Psychology and Human Performance in Space Programs: Research at the Frontier 9781138339866

Table of contents :
Dedication
Contents
Foreword
Preface
Acknowledgments
Editors
Contributors
List of Acronyms and Abbreviations
1 Physical Hazards of Space Exploration and the Biological Bases of Behavioral Health and Performance in Extreme Environments • Julia M. Schorn and Peter G. Roma
2 Spaceflight Research on the Ground: Managing Analogs for Behavioral Health Research • Ronita L. Cromwell and Joseph Neigut
3 Special Considerations for Conducting Research in Mission-Simulation Analog Environments: Challenges, Solutions, and What Is Needed • Suzanne T. Bell, Peter G. Roma, and Bryan J. Caldwell
4 Research in Extreme Real-World Environments: Challenges for Spaceflight Operations • James E. Driskell, Eduardo Salas, and Tripp Driskell
5 Technological Advances to Understand and Improve Individual and Team Resilience in Extreme Environments • Sadaf Kazi, Salar Khaleghzadegan, and Michael A. Rosen
6 Computational Modeling of Long-Distance Space Exploration: A Guide to Predictive and Prescriptive Approaches to the Dynamics of Team Composition • Brennan Antone, Alina Lungeanu, Suzanne T. Bell, Leslie A. DeChurch, and Noshir Contractor
7 Training Principles for Declarative and Procedural Tasks • James A. Kole, Alice F. Healy, Vivian I. Schneider, and Immanuel Barshi
8 Team Adaptation and Resilience: Where the Literature Currently Stands and How It Applies to Long-Duration Isolated, Confined, and Extreme Contexts • M. Travis Maynard, Deanna M. Kennedy, Scott I. Tannenbaum, John E. Mathieu, and Jamie Levy
9 Toward an Understanding of Training Requirements for Multicultural Teams in Long-Duration Spaceflight • C. Shawn Burke, Justine Moavero, and Jennifer Feitosa
10 Teamwork in Space Exploration • Jensine Paoletti, Molly P. Kilcullen, and Eduardo Salas
11 Extreme Roommates: Exploring Group-Living Skills as a Unique Team Skill Area • Lauren Blackwell Landon and Jensine Paoletti
12 Supporting Spaceflight Multiteam Systems throughout Long-Duration Exploration Missions: A Countermeasure Toolkit • Jacob G. Pendergraft, Dorothy R. Carter, Hayley M. Trainer, Justin M. Jones, Aaron Schecter, Marissa L. Shuffler, Leslie A. DeChurch, and Noshir S. Contractor
13 Human Interaction with Space-Based Systems • Kritina Holden, Jessica J. Marquez, Gordon Vos, and E. Vincent Cross II
Index

Citation preview

Psychology and Human Performance in Space Programs

Psychology and Human Performance in Space Programs Research at the Frontier

Edited by

Lauren Blackwell Landon, Kelley J. Slack, and Eduardo Salas

First edition published 2021 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2021 Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright. com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@ tandf.co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Names: Landon, Lauren Blackwell, editor. | Slack, Kelley J., editor. | Salas, Eduardo, editor. Title: Psychology and human performance in space programs / edited by Lauren Blackwell Landon, Kelley J. Slack, Eduardo Salas. Description: 1st edition. | Boca Raton, FL : CRC Press, 2020. | Includes bibliographical references and index. | Contents: volume 1. Research at the frontier — volume 2. Extreme application. Identifiers: LCCN 2020026901 (print) | LCCN 2020026902 (ebook) | ISBN 9781138339866 (volume 1 ; hbk) | ISBN 9781138339880 (volume 2 ; hbk) | ISBN 9780429440854 (volume 2 ; ebk) | ISBN 9780429440878 (volume 1 ; ebk) Subjects: LCSH: Astronautics—Human factors. Classification: LCC TL1500 .P885 2020 (print) | LCC TL1500 (ebook) | DDC 629.45001/9—dc23 LC record available at https://lccn.loc.gov/2020026901 LC ebook record available at https://lccn.loc.gov/2020026902 ISBN: 9781138339866 (hbk) ISBN: 9780429440878 (ebk) Typeset in Times by codeMantra

“For my colleagues at NASA and beyond, from whom I am constantly learning. And always, for my family, who supports me in doing challenging and meaningful work.”—Lauren Blackwell Landon, PhD “For the BHP group at Johnson Space Center, and in particular for the women of OpPsy who work tirelessly to support our astronauts.”—Kelley J. Slack, PhD “To my countless ‘team science teammates’ who have made the journey (so far) impactful, fulfilling and fun—my gratitude and admiration!”—Eduardo Salas, PhD

Contents Foreword....................................................................................................................ix Preface....................................................................................................................xvii Acknowledgments....................................................................................................xxi Editors................................................................................................................... xxiii Contributors............................................................................................................xxv List of Acronyms and Abbreviations.....................................................................xxxi Chapter 1 Physical Hazards of Space Exploration and the Biological Bases of Behavioral Health and Performance in Extreme Environments.......1 Julia M. Schorn and Peter G. Roma Chapter 2 Spaceflight Research on the Ground: Managing Analogs for Behavioral Health Research................................................................ 23 Ronita L. Cromwell and Joseph Neigut Chapter 3 Special Considerations for Conducting Research in Mission-Simulation Analog Environments: Challenges, Solutions, and What Is Needed........................................................... 47 Suzanne T. Bell, Peter G. Roma, and Bryan J. Caldwell Chapter 4 Research in Extreme Real-World Environments: Challenges for Spaceflight Operations........................................................................ 67 James E. Driskell, Eduardo Salas, and Tripp Driskell Chapter 5 Technological Advances to Understand and Improve Individual and Team Resilience in Extreme Environments................................. 87 Sadaf Kazi, Salar Khaleghzadegan, and Michael A. Rosen Chapter 6 Computational Modeling of Long-Distance Space Exploration: A Guide to Predictive and Prescriptive Approaches to the Dynamics of Team Composition....................................................... 107 Brennan Antone, Alina Lungeanu, Suzanne T. Bell, Leslie A. DeChurch, and Noshir Contractor

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Chapter 7 Training Principles for Declarative and Procedural Tasks............... 131 James A. Kole, Alice F. Healy, Vivian I. Schneider, and Immanuel Barshi Chapter 8 Team Adaptation and Resilience: Where the Literature Currently Stands and How It Applies to Long-Duration Isolated, Confined, and Extreme Contexts........................................ 151 M. Travis Maynard, Deanna M. Kennedy, Scott I. Tannenbaum, John E. Mathieu, and Jamie Levy Chapter 9 Toward an Understanding of Training Requirements for Multicultural Teams in Long-Duration Spaceflight.......................... 171 C. Shawn Burke, Justine Moavero, and Jennifer Feitosa Chapter 10 Teamwork in Space Exploration........................................................ 195 Jensine Paoletti, Molly P. Kilcullen, and Eduardo Salas Chapter 11 Extreme Roommates: Exploring Group-Living Skills as a Unique Team Skill Area.................................................................... 217 Lauren Blackwell Landon and Jensine Paoletti Chapter 12 Supporting Spaceflight Multiteam Systems throughout Long-Duration Exploration Missions: A Countermeasure Toolkit........ 237 Jacob G. Pendergraft, Dorothy R. Carter, Hayley M. Trainer, Justin M. Jones, Aaron Schecter, Marissa L. Shuffler, Leslie A. DeChurch, and Noshir S. Contractor Chapter 13 Human Interaction with Space-Based Systems................................. 259 Kritina Holden, Jessica J. Marquez, Gordon Vos, and E. Vincent Cross II Index....................................................................................................................... 295

Foreword Psych/Human Factors Visions for Moon/Mars: What the Future Holds for Those Embarking on a Long-Duration Mission Far from Home Clayton C. Anderson, retired astronaut (February 1, 2019) In my professional lifetime, I spent 15 years as an engineer and 15 as an astronaut. Every single year of those three decades was with NASA at the Johnson Space Center. Having risen through “the ranks” of the center and her hierarchy from the early age of 24 (I had a master’s degree in aerospace engineering from Iowa State University), I would learn many lessons that would apply later in my career… especially during the time I spent as an astronaut. Knowing the way different organizations and their management thought, and what they considered truly important (and maybe more critical, what they thought was not important) became keys to my being able to adapt in a NASA-world focused way more on technology and its capabilities than the talented personalities who generated that technology. I would also figure out (eventually) there is a bit of a “game” that must be played if you wanted to be successful. My ability to influence contentious scenarios, rife with flawed communicators and unbending individuals, in an ever-conscious effort to produce “win-win” scenarios, became a must for my survival and facilitated my rising to the role of a flown-inspace astronaut. These types of skills are going to matter greatly as we move from our low-earth orbital perspective to one that will eventually become the viewpoint of interplanetary travelers. To date, our country has accomplished some marvelous things. We have landed humans on the Moon and returned them safely to Earth. We have sent probes to the outermost reaches of our solar system, with Voyager 1 and 2 now sailing far beyond the orbit of Pluto, our furthest planet (and it’s not a dwarf!). Only recently, NASA’s New Horizons spacecraft returned data and photographs from a snowman-shaped asteroid dubbed Ultima Thule (a Latin phrase meaning “a place beyond the known world”), more than 4 billion miles from our sun. And of course, we cannot forget to mention my home for more than 5 months (or stated more accurately 151 days, 18 hours, 23 minutes, and 14 seconds), the International Space Station. Sailing about our planet once every 90 minutes, it has been doing so – with humans aboard – since the year 2000. Imagine for a moment, that there are young people today, who have never known a time when there WEREN’T humans living and working in outer space! I cannot believe I was a small part of those accomplishments. As a young NASA engineer, I helped devise space shuttle trajectories that enabled us to send the Galileo probe to Jupiter, the Magellan probe to Venus, and the Ulysses probe to visit our Sun. I was on a team that used the space shuttles to deploy satellites into geosynchronous orbits to monitor and protect our planet, satellites that are still performing their roles today. ix

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What America has accomplished in her storied 50-year plus space-history is nothing short of amazing. But we have so much more to do. While we – those who comprise what we call NASA – may own a storied past, it’s now truly time to figure out what to do next. In order to do that, we must learn from this past and be bold enough to take greater “leaps of faith” into the future. And I believe we must do this using a combination of both robotic and human missions. As Neil A. Armstrong – the first human to set foot on the lunar surface – once said, “A (hu)man can be amazed and amused…a robot can be neither.” At this point in time, we are poised to consider three distinct targets of opportunity for human spaceflight, and they have been discussed at length for years. Should NASA focus on the mining of asteroids, or should we head back to the moon? And what about the glamor destination of Mars? Are we even ready to attempt that one? While seemingly independent targets, with each individually intriguing, no matter which one we chose, there are issues we’re going to have to deal with. Many are not technical. They don’t involve computer software or a functioning life support system, and they aren’t concerned with a spaceship’s rocket engines and thrust vector. Yet they are all-together human; bearing the much-too-NASA-like title of psychological and human factors. Only recently, a question was posed to me that is quite relevant to this discussion. Paraphrasing, I was asked: “What are your thoughts on the U.S. sending people back to the Moon before Mars?” My response began with a statement reflective of my long-time desire for NASA’s next step…as Nike would say: “Just Do It!” With all due respect, I have not changed my tune since I was an active astronaut at the height of my spacefaring career. I have been, and will always be, an advocate for a return to the Moon BEFORE Mars. And we need to get there sooner, rather than later. I believe my logic is sound. We are not yet ready to tackle Mars...in many ways. A trip of this type will bring much different human (health) factors and psychological issues to bear than we have experienced thus far. Mitigation will require a clear and dedicated focus to develop appropriate solutions and counter-measures and we are only just now beginning to truly come to grips with specific challenges we will be facing. It is my contention that the sooner we reach the lunar surface – with crew sizes large enough to look like a colony – the sooner we can begin to make inroads into the technologies and psychologies needed to truly enable survival as a species on the rusty – and rocky – surface of Mars. As a long-duration space station crew member, and a veteran of an analogous – albeit much shorter – extreme-environment stint with NEEMO 5 (NASA Extreme Environment Mission Operations), I have personal experience in this realm. Missions of this type contrast greatly with the glory days of our space shuttle. Most shuttle missions were on the order of days in length with a training template that can roughly be described as lasting about 9 months. Crews – at least initially – were almost always comprised of commanders who wore military stripes and were jet airplane or helicopter pilots. Rounding out their crews would be mission specialists, aka scientists boasting impressive PhDs. But for a mission to Mars, are these still the proper skill sets we need? If the answer is “yes,” then I believe they’re going to need considerably more –and noticeably different – training.

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Now retired from NASA for more than 7 years, I am admittedly not within the mainstream. Yet my understanding of our current technology capability says we do not yet fully understand – nor do we have solutions for – a myriad of issues we are bound to face attempting an endeavor of this magnitude. While many will focus on the technical issues to be overcome, we will be challenged psychologically, perhaps in ways not yet foreseen. We (or at least I) do not understand the true psychological implications of a journey that will take 6–9 months just to get there. In a perfect world – assuming planetary alignment yielding a 6-month trip out and a 6-month trip back home – I cannot imagine we would land on the Martian surface and not spend at least 6 more months living and working there. It’s kind of like when you were little, and your Dad planned one of those long driving summer vacations. Remember? What may have been a simple concept in Dad’s mind, always seemed to grow into a bit of an adventure. Once you got somewhere after a long day’s drive, you were gonna stay there for a while before moving on. That means – with respect to a Mars journey – the total trip time is around 18 months (at a minimum). That’s a long time away from Earth. Oh… I don’t doubt that there will be many astronauts ready to make the journey, chomping at the bit to be the first to set foot on the rocky red planetary surface. But from where I sit, this is a trip we have not yet fully come to grips with. Living and working in small groups, especially for long periods of time, in what will undoubtedly be incredibly tight quarters (everything will be limited, simply from a cost perspective), can be fraught with issues and/or conflict. While these rifts may not be major, they will be there for sure. People like Shackleton, Amundsen, Sir Edmund Hillary, and even Ferdinand Magellan dealt with similar problems (e.g., confinement, isolation, exposure to physical hazards, altered work or rest schedules) during their arduous treks, and I have no doubt that we will deal with them too. And tragically, during my era (1998–2013) NASA provided very little preparation for issues of this regard, relying almost solely on each astronaut’s own expertise, valid or not. Leadership should no longer be optimally sought primarily from those with military jet fighter pilot and helicopter jockey backgrounds. These steely-eyed, ultra-courageous astronauts from past molds who could dog-fight with the best of them and perform military rescues that would make our heads spin, may need to be retooled a bit – or perhaps bolstered – by individuals with skill sets rivaling those of psychologists, human resources specialists, and business-like CEOs. Gone should be the give-orders, execute-orders mentality. Leading/managing (I don’t like the term “commanding” in this instance) a crew will need to be more collaborative, say, akin to managing a baseball team. Great ballpark skippers exhibit an almost chess master-like ability to make all the right moves. Success will come to the mission which leader carries a similar tool box, one that allows he or she to manipulate a not-insignificant group of extremely high achievers – all with varying skill sets, temperaments, and hot buttons – and is able to mesh those pieces together over a lengthy time period, producing championship-like results. Throw in the fact that the “team’s” entire season will be an away game (134 million miles away), and it’s easy to see (at least it is for me) the potential for looming challenges. Small things will become bigger things, nagging at individuals like a pebble in a shoe. We must develop and provide sound methodologies and solutions with clearly

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useful – and constantly executable – training, such that crews will be ably prepared when these times do come. All mission members will need to be adept with empathy, humility, and team-building strategies, while mastering psychological techniques to help battle depression, anxiety, and loneliness. And to reiterate, I believe we have NOT done this well in the past. And with no quick-return-to-earth “lifeboat” capability (aka no Soyuz, Crewed Dragon, or Starliner, coupled with the fact that our distance from Earth may be months away), issues of this type could fester for a very, very long time. Analog missions like NEEMO and the Moscow- and Hawaii-based isolation habitats may provide us with ways to better screen those we will choose to send. But how effective will these endeavors prove to be when “push comes to shove?” Will internal/external doors on a Moon or Mars habitat need to be designed such that TWO crewmembers are required to operate them, to avoid a crisis of the mind like the totally unexpected one that would lead to a padlock on the space shuttle mid-deck hatch every mission?1 Should alcohol become part of a mission manifest (don’t kid yourself, the Russians have been flying Cognac for years)2? What about the supposed calming effects claimed to be helpful by earth-bound users of food-based or extracts of cannabis? In times of stress, we, gravitationally challenged humans, often turn to the consumption of various stimulants to “turn the tide” as it were. Perhaps it’s time for us to consider the same ideas with respect to long-duration spaceflight. After all, it is one thing to get a crew as ready as possible, having (hopefully) selected the right people and mix, but quite another to ensure vehicle and habitat systems – and everything else that is needed – are there, and in the right combinations, to support them. During my time in orbit, my strength was clearly my family on earth. Based on the technology available at the time, I communicated with my wife nearly every single day and sometimes multiple times a day. Using a computer program that gave me telephone capability via Internet protocol (IP), I could “pick up” our IP phone and call anyone around the world…given an appropriate positioning of transponders, receivers, and geosynchronous satellites to guarantee a locked-on signal. I also had email capability that, while not as immediate as what I experience on earth, provided me with relatively rapid communication for work, family, and pleasure. I was further able to interact with family and friends through weekend video conferences (at least when someone felt like talking to me). Today’s high-flying astronauts can even access the Internet, something we didn’t have due to NASA’s concern with potential hackers. Now, all are graciously provided by NASA, with minimal impact to onboard schedules and a family’s time at home. Isolation from family was not something I had to deal with during my 5-month tour on the ISS. But it will be something we experience on a trip to Mars. Due to the tremendous travel distances, communication lapses will constantly plague a journey from Earth to the Red Planet. Watching the recently released movie “The Martian,” I felt the one thing they really got right was their depiction of the communication issues experienced by cinematic-hero Mark Watney. Imagine having a single – and non-visual – method of 1 2

http://www.spaceref.com/news/viewnews.html?id=1195 The Ordinary Spaceman: From Boyhood Dreams to Astronaut, Clayton C. Anderson, University of Nebraska Press 2015

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communication with your loved ones that looks a lot like sending a text today. The caveat is that it could take minutes to hours or even days to get a response to that text. The 20-minute signal delay (one way) that is guaranteed to happen will – more than likely – not allow for teleconferences, or IP phone calls. Email, which still may be viable, will undoubtedly seem more like the aforementioned texting. I often relate a humorous example of this delay to groups when I speak, based on my time on the space station. Imagine, you are poised at your spaceship’s console, ready to press the red button or the blue button, but for the life of you, you don’t know which is correct. You can’t find the procedural information you need to help with your decision, and you decide your only choice is to contact mission control. Beginning with the amount of time it takes you to type in your message and hit send, you must now add 20 minutes to the time it will take before anyone on Earth even sees your request. Now, once it’s in the hands of a brilliant team of flight controllers, how long will it take them to come to an agreement on what the correct answer is they need to send back to you? Let’s assume it’s immediate and the answer is blue. They type their response and hit send, to which another 20 minutes must be added for the message to reach us on Mars. But what if their answer isn’t immediate? What if they must form a team, hold some meetings, and hash over what the proper answer should be? My experience as a 30-year NASA employee tells me that this could take days… maybe weeks, and our favorite space movie does a pretty good job illustrating that as well! This considerable reduction in communication capability could be a huge obstacle. Crews will need to be an order of magnitude more autonomous as there will be a much smaller percentage of time that they can “call the ground.” From medical issues (and medical emergencies) to system/maintenance problems and psychological crises, their survival will depend not only on their ability to recognize, evaluate, and rectify the situation, but also on the technology level of their environment. This, in my opinion, will be one of their biggest challenges. Systems will need to be significantly more autonomous, perhaps even to a level of using Artificial Intelligence (watch out for HAL!). Procedures will need to be so clear and unambiguous (that is not the case today) that there is no question about what they are expected to do. Individuals having psychological and separation-driven issues will need the crew to be their extended family. I personally find it extremely unpalatable to think I wouldn’t have the regular contact with my wife and kids that I enjoyed on the space station. And there’s also the question no one ever seems to want to tackle – sexual activity. Will there be romantic interactions between crew members? So far from home, lacking that human sensual interaction, will this pose difficult situations given a mission that may last about 2 years? Several of my colleagues found this temptation to be quite the challenge during their training experiences in Star City, Russia, so why would we think a 2-year Mars excursion would be any different? Will we endeavor to do anything to help them maintain their families and spousal relationships, or will we just accept today’s attitude of “oh well… that’s how it goes?” Perhaps we should send couples then? Knowing whether this is a smart thing to do can be very complicated. Does the couple have children? If so, what are their ages? If both parents head for space, and tragedy ensues; have we left their children to a future without their parental guardians (this is often the argument used today

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against sending married couples to space)? And do we really know if an apparently well-adjusted couple here on earth can weather the challenges and stresses of a long-duration space voyage? Those challenges are not the same you know. It’s possible that what started out as an enviable relationship between caring partners could experience significant highs and lows when confined for months in a not-so-roomy aluminum spaceship. And will we then need a uniquely designed area of privacy? Sleep stations for two? Bigger sleeping bags? Many college campuses today have a designated “safe space” for students needing a place to “cope.” Perhaps we’ll need a designated “couples’ area?” In any event, NASA will be forced to navigate the world of an ever-inquisitive media and headlines that scream “… Is NASA studying sex in space?!” In the past, this has been something NASA avoided like the plague. During my 152-day expedition (15/16) aboard the International Space Station, part of the magic of being in space was seeing Earth. I could gaze from our insufficient windows (no cupola existed during my expedition tour in 2007) at the beautiful planet below. I was captivated by the challenge – and the resulting excitement that erupted when a much-desired photo op was successful – of capturing recognizable photographs of amazing sites like the Grand Canyon, Mount Kilimanjaro, icebergs in the southern Atlantic Ocean, and the Pyramids of Egypt. And a short work break, quietly staring through an earth-facing window could go a long way in relieving the stress of a long day or a difficult repair procedure. Using the side-facing windows of the Russian docking compartment I could wait for a sunrise, placing my face against the window’s glass to feel the warmth of the rays completing their 93 million-mile journey. For just a moment, I was home… contentedly napping in my backyard. These were things I looked forward to every day and they will be a missing aspect on a trip of planetary scale. The long trip to Mars will not afford us many luxuries. Day and night cycles, caused in low-earth orbit by our travel about the earth, will not exist. For the most part, once removed from the earth by an approximate lunar distance of 250,000 miles, it will always be sunny, until the day we finally enter Martian orbit. The further we travel from home, the more difficult it will be to see Earth. And this assumes we’ll have a spacecraft with adequate window space and an attitude control system (with ample fuel) that will allow us to maneuver and look in that direction! Arrival at Mars, and landing on the surface, will obviously re-capture our spirits, but it won’t be the same during the arduous trip to get there. The earth is our home. As humans, everything that defines us exists on the planet’s surface. Our sense of self, our loved ones, our history, it’s all there on the one place in our solar system (and perhaps the universe) where our species thrives. To journey to a new world, orders of magnitude more desolate than that discovered by the early colonists will be a jolt to our psyche. I imagine it may be as if one were lost on the ocean, safely contained on a moderate-sized boat, but with nothing to see for miles and miles. It’s a mental picture I can’t quite grasp, guessing that only after arrival will anyone be able to truly understand its impact. In order to get smarter – and we are doing that now, having recently followed an ISS crew spending nearly a full year onboard the station – we need to answer many new questions like how we will send necessary stashes of fuel, food, water, spare parts, clothing, etc. Then there are “daily life” issues like how we dispose

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of fecal matter (can’t burn it up in an atmosphere somewhere, as there will be no atmospheres to use on our interplanetary trajectory) and other waste products (aka trash). Radiation from the sun and other fusion-energy emitters from our all-encompassing Milky Way galaxy will be a bane to our existence. And in the event of a true medical emergency? A stroke, a heart attack, appendicitis…what will we do? Should our space ship and mission base have a doctor/surgeon and medical facility? How much pre-flight medical training should our crew undertake? Those things are damned expensive, they’re big, and they most certainly won’t look like those we see in movies. Our time will be spent in tight quarters, both during the journey and then once safely on the surface. While perhaps a bit larger than our travel vehicle, the surface living quarters will still be such that habitat-confinement time will be high and stressful, maybe worse than what we experience on the space station. Living in a socially dense space, isolated from loved ones, and with no ability to just “go for a walk,” will most certainly affect a person on a multi-month, multiyear mission. Even those stationed in Antarctica can go outside and “enjoy the weather,” while waving to their favorite penguin. Receiving news (both good and bad), will challenge our brave heroes mentally more than ever before. The idea of space station personal websites, full of family content and so ably updated by our “psych support” staff on earth, may be a thing of the past. Sending family photos, videos, and personal care packages may prove to be much more difficult than how it’s done today. Experts claim the surface of Mars or the Moon can provide us with “in situ (on site)” resources we may take advantage of. “They” tout our ability to concoct fuel, extract water, create oxygen, make iron bricks for building structures, simply by “living off the land.” While this may be true, I want to know HOW we will do this? Do we understand the technologies required to make these wondrous visions a reality? What infrastructure will be required? For example, has NASA subcontracted with companies like Caterpillar and John Deere to get their ideas? If we look solely at the example of pulling hydrogen and oxygen from the ice resident at the lunar poles, the task is much more daunting than we may be led to believe. With a considerable amount of the ice deep within a huge crater, there will have to be large-scale equipment upon which our extraction success will be linked. And once extracted, what is the process for reducing that ice into something useful? Referring again to our favorite space motion picture, there are very few Mark Watneys out there. And I know of none in our current corps of astronauts who are capable of the level of “MacGyverism” and knowledge that the film bestows on Watney. Improvising a farm inside a habitat using Martian soil fertilized with human feces, water produced by extracting hydrogen from leftover rocket fuel, and potatoes intended for Thanksgiving dinner, is a bit of a stretch from where I sit. And don’t get me started on how he modified the only functional rover for long-distance travel. But I digress. The responsibility for survival will indeed rest (almost) totally on the crew. There will be a daily toll – both physical and mental – on each of them as they must constantly attend to the workload issues associated with simply staying alive. There will be a tremendous sense of isolation as there is no form of “quick help” or realtime coordination with the Mission Control Center. Help will come in the form of

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their individual and combined skill sets and their ability to collectively troubleshoot across a very wide range of problems. I liken our position in human space exploration to that of the Pilgrims and their arrival on the Mayflower in 1620. Upon their initial landing at Plymouth Rock, they struggled mightily, ill-prepared to battle a robust climate, rectify their lack of sound shelter, and develop a solid food source. Although many perished, they would eventually figure it out. Using help from the already-in-place native-American residents, these brave – albeit improvising – pioneers eventually settled comfortably, living off the land by taming the wilderness with growing knowledge and tenacity. It was only then that they began to venture further into their new world. The ISS is our Plymouth Rock. It is where we are starting to “… figure it out.” As we begin to venture away from our initial outpost, I continue to favor the Moon as our next destination. A mere three days away (with proven 1970s technology), and a minimum communication delay (a few seconds), it is a place we can return to with confidence. It is a place where we may begin to develop the very technologies that will be necessities for us on Mars. To me, a lunar outpost is a valid and sensible next step. Space exploration is dangerous. It is difficult and it is hugely expensive. We must continue to learn and grow in our understanding of what exactly needs to be done. Now…when do we leave? The Martians are waiting on us!

Preface INTRODUCTION TO VOLUME 1: RESEARCH AT THE FRONTIER Our goal as the editorial team was to capture this moment in the adventure that is human spaceflight. Starting in the earliest days of the 20th century space race, psychologists, and human factors experts applied what they knew to find individuals with the “right stuff.” They tested the mental toughness and physical fitness of these astronauts and cosmonauts and informed the design of the complex vehicles that would take them to space. In many ways, these pioneers were flying by the seats of their pants. The state of the science at the time helped them make, in some cases, educated guesses as to how to be successful. Some decisions, spurred on by President Kennedy’s famous timeline to get to the moon by the end of the decade, were not necessarily based on a large body of research with firm conclusions. In the decades since, psychological research and technology related to human performance have grown in leaps and bounds, particularly with the widespread use of communication technology, the Internet, hardware and software advances, and other methods enabling data collection and analysis. The International Space Station greatly expanded opportunities to set about the business of understanding how humans cannot just survive, but thrive, in space. Researchers started looking, in earnest, toward Mars. The growing number of multi-week and multi-month mission simulation analog experiments in the early 21st century reflect this interest. Notable Russian-led simulations such as the Mars 500 and NASA’s Human Exploration Research Analog (HERA) were the setting for controlled research environments. The space agencies invited some of the best minds in the fields of applied psychology and human factors to examine how the astronaut-like crews respond and adapt to stressors. This work is time-consuming and may take years to generate robust datasets ready for analysis, but trees have begun to bear fruit. Humanity’s residency in low Earth orbit aboard the ISS also saw greater interest in optimizing where possible. For example, entertainment options allow astronauts access to a library of television shows, movies, recent articles, books, podcasts, instruments, and other items that act as a stress reliever and morale booster. Early missions were much more spartan. While the pre-ISS programs focused on individual performance and achieved successful teamwork through grueling pre-mission practice of mission tasks, the growing science around teams and teamwork over the last 20 years saw the emergence of research and operations focused on understanding how to build a flexible, resilient, and skilled team. There was more interest in moving beyond just a technically sound group of individuals and assuming they will handle team aspects. As will be described in chapters examining astronaut selection and team composition research, team-orientation, team skills, and creating the right mix of individuals for a crew has experienced a flurry of activity. Again, not just surviving, but thriving, maximizing what a crew could achieve during a long-duration mission. Human factors considerations also targeted increased xvii

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flexibility, trying to plan for new technologies, and mission objectives not yet imagined. This longer-term thinking is needed for the multi-year Mars mission. The technological advances continue to increase exponentially, rendering some features of the early ISS to seem almost antiquated. Now, we are leaping into artificial intelligence, machine learning, the complexities of human physiological responses in space, and the integration of the human with the machine in new and exciting ways. This becomes even more important as communication delays with future Mars mission crews reach over 20 minutes each way and the crew is isolated from real-time support on Earth. The year 2020 celebrates 20 years of continuous human presence in space. What do we know about working and living in space for months? What do we know about supporting those crews through careful human factors design? How does this prepare us for the near-term return to the moon through the Artemis program, and ultimately, Mars? We have some answers, and we want to share them with you. But, we still have questions, and we look to all those interested in the next chapter of exploration to use these books as a launch pad to discovery. —Lauren, Kelley, and Eduardo

CHAPTER OVERVIEWS Much like the dual-focused needs of space agencies to conduct successful operations and to create new knowledge and technology to enable future missions, we have roughly divided these two volumes into a research-focused volume and an operations-focused volume. This volume, the research-focused one, provides readers an understanding of the key threats to spaceflight, particularly as they apply to long-duration missions and missions beyond low Earth orbit to the moon and Mars. Experts in the field describe the challenges of creating environments that mimic those key threats of spaceflight, if not with mission simulations analogs, with analogous occupations of highly skilled teams in high-consequence environments. Many of the chapters in this volume explore the variety of psychological and human factors research, and the intersection of fields with the physiological demands placed on the human system during isolation and confinement. Perhaps most enlightening for readers are the descriptions of the many ongoing efforts to develop evidencebased countermeasures, and the gaps in knowledge that exist. Many of the human factors and psychology research can be applied in terrestrial settings as well. Mars is the greatest challenge to human space exploration yet, and this book serves as both an update on current knowledge, and a tantalizing glimpse into the questions that still remain. First, retired NASA astronaut Clay Anderson speaks from his experiences as a NASA engineer living in space. He highlights the concerns of astronauts as they fly beyond low Earth orbit, and offers some thought-provoking “what if?” scenarios. The next section introduces the physiological and psychological challenges of spaceflight as well as the challenges of researching crews in such extreme environments. Schorn and Roma set a foundation for all following chapters by examining the rigors of traveling to inhospitable worlds. They walk us through the five major

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hazards of spaceflight and the resulting harm to humans in space, noting that the brain is a nexus of all effects and resulting outcomes. Cromwell and Neigut describe the creation of research facilities that simulate spaceflight stressors on Earth, particularly isolation, confinement, duration, and stress. The authors approach research from the perspective of those managing such facilities, while Bell, Roma, and Caldwell consider how researchers in such environments might collect valuable data in mission-like operations that offer greater experimental controls. Bell and colleagues offer a guide to any mission-simulation analog researcher hoping to contribute to the science of long-duration space exploration. Driskell, Salas, and Driskell explore other ways of conducting spaceflight research when qualitative data may yield benefits, and how other real-world settings (e.g., polar stations, ocean voyagers or fishing operations, military) may act as a suitable analogous population to astronauts. Several chapters examine how to select individuals for future missions, create the right mix of people on a crew, and how to train that crew to live and work together effectively over many months. Kazi, Khaleghzadegan, and Rosen discuss cuttingedge research into physiological measures of psychological factors and how technology has advanced to capture that data. Antone, Lungeanu, Bell, DeChurch, and Contractor offer readers a how-to for researchers looking to extend data collected through computational modeling and virtual experiments. They include data from terrestrial analogs and spaceflight. A clear, evidence-based set of training principles for long-duration, highly complex operations is outlined by Kole, Healy, Schneider, and Barshi. This chapter on training methods for task work is complemented by a chapter on training and supporting team adaptation by Maynard, Kennedy, Tannenbaum, Mathieu, and Levy, which may apply to both technical and non-technical aspects of the mission. The next three chapters address other issues that may incur risks to the team if not managed well: interacting in a multicultural environment (Burke, Moavero, and Feitosa), general teamwork skills in operations (Paoletti, Kilcullen, and Salas), and living together viewed as a trainable work skill (Landon and Paoletti). The final two chapters examine the systems surrounding the crew in space. Pendergraft and colleagues provide a process to analyze and create a well-functioning multiteam system to support the crew in space. Mission control is often an understudied aspect to spaceflight research, and this chapter works to address this gap. Holden, Marquez, Vos, and Cross explain the many challenges of creating space-based vehicle systems to facilitate human interactions with those systems. The authors examine several case studies and operational assessments of systems developed for spaceflight. Every chapter examines long-duration exploration research with examples of studies that have been conducted in spaceflight or in analogous environments. New techniques, technologies, findings, and lessons learned from leaders in the field offer a wealth of knowledge to those seeking to understand more about what it will take to achieve a Mars mission. The journey to Mars is bigger than the crew; it depends on the work of thousands of engineers and researchers to select, compose, and support that crew. This research volume is an exciting look into the work of some of those dedicated researchers that achieve new heights.

Acknowledgments Lauren Blackwell Landon is supported by KBR’s Human Health and Performance Contract NNJ15HK11B through the National Aeronautics and Space Administration. Eduardo Salas is supported in part by grants NNX16AP96G and NNX16AB08G from the National Aeronautics and Space Administration (NASA) to Rice University, and grant NNX17AB55G from NASA to Johns Hopkins University School of Medicine and Rice University.

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Editors Lauren Blackwell Landon, PhD, is the Team Risk Discipline Scientist in the Human Factors and Behavioral Performance (HFBP) Element, a division of NASA’s Human Research Program. She is also a scientist in the Behavioral Health & Performance (BHP) Laboratory at NASA’s Lyndon B. Johnson Space Center. Her research is focused on teams in extreme environments, examining the influence of individual team-oriented characteristics, teamwork processes, and interdisciplinary areas (e.g., human factors, sleep/fatigue), as it affects team performance and functioning. Dr. Landon enjoys being a member of the award-winning NASA Astronaut Selection Team and training team skills to astronauts and flight controllers. She is also an adjunct assistant professor in the Department of Psychological Sciences at Rice University. She previously worked as an Organizational Development Consultant at the Department of Energy’s Oak Ridge National Laboratory and as a human factors researcher for the Federal Aviation Administration’s Civil Aerospace Medical Institute (FAA-CAMI). She has worked on projects funded by the U.S. Army, U.S. Navy, the U.S. military’s Special Forces, Defense Advanced Research Projects Agency (DARPA), and National Science Foundation (NSF), among other organizations. She earned all four of her degrees from the University of Oklahoma including a PhD in Industrial-Organizational Psychology. Kelley J. Slack, PhD, joined the Science and Research team at Birkman International after almost 20 years working as part of the Behavioral Health and Performance group at NASA’s Lyndon B. Johnson Space Center. There, her work centered on the psychological and psychiatric selection of astronauts. At Birkman, her work is focused on maintaining the scientific rigor of their assessments and leading research to further understand the complex relationships between personality and work. Dr. Slack studied behavioral sciences at London School of Economics and graduated with honors from Rice University with double majors in business and behavioral science. After gaining international and domestic business experience, she returned to school and earned her PhD in Industrial and Organizational Psychology from the University of Houston with a minor in Statistics. She is a licensed psychologist in the State of Texas.

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Eduardo Salas, PhD, holds the Allyn R. and Gladys M. Cline Chair of Psychology and he is Chair of the Psychological Sciences Department at Rice University. Previously, he spent 15 years at the University of Central Florida, where he was University Trustee Chair and Pegasus Professor, and the Program Director for the Institute for Simulation & Training. He also spent 15 years at the Naval Air Warfare Center Training Systems Division (formerly Naval Training Systems Center). Dr. Salas has published numerous books and scientific articles in the fields of applied psychology and human factors. He has received research funding from many agencies across the government as well as acted as a consultant in industry. His award-winning research focuses on uncovering what facilitates teamwork and team effectiveness in organizations; how and why team training works; how to optimize simulation-based training; how to design, implement, and evaluate training and development systems, and generate evidence-based guidance for those in practice. He is a fellow of the Human Factors and Ergonomics Society, the American Psychological Association, the Society for Industrial and Organizational Psychology, and the Association for Psychological Science, among others, and is one of the most cited researchers in the field of applied psychology.

Contributors Clayton C. Anderson, MS NASA Astronaut (retired) Astro Clay, LLC Houston, Texas

Yuriy Bubeev, MD SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia

Brennan Antone, BS Department of Industrial Engineering and Management Sciences Northwestern University Evanston, Illinois

C. Shawn Burke, PhD Institute for Simulation and Training University of Central Florida Orlando, Florida

Jamie D. Barrett, PhD Federal Aviation Administration Oklahoma City, Oklahoma Immanuel Barshi, PhD Human Systems Integration Division NASA’s Ames Research Center Mountain View, California Suzanne T. Bell, PhD Behavioral Health & Performance Laboratory KBR/NASA’s Lyndon B. Johnson Space Center Houston, Texas and Psychology Department DePaul University Chicago, Illinois Gary E. Beven, MD Space and Occupational Medicine Branch NASA’s Lyndon B. Johnson Space Center Houston, Texas Tiffany M. Bisbey, PhD Department of Psychological Sciences Rice University Houston, Texas

Bryan J. Caldwell, PhD Research Operations and Integration Human Health Operations Division KBR/NASA’s Lyndon B. Johnson Space Center Houston, Texas Dorothy R. Carter, PhD Department of Psychology The University of Georgia Athens, Georgia Angelina Chekalina, PhD SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia Noshir S. Contractor, PhD Department of Industrial Engineering and Management Sciences Northwestern University Evanston, Illinois Natalie Croitoru, BA University of North Carolina at Chapel Hill Chapel Hill, North Carolina Ronita L. Cromwell, PhD Baylor College of Medicine Houston, Texas xxv

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E. Vincent Cross II, PhD TRACLabs Webster, Texas

Ute Fischer, PhD Georgia Institute of Technology Atlanta, Georgia

Michael T. Curtis, PhD Bonsai Institute, LLC Richmond, Virginia

Christopher F. Flynn, MD Federal Aviation Administration Washington, DC

Leslie A. DeChurch, PhD Department of Communication Studies Northwestern University Evanston, Illinois

Laura Galarza, PhD Universidad de Puerto Rico San Juan, Puerto Rico

Donna L. Dempsey, PhD Human System Engineering & Integration Division NASA’s Lyndon B. Johnson Space Center Houston, Texas Deborah DiazGranados, PhD School of Medicine Virginia Commonwealth University Richmond, Virginia James E. Driskell, PhD Florida Maxima Corporation Orlando, Florida Tripp Driskell, PhD Florida Maxima Corporation Orlando, Florida Alexander Dudukin SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia Eric Dunleavy, PhD DCI Consulting Group Washington, DC Jennifer Feitosa, PhD Department of Psychology Claremont McKenna College Claremont, California

Vadim Gushin, MD, PhD SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia Cliff Haimann, PhD DCI Consulting Group Washington, DC Alexa Harris, MA Department of Communication Studies Northwestern University Evanston, Illinois Alice F. Healy, PhD Department of Psychology & Neuroscience University of Colorado Boulder Boulder, Colorado Kritina Holden, PhD Human Systems Engineering and Integration Division Leidos/NASA’s Lyndon B. Johnson Space Center Houston, Texas Jessica L. Hughlett, BS Behavioral Health and Performance Group KBR/NASA’s Lyndon B. Johnson Space Center Houston, Texas

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Natsuhiko Inoue, PhD Japan Aerospace Exploration Agency Chofu, Tokyo, Japan Bernd Johannes, PhD Institute of Aerospace Medicine (German Aerospace Center) Cologne, Germany Justin M. Jones, MS Department of Psychology The University of Georgia Athens, Georgia Olga Karpova SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia Sadaf Kazi, PhD Armstrong Institute for Patient Safety and Quality The Johns Hopkins University School of Medicine Baltimore, Maryland Kathryn E. Keeton, PhD University of Texas at San Antonio; Minerva Work Solutions San Antonio, Texas Deanna M. Kennedy, PhD School of Business University of Washington Bothell Bothell, Washington Salar Khaleghzadegan, BS Armstrong Institute for Patient Safety and Quality The Johns Hopkins University School of Medicine Baltimore, Maryland

Molly P. Kilcullen, MS Department of Psychological Sciences Rice University Houston, Texas James A. Kole, PhD Department of Psychological Sciences University of Northern Colorado Greeley, Colorado Tatyana Kotrovskaya, PhD SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia Lauren Blackwell Landon, PhD Behavioral Health & Performance Laboratory Biomedical Research and Environmental Sciences Division KBR/NASA’s Lyndon B. Johnson Space Center Houston, Texas Marvin Lange, CD, MD, FRCPC Canadian Space Agency, Longueuil Quebec, Canada Jamie Levy, PhD The Group for Organizational Effectiveness (gOE) Albany, New York Alina Lungeanu, PhD Department of Communication Studies Northwestern University Evanston, Illinois Jessica J. Marquez, PhD Human Systems Integration Division NASA’s Ames Research Center Mountain View, California

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John E. Mathieu, PhD Department of Management University of Connecticut Storrs, Connecticut

Jacob G. Pendergraft, BS Department of Psychology The University of Georgia Athens, Georgia

M. Travis Maynard, PhD Department of Management Colorado State University Fort Collins, Colorado

Don Pettit, PhD NASA Astronaut NASA’s Lyndon B. Johnson Space Center Houston, Texas

Jessica Mesmer-Magnus, PhD Cameron School of Business University of North Carolina Wilmington Wilmington, North Carolina

Kristen Pryor, MS DCI Consulting Group Washington, DC

Justine Moavero, MS Institute for Simulation and Training University of Central Florida Orlando, Florida

Peter G. Roma, PhD Behavioral Health & Performance Laboratory Biomedical Research and Environmental Sciences Division KBR/NASA’s Lyndon B. Johnson Space Center Houston, Texas

Kathleen Mosier, PhD Psychology Department, San Francisco State University San Francisco, California David Musson, MD, PhD McMaster University and Northern Ontario School of Medicine Sudbury, Ontario, Canada

Michael A. Rosen, PhD Armstrong Institute for Patient Safety and Quality The Johns Hopkins University School of Medicine Baltimore, Maryland

Joseph Neigut, BS Space Medicine Operations Division NASA’s Lyndon B. Johnson Space Center Houston, Texas

Eduardo Salas, PhD Department of Psychological Sciences Rice University Houston, Texas

Ashley Niler, PhD Department of Communication Sciences Northwestern University Evanston, Illinois

Gro Sandal, PhD Department of Psychosocial Science University of Bergen Bergen, Norway

Jensine Paoletti, MS Department of Psychological Sciences Rice University Houston, Texas

Daria Schastlivtceva SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia

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Aaron Schecter, PhD Department of Management Information Systems The University of Georgia Athens, Georgia Lacey L. Schmidt, PhD Minerva Work Solutions; PLLC/University of Houston Houston, Texas Vivian I. Schneider, PhD Institute of Cognitive Sciences University of Colorado Boulder Boulder, Colorado Julia M. Schorn, BS Behavioral Health & Performance Laboratory Biomedical Research and Environmental Sciences Division KBR/NASA’s Lyndon B. Johnson Space Center University of California – Los Angeles Los Angeles, California Marissa Shuffler, PhD Department of Psychology Clemson University Clemson, South Carolina Dmitry Shved, PhD SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia Walter E. Sipes, PhD Aerospace Psychology Consultants Tucson, Arizona Kelley J. Slack, PhD Birkman International/Minerva Work Solutions PLLC Houston, Texas

Annette C. Spychalski, PhD ACS People Development Julie A. Steinke, PhD The MITRE Corporation McLean, Virginia Natalya Supolkina SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia Shoichi Tachibana, MD, PhD Japan Aerospace Exploration Agency Chofu, Tokyo, Japan Scott I. Tannenbaum, PhD The Group for Organizational Effectiveness (gOE) Albany, New York Leena Tomi, MS, MA Canadian Space Agency Longueuil, Quebec, Canada Hayley M. Trainer, MS Department of Psychology The University of Georgia Athens, Geogia Elizabeth T. Turner KBR/NASA’s Lyndon B. Johnson Space Center Houston, Texas Alla Vinokhodova, PhD SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia Gordon Vos, PhD Human Systems Engineering and Integration Division NASA’s Lyndon B. Johnson Space Center Houston, Texas

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Jessica L. Wildman, PhD Florida Institute of Technology Melbourne, Florida

Contributors

Anna Yusupova, PhD SRC RF – Institute of Bio-Medical Problems of RAS Moscow, Russia

List of Acronyms and Abbreviations ABM ACTH AI ANOVA ANSMET AO AR BDNF BHP CAPSULS CNS CREWS CRH DLR DNA D-RATS EEG ESA EU EVA EXEMSI FMARS fMRI FPN GABA GCR GI HAI HCI HERA HFBP HID HIPAA HI-SEAS HMD HPA HRP HUBES

Agent-based models Adrenocorticotrophic hormone Artificial intelligence Analysis of variance Antarctic Search for Meteorites Announcements of opportunities Augmented reality Brain-derived neurotrophic factor Behavioral Health & Performance Canadian Astronaut Program Space Unit Life Simulation Central nervous system Crew Recommender for Effective Work in Space Corticotropin-releasing hormone Deutsches Zentrum für Luft-und Raumfahrt (German space agency) Deoxyribonucleic acid Desert Research and Technology Studies Electroencephalographic European Space Agency European Union Extra-vehicular activities European Campaign for the European Manned Space Infrastructure Flashline Mars Arctic Research Station Functional magnetic resonance imaging Fronto-parietal network Gamma-amino butyric acid Galactic cosmic ray Gastrointestinal Human-automation integration Human–computer interaction Human Exploration Research Analog Human Factors and Behavioral Performance Heads-in Display Health Insurance Portability and Accountability Act Hawai’i Space Exploration Analog and Simulation Head-mounted Displays Hypothalamic–pituitary–adrenal Human Research Program Human Behavior in Extended Spaceflight xxxi

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List of Acronyms and Abbreviations

HUD Head-up Displays HZE High-mass, high-charged particles IBMP Institute of Biomedical Problems ICC Isolated, confined, and controlled ICE Isolated confined extreme environment IMO Input–mediator–outcome IMOI Input–mediator–output–input IO Industrial-organizational IP International Partner IPEV Institut Polaire Francais (French polar agency) IPO Input–process–output IPV International Procedure Viewer IQ Intelligent quotient ISEMSI Isolation Study for the European Manned Space Infrastructure ISO International Organization for Standardization ISS International Space Station JAXA Japan Aerospace Exploration Agency JSC Johnson Space Center KSAO Knowledge, skills, abilities, and other characteristics LDEM Long-duration exploration missions LDSE Long-duration space exploration LDSM Long-duration space mission LEO Low Earth orbit LOA Levels of automation LRV Lunar roving vehicle MCC Mission Control Center MDRS Mars Desert Research Station MECA Mission Execution Crew Assistant MMSEV-EVA Multimission, space-exploration vehicle, extravehicular activity MPCV Orion Multi-Purpose Crew Vehicle MTS Multiteam system NASA National Aeronautics and Space Administration NBS Neutral Buoyancy Simulator NEEMO NASA Extreme Environment Mission Operations NEK Nazemnyy eksperimental’nyy kompleks (ground-based analog facility) NHST Null hypothesis significance testing NIMH National Institute of Mental Health NOLS National outdoor leadership school NSF National Science Foundation NSPIRES NASA Solicitation and Proposal Integrated Review and Evaluation System OST-D Optical See through Displays PAS Power, Avionics, and Software PFC Prefrontal cortex PLSS Portable Life Support System

List of Acronyms and Abbreviations

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Programma Nazionale Di Ricerche in Antartide (Italian polar agency) POC Point-of-contact PPV Positive predictive value PVT Psychomotor Vigilance Test RDoC Research Domain Criteria ROC Receiver operating characteristic SANS Spaceflight Associate Neuro-ocular Syndrome SCN Suprachiasmatic nucleus SFINCSS Simulation for Flight of International Crew on Space Station SFMTS Spaceflight multiteam systems SFRM Space Flight Resource Management SIRIUS Scientific International Research In a Unique terrestrial Station SPAM Situation Presence Assessment Method SRB Solid rocket booster SSE Sum of squares due to error SSM Shared mental models STEM Science, technology, engineering, and mathematics TEAMSTAR Tool for Evaluating and Mitigating Space Team Risk TLX Task load index TMS Transactive memory system TNT Trinitrotoluene USGS United States Geological Survey VMS Vertical motion simulator VR Virtual reality VSS Virtual space station VTA Ventral tegmental area

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Physical Hazards of Space Exploration and the Biological Bases of Behavioral Health and Performance in Extreme Environments Julia M. Schorn University of California – Los Angeles KBR/NASA’s Lyndon B. Johnson Space Center

Peter G. Roma

KBR/NASA’s Lyndon B. Johnson Space Center

CONTENTS Introduction ................................................................................................................ 2 Core Neurobehavioral Systems for Space Exploration.............................................. 2 Arousal/Regulatory and Sensorimotor Systems.................................................... 4 Negative and Positive Valence............................................................................... 5 Cognitive and Social Processes.............................................................................. 6 Spaceflight Hazards and Physical Risks to Behavioral Health and Performance ...... 9 Radiation ............................................................................................................... 9 Altered Gravity..................................................................................................... 10 Hostile/Closed Environments .............................................................................. 11 Isolation and Confinement .................................................................................. 12 Distance from Earth ............................................................................................ 13 Summary and Conclusion ........................................................................................ 15 Acknowledgments.................................................................................................... 15 References ................................................................................................................ 16

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Psychology and Human Performance in Space Programs

INTRODUCTION Venturing into the far reaches of the universe will challenge astronauts’ bodies and minds in extreme environments unlike anything on Earth. Although humans are highly adaptable, the physical hazards of long-duration space exploration (LDSE) pose a unique constellation of threats to biobehavioral functioning, individual and team performance, and mission success. NASA’s Human Research Program identifies five principal hazards of human spaceflight (Whiting & Abadie, n.d.). These hazards include radiation, gravity fields, hostile and closed environments, isolation and confinement, and increased distance from the Earth. Insofar as individual and team behavioral health, performance, and adaptation are regulated by the brain, the direct and indirect effects of the physical LDSE environment on the neurobehavioral mechanisms mediating behavioral, psychological, and social functioning are worthy of consideration by all stakeholders and supporters of human space exploration. The primary goal of this chapter is to provide a friendly reminder of a not-sofriendly reality: space is a dangerous place. However inspiring it may be, space exploration in any form is a physically dangerous and often life-threatening endeavor, the risks of which only compound over time. First, we provide a selective overview of key neurobehavioral systems underlying individual and team behavioral health, performance, and adaptation in extreme operational settings. We then review each of the five spaceflight hazards and consider how they may affect mission success through direct and indirect action on these neurobehavioral systems. Given the distinctively challenging physical risks of LDSE missions, our overarching goal is to encourage integrated, multidisciplinary approaches to research and operations that consider the interaction of biological, psychological, social, and environmental factors in support of long-duration space exploration crews.

CORE NEUROBEHAVIORAL SYSTEMS FOR SPACE EXPLORATION Humans are capable of adapting to a remarkably wide range of extreme environments, due in large part to the incredible plasticity of the brain. The evolution of complex neurobehavioral systems to support this adaptation can inform how individuals live and thrive in space. Here, we provide a simplified overview of key neurobiological systems that underlie individual and team adaptation in this environment. We use the National Institute of Mental Health’s (NIMH) Research Domain Criteria (RDoC) as a framework to help structure our discussion (https://www.nimh.nih.gov/ research-priorities/rdoc/index.shtml; Cuthbert & Kozak, 2013). RDoC’s primary goal is to inform new approaches to understand mental disorders across multiple levels of analysis, from genomics and molecular markers up through behavioral task paradigms and self-reports. However, it is not a categorical diagnostic model, but rather defines (dys)function by multiple overlapping neurobehavioral systems, or “domains” encompassing multiple constructs and sub-constructs, which are applicable to all individuals and teams (Clark, Cuthbert, Lewis-Fernández, Narrow, & Reed, 2017). It is important to note that RDoC is an evolving framework and does not claim to cover the full spectrum of psychological, social, and physical functions relevant to the human experience. The current six RDoC domains include

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TABLE 1.1 National Institute of Mental Health (NIHM) Research Domain Criteria (RDoC) Framework RDoC Domain Arousal and regulatory systems

Construct Arousal Circadian rhythms Sleep–wakefulness Motor actions

Sensorimotor systems

Negative valence system

Positive valence systems

Agency and ownership Habit: sensorimotor Innate motor patterns Acute threat (fear) Potential threat (anxiety) Sustained threat Loss Frustrative nonreward Reward responsiveness

Reward learning

Reward valuation

Cognitive systems

Attention Perception

Declarative memory Language Cognitive control

Working memory

Subconstruct

Action, planning, and selection Sensorimotor dynamics Initiation Execution Inhibition and termination

Reward anticipation Initial response to reward Reward satiation Probabilistic and reinforcement learning Reward prediction error Habit Reward (probability) Delay Effort Visual perception Auditory perception Olfactory/somatosensory/multimodal perception

Goal selection: goal selection Goal selection: updating, representation, and maintenance Response selection: response selection Response selection: inhibition/suppression Performance monitoring Active maintenance Flexible updating Limited capacity Interference control (Continued )

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TABLE 1.1 (Continued ) National Institute of Mental Health (NIHM) Research Domain Criteria (RDoC) Framework RDoC Domain Social Processes

Construct Affiliation and attachment Social communication

Perception and understanding of self Perception and understanding of others

Subconstruct Reception of facial communication Production of facial communication Reception of non-facial communication Production of non-facial communication Agency Self-knowledge Animacy perception Action perception Understanding mental states

Arousal and Regulatory Systems, Sensorimotor Systems, Negative Valence, Positive Valence, Cognitive Processes, and Social Processes (Table 1.1). For each domain, the levels of analysis include genes, molecules, cells, circuits, physiological measures, behavioral task paradigms, and self-report measures, although we will focus on selected constructs and the primary neural circuits, neurochemicals, and physiological and behavioral outputs most relevant to individual and team behavioral health and performance in the spaceflight operational environment.

ArousAl/regulAtory And sensorimotor systems These two domains are the most “basic” as they serve the most essential biobehavioral functions, such as sleep–wakefulness and motor actions. Arousal/regulatory systems provide homeostatic regulation of sleep, wake, and arousal. When teams live and work in space, wakefulness and attention are required to correctly execute mission tasks and maintain mission systems, which could potentially have fatal consequences if poorly performed. Within the brain, the suprachiasmatic nucleus (SCN) in the hypothalamus is critical for regulating circadian rhythms and is known as the “master clock” (Dubocovich, 2007; Ebling, 1996). Our circadian rhythm is also influenced by external cues, the most prominent being light. Sunlight stimulates wakefulness while darkness is accompanied by melatonin production. Melatonin, the neurohormone associated with sleep, is suppressed when exposed to blue-enriched white light. Light-sensitive receptor cells in the retina project to the SCN. The pathway from light receptors to SCN eventually ends in the pineal gland, which secretes melatonin. The SCN also controls related functions, such as body temperature, hormone secretion, urine production, and blood pressure. The SCN also receives input of the neurotransmitter serotonin from the dorsal raphe nucleus in the brainstem, which attenuates light-induced shifts in circadian phase (Rosenwasser, 2009). Different neurotransmitters, namely acetylcholine and

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noradrenaline, project from the basal forebrain to different areas in the cortex and support sustained attention (Dalley et al., 2001; Sarter, Givens, & Bruno, 2001). Additionally, many hormones like cortisol, testosterone, and oxytocin also exhibit natural circadian rhythms and are associated with basic sleep–wake rhythms (Amico, Tenicela, Johnston, & Robinson, 1983; Haus, 2007). Circadian rhythm disturbances are associated with multiple psychiatric conditions, including major depression, schizophrenia, and bipolar disorder (Cohrs, 2008; Pilz et al., 2018; Vadnie & McClung, 2017). Sensorimotor systems are responsible for the control, execution, and inhibition of motor behaviors. This affects many basic aspects of team performance in space, as motor actions enable individuals to move within the habitat or planetary surface, operate life-support systems, pick up and control objects, etc. Specifically, these motor behaviors are critical for all physical mission tasks, including piloting and landing, extra-vehicular activities (EVA), and telerobotics operations, as well as activities of daily living and self-care (e.g., food preparation and consumption, hygiene). Physical performance processes rely heavily on the motor cortex that projects to the basal ganglia, the brainstem, and the spinal cord, which terminate on motoneurons that innervate muscles to initiate movement (Lemon, 2008). Neurotransmitters glutamate (excitatory) and gamma-amino butyric acid (GABA; inhibitory) work together to release acetylcholine and promote muscle activity and movement (Grillner, 2015; Sian, Youdim, Riederer, & Gerlach, 1999).

Negative and Positive Valence Negative valence systems are responsible for fear, anxiety, threat, and loss, whereas positive valence systems support reward and reinforcement. Fear and anxiety usually manifest in avoidance behaviors and social withdrawal, with physiological markers of increased heart rate; decreased heart rate variability; and elevated cortisol, epinephrine, and norepinephrine. A major component of the negative valence system is stress. The “fight or flight” response is highly complex, with multiple types of stress mediators, like neurotransmitters (noradrenaline, serotonin) and hormones ­(corticotropin-releasing hormone [CRH], cortisol, and vasopressin). The ­hypothalamic–pituitary–adrenal (HPA) axis is central to the stress response. In anticipation of a threat, CRH is released from the hypothalamus to the pituitary gland, which releases adrenocorticotrophic hormone (ACTH), which enters the bloodstream and stimulates the release of cortisol and epinephrine from the adrenal gland. Cortisol returns to the hypothalamus to complete a negative feedback loop, diminishing activation (Pariante & Lightman, 2008). Additionally, during a stressful event, levels of inflammatory and immune molecules are also elevated and there are reduced nerve growth factors such as brain-derived neurotrophic factor (BDNF; Berntson et al., 1997; Dowlati et al., 2010; Howren, Lamkin, & Suls, 2009; Jaggar, Fanibunda, Ghosh, Duman, & Vaidya, 2019; Phillips et al., 1998). The limbic system deep in the brain is critical in modulating these processes. Because the limbic system is a ­functional concept, the strict definition of the anatomical structures within the ­limbic system is controversial, but it is usually agreed to include the bed nucleus of the stria terminalus, amygdala, and hippocampus (Lebow & Chen, 2016).

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Modifying behaviors is achieved through modulating neuronal function depending on the type of stress. For example, physical stressors may rely on brainstem and hypothalamic regions while psychological stressors like social tension, time pressure, or other mission-related stress may engage regions related to emotion (amygdala), memory (hippocampus), and decision-making (prefrontal cortex); (Ulrich-Lai  & Herman, 2009; Fenoglio, Brunson, & Baram, 2006). Stress may be acute, such as an emergency requiring quick action to fix, and is accompanied by rapid hormone release and neuronal activation. Acute stress can actually be a positive event, associated with boosts in physical and cognitive performance and immunity (Leonard, 2005). Chronic stress, like rising tensions between team members, is associated with sustained changes in gene expression, structural alterations in neurons, and neuronal firing changes (Joëls, Karst, Krugers, & Lucassen, 2007; McEwen, 2007). Chronic stress can fundamentally alter the structure and function of brain regions that modulate stress responses. HPA axis dysregulation is associated with psychiatric conditions as well as cardiometabolic disease, post-traumatic stress, immune dysfunction, and even dementia (Byers & Yaffe, 2011; Gianaros et al., 2017; Padgett & Glaser, 2003; Yehuda, 2001). Although the negative valence domain is critical to understand risks to physiological and psychological performance in space, the positive valence domain is no less important in the space environment. Positive valence behaviors include approach and reward-seeking, social interaction, and reduced stress responses. Reward and reinforcement processes primarily rely on the ventral tegmental area (VTA) and the nucleus accumbens (NAcc) in the midbrain. GABA and glutamate input to the VTA projects dopamine to the NAcc, which is associated with the experience of pleasure or reward (Salamone, Correa, Mingote, & Weber, 2005). Decreased dopamine responses are thus associated with anhedonia and mood disorders (Berridge & Kringelbach, 2015; Heller et al., 2009; Nestler & Carlezon, 2006; Supekar et al., 2018).

cognitive And sociAl Processes Cognitive processes like attention, memory, and cognitive control are fundamental to living and thriving in a spaceflight environment. Astronauts must rapidly acquire and retain complex knowledge, skills, and abilities that could have fatal consequences if underperformed or incorrectly executed. The taxonomy of long-term memory is typically sorted into “declarative” memory and “nondeclarative” or procedural memory (Squire & Zola, 1996). Declarative memory is often thought of as things you know that you can tell others while nondeclarative memory refers to things you know that you can show by doing, but not necessarily telling. Declarative memory is divided into two subtypes: episodic (autobiographical memory of past events) and semantic (generalized memory for facts and information). There are three subtypes of nondeclarative memory: skill learning (performing a task requiring motor coordination), priming (change in stimulus processing due to prior stimulus exposure), and conditioning (the association of two stimuli or of a stimulus and a response). Although many brain regions are involved in memory, the hippocampus is by far the most important. Hippocampal connections with the amygdala enable emotion

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to affect encoding and subsequent recall of certain evens (Squire, 1992). Key neurochemicals underlying cognitive processing include acetylcholine, glutamate, epinephrine, opioid peptides, and GABA (McGaugh, 1992). Intrinsic hippocampal circuitry (such as between the dentate gyrus, CA3, CA1, and subiculum regions) and extrinsic circuitry (parahippocampal region and higher cortical areas) contribute to the complex formation of memories. The ventrolateral prefrontal cortex is heavily involved in working memory, whereas the basal ganglia and cerebellum support procedural memory (Awh et al., 1996; Pascual-Leone et al., 1993). The prefrontal cortex (PFC) is critical for exercising cognitive control (Fuster, 2001). The PFC is thought to be involved in integrating information from different cortical areas, judgment and decision-making, planning, and abstract reasoning. Attention is modulated by well-documented neural systems: the fronto-parietal task control network, the dorsal and ventral attentional systems, and the cingulo-opercular control system (Power et al., 2011). Recent research has demonstrated that the frontoparietal network (FPN) alters its functional connectivity with nodes of other networks based on task goals. The FPN includes the dorsolateral prefrontal cortex, inferior parietal lobule, dorsal frontal cortex, intraparietal sulcus, precuneus, and middle cingulate cortex. This network is thought to initiate and modulate cognitive control abilities in a wide variety of tasks (Dosenbach, Fair, Cohen, Schlaggar, & Petersen, 2008). The FPN seems to connect brain regions that initiate attentional control in response to cues with regions that process performance feedback trial by trial to adapt rapidly. The dorsal and ventral attention systems work collaboratively and are involved in the deployment of attention (dorsal) as well as the reorientation to unexpected events (ventral). The dorsal system includes the frontal eye field as well as the intraparietal sulcus and is thought to be bilaterally organized. Specifically, the ventral system includes the ventral frontal cortex as well as the temporoparietal junction and is thought to be more lateralized to the right hemisphere (Vossel, Geng, & Fink, 2014). Finally, researchers have identified the cingulo-opercular component which provides stable “set-maintenance” over entire task epochs (rather than trial by trial). The cingulo-opercular network contains the anterior prefrontal cortex, anterior insula/frontal operculum, dorsal anterior cingulate cortex/medial superior frontal cortex, and thalamus (Dosenbach et al., 2008). The cerebellum at the base of the brain is also involved in that it processes error information to optimize performance (Fiez, 1996). Lastly, the social processes domain can inform our understanding of complex social dynamics and team performance inherent to LDSE, with the constructs of communication and affiliation/attachment within this domain are perhaps the most relevant (Landon, Slack, & Barrett, 2018; Roma & Bedwell, 2017). Sustained cooperation, communication, and coordination within a small team in isolation over extended durations pose a special challenge for mission success. Space exploration is also an international endeavor—people from dozens of countries have been in space; the International Space Station (ISS) has been occupied by astronauts from the US, Russia, Canada, Japan, and multiple European countries; and LDSE missions are expected to be multinational. Thus, social cooperation between international partners and multicultural awareness are key competencies in a spaceflight environment.

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Social neuroscience has identified many key brain areas involved in social cognition in the lateral and medial regions of the brain. Key areas include the amygdala, medial prefrontal cortex, anterior cingulate cortex, inferior frontal gyrus, intraparietal sulcus, temporo-parietal junction, posterior superior temporal sulcus, and the anterior insula (Frith & Frith, 2007). Eye gaze and facial expressions are also important in social learning and communication. Eye gaze can also be used to infer goals, and research shows that the anterior intraparietal sulcus is critical for this skill (Hamilton & Grafton, 2006). Humans also tend to automatically process faces as “trustworthy” or “untrustworthy”, and this process is mediated by the amygdala, which attaches emotional value to stimuli (Willis & Todorov, 2006). Overall, social signals, whether processed automatically or consciously, are a crucial aspect of successfully navigating a complex social world. Social communication is vital for individuals in this unique environment. Unlike other cognitive constructs such as attention or memory, social communication is always reciprocal and interactive. Social communications can be automatic or voluntary, and neural substrates have evolved to support both. Facial recognition, for example, may be an implicit aspect of social communication, whereas eye contact and reciprocation may be explicit aspects. Some molecules involved in facial communication include dopamine, GABA, oxytocin, serotonin, and vasopressin (Baribeau & Anagnostou, 2015; Lawrence, Goerendt, & Brooks, 2007; Thompson, Gupta, Miller, Mills, & Orr, 2004). Forming close relationships will vary among crewmembers; however basic social cooperation is necessary for personal and mission success. Effective communication is also an important skill for everyday life but becomes crucial for mission success with autonomous team working and living in isolation in extreme environments. Other aspects of the neurobiology of sociability and affiliation are well documented. Two hormones, oxytocin and vasopressin, have been consistently linked with positive and negative sociability (affiliation and aggression, respectively; Caldwell, 2012). The pituitary gland releases oxytocin and binds to receptors in the limbic system, which is believed to attenuate social stress (Boccia, Petrusz, Suzuki, Marson, & Pedersen, 2013). Affiliation can be thought of as engagement in positive social interactions with other individuals. It requires attention to social cues as well as social learning and memory to form and sustain relationships. While there are obviously the positive physiological consequences of social interactions, there are also negative behavioral and physiological consequences of social disruptions. When negative consequences become chronic and diagnosable, clinical representations of affiliation disruptions include social withdrawal, indifference, and anhedonia. Additionally, strong in-group bonds can influence how individuals perceive others outside of the group, which can lead to alienation and deception (Bartz, Zaki, Bolger, & Ochsner, 2011; De Dreu, Greer, Van Kleef, Shalvi, & Handgraaf, 2011; Eckstein et al., 2014; Shalvi & De Dreu, 2014). Overall, this simplified analysis of various neurobehavioral concepts reveals that the brain is a complex organ that, if compromised by the physical risks of space, can have a multitude of consequences that pervade throughout individual and team levels, jeopardizing human health and mission success. Next, we consider each of the five principal hazards of spaceflight and how they may affect mission success through direct and indirect action on these neurobehavioral systems.

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SPACEFLIGHT HAZARDS AND PHYSICAL RISKS TO BEHAVIORAL HEALTH AND PERFORMANCE rAdiAtion Radiation is energy emitted in the form of particles, electromagnetic waves, and/ or rays. In the electromagnetic spectrum, radiation can be seen in the form of visible light and felt in the form of infrared radiation. High-energy photons, X-rays, and gamma rays are not visible to the naked eye, but can be observed with special telescopes. There are three kinds of space radiation: particles trapped in the Earth’s magnetic field, solar particle events (SPEs), and galactic cosmic rays (GCRs). SPEs occur when solar flares explode on the surface of the sun, as they release massive amounts of energy in the form of protons, electrons, and HZE particles (Cucinotta, Townsend, Wilson, Golightly, & Weyland, 1994). GCR comprises nuclei from atoms that have had their surrounding electrons stripped away and are travelling at nearly the speed of light. Importantly, radiation is physical—it has mass, and exposure to radiation means particles physically entering or passing through the body and causing damage at the tissue, cell, and DNA levels. Radiation naturally exists throughout the universe; however, the Earth’s protective atmosphere and magnetic field shield us from most of it. In deep space, just the background dose rate of radiation is even higher than that on the ISS, the sun’s 11-year cycle culminates in a rapid increase of SPEs, and there is little to no natural protection from radiation. Concern about space radiation’s deleterious effects originated with the “light flash phenomenon” from high-charge and -energy (HZE) ions passing over the retina, first reported by the Apollo astronauts. Decades of research in animal models have shown that there is a known risk of acute (in-flight) and late (post spaceflight) central nervous system (CNS) effects from radiation exposure, such as fluctuations in cognition, motor function, behavior, mood, and neurodegeneration. Specifically, short-term memory, learning, emotion recognition, risk decision-making, vigilance, reaction time, processing speed, circadian regulation, fatigue, and neuropsychological changes are all affected (NASA Johnson Space Center, 2009; Nelson, Simonsen, & Huff, 2016; Strangman, Sipes, & Beven, 2014). NASA organizes space radiation risks into four domains: cancer, central nervous system, degenerative, and acute radiation syndromes. Of all the risks posed by increased radiation exposure, scientists know the least about inflight and late CNS effects. It is not ethical to experimentally manipulate radiation in humans, so scientists rely on animal models, cellular models, and clinical and epidemiological association studies to understand the risk and establish permissible exposure limits. Experimental studies with mice and rats have shown that exposure to HZE nuclei (in GCRs) at low doses significantly induces neurocognitive and operant behavioral performance deficits. For example, one such study found radiation-induced impairments in spatial, episodic, and recognition memory as well as deficits in executive function and reduced rates of fear extinction and increased anxiety (Parihar et al., 2016). They also found significant reductions in dendritic complexity, spine density, and altered spine morphology in medial prefrontal cortical neurons. HZE has also been shown to disrupt neurogenesis in mice at low doses (Rivera et al., 2013).

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Other work in rats revealed individual differences in attentional deficits emerging at five months following head-only exposure to low-dose proton radiation, effects which correlated with degradation in the functioning of the dopamine system (Davis, DeCicco-Skinner, Roma, & Hienz, 2014). Since radiation cannot ethically be experimentally manipulated in human research experiments, evidence must be collected from radiotherapy patients as well as atomic bomb survivors. However, terrestrial radiation and space radiation are fundamentally different and the dose rate is much higher than that in space (Greene-Schloesser  & Robbins, 2012; Greene-Schloesser et al., 2012). Both are imperfect models with many confounding factors that must be taken into consideration. For example, cancer patients undergoing irradiation have higher rates of chronic fatigue and depression as compared to healthy adults (Weber & O’Brien, 2017). Nevertheless, researching these special populations can lend some insights into how radiation affects cognition. Deficits in neurogenesis (growth of new brain cells) may be the mechanism by which cognitive decline occurs in irradiated patients since cognitive functioning and memory are closely associated with the cerebral white volume of the prefrontal cortex and cingulate gyrus (Schauer & Linton, 2009). A review on intelligence and the academic achievement of children after treatment for brain tumors reveals that radiation exposure is related to a decline in IQ scores, verbal abilities, and performance IQ as well as performance in reading, spelling, math, and attentional functioning (Butler & Haser, 2006). Furthermore, adult survivors of acute lymphocytic leukemia who underwent cranial radiation exposure also show a reduction in intelligence scores (Armstrong et al., 2013; Brouwers & Poplack, 1990). Atomic bomb and Chernobyl victims also provide evidence that low–moderate doses of radiation can impair memory and other cognitive functions. These victims are more frequently seen medically for psychiatric disorders and exhibit altered electroencephalographic (EEG) patterns (Bromet et al., 2011; Hall et al., 2004; Loganovsky & Loganovskaja, 2000; Loganovsky & Yuryev, 2001; Mickley, Ferguson, Mulvihill, & Nemeth, 1989; Yamada et al., 2018). Thus, although we know that chronic radiation exposure, like what is expected in an extended mission to Mars, may affect the CNS, we do not know exactly what behaviors or brain regions will be differentially affected and how this may impact the mission. Independent of acute life-threatening exposure or increased cancer risk, space radiation may pose a significant physical threat to the human body, including the neurobiological mechanisms mediating individual and team functions and performance.

Altered grAvity Although not physical mass like radiation, gravity is a physical force that acts upon the body and can alter physiological functioning, behavior, and performance capacity. LDSE crews will encounter three different gravity fields throughout their missions: microgravity in space, partial gravity (e.g., Mars is 38% of the Earth’s gravity), and the Earth’s full gravity upon return. Prolonged exposure to each gravity field and each transition can impact locomotion, motor coordination, spatial orientation and vision, balance, and bone and muscle strength. As outlined in this section, gravity

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transitions mostly affect individual health and performance. However, when an individual experiences physical impairments or distraction from the symptoms of shifting gravity fields and is tasked with a mission-critical assignment, this may lead to error and affect team performance as well. Despite the multitude of spaceflight analogues, microgravity is the only spaceflight stressor that cannot be consistently simulated on Earth. Although commonly referred to as “zero-g,” this term is misleading, as a small amount of gravity exists in space. Microgravity affects the body in a multitude of ways. When gravity is not acting on fluids in the body, blood and cerebrospinal fluid shift to the head, resulting in a “puffy” face and thin legs. This fluid shift also enables space motion sickness, headaches, and visual impairments. As pressure builds from the increase of blood flow to the brain, vision can become distorted, part of a phenomenon termed Spaceflight Associate Neuro-ocular Syndrome (SANS; Lee, Mader, Gibson, & Tarver, 2017; Marshall-Goebel, Damani, & Bershad, 2019). Dizziness and motor coordination are also affected by microgravity, especially seen when astronauts return to the Earth and can barely stand or walk in a straight line. The vestibular system in the inner ear relies on gravity to sense direction and maintain balance. Astronauts typically take a couple of weeks to adjust to microgravity in space and to gravity back on Earth. During this transition, astronauts may experience nausea and deterioration in spatial orientation, hand–eye coordination, balance, and locomotion (Bloomberg, Reschke, Clement, Mulavara, & Taylor, 2015). Microgravity also affects bones and muscles, both of which atrophy from disuse in microgravity environments. This characteristic bone loss is referred to as spaceflight osteopenia. On Earth, the elderly lose on average 1% of bone mass per year. In space, astronauts lose on average 1% of bone mass per month. Some astronauts have lost as much as 20% of bone density, while others are less affected. Bone and muscle atrophy are mitigated by exercise and physical activity (Sibonga et al., 2017). In space, exercise is essential—indeed prescribed as a mission-critical activity— to mitigate bone and muscle degradation caused by microgravity. Fortunately, this countermeasure to microgravity effects also provides neurobehavioral benefits, with evidence showing that physical activity can alleviate depression, enhance memory performance and motor acquisition, and improve sleep quality (Chang, Labban, Gapin, & Etnier, 2012; Cooney, Dwan, & Mead, 2014; Reid et al., 2010; Roig, Skriver, Lundbye-Jensen, Kiens, & Nielsen, 2012). Exercise also directly improves brain function by regulating brain-derived neurotrophic factor, insulin-like growth factor  1, and vascular endothelial-derived growth factor (Cotman, Berchtold,  & Christie, 2007). These growth factors have been shown to lessen depression, increase learning, and enhance the growth and repair of blood vessels and brain tissues (Silverman & Deuster, 2014).

Hostile/closed environments The spacecraft is not just a means of interplanetary transportation but also a complex ecosystem: it is a workplace, a laboratory, a home, and the only available habitable space for millions of miles. The habitat itself may not be a natural environment, but is no less a potential hazard, and therefore must be carefully designed. For basic

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life support, the air quality of the space station is constantly monitored. Gases like formaldehyde, carbon monoxide and carbon dioxide, and ammonia pose significant safety and health risks, especially in a closed habitat. For example, ammonia can act directly on brain cells to alter firing potential, ultimately contributing to memory impairments, attentional deficits, and sleep–wake disturbances (Bosoi & Rose, 2009), and formaldehyde exposure can impair memory and increase symptoms of depression through reduction of stress hormone receptors in the hippocampus (Li et al., 2016). Microorganisms from the crew’s own microbiomes, materials and supplies from Earth, or onboard waste management and recycling systems can be much more easily transferred and perpetuated in the fully closed environment of a spacecraft. Astronauts’ immune systems can change during spaceflight, leading to increased susceptibility to allergies, illnesses, and subsequent performance impairments. Immune system defenses against exposure to pathogens include release of multiple cytokine molecules that interact with the HPA axis stress system and hippocampus and can negatively affect learning and memory (Biron, 1994; Donzis & Tronson, 2014; McAfoose & Baune, 2009).

isolAtion And confinement Prolonged isolation is well established as a negative stressor. The limited communications bandwidth and the up to 22-minute delay each way compounds the stress involved in LDSE missions. Moreover, the lack of real-time communication with mission control, medical and behavioral health operations support, family, and friends may contribute to feelings of isolation and adversely impact health and performance (Kanas et al., 2007; Kass, Kass, & Samaltedinov, 1995). Currently, ISS crew members work in rotation approximately every three months and receive a regular supply of goods. However, a planetary mission to Mars allows for no crew rotation or resupply, which means crews will likely not receive care packages, fresh food, or an influx of new crew members, all of which psychologically contribute to more isolation. Perceived isolation has contributed to depression-like symptoms among astronauts on the four-month missions to Mir space station (Burrough, 1998) and ISS astronauts have reported similar symptoms in other studies (Stuster, 2010). At a team level, isolation leads to decreased morale and interpersonal tensions (Sandal, 2001), as well as decreased communication between crew members (Kanas et al., 2007). Astronauts may focus on their own priorities, away from team goals, due to the stress induced by isolation (Orasanu, 2009). Apart from direct physical impacts from environmental toxins and microorganisms, habitability of the constructed space is a very important aspect of the confined environment, which can indirectly impact the brain and shape behavioral health, functioning, and performance. For example, temperature and pressure regulation, lighting, noise, and adequate volume for task performance all affect how astronauts live and work in space and can serve as compounding and cumulative stressors. The ISS has a habitable volume of 388 m3, approximately the size of an average four-bedroom home. Typically, up to six crew members share this space

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(~64 m3 per  person). Living spaces will be even more confined in future LDSE missions. Subject matter experts determined that the minimum acceptable net habitable volume for a Mars mission is 25 m3 per person, with functional areas of sleep/ private quarters, dining and communal activities, work space, exercise, hygiene, pass-throughs, and stowage (Whitmire et al., 2015). Living in close, confined quarters for months or years at a time in an unfamiliar and austere environment can be regarded as a chronic stressor; thus, special care must be taken to ensure that the habitat itself not only provides for efficient mission operations but also adequate sensory stimulation, comfort, and control for autonomous crews. Sensory stimulation can provide restorative relaxation and therapeutic release of emotion (Vessel & Russo, 2015). In space, environmental design is not confined to floors, walls, and ceilings. Every inch of space can and must be utilized. At the 2015 Human Research Program Investigators’ Workshop, astronaut Peggy Whitson noted that private space is the most crucial habitability factor, and experts agree that private crew areas are extremely important for individual and team behavioral health and performance (Kanas & Manzey, 2008; Santy, 1983; Simon, Whitmire, & Otto, 2011; Whitmire et al., 2015). Indeed, teams in confined spaces over extended periods may wish to spend considerable time alone as a countermeasure, but if unbalanced, they may indicate social withdrawal and risk team functioning (Basner et al., 2014). Other design factors can affect well-being in the austere spaceflight operational environment, including colors, which, in addition to sensory stimulation, can be used to orient crew members in the absence of gravitational cues (Raybeck, 1991; Stuster, 1996). Windows are also an important psychological countermeasure to mitigate the sense of confinement and environmental monotony (Haines, 1991). Exposure to nature can be restorative and stress-reducing, which also supports the importance of windows for viewing the cosmos. Growing plants within the habitat can enhance the environment, maintain connection to Earth, and offer a potential source of pride in maintaining life in such an extreme environment (Massa, Wheeler, Morrow, & Levine, 2016; Simon et al., 2011; Zabel, Bamsey, Schubert, & Tajmar, 2016).

distAnce from eArtH Exploration inherently means leaving behind everything and everyone we know, often for an extended duration across a great distance. LDSE missions are the ultimate journey and will require meticulous planning and astronaut self-sufficiency. The moon is 239,000 miles (385,000 km) from the Earth, and the manned Apollo missions were completed in less than a week. However, the distance between the Earth and Mars ranges from 33 to 248 million miles (54–400 million km), with planetary surface missions expected to last nearly three years overall. Although common logistical issues or potential problems can be anticipated from astronauts’ experiences on the ISS, there is certainly no way to ensure what complications may arise under LDSE mission conditions of necessarily high autonomy and constrained resources with no real-time support, resupply, or evacuation strategies. The physical risks that the distance from the Earth creates include the limited medical care, specifically the potential unavailability, ineffectiveness, unintended

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side-effects, or degradation/toxicity of medications (Wotring, 2011). Astronauts use medications most often for sleep problems, pain, congestion, and allergy relief at comparable rates with healthy adults except for the more frequent use of sleep medications (Barger et al., 2014; Wotring, 2015). Many of the limited complement of medications expected to be included on LDSE missions act directly on the brain and can affect behavioral health and performance, either by design (e.g., sleep aids, anxiolytics, alertness aids) or as side effects (e.g., antihistamines, steroid hormones). Of considerable concern is the fact that without resupply, medications must remain stable for the entire duration of the mission. Loss of potency confers risk, but increased radiation exposure could also promote degradation or conversion into other, potentially toxic, chemicals with unknown effects on physical and behavioral health and performance (Mehta & Bhayani, 2017). Perhaps, the most prominent physical hazard of the distance from Earth with behavioral health and performance implications is in the highly constrained food system required for LDSE missions. Due to storage and mass constraints, food on the ISS comes from a standard menu that is somewhat restricted in quantity and variety. For LDSE missions, food scientists face the daunting task of creating food that is palatable, nutritious, and even more mass- and cost-efficient. Shelf stability and sustained acceptability are critical, as nutritional deficits can impact physical health, performance, and individual and team behavioral health. Zinc, vitamin D, and omega-3 fatty acid deficiencies are associated with depression, anxiety, and other mood disorders (Mitsuya et al., 2015; Patrick & Ames, 2015). Foods can also indirectly affect the brain through interfacing with the gastrointestinal (GI) microbiome. The GI system metabolizes food into fatty acids, peptides, phenolic acids, and neurotransmitters, which can easily travel to the brain and affect social behavior and cognition (Tengeler, Kozicz, & Kiliaan, 2018). Flight psychologists and crew alike have extolled the importance of nutritious food in a spaceflight environment as a way of fostering team cohesion, promoting morale, and replenishing key nutrients needed to maintain health and performance (Douglas, 2016; Stuster, 2010). Various reviews of food systems in spaceflight settings have reported that food variety, quality, and availability affect behavioral health, team morale, and social cohesion, particularly with multi-national crews (Douglas, 2016; Smith, Zwart, & Heer, 2015; Stuster, 2010). Food can act as a highly effective behavioral health countermeasure when it comes in care packages filled with fresh food and personal favorite snacks from home. Unfortunately, all of these food-based countermeasures will not be available for LDSE missions. In order to meet nutritional requirements under harsh spaceflight conditions, the LDSE food system must remain pre-packaged and be exceptionally shelf-stable. The entire food supply for the surface operations and return transit phases of the mission will likely be pre-positioned on the surface of Mars prior to the crew’s arrival. Production will begin long before crew selection, thereby precluding personalized menus and preference foods. There will be no resupply, and thus no influx of fresh food, with at most one or two grown vegetables such as leafy greens as an occasional supplement to the food system. Clearly, the distance from Earth that defines LDSE missions poses considerable physical and psychological risks to individual and team performance and functioning.

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SUMMARY AND CONCLUSION Long-duration space exploration crews will face a unique set of physical hazards in the spaceflight operational environment, including radiation, gravity fields, hostile/ closed environments, isolation and confinement, and distance from the Earth. Each of these hazards can physically affect the neurobiological and neurobehavioral mechanisms underlying individual and team behavioral health and performance, as outlined in the National Institute of Mental Health’s Research Domain Criteria framework, including arousal and regulatory systems, sensorimotor systems, negative valence systems, positive valence systems, cognitive systems, and social processes. Given the distinctively challenging physical risks of LDSE missions, we encourage integrated, multidisciplinary approaches to current and future research and operations that consider the interaction of biological, psychological, social, and environmental factors. Our effort to elucidate some of the biological bases of behavioral health and performance in extreme environments is by no means a call for those focused on psychological, social, and organizational factors in spaceflight to abandon their disciplines, or to add unnecessary complexity to already complex problems. Rather, we assert that complexity is necessary because it is inherent to the space exploration enterprise, and somewhat paradoxically, a multidisciplinary approach ultimately serves parsimony. Mission risks in complex operational environments rarely present themselves as unidimensional phenomena. For example, poor performance on a cognitively demanding task may indeed be not only due to poor communication due to interpersonal tensions, or compromised cognition from sleep deprivation or cumulative stress of isolation, but perhaps just as likely an artifact of sensory modality (e.g., vision-based task compromised by SANS from microgravity and radiation) or response modality (e.g., impaired muscle strength or motor control to execute the task). With a multitude of factors potentially interacting to contribute to performance decrements and behavioral health dysfunction in the resource-constrained closed system of LDSE missions, an integrated multidisciplinary approach can help identify the most direct or effective pathways to maintaining and restoring performance. Ultimately, a more complete understanding of how the LDSE mission environment affects the body and brain can then guide the development of effective mission architectures; habitat and environmental systems; tools for prevention, training, monitoring, and maintenance; and operational countermeasures in support of those who will work, live, serve, and explore on the final frontier.

ACKNOWLEDGMENTS JMS and PGR are supported by KBR’s Human Health and Performance Contract NNJ15HK11B through the National Aeronautics and Space Administration. JMS and PGR are also supported in part by NASA Human Research Program Directed Project CBS Operational Performance Measures (P. G. Roma, PI), and JMS is supported in part by UCLA’s Graduate Research Mentorship Fellowship. The authors of this chapter are entirely responsible for its content and the decision to submit the work for publication. We thank Kerry A. George and Cara A. Spencer for critical comments on the manuscript.

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Psychology and Human Performance in Space Programs

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US Government, the National Aeronautics and Space Administration, KBR, or the University of California, Los Angeles. The authors have no interests that may be perceived as conflicting with the work described or proposed here. Author contributions: PGR conceived the project and designed the review. JMS and PGR wrote the paper. All authors made substantial contributions and reviewed and approved the final content.

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Spaceflight Research on the Ground Managing Analogs for Behavioral Health Research Ronita L. Cromwell Baylor College of Medicine

Joseph Neigut NASA’s Lyndon B. Johnson Space Center

CONTENTS Introduction .............................................................................................................. 23 Existing Analogs and Characteristics....................................................................... 28 Comparison of ICE and ICC Analogs...................................................................... 31 Developing and Creating an Analog ........................................................................ 34 Use of Analogs for Research.................................................................................... 37 Research Implementation in Analogs....................................................................... 39 Summary and Conclusions....................................................................................... 43 References................................................................................................................. 44

INTRODUCTION Ground-based analogs are used extensively in spaceflight endeavors, both for informing and developing operational concepts and for answering research questions. A spaceflight analog in simple terms replicates on Earth a space environment or condition, or produces physiological or behavioral effects on the human body similar to those experienced in spaceflight. Analog environments provide the potential to study the effects of spaceflight on the human body in the relative convenience of being on Earth. Analogs on one end of the spectrum can be simple and straightforward or on the other end complex and elaborate. For example, an investigator evaluating new software for a manual vehicle docking system could easily set up this simulation in a ground-based laboratory. However, if the objective is to evaluate human performance using this software during a mock emergency with a sleep-deprived crew, this would be a much more complex project requiring a more elaborate facility. Both of these problems and approaches qualify as analogs.

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Analogs, and the resulting research and operations conducted in their environment, are essential to space agencies for fulfilling space exploration objectives. Within analogs, agencies perform space-oriented research; develop processes and procedures for exploration missions; prove test concepts in a realistic environment; and train flight crews and ground personnel on hardware, software, and general operations. A wide variety of analogs are in use worldwide to support spaceflight research and training. No single analog perfectly emulates all possible spaceflight consequences to the human being. Each analog possesses specific characteristics that make it suitable for the study of particular effects of spaceflight. Having a portfolio of analogs to choose from will aid in the selection process. Selection of the appropriate analog must carefully consider the match between analog characteristics, and the requirements of the training or research conducted in the analog. Users may find that due to limitations of individual analogs, multiple analogs may be needed at different stages to accomplish all objectives. NASA has a rich history of using analog settings for its research and training objectives. NASA’s need for spaceflight analogs began out of necessity to prepare and train early crews for their missions in space. Replicating different aspects of these missions on Earth was essential for these crews, and engineers were tasked with creating and developing novel, analogous environments and hardware that mimicked the space flight environment. One of the early microgravity simulators allowed Ed White to prepare for the first US spacewalk by floating on a cushion of air (Figure 2.1) (NASA, 2015). Devices of this type are still in use. Spacewalkers who followed eventually trained for their extravehicular activities (EVAs) in the spaceflight analog environment provided by the Neutral Buoyancy Simulator (NBS) Facility, in Huntsville, Alabama (Figure 2.2). The NBS is the

FIGURE 2.1 This analog creates a cushion of air above a steel floor simulating zero gravity conditions in two axes. Ed White is depicted doing spacewalk training, practicing with his hand-held maneuvering unit.

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FIGURE 2.2 View of the Neutral Buoyancy Simulator. The space shuttle mock-up can be seen at the bottom of this 40-foot deep tank.

predecessor to the Neutral Buoyancy Lab that is currently in use at the Sonny Carter Training Facility near the Johnson Space Center in Houston, Texas. The NBS was NASA’s first large, water-filled tank designed for training purposes. It created on Earth an experience very close to the weightless conditions of working in space. Full-sized mock-ups of space hardware and training structures were submerged in the NBS. Uses of the NBS included confirming engineering designs and procedures, and practicing mission maneuvers by astronauts (Library of Congress, 2019). As NASA’s mission objectives changed, so did the analogous locations for training. Lunar-based training sites were selected in many locations including Hawaii, Alaska, and several sites in the Western USA (Oregon, California, New Mexico, Arizona, Nevada, and Texas). International sites such as Mexico and Iceland were also used for training purposes. Most of these locations were chosen for the geology training necessary for lunar exploration. These training opportunities eventually evolved into lunar simulations known as the “Moon game” in which astronauts were paired up and placed in unfamiliar terrain. Equipped with radios, the participants communicated with their field guide instructors remotely. The transmissions were recorded so that procedures for communications between scientists and astronauts could be analyzed and improved for future lunar exploration (Phinney, 2015). In addition to utilizing natural geology, NASA and the United States Geological Survey (USGS) began to create environments to test tools, scientific equipment, procedures, and hardware that ultimately would go to the lunar surface. One of these early analog facilities was the “Moon Room” located at Ellington Field in Houston, Texas (Figure 2.3) (Phinney, 2015). Beginning in 1967, NASA and the USGS set out to create a realistic moon-scape by using explosives to mimic the impact craters found on the moon (Figures 2.4 and 2.5). These areas continued to grow in scale, culminating in a 35-acre site near Flagstaff, Arizona. This site was modified to include 380 craters made by 850 t of trinitrotoluene (TNT) and 43 tons of ammonium nitrate.

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FIGURE 2.3 The “Moon room” at Ellington Air Force Base used by Apollo astronauts to simulate the lighting conditions and surface structure of the lunar surface.

a FIGURE 2.4 Apollo 15 crew members ride the Lunar Roving Vehicle during geology training at the Cinder Lake crater field in Arizona. They are stopped at the rim of a 30-foot deep crater to examine the terrain.

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FIGURE 2.5 Rusty Schweickart examines brecciated rock on a crater wall at the Nevada test site, Yucca Flat.

These sites were used to practice driving the Lunar Roving Vehicle (LRV) simulator as well as develop procedures for new hardware including scientific equipment destined for the moon (Phinney, 2015). Lunar gravity conditions were simulated in a number of ways during the Apollo program. Physical performance was tested in a simulated lunar gravity field at Langley Research Center (Figure 2.6) (NASA, 2008). Pressure suits supported by a system of slings and cables were worn by astronauts as they controlled a trolley and performed maneuvers. Researchers studied astronauts’ ability to walk, jump, and run using this

FIGURE 2.6 program.

Photograph of the Langley Reduced Gravity Simulator used during the Apollo

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ingenious lunar-gravity simulator. Other studies examined astronauts’ fatigue limits and energy expenditures in this simulation of one-sixth gravity. In addition to the land-based analogs, NASA also utilized the KC-135 Parabolic Flight aircraft and the NBS to simulate the 1/6 g gravity environment of the moon (Phinney, 2015). Over time, evolution of analog environments and facilities by NASA continued to become more extensive, complex, and innovative to meet the demands of the program’s objectives. New analogs were created to simulate particular aspects of the spaceflight mission to meet requirements for training of spaceflight maneuvers, testing engineering hardware, and assessing physical performance of astronauts. This chapter examines a variety of analogs that currently exist for spaceflight ground research. Analogs are compared and contrasted to examine the unique characteristics of each of them. Discussion of analog development addresses key issues of analog fidelity, and assembling a facility to meet research and programmatic requirements. Presentation of analogs as used for research purposes explores access to analogs, and how analogs are part of a stepwise process for taking research to spaceflight. Finally, a discussion of research implementation in analogs provides insights into study integration, participant selection, and mission readiness.

EXISTING ANALOGS AND CHARACTERISTICS AnAlog cAtegories Analogs used for studying spaceflight can be broadly categorized as those that (1) change human physiology in ways similar to spaceflight and (2) simulate spaceflight missions. Analogs used to induce physiological changes typically do not simulate a spaceflight mission. Rather, they produce adaptations in physiology similar to what is experienced in spaceflight. One analog used for this purpose is head-down tilt bed rest. Long duration (60 days or more) (Sundblad & Orlov, 2015) head-down tilt bed rest can produce bone and muscle atrophy, and create headward shifting of body fluids similar to the case of spaceflight (Spector et al., 2009; Platts et al., 2009). Dry immersion is another analog that produces similar effects as bed rest on muscle, bone, and cardiovascular systems with added effects on the sensorimotor system of both decreased muscle tone and afferent input (Navasiolava et al., 2011). To achieve these effects, an individual is immersed into a tank up to their neck and surrounded by a bladder filled with thermo-neutral water. The individual is suspended within the water, but remains dry (Navasiolava et al., 2011). Analogs that change physiology are usually not utilized for research related to behavioral health. These analogs are not designed to study phenomena such as the effects of isolation, crew autonomy, and team cohesion. Typically, subjects participating in long duration bed rest studies will experience physiological changes similar to spaceflight that can be studied by investigators. However, these subjects routinely interact with medical staff and scientists, and participate in activities to alleviate boredom. These conditions do not permit the study of behavioral effects of spaceflight. Indeed, interactions with staff and participation in activities can be thought of as countermeasures to the effects of isolation and disqualify bed rest as an analog to study many behavioral health objectives.

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However, investigators are beginning to examine behavioral responses to the bed rest analog (Gouvier et al., 2004; Styf et al., 2001). In these studies, modifications were made to the bed rest analog and psychological measures were assessed. In one study, the 6° head-down tilt bed rest analog was modified to include daily doses of 3,5,3’-triiodothyronine (T3) to accelerate muscle and bone atrophy associated with 28 days of head-down tilt bed rest (Gouvier et al., 2004). In one arm of this study, pharmaceutical countermeasures of testosterone and alendronate were provided to counteract the accelerated muscle and bone loss. Neuropsychological assessment using 82 measures was implemented to understand the response to simulated microgravity using this enhanced bed rest platform with added countermeasures. Overall, findings indicated that the testosterone and alendronate countermeasure groups demonstrated improved psychological adjustment over the group receiving T3 without a countermeasure. In another study designed to examine spaceflight-related back pain, the 6° headdown tilt bed rest model was modified to include counterbalanced traction where 5% of the body weight was added to each leg via a pulley system at the foot of the bed. This model was compared with horizontal bed rest that served as a control. Behavioral health testing examined pain, mood state, and depression to assess the psychological impact of these bed rest models over 3 days of bed rest. Results indicated that subjects in 6° head-down tilt bed rest positions using counterbalanced traction had greater lower back and abdominal pain, and lower activity scores on the mood state questionnaire. Depression was not significantly different between subject groups. These studies illustrate the importance of assessing psychological effects of being exposed to analog models that simulate spaceflight-related changes in physiology. Behavioral health results can provide valuable insights as investigators interpret their physiological findings. Analogs that simulate spaceflight missions can be further divided into two general categories. One is an isolated, confined, and extreme (ICE) environment, and the other is an isolated, confined, and controlled (ICC) environment. ICE analogs are remotely located facilities in environments that produce extreme conditions. Extreme conditions include high-risk environments such as dangerously cold weather or undersea habitation. Travel to these analogs is often difficult with little or no opportunity for emergency evacuation once the crew members arrive. Missions conducted at ICE locations are typically centered on training exercises, or field research such as geological, environmental, or marine science. Human spaceflight investigations become an adjunct to the primary mission. The group conducting training exercises or fieldwork is a sample of convenience for the spaceflight ground study. In effect, the group constitutes a crew on a mission who serves as an analog for astronauts on a spaceflight mission. During the mission, the crew then participates in research studies that include, for example, examination of responses to isolation and stress. Some examples of ICE analogs include the Haughton-Mars Project on Devon Island, Canada (Mars Institute, 2018); a number of Antarctic stations in Antarctica (NSF, 2018); and the Aquarius Undersea Laboratory off the coast of Key Largo, Florida (FIU, 2018). The varying sizes of these analog facilities must be considered when choosing an analog. For example, McMurdo Station is the largest station in Antarctica located on Ross Island (“Virtual Tour – McMurdo Station,” 2019). It comprises more than 100 buildings and includes a harbor and Williams Field airport.

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McMurdo Station is much like a small village and residents have access to many resources such as housing, research, and conference facilities. Conversely, the Antarctic Search for Meteorites (ANSMET) program conducts field research during the Antarctic summer months (ANSMET, 2018). Small teams of scientists (five to seven explorers) spend the Antarctic summer camping on the ice in tents as they search for meteorites in the Transantarctic Mountains (Figure 2.7). In addition to primitive living conditions, they must also bring all necessary supplies and resources with them to sustain throughout the mission. Mission durations also vary among analog sites and may be a factor to consider when conducting research. An Antarctic winter-over season lasts from March through October. This duration may be useful for some studies, as it is similar to a 6-month stay on the International Space Station (ISS). In contrast with this, the Aquarius Undersea Laboratory hosts the NASA Extreme Environment Mission Operations (NEEMO). Studies conducted as part of NEEMO training missions last only 7–21 days. This length of stay was similar to space shuttle missions and will be analogous to upcoming missions in the NASA Artemis program. As NASA looks toward long-duration exploration missions to the moon and Mars, use of the ISS as an ICE analog becomes increasingly important. Under typical mission conditions, astronauts on the ISS are provided with constant communications from ground; and upon landing, they receive immediate assistance and medical support. Plans for using the ISS to simulate exploration conditions are under way (Robinson, 2019). Astronauts will spend a 2-week period of communication delays with ground support to gain experience and determine how best to handle these delays. They will also complete simulated medical procedures without ground support to test crew autonomy capabilities. At the landing site, deconditioned astronauts will carry out tasks typical of the first 2–3 days of planetary habitation. These types of studies will allow the ISS to be used as a deep space analog for exploration missions.

FIGURE 2.7

Camping on the ice for an ANSMET mission.

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Another type of analog that simulates a spaceflight mission is the ICC analog. The ICC provides isolation and confinement under controlled conditions. ICC facilities tend to be more easily accessible and can be located in cities or within close proximity to a space center. In the ICC environment, research subjects who serve as crew members are typically confined to a habitat that simulates, for example, an exploration transit vehicle or a planetary habitat. Mission scenarios are created to provide meaningful work and immerse subjects in the spaceflight experience. While missions in ICC analogs are simulated, the need to create realistic mission scenarios and tasks is of critical importance for the successful use of the analog. As they enter the habitat, subjects are fully aware that they can leave at any time and easily return to their normal daily activities. However, carefully designed missions that include realistic spaceflight operational tasks will allow subjects to completely immerse themselves as though they were participating in a real spaceflight mission. While subjects understand that they are not really traveling through space or living on a planetary surface, the sense of importance of the work they are doing keeps them engaged and dedicated to mission success. In addition to designing realistic mission scenarios and tasks, conditions within the ICC analog can be standardized and controlled. For example, environmental conditions of temperature and humidity are often controlled. Mission duration and sleep/wake cycles can be standardized. A uniform diet can be provided to all subjects. Standardizing these conditions provides a consistent platform to limit confounding variables for research studies. Research studies are then integrated as part of the mission similar to what occurs on actual spaceflight missions. These investigations typically examine phenomena such as crew autonomy, team cohesion, and effects of stressors like fatigue and communication delays. Examples of ICC facilities include the Japan Aerospace Exploration Agency (JAXA) Isolation Chamber at the Tsukuba Space Center, Japan (JAXA, 2018); the Institute for Biomedical Problems (IBMP) Ground Based Experimental Facility (in Russian the “Nazemnyy eksperimental’nyy kompleks” abbreviated as NEK) in Moscow, Russia (NASA, 2018); and the NASA Human Exploration Research Analog (HERA) facility at NASA Johnson Space Center in Houston, Texas (NASA, 2018). ICC analog facilities can vary in size. For example, the NEK facility consists of four modules that can support habitation and includes sleeping quarters, kitchen facilities, an exercise gym, medical examination space, and a planetary landing ship (Figure 2.8). In addition to the four habitable modules, there is also a large module to simulate a planetary surface (NASA, 2018). In contrast, the NASA HERA facility is a single, two-story module with an attached hygiene module and simulated airlock (Figure 2.9). Mission durations at these facilities are also variable. The NEK facility hosted the Mars-500 project that ran for 520 days to simulate a trip to Mars. Early missions in the NASA HERA facility lasted 7 days with more recent missions extending to 45 days (NASA, 2018).

COMPARISON OF ICE AND ICC ANALOGS ICE and ICC analogs are used to create terrestrial missions as a platform for studying the effects of spaceflight. Each type of analog has unique characteristics that should be considered by researchers when designing analog investigations. These characteristics are summarized and contrasted in Table 2.1. The environment where

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FIGURE 2.8 The IBMP NEK analog used for long duration isolation missions.

FIGURE 2.9 The NASA HERA facility as it currently stands indoors at the NASA Johnson Space Center. The sleeping loft was added in 2011 (University of Wisconsin, 2011) prior to being acquired by the Human Research Program in 2013.

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TABLE 2.1 Characteristics of ICE and ICC Analogs ICE

ICC

Extreme environment Real mission Research is an adjunct to the mission Limited or no experimental control of conditions Crew selected for field work or training mission Crew size is variable

Local environment Simulated mission Research is the purpose for the mission Experimental control of conditions Crew selected to meet astronaut criteria Crew size can be controlled

Note: ICE = isolated, confined and extreme; ICC = isolated, confined and controlled.

the analog resides can contribute to the realistic nature of the mission. ICE facilities located in extreme environments are analogs for the remoteness and danger associated with space travel. ICC facilities however, are usually situated locally near cities or towns, and are more easily accessible. This may detract from the realism of the mission for some research participants, especially when airplanes, thunderstorms, and car alarms can be heard from inside the habitat. The ICE environment provides a real mission scenario. Crew members have a purpose for their mission such as climate research, astronomy and astrophysics investigations, and marine life and ecosystem studies (Scientific Committee on Antarctic Research, 2018). For ICE analogs, spaceflight research is an adjunct to the purpose of the mission. Because spaceflight research is an add-on to the primary purpose, the ability to control experimental conditions is either limited or not possible. Spaceflight research studies then examine crew responses to the mission as it occurs. Often, data collection can be hampered due to the remote location, distraction from the actual objectives, and lack of reliable logistics for return of data and biological samples. In contrast, the ICC mission is a simulated spaceflight mission. Typically, isolation facilities are used to simulate a space vehicle or planetary habitat, and a mission is carefully constructed within it. Design of the analog mission supports spaceflight research studies and takes into account the requirements for each study. The purpose for conducting a mission is to provide a ground-based platform for spaceflight research data collection. Experimental control of conditions is implemented to avoid confounds and provide consistency across each of the individual missions. Crew selection and subject sample size are characteristics to consider when designing research for the ICE and ICC analogs. Selection of crew for ICE missions is typically done by the agency supporting the field work. Crew members are selected based on their scientific background or technical expertise as related to their field work to be completed. For ICC analog missions, crew selection criteria are determined by the spaceflight research studies and are often aligned with astronaut selection criteria. Sample size will vary depending on the ICE or ICC facility being used. The number of people at any particular ICE facility can vary widely and may depend on the time of year. For example, in the summer, the McMurdo station in Antarctica can host more than 1,000 people. During the winter months, this number decreases to around 200 people.

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In contrast, the ANSMET project sends small teams of about five to seven people to conduct field research (ANSMET, 2018). These small groups live in tents on the ice as they search for and collect meteorites to study. The sample size for ICC analogs tends to be similar to astronaut crews and typically ranges from four to eight crew members. In the ICE or ICC analog, research investigators often wish to study crew sizes that simulate a spaceflight mission (i.e. four to eight crew members). However, to obtain the complete statistical analyses of the data, a larger sample size is needed. To achieve this larger sample size, data are collected over multiple missions and pooled for analyses. Because of the ability to control conditions at an ICE analog is limited, pooling of data is not always possible for studies completed in this type of analog. In the ICC analog, multiple missions each with a new set of crew members are often run using the same standardized conditions. Crew members on each mission experience the same activities and environmental conditions making pooling of data easier. Types of analogs used by space agencies can be thought of as those that change physiology, or provide missions that simulate spaceflight. Analogs that change physiology such as bed rest or dry immersion provide insights to physiological mechanisms related to spaceflight adaptations. Isolation analogs simulate spaceflight missions. The ICE analog serves as an actual mission where scientists and technicians completing fieldwork are studied for their responses to the isolated extreme conditions. In the ICC analog, missions are created and conditions are controlled to support research studies. Each type of analog possesses unique characteristics and supports a wide variety of research investigations.

DEVELOPING AND CREATING AN ANALOG To be successful in developing analog environments, researchers and other users must determine the main purpose and requirements for creating the analog, and then determine how to create, or replicate, that environment in a convenient location. There is no ideal analog other than the exact environment or mission the analog is attempting to recreate; analogs, by definition, are imperfect. One of the key factors to determine when setting up and using an analog is the level of fidelity required for mission and research success. Typically, the highest fidelity analogs are the most expensive and complex. High-fidelity analogs can bring the greatest return for the most users if the mission objectives and study requirements are successfully integrated. However, cost for requirements to achieve a high-fidelity analog often exceeds funding capabilities of the user or sponsor. To address this, compromises are made among the users; partnerships are established to provide hardware or services; and secondary and tertiary objectives are considered. In addition, perception of analog fidelity is unique to the user and their study requirements. What one user thinks is necessary for fidelity of the analog may not be perceived as such by other users. For example, the use of actual flight hardware in an analog may be critical for a particular mission test objective. However, if the participants are not future astronauts, but instead come from the general population of test subjects, there is a mismatch between the experience of the operators and the complexity level of the hardware. This leads to a potential problem in meeting the test objectives due to the extensive amount of training required for participants to use the flight hardware.

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Fidelity of the ISS as an analog is considered low in some instances particularly when attempting to study the isolation of a deep space exploration mission. Use of the ISS to replicate the expected isolation of a transit flight to Mars is difficult to simulate completely. For example, on a mission to Mars should a medical emergency occur, astronauts would handle the emergency with resources supplied on the vehicle without the possibility of returning to Earth for treatment. In contrast, in the event of an emergency on the ISS, an astronaut can be evacuated and returned to Earth within hours. In addition, ISS astronauts have many tools to keep them in touch with their families, news, sports, and politics in near real-time, which will not be possible in transit to Mars. Asking an ISS astronaut to forgo this communication and connection to home while on ISS for the sake of research is possible, but with much less fidelity than the actual trip to Mars. In order to replicate successfully reduced communications and increased communication delays of a Mars transit, the entire crew and ground operators need to be willing participants. This is a difficult undertaking and can introduce significant risk to ISS operations if done for the entire mission. Consequently, plans to use the ISS to examine communication delays are starting with short durations (14 days) with the potential for extending this duration to 30–45 days (Robinson, 2019). Processes for developing analog facilities vary by agency and available resources. However, setting up the NASA HERA facility provides a good example of factors to consider when developing an ICC analog for spaceflight research. The HERA facility was originally constructed as a portable habitat for NASA Desert Research and Technology Studies (D-RATS) (NASA, 2013b) (Figure 2.10). At that time, it was called the Habitat Demonstration Unit. It was later moved to the Johnson Space Center where it was set up indoors and used for technology demonstrations and short duration tests. The NASA Human Research Program acquired the facility in 2013 to conduct isolation studies for deep space exploration missions (Figure 2.9). To begin to set up the isolation studies, the facility was first assessed for its current capabilities. An inventory was taken to determine any applicable hardware remaining in the habitat, and it was assessed for human habitability over long durations. This information was used to inform scientists and managers who would either be conducting studies in HERA or providing resources to support infrastructure and funding for these studies. Numerous meetings were conducted with these stakeholders to determine equipment and conditions necessary to study deep space isolation in HERA. These requirements generated by the stakeholders were evaluated for cost and feasibility of implementation. Requirements were then prioritized considering immediate needs and cost. A phasing plan was developed for bringing in lower priority items. To begin preparing for study implementation, the HERA was outfitted with necessary communication equipment simulating what one would expect on a spacecraft. An accompanying mission control center outside the facility was built for a high-fidelity communication link (including one-way video downlink) between the habitat and mission control operators. Once the HERA was ready, a short 7-day pilot study was conducted to ensure that the facility operated as planned, and to provide a dry-run for HERA mission operations. Once the 7-day study was complete, NASA gradually extended mission durations and is now conducting 45-day mission operations (NASA, 2018).

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FIGURE 2.10 The Habitat Demonstration Unit used for NASA D-RATS missions prior to becoming what is now the HERA. Lunar rovers are shown to the right and left of the habitat. Notice the sleeping loft does not yet exist.

The importance of using a structured process for setting up an ICC analog like HERA is critical for ensuring mission success. Once an analog is developed to meet certain requirements and fidelity characteristics, incorporating a new requirement can disrupt or confound other analog capabilities, or research studies. Retroactively attempting to incorporate additional tests or research objectives is typically unsuccessful and often very difficult to do once mission planning is complete. For example, it may seem simple to add testing of a specialized diet where half of the crew members eat a specialized diet, and the other half eat the standard diet. To do this one might think that the investigator need only supply the food necessary for the specialized diet. However, there are factors to consider before adding such a study to an existing mission even if the study were added prior to the start of the mission. Addition of equipment necessary for food preparation would need to be evaluated. Implementation of the diet for half of the crew needs to be carefully thought through, particularly if there are multiple missions using the same complement of studies. Implementation may include feeding the specialized diet to half of the crew members for each individual mission. Alternatively, it may include feeding the specialized diet to all of the crew members on half of the total number of missions. More importantly, if the logistics can be determined, consideration also needs to be given as to how the last-minute addition of this investigation would affect all other investigations in the complement of mission studies. Addition of specialized food may affect the behavior of crew members and influence the outcomes of other studies.

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While there is no one perfect analog, optimization of analog fidelity and research requirements can provide the best opportunity for success. ISS as an analog is not optimal for studying the isolation of deep space exploration without first making some modifications to mission operations. Use of a structured process to set up an analog helps to ensure that mission objectives are met for all research studies. Changing or adding requirements once mission planning is complete must be done with careful thought, as changes and additions may confound existing investigations in the study complement.

USE OF ANALOGS FOR RESEARCH There are a number of benefits to using spaceflight analogs for research. Analogs provide an expedient means for completing research. Analog studies can be done more quickly and less expensively than those conducted on the ISS. In addition, multiple studies can be accomplished in an analog mission by utilizing the same research participants. Comparisons can be made across studies and answers to additional research questions can be found. In some cases, standardized conditions are applied to facilitate research, and pooling of data over multiple missions is allowed to achieve a desired sample size. In addition, research studies completed in spaceflight analogs provide valuable insights into the effects of spaceflight on humans, and help to determine appropriate countermeasures prior to using them in flight. No single analog perfectly simulates all aspects of spaceflight and its effects on the human body. Therefore, analogs selected for research studies must sufficiently reproduce the effects of spaceflight related to the phenomena under investigation. The use of any particular analog for spaceflight research is carefully chosen to fit the research questions. For example, a study examining the effects of circadian disruptions may be appropriate for an Antarctic station, as the summer months can produce 24 hours of daylight and winter months produce 24 hours of darkness. Alternatively, an ICC analog can be used where sleep schedules are altered to disrupt circadian rhythms. Analogs, or a portfolio of analogs, are often used along the development path for spaceflight hardware and countermeasures. An investigator typically begins the development phase in a laboratory setting. Once developed in the laboratory, refinement takes place in lower-fidelity analog environments. During this development and refinement stage a project can potentially be tested in several analog facilities with increasing fidelity. In the final step, testing is completed during a spaceflight mission. Development of the Psychomotor Vigilance Test (PVT) provides an excellent example of this process. The PVT is a tool that provides objective feedback on neurobehavioral changes in vigilant attention, psychomotor speed, state stability, and impulsivity. This tool also captures subjective ratings of workload, sleep (timing and quality), tiredness, fatigue, and stress. The PVT was first developed in a laboratory setting where it was tested for accuracy, validity, and sensitivity (Basner & Dinges, 2011, 2012; Basner, Mollicone, & Dinges, 2011). The tool was then utilized in an analog setting as one of the investigations in the Mars-500 study completed in the NEK in Moscow, Russia (Basner et al., 2013; Basner et al., 2014). More recently, the PVT was tested on the ISS (Dinges & Basner, 2018), and used during the NASA Twins Study on NASA’s 1-year mission (Garrett-Bakelman et al., 2019).

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Deciding which analog to use may also include consideration of other factors such as, the amount of experimental control required, the sample size needed, and the timing between study selection and the start of the mission. The amount of time between selection and mission initiation is important, as time is needed to obtain the required approvals and plan implementation. Two processes are primarily used by space agencies for identifying the most suitable analog for conducting specific research investigations. In the first process, an analog is selected to fit the requirements of a research study. In the second process, the analog is first identified and investigators then design their studies to fit the characteristics of the analog platform. The first process, where the analog is selected to fit research requirements, is generally used when there are a small number of studies that require analog use. In this case, investigators have typically responded to a research solicitation and are proposing the use of an analog as the optimal platform to conduct their study. In the solicitation, investigators are asked not to specify the exact analog that they would like to use. Rather, they are encouraged to provide significant detail regarding the requirements of their study. The requirements are then evaluated against characteristics of available analogs and a suitable analog is chosen for the study. The analog characteristics listed in Table 2.1 are some examples of those considered when matching research studies with analogs. Placement of studies into analogs from this type of research solicitation will also depend on analog availability during the time frame of the study. For example, study characteristics may be suitable for placement at an Antarctic station for a winter-over season. However, if planning for the Antarctic winter-over season is already underway, the selected study would either have to wait until the next planning cycle (1–2 years), or be placed in a more easily available ICC analog such as the NASA HERA facility. In cases like this, a study may collect data from the ICC analog, and then enter the next planning cycle for an Antarctic winter-over season. The second process used for selecting analogs for spaceflight research involves the identification of an analog where platform conditions are defined in advance. Studies are then designed to accommodate specifications of the analog platform. This process is often used for studies conducted in ICC analogs where experimental controls of the analog platform are possible. Examples of these predetermined conditions can include (1) mission description such as a transit mission or planetary habitation; (2) mission duration; (3) internal environment conditions such as temperature and humidity control; (4) EVAs planned during the mission; (5) sleep deprivation; and (6) communication delays. Information on the predefined analog platform is included in the research solicitation so that investigators are aware of these conditions, and can design their study to run easily in the analog platform. Below is an example of this type of analog description from a NASA Research Announcement in 2014 (NSPIRES, 2019). Several research topics being solicited in this appendix will require proposals that will utilize NASA’s Human Exploration Research Analog (HERA). Proposals in response to this solicitation should be written keeping in mind the characteristics that will define the HERA Missions. Missions will simulate exploration to a near-Earth asteroid. Travel to and from the simulated asteroid will be at a distance from Earth that

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allows for communication delays during the mission. In addition to communication delays, other events that induce realistic mission stress will be included such as, time pressures, task ambiguities, high workloads and loss of sleep. Missions will consist of 30 days of confinement within the HERA analog. Four missions in Appendix A -4 are planned for 2016 and are tentatively scheduled for January, April, June and August. Four crew members will participate in each mission for a maximum subject sample size of 16 subjects.

Typically a group of investigations is selected and integrated to form a complement of studies that optimizes experimental testing schedules and resources. All studies then utilize the same analog platform and prescribed conditions. Multiple missions can be conducted to increase the sample size for statistical power. The process for access to analogs for spaceflight research will vary depending upon the entity that owns and operates the analog. Some ICC analogs are owned and operated by space agencies providing convenient access for spaceflight research. The JAXA Isolation Chamber (JAXA, 2018), the IBMP Ground Based Experimental Facility (NASA, 2018), and the NASA HERA facility (NASA, 2018) are examples of ICC analogs owned and operated by space agencies. ICE analogs are usually owned and operated by entities other than the space agencies. Access for spaceflight research is typically negotiated by the space agency with the entity that supports the ICE analog. Examples of these types of ICE facilities include the Haughton-Mars Project (Mars Institute, 2018), a variety of Antarctic stations (NSF, 2018), and the Aquarius Undersea Laboratory (FIU, 2018). Choosing the optimal analog for conducting research depends on the questions being addressed by the investigation, and the requirements for study implementation. Once selected, an analog can serve as an ideal platform for developing and refining tools and countermeasures prior to testing them in spaceflight. Access to analogs for spaceflight research is typically done through the space agency. Agency research solicitations either request study requirements to match with analog characteristics or define the analog platform and conditions in the research solicitation. Gaining physical access to the analog facility is typically done by the space agencies as they own and operate the facility or negotiate access with the facility owner.

RESEARCH IMPLEMENTATION IN ANALOGS The process of implementing investigations in an analog environment is complex and unique to each individual analog and the studies being conducted. However, there are some key factors that must be considered, as studies are conducted particularly in the ICC analog environment. These factors include integration of research studies into an optimized complement, participant selection, and assessment of readiness for implementation. Once a group of investigations is identified for an ICC analog environment, studies are integrated into an optimized mission complement for ease of implementation. Integration of multiple users who have diverse and different objectives is challenging, as requirements for all studies must be carefully considered. Further, it must be understood that all studies in the mission complement complete all assigned missions. Because ICC analog missions often rely on pooling of data to achieve

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statistical power, all participants must be exposed to the same testing protocols and conditions. To integrate a study complement, multiple meetings of the investigators and operations team are conducted to step through requirements and determine the needs of each individual study. Next, a comprehensive schedule that includes all testing requirements and mission activities is drafted. The draft schedule is reviewed to identify confounds, conflicting requirements, and opportunities for data sharing. Resolution of confounds and conflicting requirements through compromise is critical for a smooth and seamless implementation of studies. For example, it may be desired by all investigators to have one last data point collected on the final day of the mission. While this approach is scientifically sound, conducting such a volume of testing on the last day of the mission may not be possible. In cases like these, it is critical for the operations team to look across requirements of all studies and ask key questions to determine which studies can collect final in-mission data points earlier. These findings are discussed with investigators and compromises are determined. Finding this balance between what is ideal and what is practical, is essential for mission success. Opportunities for data sharing among investigators can improve efficiency of study operations. For example, it may be the case that five investigators are planning to use a survey to assess mood state during the mission 5 times each week. Implementing 25 surveys per week is difficult from a scheduling perspective, detracts from the realism of the mission, and causes subjects to experience survey fatigue affecting their responses. Entertaining the use of the same survey by all investigators and optimizing the schedule to decrease testing frequency are items to consider when resolving this issue. Investigations that test countermeasures can create challenges and opportunities for study integration. Testing a countermeasure to reduce the effects of isolation on the crew could confound studies that need to examine isolation effects. In these types of situations, a separate set of missions could be designed to accommodate testing of the countermeasure. Other studies added to the mission complement would be those that are not affected by the countermeasure such as technology demonstrations. Alternatively, having a countermeasure study within the complement could provide an opportunity to answer additional research questions. Often, testing of a countermeasure requires a parallel set of control subjects who do not receive the countermeasure or control subjects who receive standard of care. In the case of the aforementioned example, two arms of the study could be set up to accommodate missions that test the isolation countermeasure and missions that do not use the countermeasure. A group of studies can then be integrated to run in both scenarios and measures from all studies can be collected. By structuring missions in this way, a study that assesses sleep patterns for example, could meet its study objectives in the control missions. Additional research questions regarding the effect of the isolation countermeasure on sleep patterns could be answered in the second arm of the study providing a more robust assessment of the countermeasure. Once studies are integrated, complete documentation of schedules, testing constraints, and data sharing is necessary for smooth implementation. This type of

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information is often placed in a requirements document that reflects the full mission complement. Tools are developed to assist operations such as an integrated testing schedule that reflects the complete schedule for testing and mission tasks throughout the entire mission. An example of an integrated testing schedule for a 2-week mission is provided in Figure 2.11. Daily schedules are also developed from the integrated testing schedule to guide day-to-day operations. Once studies are integrated and approvals are obtained from relevant ethics committees, participant recruitment can begin. Selection criteria of research participants are important to consider when designing ICC missions. Participants must be willing to commit to the extended duration of the study, as ICC missions are frequently designed to simulate long duration spaceflight. Younger adults (18–25 years old) are often willing participants and can be convenient to recruit. However, research results must be generalized to the population of interest. For spaceflight, this population comprises astronauts who possess a unique set of demographics. These demographics were considered as participant selection criteria were developed for the NASA HERA missions (Table 2.2). The age range of participants for HERA is similar to that of the active astronaut corps (NASA, 2013a). Physical and psychological examinations for HERA subjects are also similar to the exams meant for astronauts. Education requirements of astronauts were included in the HERA participant criteria with a preference for advanced degrees and job experience. Finally, human factors criteria were considered and maximum participant height was determined. The height requirement allows participants to easily move within the HERA habitat and fit comfortably in the sleeping quarters. Prior to admitting participants to the analog mission, preparations must be complete. Training sessions for participants are developed to provide adequate instruction for mission tasks and investigator study requirements. Training sessions should be scheduled close enough to the mission start to be confident that participants possess up-to-date knowledge of procedures as they enter the analog habitat. Dry-runs of critical processes and emergency procedures should be completed by the operations team to test procedures and prepare to support the mission. A final readiness review of all infrastructure, operations, and investigations needs to be completed prior to starting the mission. This review typically involves a meeting of all mission stakeholders to confirm readiness to support their respective component of the mission. The readiness review ensures that relevant approvals were obtained; and that infrastructure, operations, and investigator study support are prepared. This review should be completed far enough ahead of the mission to correct any problems that may arise so that the mission starts on schedule. Implementing an analog mission is a challenge. Integration of research studies is important to avoid confounding experiments and efficiently form a mission complement. Documentation of study integration is necessary for ease of implementation. Selection of participants that match astronaut characteristics provides a study sample whose results can be generalized to the astronaut population. Conducting a readiness review prior to the beginning of the mission confirms that all aspects of the mission are ready, and helps to ensure smooth operations during the mission.

0 0 5 3

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Investigator Science Pre-Mission Training Acoustic Monitoring Pre-Mission Behavioral Data Collection Behavioral Testing Morning Questionnaire Afternoon Questionnaire Post-Mission Behavioral Data Collection Behavioral Measures- Team Audio data collection Pre-Mission Training

MD-9 45

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Total Time (hrs)

FIGURE 2.11 Example of an integrated testing schedule for a 2-week ICC m ission. Mission tasks and investigator science a re represented on a single schedule. Time frames a re depicted for pre-, in, and post m ission testing, along with any follow up testing required. The integrated testing schedule provides a single source for all m ission activities and tim ing.

Investigator 6

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Meaningful Work Meteorite Obsevation Analysis Crystal Growth Emergency Sim Exercise/Hygiene Exercise Monitor Evening Daily Planning Conference (eDPC) Multi-Mission Space Exploration Vehicle Housekeeping Microbiome activity Morning Daily Planning Conference (mDPC) Public Affairs Event Seeds growth Private Family Conference Private Medical Conference Private Psych Conference System Maintenance System Status Check

MD+5

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42 Psychology and Human Performance in Space Programs

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TABLE 2.2 HERA Participant Requirements • Pass a medical physical examination • Pass a psychological examination • Be a US citizen or hold a US Permanent Resident Card • Range in age from 30 to 55 years • Be no taller than 6 feet 2 inches • Possess technical skills as demonstrated through professional experience and education • Demonstrate motivation and work ethics similar to the current astronaut population • Meet astronaut educational criteria as used for astronaut selection: • Bachelor’s degree from an accredited institution in engineering, biological science, physical science, or mathematics • An advanced degree in a STEM (science, technology, engineering, and mathematics) field is preferred and may be substituted for experience as follows: • Master’s degree = 1 year of experience • Doctoral degree = 3 years of experience • Military experience may be considered equivalent years of experience

SUMMARY AND CONCLUSIONS NASA has a long history of analog-use dating back to the early days of spaceflight training up to the current use of analogs for research purposes. A wide variety of analogs are used by NASA and other space agencies throughout the world. Each analog has unique characteristics that are considered when selecting analogs for research. Bed rest and dry immersion are useful platforms for examining physiological adaptations seen in spaceflight. The ICE and ICC analogs each provide an isolated environment, but have different mission structures. Spaceflight research takes advantage of the ICE analog extreme environment, and existing scientists and technicians who form the simulated astronaut crew. ICC analogs provide the opportunity for a well-controlled research setting to obtain results from a larger number of participants over multiple missions. Developing an ICC analog is challenging. Optimization of analog fidelity to meet research requirements is critical to support successful research. Even the ISS is not a perfect spaceflight analog as it does not simulate a deep space exploration mission. Analogs when used for the development of tools or countermeasures provide an excellent intermediate testing ground for refinement prior to testing in spaceflight. Space agencies can provide access to analogs for spaceflight research as facilities are either owned by the agency or agencies can negotiate for analog use. Implementation of research in analogs requires integration of research studies with structured documentation to support operations. Participant selection that closely matches the astronaut population allows for generalization of the results. Readiness reviews ensure that infrastructure, operations, and investigations are prepared for the mission to begin.

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The use of analogs will become increasingly more important as NASA moves ahead to exploration missions. Fewer missions will be launched with fewer astronauts on board. There may be less opportunity for research, as payload mass and volume will be less than what is currently launched to the ISS. Given these constraints, fully utilizing capabilities of ground-based analogs for spaceflight research will help answer critical questions as NASA plans to explore deep space.

REFERENCES ANSMET. (2018, December 12). The Antarctic search for meteorites. Retrieved from http:// caslabs.case.edu/ansmet/ Basner, M., & Dinges, D. F. (2011). Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss. Sleep, 34(5), 581–591. Basner, M., & Dinges, D. F. (2012). An adaptive-duration version of the PVT accurately tracks changes in psychomotor vigilance induced by sleep restriction. Sleep, 35(2), 193–202. Basner, M., Dinges, D. F., Mollicone, D., Ecker, A., Jones, C. W., … Sutton, J. P. (2013). Mars 520-d mission simulation reveals protracted crew hypokinesis and alterations of sleep duration and timing. Proceedings of the National Academy of Sciences, 110(7), 2635–2640. Basner, M., Dinges, D. F., Mollicone, D., Savelev, I., Ecker, A., Di Antonio, A., … Sutton, J. P. (2014). Psychological and behavioral changes during confinement in a 520-day simulated interplanetary mission to Mars. Public Library of Science One, 9(3): e93298. Basner, M., Mollicone, D. J., & Dinges, D. F. (2011). Validity and sensitivity of a brief Psychomotor Vigilance Test (PVT-B) to total and partial sleep deprivation. Acta Astronautica, 69(11–12), 949–959. Dinges, D. F., & Basner, M. (2018). Psychomotor vigilance test (PVT) on the ISS. NASA Taskbook. FIU. (2018, November 7). Medina aquarius program. Retrieved from https://aquarius.fiu.edu/ Garrett-Bakelman, F. E., Darshi, M., Green, S. J., Gur, S. J., Lin, L., Macis, B. R., … Turek, F. W. (2019). The NASA twins study: A multidimensional analysis of a year-long human spaceflight. Science, 364(6436), eaau8650. Gouvier, D. W., Pinkston, J. B., Lovejoy, J. C., Smith, S. R., Bray, G. A., Santa Maria, M. P., … Browndyke, J. (2004). Neuropsychological and emotional changes during simulated microgravity: effects of triiodothyronine, alendronate, and testosterone. Archives of Clinical Neuropsychology, 19: 153–163. JAXA. (2018, November 7). Astronaut training facility. Retrieved from http://iss.jaxa.jp/ssip/ ssip_atf_e.html Library of Congress. (2019, November 13). Library of Congress, Marshall Space Flight Center, Neutral Buoyancy Simulator Facility, Rideout Road, Huntsville, Madison County, AL. Retrieved from https://www.loc.gov/item/al1193/ Mars Institute. (2018, October 17). Recent news. Retrieved from https://www.marsinstitute.no/ NASA. (2008). NASA Langley Research Center’s Contributions to the Apollo Program (April 22, 2008). Retrieved from https://www.nasa.gov/centers/langley/news/factsheets/ Apollo.html NASA. (2013a). Astronaut fact book (NASA document number NP-2013-04-003-JSC). Retrieved from https://www.nasa.gov/pdf/740566main_current.pdf NASA. (2013b, March 7). Beyond Earth: Analog missions and field testing. Retrieved from https://www.nasa.gov/exploration/analogs/desertrats/ NASA. (2015). Training for the void (November 13, 2019). Retrieved from https://www.nasa. gov/image-feature/training-for-the-void NASA. (2018, October 17). Analog missions. Retrieved from https://www.nasa.gov/analogs

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Navasiolava, N. M., Custaud, M. A., Tomilovskaya, E. S., Larina, I. M., Mano, T., … Kozlovskaya, I. B. (2011). Long-term dry immersion: review and prospects. European Journal of Applied Physiology, 111, 1235–1260. NSF. (2018, November 7). Office of polar programs site map. Retrieved from http://www.nsf. gov/geo/plr/sitemap.jsp NSPIRES. (2019, March 13). NASA solicitation and proposal integrated review and evaluation system. Retrieved from https://nspires.nasaprs.com/external/helpEnterprises.do Phinney, W. C. (2015). Science training history of the Apollo astronauts (NASA document number NP-2013-04-003-JSC) (November 13, 2019). Retrieved from https://www. hq.nasa.gov/alsj/PhinneySP-2015-626.pdf Platts, S. H., Martin, D. S., Stenger, M. B., Perez, S. A., Ribeiro, L. C., Summers, R., & Meck, J. V. (2009). Cardiovascular adaptations to long-duration head-down bed rest. Aviation Space Environmental Medicine, 80(5, suppl): A29–36. Robinson, J. (2019). Analogs for Mars explorations, Humans 2 Mars Summit (November 11, 2019). Retrieved from https://h2m.exploremars.org/wp-content/uploads/2019/07/ Robinson_ISS-as-an-Exploration-Analog-H2M-2019-05.pdf Scientific Committee on Antarctic Research (SCAR). (2018, December 12) Homepage. Retrieved from https://www.scar.org/ Spector, E. R., Smith, S. M., & Sibonga, J. D. (2009). Skeletal effects of long-duration headdown bed rest. Aviation Space Environmental Medicine, 80(5, suppl), A23–28. Styf, J. R., Hutchinson, B. S., Carlsson, S. G., & Hargens, A. R. (2001). Depression, mood state, and back pain during microgravity simulated by bed rest. Psychosomatic Medicine, 63, 862–864. Sundblad, P, & Orlov, O. (Eds.). (2015). Guidelines of standardization of bed rest studies in the spaceflight context. Paris: International Academy of Astronautics. Available for download at: http://www.nasa.gov/hrp/important_documents. University of Wisconsin-Madison. (2011). UW-Madison undergrads win NASA habitat design competition (November 11, 2019). Retrieved from https://news.wisc.edu/ uw-madison-undergrads-win-nasa-habitat-design-competition/ Virtual Tour McMurdo Station, Antarctica. (2019, March 12) McMurdo. Retrieved from http:// astro.uchicago.edu/cara/vtour/mcmurdo/

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Special Considerations for Conducting Research in Mission-Simulation Analog Environments Challenges, Solutions, and What Is Needed Suzanne T. Bell DePaul University KBR/NASA’s Lyndon B. Johnson Space Center

Peter G. Roma and Bryan J. Caldwell KBR/NASA’s Lyndon B. Johnson Space Center

CONTENTS Introduction .............................................................................................................. 47 Challenge 1: Identifying and Selecting Analogs...................................................... 48 Challenge 2: Obtaining Access to Analogs .............................................................. 53 Challenge 3: Data Challenges .................................................................................. 57 Challenge 4: Disseminating Findings and Maintaining Participant Confidentiality ....................................................................................................... 61 Conclusion................................................................................................................ 63 Acknowledgments.................................................................................................... 64 References ................................................................................................................ 64

INTRODUCTION Future space exploration, such as missions to the moon, asteroids, and Mars, present a uniquely challenging environment within which astronauts and crews must operate. Individuals and teams will work and live for extended durations under physical conditions of zero or low gravity, limited variety of food and exercise, and increased radiation exposure, combined with psychosocial stressors of confined space, limited privacy, variable workload, and isolation from family and friends. Although much is 47

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known about how individuals and teams operate in traditional work environments, the extent to which our understanding of individuals and teams generalizes to the final frontier is not always clear. Research is conducted in analog environments, or settings that share one or more defining features of the target environments or populations to which the research is expected to generalize, to characterize the risks associated with spaceflight. As an example, bed rest studies, in which individuals remain in a head down tilted position for extended periods of time, are used to better understand the effects of microgravity including fluid shifts, bone and muscle loss, cardiac alterations, sensorimotor deficits, and visual impairments. Analog research also allows for problems to be solved or countermeasures to be developed and refined before deployment to an operational environment. For example, lighting changes to help regulate sleep/ wake rhythms can be tested in analog environments before they are installed in the International Space Station (ISS). Although research in analog environments can provide critically important data on the risks of working and living in space with fewer constraints than the target operational setting, it has considerable challenges. The purpose of this chapter is to communicate key challenges, current solutions, and necessary developments for conducting research in space analog environments. We narrow our focus to a few key issues that are fairly unique to conducting research in space analog settings. Challenges and issues that affect research more broadly also apply to analog research (e.g., ethics of risk-benefit to participants), but are beyond the scope of this chapter. We focus on issues related to identifying and selecting analogs; obtaining access to analogs; data collection challenges including the logistics of data collection in hard-to-access locations, lack of criterion data, and small sample sizes; and disseminating findings while maintaining confidentiality. For each of these, we describe the challenges, current solutions, and identify what is needed. Key ideas are summarized in Table 3.1.

CHALLENGE 1: IDENTIFYING AND SELECTING ANALOGS Several analog environments are available for research. A primary challenge is identifying the most appropriate one(s) for specific research questions. There is no comprehensive list of analog environments, in part because analogs are defined by the research questions and populations to which they are expected to generalize. Almost all the major space agencies operate or have affiliations with analog environments around the world. For those unfamiliar with spaceflight analogs, most can be found via an Internet search. For example, the National Aeronautics and Space Administration (NASA) keeps an extensive list of analogs they operate, fund, or are affiliated with (https://www.nasa.gov/analogs/types-of-analogs). Literature searches in academic databases and space agency databases, technical manuscripts, evidence reports, key reviews, attending space-oriented research conferences, and Internet searches of moon and Mars nonprofit organizations also are a fruitful means of identifying potential analogs. There are many risks to consider, and every analog or potential analog has its own unique set of physical, human factors, and operational features and limitations. Because analog environments are not the target environment (e.g., a long-duration

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Challenges, Solutions, and What Is Needed

TABLE 3.1 Key Challenges, Current Solutions, and What Is Needed Specific Challenges

No comprehensive list of analogs Appropriateness of an analog is dependent on the research questions and populations of interest to which the data are expected to generalize

Limited access for researchers, with varying processes to obtain access across different analogs Time-consuming process

Hard to access location A lack of criterion data Small sample sizes Inappropriateness of inferential statistics and the null hypothesis significance testing framework for small sample size research

Current Solutions

What Is Needed

Challenge 1: Identifying and Selecting Analogs Frameworks for suitability of an analog Research (e.g., meta-analysis presented in Keeton et al. (2011), and and experimental manipulation) Landon et al. (2018) to determine which features of Systematic approaches to describe the the context impinge on context (Bell et al., 2018), which can individual and team functioning facilitate a comparison between the target Descriptions of commonly used and analog environment analogs that capture key features of the context, as well as updates and manipulations over time Challenge 2: Obtaining Access to Analogs Agencies have eliminated some Improving the predictability of redundancies (e.g., dual application, when parts of the research review process) process are likely to occur Researchers can create a “best-case” Creating efficiencies so that the protocol and a “minimum acceptable data process is less cumbersome yield” protocol Orientation training to new Research funding expenditures should researchers, so that the account for data collection delays researchers and analog Knowledge transfer should be actively managers develop a shared managed across the lifespan of the understanding of the research research process in the analog Challenge 3: Data Challenges Establishing good rapport and a shared understanding with a local point of contact Identifying unobtrusive and existing data that are practical, relevant, and sensitive Using alternative data analysis approaches and simulation where appropriate Collecting data in such a way so that it is comparable across multiple analogs (e.g., same measures for similar constructs; Standardized Measures for Bed Rest Studies)

Ensuring that appropriate inferences are being made from small sample research Improved understanding of the appropriate analysis of and inferences from small sample size research Development and adoption of international standardized measures if not already established

Challenge 4: Disseminating Findings and Maintaining Participant Confidentiality Development of a standardized Maintaining data and Scrutinizing tables, figures, and other media training, which includes protocol descriptions for breeches of special issues related to data confidentiality while confidentiality and protocol integrity in disseminating Principle investigators proactively creating a coordinated outreach plan, key talking analogs findings points, etc.

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space exploration mission), they are necessarily imperfect approximations. Because of this, the appropriateness of an analog depends on the research questions and populations of interest to which the data are expected to generalize. Any given analog may be more appropriate for some research questions than others. “Natural” analogs are typically professional or operational settings that exist for purposes outside of data collection. For example, Concordia Station in Antarctica often serves as an analog for biomedical and behavioral research, as well as countermeasure testing through science campaigns organized by the European Space Agency (ESA). A crew of approximately 13 researchers and support personnel stay at Concordia during each winter-over season (typically February through October). Small crews, the dangerous physical environment surrounding the base, and inaccessibility during the winter-over months (~February through October) create an isolated, confined, and autonomous operational environment analogous to what is similar to a long-distance space exploration mission. Such an environment may be appropriate for research questions focused on individual and team behavioral health, performance, and biopsychosocial adaptation over long periods of time. However, Concordia Station or other remote outposts may not be suitable for studying other spaceflight relevant topics such as multiteam systems because these bases often operate autonomously, with virtually no “Mission Control” and very little remote mission support. In this case, more controlled analogs or isolation chamber habitats that have a mission control, such as the Institute of Biomedical Problems (IBMP) sponsored ground-based experimental complex (abbreviated NEK for its name in Russian) in Moscow may be more appropriate. Further, isolated, confined, and controlled environments, such as chamber habitats, more readily allow for more experimental manipulations and environment alterations than do natural analogs.

solutions Ideally, a comprehensive and continuously updated listing of all analogs would be available, outlining strengths and limitations for modeling aspects of space exploration. Keeton and colleagues (2011) provide a framework for systematically assessing the suitability of different analogs for research, with some updates provided by Landon, Slack, and Barrett (2018). However, a truly comprehensive list does not exist. Given the wide variety of potential analogs, unpredictable availability of existing analogs, increasing emergence of new analogs, and multiple (and often changing) organizations responsible for managing any given analog, a singular resource that is current and regularly updated is unlikely. Instead, there are some general guidelines that researchers should incorporate into the decision-making process when identifying and selecting analog environments for research. The appropriateness of an analog environment is determined by identifying the phenomena of interest, and then systematically identifying key contextual features including the characteristics of the population of the operational environment to which the research is intended to generalize that are likely to impinge on the phenomena of interest. Bell, Fisher, Brown, and Mann (2018) outline an approach for doing so in extreme team research. Extreme teams complete their tasks in

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“unconventional performance environments and have serious consequences for failure” (p. 2, Bell et al., 2018). Astronaut teams are an example of extreme teams. The approach outlined can also be applied to individual-level analog research. Context is defined as the situational opportunities and constraints that affect the occurrence of and meaning of behavior, as well as the functional relationships between variables (Johns, 2006). Context operates at two levels: the omnibus context and the discrete context. The omnibus context is the context broadly considered as a “bird’s eye” view of the context of interest. One approach to describing the omnibus context is to take a journalistic approach – identifying the who, what, when, and why of the context of interest (Bell et al., 2018; Johns, 2006). The discrete context is the particular variables (e.g., isolation, interdependence) that shape human affect, behavior, and cognition (Johns, 2006). Features of the discrete context that are likely to impinge on the phenomena of interest should be driven by theory, and prior empirical research where possible (e.g., autonomy was manipulated and showed an effect on communication patterns). Importantly, context should be considered systematically, and features of the discrete context as relevant to the phenomenon of interest should be documented. Bell et al. (2018) provide tables of the omnibus context and discrete context of future longdistance spaceflight as it pertains to team composition research. Such summaries can be combined with the specific research questions of interest to identify key features necessary in the analog environment. These summaries also can be reported in later publications and technical reports to better lend the research to integration across studies, an important consideration for analog research because small sample sizes are common. After systematic identification and description of contextual features, the psychological and physical fidelity of a potential analog environment and the intended population should be compared. Note that fidelity is not a feature of the analog per se; it is dependent on the research topic and population of interest. Once context has been described systematically, inductive reasoning will often be enough to determine the suitability of the analog in representing the target environment. This process, however, requires the researcher to clearly articulate their needs and the key contextual features likely to impact the phenomena of interest, and for those involved with the administration or operation of analog environments to effectively communicate the  characteristics of their analog. Data on the physical and psychological fidelity of the analog can be collected from facility description documents, subject matter experts, and previous or current participants, as well as the extent to which specific features of the context are present (e.g., participant ratings of autonomy). Fidelity information can be communicated in several ways. As an example, in their review of team composition research conducted in analog environments, Bell, Brown, Outland, and Abben (2015) provide a numeric rating of the fidelity of the analog research to long-distance space exploration. Importantly, it will be common that an analog does not share all the contextual or sample characteristics likely to affect the phenomena of interest. When that occurs, the limitations to the generalization of the analog research to the target population should be clearly communicated in any report or publication.

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WHAt is needed Two aspects of the approach noted above are particularly difficult: determining the key feature(s) of the context likely to impinge on the phenomena of interest and having an adequate understanding of the analog to determine the fidelity and generalizability of the research to the target environment or population. A better understanding of how features of the context affect individual and team behavior and performance is necessary to appropriately select a site for research and countermeasure testing. Documentation and communication of the context and populations used with the analogs also are needed. We suggest two ways that research can provide insights into which contextual features affect the phenomena of interest. First, within one study features of the context can be varied (e.g., workload) so that the effect of the context on the focal construct can be estimated. Second, the influence of the context on the phenomena of interest can be investigated through meta-analysis or systematic review. Meta-analysis provides a systematic means of quantitatively aggregating multiple primary studies so that summary statements can be made regarding the phenomena of interest. One of the strengths of meta-analysis is that moderators (such as context) can vary across studies rather than within one. So, for example, a meta-analyst could examine the relationship of mood and instances of conflict with the habitable volume of a chamber as a moderator, even if habitable volume was not manipulated in the same primary study. The meta-analyst could code the effect size of the mood and conflict relationship, code the habitable volume of each of the analog environments from which they calculated an effect, and then determine if habitable volume moderated the effect using meta-analytic approaches. To make progress in understanding the influence of features of the context on relationships of interest requires systematic reporting of the context in primary studies; in this case, habitable volume of each of the analog environments would need to be reported in the primary study or located in other ways (e.g., documentation of the analog). Consistent systematic description of the context is necessary in analog research. There are several analog environments that are consistently used in research (e.g., McMurdo and Concordia bases in Antarctica; NASA’s Extreme Environment Mission Operations [NEEMO] and Human Exploration Research Analog [HERA], Hawai’i Space Exploration Analog and Simulation [HI-SEAS], the German space agency’s DLR :envihab facility, IBMP’s NEK). Detailed, systematic description of these analogs that are available to researchers is necessary. Features that are consistent across campaigns or missions can be provided in documentation or reports, and changes made or additional features for each campaign or mission should be reported as an appendix or similar for use by researchers. Such descriptions exist for some analogs but not others. They should be developed for those that do not have them. Researchers could use detailed documentation about analogs to design and propose better experiments with realistic expectations in mind. Given that the important features will vary somewhat according to the phenomena of interest, identifying an appropriate analog requires an iterative top-down (those who run analogs communicating key features) and bottom-up (researchers

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using their expertise to estimate what might impinge on functioning for their phenomenon of interest) effort. This process can start with researchers providing requirements via tables describing the omnibus and discrete context as discussed earlier (see Bell et al., 2018 for examples), and the organization who sponsors the analog providing a systematic description of the analog. Then the circumstance a researcher opts to generalize and the analog of interest can be compared for fidelity for a specific phenomenon to identify appropriate analogs for research questions. In some cases, the appropriateness of the analog for the research question could be determined from existing documents; however, most often additional communication will be required. Even so the documentation can help develop a shared understanding between the researcher and those managing the analogs, and to guide conversations used to determine the appropriateness of the analog for research.

CHALLENGE 2: OBTAINING ACCESS TO ANALOGS Admittedly, obtaining access to research analogs is difficult. From Antarctic research stations to isolation chamber facilities built and managed by space agencies and universities, analogs vary wildly in construction, location, operations, and management. Each analog has its own requirements for obtaining access; however, there are common challenges across many analogs. First, it is typically difficult to get approval to collect data in an analog – access is limited to a small group of researchers, and sometimes the process for accessing analogs is mysterious. Second, obtaining access to analogs is almost always time-consuming. Regardless of the location of the analog, duration of the study, and status of funding, an analog researcher should always expect an extensive planning effort, interdependence among multiple parties, and a multiyear process from proposal of a project to final return of data and equipment. For analogs sponsored by space agencies or their research arms, the avenue is typically to propose research when requests for proposals are issued. Access is typically limited to investigators funded by the sponsoring agency or partner agencies. As an example, access to NASA-sponsored analogs currently happens by submitting proposed research to calls on the online NASA Solicitation and Proposal Integrated Review and Evaluation System (NSPIRES) system, clearing the multimonth peer-review and programmatic approval processes, and then conducting the awarded research. Most requests for proposals require the proposer to indicate their requirements from a NASA-sponsored analog. NASA then facilitates entry into the NASA-sponsored analog, or an analog of another agency whom with they may have a cooperative agreement such as the National Science Foundation (NSF), IBMP, or Deutsches Zentrum für Luft-und Raumfahrt (DLR). Likewise, analogs with other agencies have their own systems. The European Space Agency’s (ESA) website provides opportunities for data collection at Concordia Station and posts Announcements of Opportunities (AOs). Additional requirements vary by agency and analog. For example, Concordia Station in Antarctica is jointly owned and operated by the French and Italian Polar Agencies (IPEV and PNRA, respectively);

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ESA oversees all biomedical and behavioral research conducted there, and all principal investigators’ home institutions must be based out of the European Union (EU). Analogs that are not owned or directly managed by space agencies each have their own process. For example, HI-SEAS, which is owned by the Blue Planet Foundation and operated by the University of Hawai’i through grants, has included “opportunistic research” projects, where a researcher may propose their ideas directly to the primary project team. Analog environments run by independent nonprofit organizations (e.g., Mars Desert Research Station [MDRS] via The Mars Society) often let researchers apply directly to conduct studies and field tests, and information on how to apply can be found on their websites. Many analogs will require what is similar to a grant application. This can create a double review process, because the funding is typically from the investigators’ sponsoring agencies (so the merit was reviewed when applying for funding), then the merit is reviewed again for inclusion in an analog mission(s). Proposals are almost always subject to review for scientific merit, feasibility (including potential interference with other protocols), and probability of success. For example, for Concordia Station, ESA typically accepts four to five proposals per campaign. The proposals need to be either innovative science or a field test of a countermeasure or product ready for validation in an extreme environment or population. Almost all analogs, especially those sponsored by space agencies, will include multiple projects with potentially overlapping or competing protocol demands. Importantly, those who manage analogs look to reduce redundancies, and will often ask for researchers to combine parts of protocols and enter into data-sharing agreements. Obtaining access to analogs is often a time-consuming process: analog research can span multiple years. An example project timeline may include: (a) year 1: a letter of intent or brief proposal submitted to the sponsoring agency, and if invited, development of a full proposal, review, and decision notification; (b) year  2: preparation, logistics, and data collection, or if a current campaign or mission is finishing, waiting months or another year to begin data collection; (d) years 3 and 4: data collection that often spans multiple missions to amass a meaningful sample size; and (3) years 5 and beyond: return shipment of equipment, biological samples, and raw data; data management and analysis; and writing final reports and manuscripts for publication. The time-consuming process is a significant barrier, as it would often seem ill-advised for tenure-track professors or professionals working in limited-term appointments (e.g., postdoctoral fellows) to commit to this process. Given the extreme length of obtaining access to analogs combined with the longitudinal nature of research, the research project team is likely to be dynamic over the course of the project. Research assistants, students, and postdocs may come and go during a project’s lifespan. Collaborators, consulting companies, subcontractors, and subteams may join or leave during a grant. For example, a consulting company may build technology in the first year or two that is used for data collection for the remainder of the project. There are often “follow-along” projects that are designed to build on previous research. Knowledge transfer must be actively managed.

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The extreme length of analog research (sometimes 5 years or greater) necessitates maintaining detailed records and ensuring knowledge transfer across multiple parties that are involved over the course of a project. First, technology and software can be used to manage knowledge transfer. For example, team collaboration software such as Slack or Microsoft Teams can be used to organize workflows and keep multiple teams and team members in the communication loop for a given topic. They also provide a history of the interactions and decisions made by the research team. Another strategy is to have research assistants and other parties create an informal presentation in PowerPoint or Google Slides for bi-weekly updates and reporting. These updates can be used to ensure that progress is systematic and focused on research objectives, while also documenting information and decisions for future parties to refer to when joining the research team. In addition, documents can be stored on a cloud service such as Google Drive or Dropbox. A “readme” file or folder can be kept with the proposal, along with background documents, and a Gantt (or similar) chart that delineates key responsibilities over time. Researchers can use this folder as an “onboarding” for new research team members. It should be noted that cloud services may be appropriate for some documentation (e.g., nonproprietary protocols, information about the analogs), but not others (e.g., proprietary or confidential information such as data). Further, often there will be a long gap between methodological decisions and write up for publication, final reports, or submission of protocols and cleaned data to the funding agency. To help in the documentation of these decisions, a “working manuscript” can be assigned for each aspect of the research project. As an example, if interaction data are being coded, a “Method” section should be written in real-time as decisions are made. While this may be trimmed for some publications or reports, the working manuscript provides documentation or a reminder of the decisions that were made for current and future research team members.

WHAt is needed To the extent possible, the process should continue to be demystified and streamlined to improve researcher participation. Many of the solutions mentioned increasingly are being implemented by researchers, organizations, and agencies. In addition to these, three other things are needed to address the challenge of obtaining access in analogs. First, where possible, it is important to improve the predictability of when parts of the research process are likely to occur. As timelines change or are established, this should be quickly communicated to the researchers. Researchers should solicit this information regularly. Given that analog research is a multiyear process, it is likely that externally funded researchers will be engaged in other activities, whether research, teaching, or other commitments. Improving the predictability of timing can help researchers better manage multiple obligations, conduct higher quality research that is better integrated, and wisely spend public funds with which they and the sponsoring agency have been entrusted. Second, efficiencies should be made so that the process is less cumbersome. The removal of multiple scientific reviews is an example of this that is already being done. Finally, agencies and sponsors of analogs should provide orientation training to new researchers, so that the researchers and analog managers develop a shared understanding of the parties involved and

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the research process in the analog. For example, a new researcher may not understand the importance of science integration meetings or science requirements documents. A lack of understanding may result in researchers not prioritizing important meetings or deadlines, and inefficiencies such as multiple drafts of documents. This chapter, as well as orientation training to a specific analog and its process, may help researchers provide better and timelier information to agencies. Researchers should regularly ask for the purpose of different meetings and requirements, so they understand their role in preparing for and facilitating data collection.

CHALLENGE 3: DATA CHALLENGES Analog environments share one or more of the contextual features of the extreme environment. These particulars often lead to significant data challenges. For example, teams of people wintering over in the Antarctic are subject to a hostile physical environment, including extreme temperature, and may not be accessible during the winter-over period. While these features help researchers understand individual and team function in isolation while surrounded by a hostile environment, the hard-to-access locations can make data collection difficult. More details on the challenge of collecting data in hardto-access locations, a lack of criterion data, and small sample sizes are provided next. First, difficult-to-access locations can lead to data-collection challenges. The researcher may never actually travel to the site (e.g., Antarctica) which may result in an inadequate understanding of the context and how best to frame the research and manage data (e.g., providing appropriate instructions for administering the protocol, identifying whether measures need to be translated). Second, remote locations may have inadequate power for equipment and confined spaces may limit the amount of equipment a researcher may provide. Third, by nature of the individuals and team living in an isolated environment, they are likely to have limited access to the Internet (availability, stability, or bandwidth). This means that data will often be collected by other means such as paper-and-pencil measures or interview, or, if the Internet is intermittently available, the need for paper-and-pencil measures as backup. Second, because of the extreme features of the environment, data collection can often be limited in amount or kind. As an example, a lack of criterion data, such as performance measures is a common problem. Participants in natural analogs and operational environments are there for purposes other than the research. This has two implications: privacy concerns may significantly limit access to some types of data (e.g., mental health, performance), and the number of hours participants dedicate to research may be significantly limited. In controlled analogs, particularly for longer missions there may be more time to collect criterion measures. Even so, adequate criterion measures can be time-consuming to develop. Third, the sample size is often small in analog research, particularly in higher fidelity settings. For example, to examine the effects of prolonged isolation, the Mars 500 experiment placed a crew of 6 people in isolation for 520 days. These chamber simulations are logistically intense to prepare for and operate, which makes a large individual and team sample size unrealistic. A recent quantitative review of team dynamics research conducted in analog environments found that 80% of articles included a sample size of fewer than five teams (Bell, Brown, & Mitchell, 2019).

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Small sample sizes in analog research also have been noted for individual-level phenomena (Shea, Slack, Keeton, Palinkas, & Leveton, 2011). Small sample size is one of the greatest data collection challenges of analog and operational research. Generalizability of research is made through a series of inferences, including statistical inferences. In human research with adequate sample sizes, the null hypothesis significance testing (NHST) framework is often used to provide evidence that an observed effect in the sample is present in the population of interest. A hypothesis is specified and then data are analyzed with inferential statistics such as t tests, analysis of variance (ANOVA), and multiple regression to infer whether observed effects (i.e., the difference between two means) is more likely than would be expected by chance. Small sample sizes typically have inadequate statistical power, thereby decreasing the likelihood that the results will be reproducible (Button, Ioannidis, Mokrysz, Nosek, & Flint, 2013). Button et al. (2013) provide a detailed discussion and demonstration of the consequences of low power for replicability. As they indicate, low-powered studies are more likely to produce more false negatives; they have a lower chance of finding a true effect when one exists in the population. Lower power also lowers the positive predictive value (PPV), or the probability that a positive research finding reflects a true effect. A lower-powered study also makes it less likely that an observed effect that passes a significance threshold (e.g., P < .05) reflects a true effect. Finally, even when an underpowered study discovers a true effect, the estimate of the magnitude of the effect is likely to be exaggerated, especially when the effect is newly discovered, called the ‘winner’s circle’ (Ioannidis, 2008). For these reasons, inferential statistics and the NHST framework are largely inappropriate in small sample size research. As such, other approaches must be used to provide evidence that an observed effect is likely to occur in the target population.

solutions Current solutions and best practices exist for each of these challenges. First, because the researcher is unlikely to travel to hard-to-access locations, it is critically important that the researcher identify a point-of-contact (POC) and establish good rapport and a shared understanding of the research project with the point of contact. The POC can significantly influence the success of a project; for example, the POC in natural analogs will often help with the recruitment of participants. The researcher should discuss the purpose of the project, the target population to be recruited, and next steps for individuals who show interest in participating. The same person is often the POC for several studies; thus it is important to provide clear and concise documentation that summarizes “what is this project about?”, “who can participate?”, and a few bulleted points on what administrative behaviors can significantly strengthen or weaken the data (e.g., biological samples must be frozen immediately, must use a quiet room for cognitive testing). The POC can ensure any compliance with the protocol and communicate anomalies back to the researchers. Second, it is necessary to overcome inadequate criteria in good analog research. Potential solutions to the lack of criterion data involve a two-step process. As a first step, the researcher must adequately define criteria of interest. This is important for being able to judge the relevance of possible criteria in the next step. Generally,

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criteria of importance are aligned with organization goals, for example, identified risks in NASA’s Human Research Roadmap. An example is the risk of performance and behavioral health decrements due to inadequate cooperation, coordination, communication, and psychosocial adaptation within a team (NASA Human Research Program’s “Team” risk). These risks identify important criteria to the organization (in this case NASA), and should serve as the basis for criteria in research. Second, researchers can judge currently available information, or potential criteria they could develop for their usefulness. Usefulness of criteria is judged by its practicality, relevance, and sensitivity (Cascio & Aguinis, 2019). Practicality reflects the extent to which criterion data collection will interfere with ongoing operations. Relevance captures the extent to which a measure is logically related to the criterion domain of interest. Sensitivity is the extent to which the criteria can discriminate between individuals and teams that are doing well or not doing well. To increase the likelihood of practicality in an analog setting, where possible, unobtrusive measures or already existing data should be used. As examples of unobtrusive data, audio and video recordings can be used to determine the presence and patterns of behaviors. Gushin et al. (2016) provide an example of capturing the wellbeing of crew members via content analyses of communications. Existing measures also should be used where possible, and existing measures or tasks can be modified or recoded to improve criterion relevance. As an example, the multimission, spaceexploration vehicle, extravehicular activity (MMSEV-EVA) is a team performance simulation in NASA’s HERA. Fuel consumption is an efficiency metric automatically generated by the program; however, the simulation provides a wealth of additional information about team performance. The first author’s research team was able to code and score existing MMSEV-EVA data to provide a quantitative measurement of the extent to which the team met its objectives, which was a definition of team performance more relevant for their research questions. Without collecting additional criterion data, they were able to identify predictors of team performance over time. In some cases no relevant criterion can be identified, which will require criterion development. Best practices in criterion development should be used to ensure criteria are relevant, reliable, and able to be combined. Importantly, when such criteria are used in an analog, they should go through extensive validation before use in that setting. The special population and analog context need to be considered, and criteria must be carefully crafted to ensure distinguishability between those high and low on the criterion of interest. For example, astronauts and astronaut-like individuals may have excellent cognitive function, requiring tests of cognitive function to be refined so they may detect meaningful decline for participants who typically score at the top end of the distribution. Participants in analog research may also engage in impression management for several reasons, such as the desire to become an astronaut, or because they are working in their professional operational environment (e.g., Antarctic stations). Good criteria must be able to distinguish between desirable levels on a criterion and impression management. Finally, there are several potential solutions to small sample research (Bell et al., 2018). Within a given study, strategies can be used to increase the statistical power. For example, when appropriate for answering the research question, the sample size can be increased by collecting data at a lower level (e.g., interaction, instead of

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individual, level data), or collecting repeated measures over time. Further, alternative research and data analytic approaches better suited to small sample research can be used. For example, computer simulations, such as agent-based modeling, can be used to conduct virtual experiments and alternative scenarios to those present in the analog. Bayesian analyses are particularly useful for small sample size research if the data are normally distributed (see Bell et al., 2018; McNeish, 2016; Van De Schoot, Broere, Perryck, Zondervan-Zwijnenburg, & Van Loey, 2015). Descriptive statistics and effect sizes also may effectively communicate trends. Finally, it is often prudent to conduct debrief interviews to contextualize the data for better interpretation of small sample research and to explore new issues. Further, because any one small sample study is unlikely to provide enough evidence for firm conclusions, small sample analog research needs to be deliberately conducted with eventual data aggregation in mind. Bell et al. (2016, 2019) provide guidance for doing so as summarized next, including collecting (a) enough data so that an effect size can be generated for eventual comparison in a meta-analysis and (b) raw data in such a way that it can be analyzed and summarized across multiple studies. First, when possible, data should be collected in such a way that an effect size estimate can be generated. Effect sizes standardize (e.g., d, r) the strength and direction of a relationship and allow for comparisons across studies. Meta-analysis, the preferred means for summarizing quantitative data across studies and generating cumulative knowledge, requires that an effect size is generated from each study. Second, when possible, data should be collected using the same measures and response formats that have been used in previous analog studies. For example, an International Standardization of Best Rest Studies Measures (Sundblad & Orlov, 2015) has been adopted that allows for researchers in different analogs to collect comparable data on frequently examined domains. Comparable data across analogs allow researchers to benchmark new results as well as conduct analyses with larger data sets. Further, because studies will often include too small of a sample size to generate an effect size estimate, traditional metaanalysis may not always be possible to aggregate analog research. The use of the same measures allows for systematic reviews of the research through other means. As an example, in their review of long-distance space exploration mission-analog research on teams, comparable measures across analogs allowed Bell et al. (2019) to generate descriptive figures reflecting changes over time as well as upper and lower limits in team cohesion, team efficiency, team conflict, communication with mission control, and team mood across multiple analog studies. The ability to aggregate data across studies becomes significantly limited when different measures or different response formats are used (e.g., some scales have a neutral, some do not). Thus, a critical step in analog protocol development is to identify measures of constructs that have been previously validated in analog research, and to use these measures in the protocol when they will adequately capture the phenomena of interest.

WHAt is needed? While many solutions exist, continued work in this area is needed. First, agencies and researchers need to make the reproducibility and replication of research a priority and adhere to best practices. Researchers and other stakeholders (e.g., funding

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agencies, operations, journal editors) need to ensure that appropriate inferences are being made from small sample research. Research should be judged on quality and appropriateness of the inferences, rather than the presence or absence of an effect. Second, continued understanding of the appropriate analysis and inferences of small sample size research is needed (Bell et al., 2018). For example, future research could explore the accuracy of different meta-analytic approaches for use with extremely small sample sizes (Bell et al., 2019), and alternative strategies for best representing the available data can be developed. Finally, as noted, consistent measures should be used across analogs. International agreements, such as was done with the International Standardization of Best Rest Studies Measures, can be done in additional individual and team research domains to facilitate knowledge accumulation. Analog research that operates outside of these agreements, for example, that are run by private organizations, should still adhere to these standards when possible, so that their research can be compared with, and contribute to, an emerging body of analog research. Note, the use of these standardized measures should not preclude innovation or the collection of data on constructs ought not to be captured by the standardized measures. However where possible, the use of measures and response formats that allow for comparisons across multiple studies and analogs is essential.

CHALLENGE 4: DISSEMINATING FINDINGS AND MAINTAINING PARTICIPANT CONFIDENTIALITY The distinctive features of the analog research setting, including the high-profile nature of analog environments, public and media interest, small sample sizes, and crew members that are publicly identified, require special consideration to how findings are disseminated. Research should be disseminated through traditional outlets, such as publications and presentations; however, researchers are often provided opportunities for additional outlets likely to reach the broader public, such as education outreach, press, broadcast media, and creative works like documentaries. Researchers participating in these public information forums play an important role in engaging and fostering the public’s interest in space exploration, science, and technology. Social media platforms also are increasingly used to disseminate research information or used to prime the public before more comprehensive dissemination in long form media. There are important considerations for the content and timing of what should be presented, published in articles, or discussed in public forums. Importantly, confidentiality of a participant’s information or data should be maintained throughout the process. Researchers cannot disclose human participant information that violates research ethics guidelines and regulations (e.g., HIPAA) set out by the study Institutional Review Boards even after a study ends. While most researchers are likely familiar with the ethical mandate of maintaining confidentiality, small sample sizes require extra thoughtful consideration of what is discussed or presented publicly. Small sample sizes may inherently make crews or participants identifiable when they are unique in their standing on a particular construct and this information is presented or revealed (e.g., mission length, gender composition, role). Confidentiality of participant data is put further at risk because crew member identities are often made

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available online through the press or other coverage, or even publicly self-disclosed by participants. This risk is further compounded by the often inherent familiarity participants may have with each other’s data, and the potential for violating others’ rights and the researcher’s responsibility to protect confidentiality. Research protocols can be ongoing for several missions, requiring that elements of the protocol, measures, and methods be kept confidential so that the research integrity can be maintained in a future mission, or even for operational use. For example, key manipulations for which data are still being collected should not be revealed to the press. Future participants are often interested in space and may encounter the information in the media, leading to the potential for confounded results when they participate, or potential loss of suitable volunteers in response to misinformation. As with participant confidentiality, researchers must strike a careful balance between describing their study and depicting results transparently while not breaching the confidentiality of protocols and manipulations that are still being implemented.

solutions Maintaining confidentiality is paramount during the dissemination process. Methods, descriptions, and results sections including tables and figures should be carefully inspected for the extent to which that information could be combined with publicly available information to match an individual to their results. For example, it may be inappropriate to present data in a figure that shows mission length and crew role information (e.g., commander) in datasets that include missions of uniquely different lengths. Codes rather than crew or position information can often be used to mask participant identity and still adequately represent the data (e.g., position A). These codes should not be the participant IDs assigned by researchers as the first level of participant identity protection. In media interactions, descriptions of analog results whether empirical or anecdotal should be careful not to reveal an individual participant’s data. Principal investigators and their research teams can be proactive in guiding how the research team will interact with the media. This can be as simple as specifying who can communicate with the media about the research, and what content and key talking points are appropriate, or a more formal coordinated public outreach plan across researchers made in conjunction with their public relations department. For example, researchers can contribute to an agency press release when there is a new finding that is released to multiple, reputable news agencies. Websites that define the project in general terms with findings added as data collection for key manipulations can be created. This information can serve as the official public statements on the project to which initial inquiries may be referred. Researchers may be asked for interviews, either as a follow up to publications, press releases, or other information. For these interviews, the research team should identify key talking points ahead of time. These talking points should succinctly summarize key findings from the research and their implications for spaceflight. The sponsoring organization, such as a space agency, also may have talking points when new larger initiatives are implemented (e.g., return to the moon; Artemis). Preparing talking points ahead of time can help the researcher stay focused and avoid misstatements including breeches of confidential information during an interview. The press

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is often looking for ‘soundbites’ for traditional and social media platforms, which talking points can lend themselves to nicely when properly prepared. Further, the researcher should not necessarily be limited to key findings and should feel comfortable speaking from their expertise. However, it is usually prudent for a researcher to articulate when they are speaking from general expertise or reporting on others’ research projects, as the press may report all comments as the results of the researcher’s study. Formal development of science communication skills for public outreach is rarely offered as part of professional science training, but science communication has become a field unto itself, and organizations are increasingly investing in such training (www.aldacenter.org).

WHAt is needed? While researchers are often well versed in technical writing for publication, they are less likely to be familiar with engaging the media. Media training (or coaching) from the researcher’s home institution is highly recommended for researchers who may eventually engage in many forms of public outreach that require specific interview techniques or key messaging formats. An analog itself will have an identity and operational context that it wants to present to the research community and the public, and researchers and their personnel should be provided with messaging guidelines from the analog so that the analog is accurately represented in the media. Just as important is the development of a standardized guide for ethical considerations in disseminating analog research results. Some aspects of media inclusion and public outreach will vary according to the specific analog; however, much of what constitutes appropriate disclosure of protocols, other participants’ experiences, and roles of the participants is the same across analogs. For example, we cannot think of a time when revealing details to the press about a specific participant’s experience, data, or the details of a manipulation or intervention from an ongoing protocol would be appropriate. Because of these concerns and their ethical implications, it is worthwhile that a standardized training related to these privacy considerations is created for distribution to all participants and key personnel including researchers.

CONCLUSION Analog research plays an important role in preparing for future space exploration missions. It allows for the characterization of the risks of spaceflight, allows problems to be solved, and countermeasures to be developed and refined before deployment to an operational environment. Despite its importance, analog research has significant challenges. We focus on a few key issues related to identifying and selecting analogs; obtaining access to analogs; data collection challenges including the logistics of data collection in hard-to-access locations, lack of criterion data, and small sample sizes; and the challenges to protecting subject identity in research dissemination and when interacting with public forums. We highlight key challenges and current solutions, also where more work is needed so that analog research can best inform our understanding of human behavior and performance at the final frontier.

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ACKNOWLEDGMENTS Work related to this project conducted by STB was supported by National Aeronautics and Space Administration grants NNX16AQ48G, NNX15AM32G, NNJ15HK18P,  and NNJ13HF07P. STB, PGR, and BJC were supported by KBR’s Human Health and Performance Contract NNJ15HK11B through the National Aeronautics and Space Administration. Author contributions: STB and PGR conceived the project. STB, PGR, and BJC wrote the chapter. Any opinions, findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect the views of any organization affiliated with the research. The authors declare that there are no conflict(s) of interest.

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Shea, C., Slack, K. J., Keeton, K. E., Palinkas, L. A., & Leveton, L. B. (2011). Antarctica meta-analysis: Psychosocial factors related to long-duration isolation and confinement. (NASA/TM-2011-216148). Houston, TX: National Aeronautics and Space Administration/ Johnson Space Center. Sundblad, P., & Orlov, O. (Eds.) (2015). Guidelines for standardization of bed rest studies in the spaceflight context. Paris: International Academy of Astronautics. Available for download at: http://www.nasa.gov/hrp/important_documents. Van De Schoot, R., Broere, J. J., Perryck, K. H., Zondervan-Zwijnenburg, M., & Van Loey, N. E. (2015). Analyzing small data sets using Bayesian estimation: The case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European Journal of Psychotraumatology, 6(1), 25216.

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Research in Extreme Real-World Environments Challenges for Spaceflight Operations James E. Driskell Florida Maxima Corporation

Eduardo Salas Rice University

Tripp Driskell Florida Maxima Corporation

CONTENTS Introduction .............................................................................................................. 67 Extreme Environments ............................................................................................. 68 Natural Disasters....................................................................................................... 72 Military Settings....................................................................................................... 72 Polar Exploration ..................................................................................................... 73 Spaceflight and Spaceflight Analogs........................................................................ 76 Summary .................................................................................................................. 81 References ................................................................................................................ 82

INTRODUCTION There is an expression “where the rubber meets the road” that refers to a setting where one’s intentions, hopes, or expectations and performance are tested in a critical setting. To continue this automobile analogy, a race car may look good in practice, but the “rubber really meets the road” on the banks of the Daytona Speedway. Thus, the environment in which the rubber meets the road for a firefighter is in fighting an inferno; for the soldier, it is the combat battlefield; for the mountaineer, it is the trek to the summit; and for the astronaut, it is the journey into space. Many of these types of settings are what we term extreme environments – high-demand environments that test the individual’s skills, preparedness, and 67

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temperament. Research that examines the performance of individuals and teams in extreme contexts has a long and rich legacy. In this chapter, we provide an overview of the research literature on performance in extreme environments and describe the challenges in maintaining effective performance under high demand. We discuss research in several relevant contexts, including natural disasters, military settings, polar explorations, and spaceflight analogs. We discuss the value of this research for the spaceflight setting, and some of the unique challenges in supporting individual and crew well-being and performance in future space missions.

EXTREME ENVIRONMENTS We first discuss what we mean by the term extreme environments. Broadly, we think of extreme environments as settings that are demanding, dangerous, and those that test the limits or capabilities of individuals who work in them. Commercial fishing provides one example. Commercial fishing is one of the most hazardous occupations in the United States, with a worker fatality rate that is 29 times higher than the national average. Specifically, fishing workers have a fatal injury rate of 99.8 per 100,000 workers, compared with an overall fatality rate among all workers in the United States of 3.5 per 100,000 workers (Bureau of Labor Statistics, 2018). The Aleutian Enterprise accident provides an illustrative example. The Aleutian Enterprise was a commercial fishing and processing vessel that capsized and sank in the Bering Sea in 1990, killing 9 of the 32 persons onboard (National Transportation Safety Board, 1972). As a net loaded with fish was being hauled onto the ship, the net ripped, spilling fish onto the deck and causing the ship to list severely to one side. Focusing on fish production, the ship’s master (e.g., captain) ordered the winching of the net to continue and water began to enter the hull openings on the port side of the ship (they could not be closed because the closures were inoperative). After an attempt to sound the general alarm (which did not work), the ship capsized, and crewmembers entered the 32° water, most without immersion suits or life rafts, which were also inoperable. Nearby fishing vessels were able to pull most of the crewmembers to safety. Commercial fishing accidents often reflect a litany of dangers—rough seas, bad weather, icy surfaces, dangerous and often poorly maintained equipment, and poorly trained crews. Certainly, most people would consider commercial fishing to be an extreme work environment. Coal mining is another industry that has traditionally been viewed as hazardous, with limited lighting, heavy machinery, explosive gases, and the constant potential for mine collapses and cave-ins. In China, the Benxihu Colliery mine disaster killed 1,549 workers in 1942. Even today, mining has a worker fatality rate that is approximately five times the national average of all workers (Bureau of Labor Statistics, 2018). Although the potential for harm does not in itself define an extreme environment, it does reflect our general expectation that extreme environments are more dangerous for those who work in them than are “normal” task environments. A number of authors have offered more comprehensive and precise definitions of this term. Driskell, Salas, and Driskell (2018) define the term extreme environment

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as a setting in which there are significant tasks, social, or environmental demands that entail high levels of risk and increased consequences for poor performance. Bell, Fisher, Brown, and Mann (2016) define extreme environments as (a) task contexts that are atypical in terms of the level of demands (e.g., time pressure) or the type of demands (e.g., confinement, danger), and (b) contexts in which ineffective performance has severe consequences. Hannah, Uhl-Bien, Avolio, and Cavarretta (2009) define an extreme context as one in which one or more extreme events occur (defined as events of high potential consequence) and are likely to exceed the organization’s capacity. Power (2018) describes emergency contexts as “complex, dynamic, high-stakes, and fast paced contexts, wherein successful resolution is contingent on effective teamwork and collaboration” (p. 478). Golden, Chang, and Kozlowski (2018) describe the more specific term of isolated and confined environment (ICE) as a setting marked by (a) isolation from others outside of the setting, (b) close confinement with those within the setting, and (c) extreme task or performance demands (see also Harrison & Connors, 1984). Maynard, Kennedy, and Resick (2018) refer to “intense, risky, and often dangerous environments that place unique demands on the teams operating within them” (p. 695). Stewart and Bostrom (2002) summarized several properties that are characteristic of extreme decision-making environments. Extreme environments comprise events that are (a) often rare, and for which relevant experience may be lacking; (b) of high consequence; (c) are uncertain and difficult to predict; (d) involve time pressure or limited time for analysis; (e) disrupt normal activities; and (f) pose complex, ill-structured problems. A commonality among these various conceptualizations is that they all refer to a highly demanding performance context. The specific threats or demands encountered will vary among specific work contexts, but may include the types of demands illustrated in Table 4.1. Second, we describe extreme environments in contrast to “normal” task environments, but it is useful to conceptualize the topography of extreme environments along a continuum. For example, if we assume that extreme environments differ from normal task environments in terms of the types and magnitude of demands that are imposed on workers or operators, we could place a “normal” task environment in the middle of a continuum, as illustrated in Figure 4.1. In Figure 4.1, a normal task environment represents a “Goldilocks zone”—not too much time pressure and not too little time pressure; not too much workload and not too little workload; not too much complexity and not too little complexity; and not too much challenge or too little challenge. An extreme environment would be conceptualized as an environment that is characterized by extremes in these contextual features or that are atypical in the type or magnitude of demands. It is interesting to note that environments such as long duration spaceflight, as well as other occupations such as firefighters, may contain periods of both high workload and low workload. A third commonality among different conceptualizations of extreme environments is that they require coordinated responses to successfully address the demands and challenges that are faced. That is, complex and demanding tasks place a high priority on cooperation and coordination of team members to attain desired goals.

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TABLE 4.1 Typical Task and Environmental Demands Demand

Definition

Time pressure Task load Role conflict Role ambiguity Threat Performance pressure Coordination requirements Uncertainty Complexity/difficulty Ambiguity Novelty Fatigue Environmental stressors

Defined as a restriction in time required to perform a task. Defined as performing two or more tasks concurrently. Conflicting task demands stemming from the nature of the task or from conflicting supervisor or subordinate demands. Lack of clarity in job demands or procedures. Refers to the anticipation or fear of physical or psychological harm. Refers to the increased consequences for error in a high-stress environment. The increased demands of coordinating task performance with multiple others. A property of the task environment defined by unclear, shifting, or ill-defined goals. Complexity or difficulty of the task. A property of the task environment defined by missing, unreliable, or inaccurate information or task data. A property of the task environment in which events occur that are unique or unanticipated. Physical or cognitive fatigue resulting from sleep deprivation or continuous operations. Immutable features in the task environment such as noise, temperature, vibration, motion, and so on.

Extreme Environment

Normal Task Environment

Extreme Environment

Very Low Workload

Moderate Workload

Very High Workload

FIGURE 4.1 Demand continuum.

Thus, we have seen a proliferation of research on how “extreme teams” operate in these environments (Bell et al., 2016; Burke, Shuffler, & Weise, 2017; Driskell & Salas, in press; Landon, Slack, & Barrett, 2018; Vessey & Landon, 2017). Finally, extreme environments are challenging and tax the capacity of those who work in them. However, as Driskell and Salas (in press) have noted, demanding task environments may also offer the opportunity for exceptional performance – for the highest peaks to be scaled or for space to be explored. Thus, these conditions are both a burden and an opportunity.

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It may be useful to discuss the type of research approach that is typically employed in examining performance in extreme environments. Driskell, King, and Driskell  (2014) defined applied experimental research as research that applies or extends theory to an identified real-world problem with a practical outcome in mind. In other words, applied experimental research is research that enhances our understanding of behavior and has practical implications within specific real-world contexts. This type of research shares aspects of two broader research traditions, qualitative research, and experimental research. Consistent with qualitative methods, this approach emphasizes the observation of human behavior in context – in realworld applied settings with real persons doing real tasks. Observing and analyzing performance in situ or during actual operations is often challenging to accomplish for a number of reasons. First, it is difficult (and often ill-advised) to conduct research in inherently dangerous real-world settings. For this and other reasons, well-designed simulations such as NASA’s Human Exploration Research Analog (HERA) are valuable. Second, in real-world task settings, observation or measurement of behavior can interfere with the task and performance of the operators. Thus, unobtrusive measures that do not require the operators to stop the task they are doing and fill out a questionnaire are valuable. Moreover, the ongoing unobtrusive measurement of individual and team processes may allow countermeasures to be implemented in near-real time to address potential deficiencies (Landon et al., 2018). A third challenge (consistent with qualitative field studies) is engaging and gaining support from those workers or operators or crew members who are the focus of the research. Any type of behavioral monitoring and assessment (unobtrusive or otherwise) involves some degree of invasion of privacy, so it is incumbent on the researcher to keep in mind whose turf they are on and to conduct their research with the least possible interference and intrusion. Consistent with more traditional experimental methods, this approach also emphasizes the value of theory-driven research and quantitative analysis. Theory guides the researcher in terms of identifying the types of behaviors to be examined (e.g., the types of behaviors that are most likely to impact the behaviors or processes of interest). Without theory, the researcher is, to use an analogy, searching randomly for a set of keys in a dark room. Theory guides the researcher to relevant avenues or potentially valuable paths of exploration. Thus, applied experimental research takes concepts and principles from relevant theories and uses them to solve real-world problems. Further, given that many types of unobtrusive measures, such as lexical or speech analysis (Driskell, Driskell, & Salas, 2017) or team interaction sensors (Kozlowski & Chao, 2018), can capture a large amount of data over long durations, theory provides guidance on the quantitative analysis and interpretation of this data. This type of research, although challenging, has been ongoing in polar, space, military, and other extreme settings for a considerable period of time. More recently, however, there has been an increased emphasis on incorporating team science and research to optimize performance (see Landon et al., 2018) and on advances in technology and methods to examine complex relationships (see Kozlowski & Chao, 2018).

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In the following sections, we will discuss performance in extreme environments in several relevant contexts, including natural disasters, military settings, polar explorations, and spaceflight analogs.

NATURAL DISASTERS One of the pioneering efforts to understand human responses to extreme environments was conducted in the 1950s and 1960s in response to the US military’s requirement to understand how civilians might respond to a catastrophic disaster, such as an unexpected nuclear attack. This resulted in a body of “disaster research” that examined individual reactions to natural disasters that were deemed to be analogous to wartime disasters, including earthquakes, tornadoes, floods, mine disasters, and aviation accidents (Quarantelli, 1987). The research topics were broad practical concerns as how do individuals react in extreme situations; how do we manage panic, herd reactions, and the potential for riots; is there evidence of confusion, disorganization, and anti-social behavior? In a typical study, researchers would attempt to arrive at a disaster site as early as possible, while emergency operations were underway. Quarantelli (1987) stated that in the 1984 Alaska earthquake, the Disaster Research Center had five researchers on site within the first 24 hours. The goal was to observe ongoing recovery operations and conduct field interviews to understand the characteristics and consequences of disaster behavior. The overall results from this body of research were striking: Despite the extreme conditions, people generally respond to emergencies in a rational, pro-social, and controlled manner. As Quarantelli (1984) summarized, individuals “rise to the occasion and deal rather effectively with the personal challenges presented by the disaster” (p. 9). Thus, the original concerns regarding negative reactions came to be termed “disaster myths” (Drury, Novelli, & Stott, 2013). That is, people in extreme or emergency conditions generally do not panic, their actions are rational rather than disorganized, their actions are altruistic and often oriented towards helping others, and there is little evidence of breakdown in the social order or antisocial behavior. The picture that emerged from this research was that, contrary to prevailing concerns at that time, individuals are resilient in the face of extreme demands and respond in a positive manner. This research also highlighted the distinction between disaster management (i.e., post-disaster response) and disaster planning (pre-disaster preparedness). Those communities that were more prepared were better able to respond, or were less disrupted, by the disaster event (Quarantelli, 1987).

MILITARY SETTINGS War is uncertain, mentally complex, physically demanding, and intensely… emotional (U S Army, 2002, p. 18).

A rich legacy of research has been conducted over the years on performance in high-demand military settings (see Driskell & Olmstead, 1989; Radloff & Helmreich, 1968; Goodwin, Blacksmith, & Coats, 2018). These studies have included research

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on specific military tasks such as bomb disposal (Rachman, 1983), diving (Radloff & Helmreich, 1968), and parachuting (Burke, 1980) that are uniquely high stress tasks. For example, MacDonald and Labuc (1982) observed military personnel at various stages in parachuting training. They measured the performance of novice and experienced parachutists on a series of cognitive tasks during a baseline period in advance of parachute training, before jumping, and after jumping. Significant decrements in performance on the tests were found in nearly all phases of training. Weltman, Christianson, and Egstrom (1970) and Mears and Cleary (1980) found psychological stress to be a significant factor in degrading underwater (diving) performance. Altman, Haythorn, and colleagues conducted a series of studies on interpersonal relations in isolated and confined groups of military trainees (Altman & Haythorn, 1965; Taylor, Wheeler, & Altman, 1968). Research was conducted to examine social and psychological problems of crew members under prolonged marine submergence (Weybrew, 1963) and in underwater habitats such as the Navy’s SeaLab (Helmreich, 1966). Other military studies have examined performance in high stress, realistic simulations. Keinan (1988) examined the performance of military personnel in a training setting using live artillery fire. Berkun, Bialek, Kern, and Yagi (1962) developed a realistic series of stress studies for the army, which involved simulated emergency situations. Lieberman et al. (2005) examined performance decrements during intense military training exercises (US Army Ranger training and Hell Week of US Navy Seal training) and found that under these conditions “every aspect of cognitive function assessed was severely degraded” (p. 7). Although the performance conditions were severe, the authors proposed that specialized training may mitigate some of the decrements. A number of broad lessons were learned from these studies. First, this research established that simulations or simulated environments served as useful analogs for real-world environments (such as combat) that were difficult to study directly. Certainly, a training exercise or live drill does not capture the entirety of being in combat, but it may approximate the conditions that are present in the criterion setting (and avoid conditions that would be too dangerous for training purposes). Second, these studies documented the types of severe performance decrements that can be incurred in extreme task environments. Third, this research has led to the examination of interventions that may lessen negative performance effects in high-demand settings, such as specialized stress training (Driskell, Salas, Johnston, & Wollert, 2008; Driskell, Salas, & Johnston, 2006).

POLAR EXPLORATION Long duration exploration missions – such as a mission to Mars – have been generally characterized by extreme conditions such as difficult, dangerous, and stressful working and living conditions. As such, organizations such as NASA have called attention to the relevance and value of analog isolated, confined and extreme (ICE) environments such as polar expeditions and teams wintering-over on scientific bases. The rationale is that ICE environments sufficiently approximate conditions that humans can be expected to work and live in during long duration exploration

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missions and thus, findings from these environments can inform these future missions. Fortunately, there is a rich history of research carried out in these settings to draw upon (e.g., Gunderson & Nelson, 1963; Nelson, 1965; Taylor & McCormick, 1985). The crew of the Belgica Expedition (1898–1899) was the first to experience the trials and tribulations of wintering-over in the Antarctic. As quoted in Palinkas (2003), Fredrick A. Cook paints a rather dim account of life on the Antarctic: The curtain of blackness which has fallen over the outer world of icy isolation has descended upon the inner world of our souls. Around the tables, in the laboratory, and in the forecastle, men are sitting about sad and dejected, lost in dreams of melancholy from which, now and then, one arouses with an empty attempt at enthusiasm. (Cook, 1908/1998, p. 282)

In another telling account, Admiral Byrd describes interpersonal relations among his crew during a winter-over on the Antarctic: The time comes that one has nothing to reveal to the other; when even his unformed thoughts can be anticipated, his pet ideas become meaningless drool, and the way he blows out a pressure lamp or drops his boots on the floor or eats his food becomes a rasping annoyance…You are hemmed in on every side by…the crowding pressures of your associates. (Byrd, 1938, pp. 16–17)

Thus, it was clear from the beginning that individual and interpersonal psychological issues would be a central concern for crews working in these extreme conditions. Much of the research carried out in polar settings was spurred by the initiation of the 1957 International Geophysical Year program that saw the creation of the first permanent research bases on the Antarctic (Sandal, Leon, & Palinkas, 2006). A member of a US crew sent to the Antarctic to establish a research base experienced a psychotic episode and was incapacitated for the remainder of the mission (Driskell et al., 2018). This psychotic episode compelled program managers to initiate research to further examine small groups in isolation and, especially, the dynamics of groups that winter-over in the Antarctic. Numerous reviews documenting psychological adaptation to the isolated, confined, and extreme environment of Antarctica have been published for decades (e.g.,  Mullin, 1960; Gunderson, 1974; Harrison, Clearwater, & McKay, 1989; Palinkas, 1991; Jenkins & Palmer, 2003; Golden et al., 2018). A review by Jenkins and Palmer (2003) highlights some of the more consistent findings from the literature. First, a set of symptoms, dubbed “winter-over” syndrome, have been almost universally reported by researchers. These symptoms include degrees of depression, irritability and hostility, insomnia, and cognitive impairment. The authors also note the possibility of the “third-quarter phenomenon” in which mood deteriorates in the third-quarter of the mission, but recognize that findings on this phenomenon are mixed. Similarly, findings regarding heightened levels of anxiety are mixed. Regarding interpersonal interactions, numerous researchers have reported heightened levels of friction, anger and irritability, and conflict. The authors note that interpersonal relationships and the adjustment demands present in wintering-over represent the most significant stressors for crews. Jenkins and Palmer (2003) also call attention to the potential salutogenic, or positive, effects of stress on individuals who winter-over. Studies have identified positive effects including an increase in

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independence, self-reliance, greater self-discipline, tolerance, flexibility, patience, self-understanding, an increased capacity for intimate involvement and enhanced self-esteem and self-efficacy stemming from the ability to overcome the challenges of wintering-over in the Antarctic. In a more recent review of the literature, Golden et al. (2018) document the findings from the ICE literature and organize these findings using an input-mediatoroutput framework. From the 90 studies reviewed, three core themes of ICE research emerged. The first theme was that team members who request social support tend to experience more negative emotions and stress from interpersonal relationships, as well as poorer well-being. The second theme was that team members often experience an increase in negative emotions and tensions across time. Moreover, cohesion tends to decrease across time. The final theme was that team members demonstrate an increase in avoidance responses (e.g., giving up on problems) across time. Although it is clear that research from polar settings can effectively inform future space missions, it is important to identify one caveat regarding the characterization of exploration type space missions. As represented in Figure 4.1, extreme environments may be characterized by both high and low ends of a demand continuum. This view departs from the typical mental image evoked when describing an extreme environment as a continuous high-risk, high-demand setting. While future exploration missions will necessarily be isolated and confined, they do not necessarily have to be extreme in the sense of high-demand and risk over the entire duration of the mission. That is, portions of exploration type missions may be downright boring. Thus, we can expect the relevancy of certain ICE environment studies to be more or less applicable during various phases of a mission. Smith, Kinnafick, and Saunders (2017) note that the myriad of studies carried out in the Antarctic can be broadly classified as either (a) studies of individuals and teams working at scientific bases and overwintering in isolated and confined conditions or (b) mobile expedition groups. Furthermore, these authors note the importance of this distinction as “the physical and psychosocial stressors experienced may be somewhat different.” Below we present an initial framework (Figure 4.2) for how we believe this distinction can be applied to long duration exploration missions. Figure 4.2 represents an entire mission cycle from the departure from earth, to landing on Mars, and the return trip. This type of mission can be broken down into three primary sub-missions: the trip-toMars, Mars surface operations, and the return trip-to-Earth. Moreover, these submissions can be further broken down to correspond with the two types of ICE studies identified by Smith et al. (2017). That is, the beginning and ends of the sub-missions can be characterized by expedition-type activities what we term transition activities. Transition activities refer to active, dynamic periods of change, including major launch and return events (e.g., sequences such as Earth-launch-space; space-landingMars; Mars-launch-space; and space-landing-Earth), as well as emergency transition periods (e.g., from nominal conditions to emergency conditions back to nominal conditions). Transition activities are likely to include greater physical and mental exertion, more acute stressors, greater reliance on team coordination, and are more rapidly paced. A Mars landing provides a good example of this type of activity. The long middle phase of the missions may be characterized better as overwintering-type activities, what we term transit activities. Transit activities refer to somewhat more

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Earth Transition

Transition

Transit

Transit

Transition

Transition Mars Terra activities

FIGURE 4.2

Long duration exploration missions.

stable “standard” operations such as science experiments and other nominal day-today activities. These extended periods may result in different types of stressors, such as interpersonal friction or boredom. Finally, Mars surface operations – what we term terra activities – are initially most likely to be analogous to transition activities given the novelty, uncertainty, and significance, both for the astronauts and mankind, of carrying out operations on another planet. However, when Mars missions become more commonplace and astronauts become more familiar and comfortable with surface operations, terra activities may share characteristics of both transition and transit activities.

SPACEFLIGHT AND SPACEFLIGHT ANALOGS The journey to Mars will be a particularly challenging test of human ingenuity and capability. The challenges of such a mission are expected to be numerous and varied. While many of these challenges will parallel those experienced during previous manned spaceflight operations, we can expect novel challenges to arise in long duration exploration missions that may differ from previous missions. These challenges are summarized in Table 4.2. Table 4.2 also provides estimations as to whether we expect these challenges to be (a) novel to Mars exploration, (b) higher, lower, or similar to past missions, or (c) exacerbated or worsened in exploration missions. Over the past two decades, Stuster (2016) has collected a particularly rich dataset of astronaut journals to examine potential behavioral issues associated with longduration space flight. This dataset includes personal journals from NASA astronauts during 20 expeditions to the International Space Station (ISS). Ten of the journals were from astronauts who served on two- and three-person crews and ten of the journals were from astronauts who served on six-person crews. A content analysis of the journals allowed for a rank-ordering of behavioral issues in terms of importance. The content analysis identified 24 major categories of issues: Work, Outside

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TABLE 4.2 Long Duration Exploration Challenges Relative to Previous Missions Challenge

Comparison to Previous Missions

Autonomy

Higher

Confinement and isolation

Higher

Earth-out-of-view

Novel

Cultural differences

Exacerbated

Personality differences

Exacerbated

Boredom

Higher

Social Support (e.g., countermeasures)

Lower

Crew tension

Exacerbated

Asthenia (physical conditions) Skill degradation

Higher

Gender issues

Exacerbated

Environmental stress

Higher

Temporal effects Leadership issues

Higher Exacerbated

Language barriers

Exacerbated

Professional differences Religion differences

Exacerbated Exacerbated

Higher

References Kanas et al. (2009), Manzey (2004), De La Torre et al. (2012), Suedfeld (2010), Smith-Jentsch (2015) Kanas et al. (2009), Manzey (2004), Holland (2000), De La Torre et al. (2012), Suedfeld (2010), Bachman, Otto, and Leveton (2012) Kanas et al. (2009), Manzey (2004), Suedfeld (2010), Kearney (2016a) Kanas et al. (2009), Manzey (2004), Holland (2000), De La Torre et al. (2012), Burke and Feitosa (2015) Kanas et al. (2009), Holland (2000), Bartone et al. (2017), Bell, Brown, Outland, and Abben (2015), Landon, Rokholt, Slack, and Pecena (2016) Kanas et al. (2009), Manzey (2004), Holland (2000), De La Torre et al. (2012), Suedfeld (2010), Britt, Jennings, Goguen, and Sytine (2016), Morgeson (2015) Kanas et al. (2009), Manzey (2004), De La Torre et al. (2012), Vasterling and Deming (2016) Kanas et al. (2009), Manzey (2004), De La Torre et al. (2012), Maynard and Kennedy (2016) Kanas et al. (2009), De La Torre et al. (2012) Kanas et al. (2009), Manzey (2004), Smith-Jentsch et al. (2015) Kanas et al. (2009), Holland (2000), De La Torre et al. (2012), Mark et al. (2014) Kanas et al. (2009), Driskell, Salas, and Driskell (2018), Schmidt, Landon, and Patterson (2015), Kearney (2016b) Kanas et al. (2009), Holland (2000) Kanas et al. (2009), Holland (2000), Suedfeld (2010), Burke et al. (2018) Kanas et al. (2009), Holland (2000), De La Torre et al. (2012) Kanas et al. (2009), Holland (2000) Kanas et al. (2009) (Continued)

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TABLE 4.2 (Continued) Long Duration Exploration Challenges Relative to Previous Missions Challenge

Comparison to Previous Missions

Adaptation to microgravity Personal privacy

Higher Lower/Exacerbated

Onboard emergency (e.g., illness, accident) Other emergency (e.g., death in family) High workload

Higher

Equipment failure Insufficient onboard control Communication time lag

Higher Higher Novel

Critical tasks with limited tools Ground-crew incident (e.g., uplink wrong procedure, displacement) Relative crewmember experience Crewmember assigned roles Teamwork issues Multi-phasic mission Danger without possible rescue

Novel

Holland (2000), Suedfeld (2010), Driskell, Salas, and Driskell (2018) Holland (2000) Holland (2000) Holland (2000), De La Torre et al. (2012), Fischer and Mosier (2014) Holland (2000), Suedfeld (2010)

Higher

Holland (2000), De La Torre et al. (2012)

Exacerbated

Holland (2000)

Novel Exacerbated Novel Novel

Holland (2000), Burke et al. (2017) Landon, Slack, and Barrett (2018) Suedfeld (2010) Suedfeld (2010)

Higher Same

References Manzey (2004) Manzey (2004), Holland (2000), Whitmire et al. (2015), Kearney (2016b) Manzey (2004), Holland (2000), Suedfeld (2010) Holland (2000)

Communications, Adjustment, Group Interaction, Recreation/ Leisure, Equipment, Event, Organization/Management, Sleep, Food, Logistics/ Storage, Exercise, and other challenges. In journals from two- and three- person missions as well as from six person missions, the highest ranked behavior issues included work, adjustment, and outside communications (see Table 4.3). Other salient issues included group interaction, recreation/ leisure, and equipment concerns. Stuster found support for a “third-quarter phenomenon” or a general decline in morale and mood during the third quarter of a mission. It is also worth mentioning that journaling in and of itself was seen by astronauts as an unintended benefit. As Stuster notes, the journals served as an outlet to express their frustrations, but if expressed verbally, those could damage relationships. Moreover, journals can allow individuals to self-reflect, which can be beneficial. These results are consistent with those of recent research conducted to determine competencies for future long duration space missions (Barrett, Holland, & Vessey, 2015). The resulting 18 astronaut competencies highlighted the importance of

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TABLE 4.3 Rank Order (Top 6) of Behavioral Issues Associated with Long Duration Two- and Three-Person Crews Work Outside communications Adjustment Group interaction Recreation/leisure Equipment

Six-Person Crews 1. Adjustment 2. Work 3. Outside communications 4. Group interaction 5. Equipment 6. Recreation/leisure

teamwork, small-group living, communication, and adaptability (see also Landon, Vessey, & Barrett, 2015). In preparation for Mars, international space agencies have taken isolation and confinement analog studies to new limits. This is evidenced by the Mars500 experiments – a joint venture between the Russians, Chinese, and the European Space Agency – which included a 6-person 105-day mission (March–July 2009) and a 6-person 520-day mission (June 2010–November 2011). Although data from the Mars500 experiments are still forthcoming, Seedhouse (2015) notes that the findings regarding psychological adaptation to an isolated and confined environment are comparable to the findings borne out from the decades of previous research. Over the past two decades, isolation and confinement research has been, or is being, conducted worldwide in the United States, Russia, Canada, Europe, Antarctica, and the Arctic (Tafforin, 2015). This research has been carried out in research-specific facilities including the Canadian Astronaut Program Space Unit Life Simulation [CAPSULS], Isolation Study for the European Manned Space Infrastructure [ISEMSI], European Campaign for the European Manned Space Infrastructure [EXEMSI], Human Behavior in Extended Spaceflight [HUBES], Simulation for Flight of International Crew on Space Station [SFINCSS], NASA’s Human Exploration Research Analog [HERA], Hawaii Space Exploration Analog and Simulation [HI-SEAS]), underwater training facilities (e.g., NASA Extreme Environment Mission Operations [NEEMO]), desert stations, (e.g., Mars Research Desert Station [MRDS]), polar stations (e.g., Flashline Mars Arctic Research Station [FMARS], Concordia Research Station), polar field expeditions (e.g., Antarctic Search for Meteorites [ANSMET]), and polar sailing expeditions (e.g., Tara expedition). These studies ranged from a week in duration to almost a year and a half and varied in terms of gender composition, crew nationality, and crew size (see Tafforin, 2015). Research conducted in analog settings is as diverse as the analogs themselves. In the following section, we briefly review several research efforts that utilize unobtrusive means to measure various psychological states of individuals and teams. One practical goal in these operational settings is to assess operator state in an unobtrusive manner, without interfering with or disrupting ongoing performance

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(see Landon et al., 2018; Landon, Rokholt, Slack, & Pecena, 2016). Unlike teams in the experimental laboratory that can be examined “under a microscope,” teams in the real world operate autonomously, apart from direct observation and supervision, and operate in a fluid, dynamic manner to achieve the team’s objective (Driskell, Burke, Driskell, Salas, & Neuberger, 2014). Moreover, as Landon et al. (2018) have noted, “It is particularly important to avoid disturbing astronauts while they conduct highly complex tasks such as spacewalks; monitoring team behaviors without distracting from the task at hand may be the difference between life or death.” Nonobtrusive measures provide a means of detecting performance deficits, stress, fatigue, or interpersonal friction in situ without the intrusion of the psychologist’s typical array of questions. Driskell and colleagues (Driskell et al., 2014, 2017) conducted research to examine the use of lexical measures to assess cognitive/emotional state of crewmembers in the NASA HERA analog. The basic premise of this work is that spontaneous verbal output provides a natural and valid indicator of basic cognitive processes (Pennebaker, Mehl, & Niederhoffer, 2003). Natural word use is not prone to the typical limitations of self-report measurements. That is, natural language use is less subject to social desirability bias, and can be derived without interfering with the cognitive processes being measured, and without interrupting team performance. Central to this approach is the emphasis on the importance of language as a means to draw inferences regarding the psychological state of the speaker. Moreover, reasonable success has been achieved in examining word usage to uncover linguistic correlates of various psychological constructs of interest. Fischer, McDonnell and Orasanu (2007) have examined linguistic correlates of team performance, Khawaja, Chen, and Marcus (2012) examined the communication output of bushfire management teams to examine cognitive load and other indices of collaborative communications, and Driskell, Blickensderfer, and Salas (2013) used a lexical analytic approach to assess rapport in law enforcement investigative interviews. Waller and Zimbelman (2003) have observed that the use of these types of textual/verbal materials allow the researcher to identify the “cognitive footprint” of ongoing, internal psychological processes from textual or verbal records. This research involved the development of a lexical analysis tool, termed STRESSnet, to provide a non-obtrusive means of detecting stress and related deficits in spaceflight through the assessment of spontaneous verbal output in crew communications. Results from analysis of crew communications from three HERA campaigns indicated that (a) the core STRESSnet lexical measures of Negative Emotion, Social Impairment, Anxiety, Cognitive Load, and Attentional Focus were consistent with traditional pen-and-paper measures of these same constructs, and (b)  STRESSnet measures for each of the core facets consistently differentiated between high and low workload days in HERA. Furthermore, this analysis proved valuable in proactively identifying other operational variables of interest; for example, the identification of an elevated level of terms related to somatic anxiety in a crewmembers’ speech predicted that person’s health-related attrition. By assessing spontaneous verbal output in crew communications, this tool can serve as an “astute listener” that can track cognitive and emotional state from speech to provide an indicator of individual and team functioning.

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In an effort to investigate team dynamics, and in particular team cohesion, Kozlowski and colleagues (Kozlowski, Chao, Chang, & Fernandez, 2015; Kozlowski & Chao, 2018) have developed a wearable sensor or “badge” that is designed to assess the frequency, duration, and quality of crewmember interactions. These badges are able to capture team dynamics through multiple modalities, including face time (face-to-face interactions), physical movements (motion), vocal activity (duration, interval, and intensity of vocalization), and heart rate (beats per minute and heart rate variability). In short, this technology tracks the movements of crew members across time and allows the researchers to see who interacts with who and how long these interactions take place. These data can then be fed into computational models to infer team states, such as cohesion and affect. Testing and revision of this technology has been carried out in laboratory settings as well as in NASA analog studies, and initial evidence indicates that positive and negative affective reactions to specific team member interactions can be predicted from these data streams. The goal of this research is to create a technology to monitor team cohesion and guide interventions to maintain it. One challenge of an extreme environment such as long-duration spaceflight is that it is even difficult to get a question answered. Recent research has examined the impact of communication delays in space-ground communications during longduration spaceflight. Because of the distance between Earth and Mars, communications may be delayed up to 20 minutes one-way, which poses significant difficulties in maintaining effective communication between space crews and ground crews, such as Mission Control. In spaceflight, space-ground communication is critical, and time delays in communication can result in difficulties in keeping track of messages and a loss of common ground or mutual understanding. Fischer and Mosier (2014) found that communication delays of 5 minutes disrupted collaboration in distributed teams, regardless of the communication medium (voice or text). To address this problem, Fischer and Mosier (2015) developed a communication protocol to train communicators to keep track of conversational threads and maintain common ground in communications. In initial tests in both the NASA NEEMO and HERA analogs, results suggested that these protocols were effective in mitigating the negative impact of communication delay.

SUMMARY Perhaps the effects of extreme environments on human performance can be summed up best by NASA psychologist Al Holland (2000): “Just as wear and tear occur on hardware and physical systems placed into extreme service, humans experience wear and tear when placed into extreme service” (p. 7). We have tried to capture some of the dynamics of extreme environments—the risk, the time pressure, the extreme stressors, the task demands, the complexities, as well as the requirement for a collaborative or team response to address these challenges. Scholarly attention to performance in extreme environments has increased significantly in recent years. There are several likely reasons for this increased attention. First, work environments have become more complex and arguably more dangerous than in previous eras. The types and magnitude of demands that are faced

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are formidable. There is increased complexity in technology, in equipment, and in coordination. Second, the consequences for failure are greater. We are more vulnerable in that there is little room for error and tight margins for recovery. Third, as former NASA administrator Daniel Goldin stated, “We are explorers. It is written into our genetic code” (Goldin, 1994). We constantly push the envelope to scale higher peaks, to explore new worlds, and to reach further into space. Fortunately, our capacity to address these challenges is also growing. Research on performance in extreme environments provides the incremental knowledge to make these goals attainable.

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Technological Advances to Understand and Improve Individual and Team Resilience in Extreme Environments Sadaf Kazi, Salar Khaleghzadegan, and Michael A. Rosen The Johns Hopkins University School of Medicine

CONTENTS Introduction............................................................................................................... 88 Wearables and Other Unobtrusive Measurement Data Streams............................... 89 Physiological Measurement................................................................................. 89 Linguistic and Paralinguistic Communication..................................................... 89 Geospatial Sensing and Activity Traces...............................................................90 Framework of Team Interaction Measure Development...........................................90 Conceptual Domain..............................................................................................90 Data Acquisition and Measurement Domain....................................................... 91 Data Analysis Domain......................................................................................... 91 Using Sensors to Obtain Cues about Brittleness and Resilience in Individual and Team Health...................................................................................... 91 Early Warning Signs of Low Resilience..............................................................92 Deploying Countermeasures for Individual and Team Health..................................96 Countermeasures to Minimize the Impact of Stressors........................................ 98 Countermeasures to Manage Inflight Stressors....................................................99 Countermeasures to Mend after Stressor Exposures.......................................... 100 Conclusions and Future Directions......................................................................... 100 Acknowledgment.................................................................................................... 100 References............................................................................................................... 101

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INTRODUCTION High performance on complex tasks in safety-critical dynamic environments often relies on the ability to work with others as part of a team (Landon, Slack, & Barrett, 2018; Leonard, Graham, & Bonacum, 2004; Salas, Tannenbaum, Kozlowski, Miller, Mathieu, & Vessey, 2015). Teamwork competencies have been traditionally measured using observations and surveys (e.g., Sexton, Thomas, & Helmreich, 2000; Wildman, Salas, & Scott, 2014). The emergence of wearables, including those capable of measuring physiology, activity, and communication data, now provides researchers with means to collect team interaction data in a relatively unobtrusive and automated manner (Fusaroli, Bjorndahl, Roepstorff, & Tylen, 2016; Gorman et al., 2016). These new technologies can enable near real-time decision-making for a range of team development activities, from individual selection, crew or team composition, to performance support during missions. Although unobtrusive measures may be helpful for any organization looking to assess team performance and individual well-being, they are especially critical for teams that work in isolated, confined, and extreme (ICE) environments. The unique stressors of Long Duration Space Exploration (LDSE) make them a special case of ICE environments (Golden, Chang, & Kozlowski, 2018). Psychological well-being in LDSE missions may be adversely affected by isolation because of communication delays. Limited access to support structures and communication from Earth, including inconsistent interaction with family and friends may adversely affect the ability to manage stressors (Landon, Slack, & Barrett, 2018). Extra-habitat extreme atmospheric, temperature, and radiation conditions that endanger life will result in crew confinement to the habitat for the majority of in-flight and on-planet mission phases (Gabriel et al., 2012; Golden et al., 2018). Maintaining individual and team wellbeing and high performance under acute and chronic stressors of the LDSE environment will be a challenge (Driskell, Salas, & Driskell, 2018; Szocik, Abood, & Shelhamer, 2018). Work in ICE environments has already been documented to result in increased rates of maladaptive behaviors such as substance abuse, and altercations that have resulted in reduced team functioning (Nelson, Gray, & Allen, 2015) and fatal injuries (Daley, 2018). Unobtrusive measures that are capable of detecting aberrant patterns of physical, physiological, and psychological functioning can serve as valuable tools in managing individual and team health in LDSE missions (Roda et al., 2018). In this chapter, we provide an overview of key technologies that can be used to provide support to crews operating in ICE conditions while on LDSE missions. We will focus primarily on the use of unobtrusive measures not only to better understand current individual and team member states and processes, but also to support performance and well-being. Specifically, we address four goals related to this rapidly changing landscape of technology innovation. First, we briefly review existing technologies and the data streams they generate. Second, we outline a three-part framework for the systematic design of measurement systems using unobtrusive measures. Third, we detail patterns of resilience and their use in interpreting and making use of unobtrusive measures. Fourth, we review the role technological advances play in key countermeasure strategies for LDSE missions.

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WEARABLES AND OTHER UNOBTRUSIVE MEASUREMENT DATA STREAMS Wearable and environmental sensing systems provide a wealth of data about human interaction. However, these data can be voluminous, context-lean, and difficult to interpret. In this section, we provide a brief introduction to three critical data streams for individual and team performance: physiological data, linguistic and paralinguistic communication, and geospatial sensing and activity traces.

PHysiologicAl meAsurement The measurement of physiological activity during interpersonal interaction, including cardiovascular and electrodermal arousal, has been used to successfully predict relationship satisfaction and divorce rates of couples (Levenson & Gottman, 1983, 1985). Since these seminal studies, the use of physiological measures to capture the nature and quality of interaction has gained momentum in psychology (e.g., Elkins et al., 2009; Henning, Boucsein, & Gil, 2001). Researchers have investigated teamwork variables that are critical to the success of LDSE missions, such as cooperation and leadership–followership, using cardiovascular measures (blood pressure, heart rate variability, respiratory sinus arrhythmia, etc., Fusaroli et al., 2016), skin conductance (electrodermal activity, Guastello et al., 2016), emotion expression (facial electromyography; e.g., Håkonsson, Eskildsen, Argote, Monster, Burton, & Obel, 2016), neuroimaging (EEG, fMRI, etc.; see Gorman et al., 2016), and biochemical and hormonal variations (e.g., changes in cortisol level). The resulting team physiological dynamics are then expressed using aggregates of physiological arousal across all team members, the degree of physiological similarities and differences across the team, or patterns of stability and disorganization (Kazi et al., 2019). Although physiological measures have been used to study different aspects of team interaction for decades, recent breakthroughs in wearables has provided the opportunity for researchers to unobtrusively collect physiological measures.

linguistic And PArAlinguistic communicAtion Team communication has been traditionally measured using surveys and observations (Marlow, Lacerenza, Paoletti, Burke, & Salas, 2018). Since the late 2000s, advancements in unobtrusive measurement of team communication and automated near real-time natural language processing and transcription are being used to complement traditional methods of team interaction (e.g., Gatica-Perez, 2009; Schuller & Batliner, 2013; Burgoon, Magnenat-Thalmann, Pantic, & Vinciarelli, 2017). Measuring the content of communication has been useful in linking constructs such as team cohesion and performance to linguistic style matching (i.e., a measure of the degree to which members of small groups mimic one another’s use of function words—articles, prepositions, conjunctions, and other non-content words); (Gonzales, Hancock, & Pennebaker, 2010). Analyzing the usage pattern of politeness words such as expressions of gratitude, directives, among others, has also been used to measure power distance in social relationships in the United States Navy (Wu, Rye, Miller, Schmer-Galunder, & Ott, 2013).

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Paralinguistic communication refers to vocal features devoid of actual communication content, including the speed, volume, and pitch, in addition to other non-verbal cues, including communication flow, gestures, posture, facial expression, and gaze behavior (Schuller & Batliner, 2013). Paralinguistic communication can be assessed through audio recordings (both traditional and recordings that only consist of vocal features without the actual words), and video recordings (e.g., measuring gesture and posture through analysis of video data) or instrumenting individuals with sensors that record body movement and muscle activation, and measuring gaze behavior by using eye-tracking methods.

geosPAtiAl sensing And Activity trAces By combining an individual’s geospatial position with their activity, sensors can be used to capture different patterns of interaction such as physical proximity of team members, as well as collective patterns of behavior or physical activity levels of team members. An additional source of geospatial data is tracing the activity of team members through byproducts of interaction captured by information systems used for collaboration such as email and paging. Although using this type of methodology in the field is still relatively new, there are exciting findings that suggest that measuring constructs related to teamwork, task performance processes, and team outcomes is feasible with unobtrusive sensor-based systems (Rosen & Dietz, 2017).

FRAMEWORK OF TEAM INTERACTION MEASURE DEVELOPMENT In order to drive decision-making and performance support, the data from physiology, communication, and geospatial activity must be used within a formal measurement system. Rosen, Dietz and Kazi (2018) provide a framework for using emerging technologies in team interaction measurement centered around three domains: conceptual grounding, data acquisition and methodology, and analysis. The team interaction measurement framework is guided by the Input-Mediator/Process-Output framework (Ilgen, Hollenbeck, Johnson, & Jundt, 2005).

concePtuAl domAin The conceptual domain involves a theoretical description of the attributes of interest within team interaction analysis. Developing this domain requires attention to multiple theoretical components. First, researchers must identify the construct of interest (e.g., individual and team stressors, adaptation, and resilience in ICE environments, e.g., Palinkas, 2002) and consider how it relates to existing frameworks (e.g., resilience may be conceptualized as an input and a process variable in the Input-Mediators/Processes-Outputs framework, Ilgen et al., 2005) and theories (team resilience, Alliger, Cerasoli, Tannenbaum, & Vessey, 2015). Next comes identifying and defining attributes of the social unit of analysis (e.g., size of the space crew, team member roles [e.g., commander, flight engineer, and mission support specialist], patterns of interdependencies in team tasks, etc.). Finally, one must consider the unit level of aggregating data from individuals to the level of the team, and the time period when measurements are taken.

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D

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in unobtrusive measures that may indicate individual and team performance capacities. Brittleness can be understood in contrast to resilience, the capacity of an organism or a system to not only maintain its functions in the face of challenges (Hodgson, McDonald, & Hosken, 2015; Scheffer et al., 2018), but also effectively recover from disruptions (Hodgson et al., 2015). Researchers in human factors and systems engineering also highlight the adaptive capacity of the system as key to understanding resilience (Woods & Cook, 2006). Several terms help characterize the state of the system in terms of its resilience. Resistance refers to the inherent ability of the system to deflect disturbances (Hodgson et al., 2015). For example, the ability to avoid depression in ICE environments may reflect the inherent psychological resistance of crew members. Elasticity is the rate of recovery of a system from a disturbance (Grimm & Wissel, 1997), for example, how quickly the space crew can regain cohesion after facing an accident. Reductions in resilience may be signaled through subtle changes in system states that may be hard to detect. Several subtle shifts may gradually accumulate until they reach a critical threshold. This threshold, called a tipping point, refers to the point at which positive feedback loops accelerate the change of the system into a contrasting (and for the purpose of this chapter, often adverse) state (Scheffer et al., 2009; van Nes et al., 2016), and from there, to a new state of homeostasis or stability. Latitude refers to the distance past the tipping point when the system approaches a new state of stability (Hodgson et al., 2015). Trade-offs between resistance, elasticity, and latitude may determine the overall resilience of the system. For example, space crews that are at the tail end of the mission may have a reduced latitude compared with earlier points in the mission. However, because of historical accumulation of strategies to manage stressors throughout the mission they may have greater resistance to stressors with increasing duration of the mission.

eArly WArning signs of loW resilience Crews in ICE environments may face a variety of stressors throughout the mission. Stressors to individual health may manifest through mood symptoms such as depression and irritability (Palinkas, Gunderson, Johnson, & Holland, 1999), whereas stressors to the team may be manifested through lower crew efficiency, increased frequency of conflicts, and low morale (Kraft, Lyons, & Binder, 2003; Kanas, 1990; Orasanu, Fischer, Tada, & Kraft, 2004). Not all stressors interrupt the ability of crew members to maintain their health or collaborate effectively with team members to accomplish important tasks. However, because it is difficult to restore the system to the stable state prior to the tipping point, it is essential to determine cues or early warning signs (Scheffer et al., 2009) that signal deteriorating individual and team health. Early warning signs have been shown to exist universally across systems that range in scope from biological functioning to the functioning of entire ecosystems. These signs include critical slowing down and slow recovery from challenges, high temporal autocorrelations between system states, high variance, and high cross correlations (Scheffer et al., 2009, 2018), and can be captured by time-series data. Unobtrusive measures such as those used to capture physiological, physical, and communication activity generate continuous data, making them well-suited at capturing signals of deteriorating individual or team health. Table 5.1 shows how the

Slow recovery rate

Slower than average return to baseline physiological metrics (e.g., heart rate variability, electrodermal activity) across tasks (e.g., tasks requiring low-level physical activity to high-intensity cardiovascular such as exercising) and stressors (e.g., interpersonal conflicts) Increased frequency of disease onset/ fewer periods of physical health Longer-lasting illnesses

Physical and physiological

Cognitive (task-related)

Slower than average return of Higher response time to mood to baseline from tasks presented through stressors presented through AR and VR and work AR and VR, and individual tasks and team work Higher, more frequent Increased frequency of disease error rate to tasks onset/ fewer periods of presented through AR psychological health and VR and work tasks Longer-lasting psychological Slower detection of and illnesses recovery from errors on Slow recovery from AR and VR and work psychological illness tasks

Psychological (well-being)

Individual

TABLE 5.1 Individual and Team Indicators of Drift to Low Resilience

(Continued)

Work team (crew + Earthbased mission control) Increased frequency of and repeated recovery from small-scale disturbances (e.g., interpersonal conflicts) Slower than average resolution of interpersonal conflicts in team simulations or work interactions

Social/Team-Based Non-work (family/ friends) Increased frequency of interpersonal conflicts Slower than average resolution of interpersonal conflicts

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Physical and physiological

Note: AR = augmented reality; VR = virtual reality.

High cross correlations

High temporal autocorrelation

Cognitive (task-related)

Social/Team-Based

Psychological (well-being)

Non-work (family/ Work team (crew + Earthfriends) based mission control) Larger than average variation in Larger than average day-to-day Larger than average Larger than average Less frequent/ more minute-by-minute day-to-day fluctuations fluctuations in speech day-to-day fluctuations unpredictable physiological metrics during in off-task social (linguistic and paralinguistic) in reaction time to tasks interactions with and after engaging in physical features in tasks presented presented through AR family & friends activity patterns activity through AR and VR and VR Larger than average (e.g., moving towards Higher rate of physiological day-to-day fluctuations more time spent in reactivity to minor stressors in speech (linguistic isolation) and paralinguistic) features in interactions with friends and family Frequent, longer-lasting bouts of low-level illness following high workload situations (if systems have high autocorrelation then high cognitive demand may also affect the physical system faster and vice versa) Interpersonal conflicts adversely impact individual physical and mental health, and quality of individual and team tasks

Individual

TABLE 5.1 (Continued) Individual and Team Indicators of Drift to Low Resilience

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indicators of critical slowing are reflected at multiple levels of individual physical and psychological health, as well as social groups and work teams. Slow recovery from challenges. The most well-known and common indicator of transition to a tipping point is a critical slowing down of the system. Critical slowing is revealed in the form of reduced speed of recovery of the system from minor perturbations. In ICE environments such as LDSE missions, reduced recovery rate may be reflected through slower than average return to baseline metrics or stable states after regularly scheduled activities (e.g., exercise, work), or unanticipated stressors (e.g., accidents that threaten safety). Physiological metrics such as electrodermal and cardiac activity may be slower to return to pre-mission baselines after physical exertions. Slow recovery of physiological stability may translate to reduced ability of crew members to recover from minor physical perturbations and increased susceptibility to illnesses because of reduced physical and physiological elasticity. Longer periods of illnesses and fewer periods of health may occur because of weakened resistance. Similarly, there may be slow recovery of psychological health after experiencing stressors. This may be signaled by longer persistence of unpleasant moods and slower recovery of neutral moods after engaging with challenges from work and interpersonal interactions. Advances in wearables can be leveraged to predict levels of stress in individuals (Muaremi, Arnrich, & Tröster, 2013). In ICE environments, crews may be at risk for depression due to prolonged isolation and limited ability to interact in real time with family and friends (Lugg, 2005). The measurement and analysis of the content of speech, such as slowing down in speech productivity, may be a useful cue to alert to stress and cognitive deficits (Lieberman et al., 2005). Similarly, speech features such as a slow rate of speaking or greater pauses (Cannizzaro, Harel, Reilly, Chappell, & Snyder, 2004) and increased shimmer and jitter (Vicsi, Sztaho, & Kiss, 2012) can signal depression. Sensors that track patterns of physical activity and sleep have found general reductions in physical activity (Helgadottir, Forsell, & Ekblom, 2015), and specific reductions in activity during the evenings (Benedetti et al., 2007) to be associated with depression. Similarly, metrics from task logs such as increased response times and high error rates may also signal reduced cognitive capacity. The concept of slow recovery can also be applied to interpersonal relationships. As with physical and psychological health, there may also be slow resolution of interpersonal conflicts. This may be manifested in work relationships (e.g., with crew members or mission support personnel) and non-work relationships (e.g., family and friends). Longer durations of strained interpersonal relationships may make it more difficult to repair and recover from conflicts. Higher temporal autocorrelations and variance. Moment-to-moment fluctuations in a system’s state reflect attempts of the system to recover from minor challenges. System slowing results in fewer fluctuations in the states of the system, which in turn results in greater similarity of system states at consecutive time periods. This results in increased frequency of temporal autocorrelations (Scheffer et al., 2009). System slowing also results in longer persistence of effects of perturbations of the system. Gradual accumulation of multiple perturbations is manifested as increased variance in system states (Scheffer et al., 2009, 2018). Capturing continuous physiological, activity, and speech data from wearables can help in understanding and predicting patterns of high temporal autocorrelations

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and variance. If baseline data on these metrics is available, it may be possible to detect larger-than-average variation in physiological dynamics when engaged in solitary work as well as work in teams. The existence of team conflicts may be revealed through location and activity tracking when crew members show fluctuations in off-task activity patterns (e.g., members spend more time away from each other in isolation, Kanas, 2014) or interaction patterns (e.g., large day-to-day variance in duration of interaction with family/friends). During task work, higher fluctuations in patterns of physiological synchrony may also signal the need to re-establish a shared mental model. High cross-correlations between systems. Couplings between sub-systems can help overall system resilience. The effects of failures faced by one sub-system may be compensated by coupled, but stronger systems that may temporarily take over the functions of the failed system. For example, if all team members are familiar with performing each other’s essential tasks then in case of illness experienced by a member, continuity of functions is ensured when others can temporarily take over. However, tight coupling between sub-systems is a marker of low resilience (Scheffer et al., 2018). This is because malfunction in one sub-system may spread through the network of couplings and similarly disable other sub-systems. For example, the persistence of poor-quality work relationships over a long time may adversely impact individual members’ physical and psychological health. Similarly, a team member who is ill for a long duration may be unable to contribute to team tasks, forcing other members to take over essential functions performed by the ill member. The increased workload for healthy team members may contribute to deteriorations in their health and well-being, especially if it persists for a prolonged duration.

DEPLOYING COUNTERMEASURES FOR INDIVIDUAL AND TEAM HEALTH Improvements in methods for measuring individual and team functioning provide strategies for improving or maintaining performance levels. The measurement approaches discussed above are valuable research tools for developing and testing theory and expanding the science of individual and team performance. They are also valuable tools for practice, specifically to drive interventions through more accurate diagnostic assessment of individual and team functioning. The term countermeasure describes a broad range of approaches designed to keep astronauts healthy and high functioning during spaceflight operations. Countermeasures can be proactive or reactive and deployed pre-flight, during mission, and after mission. This includes, but is not limited to, procedures, systems or devices, medications, and training programs (Flynn, 2008). Spaceflight operations have detrimental effects on human physiology, and ICE conditions pose risks to individual and social well-being. The success of the mission depends on the crew’s collective ability to withstand a wide range of chronic and acute stressors, and countermeasures are a critical component of efforts to build and maintain a resilient capacity in astronaut crews during extended missions. Alliger et al. (2015) provide a descriptive framework of team resilience, a construct they define as “the

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capacity of a team to withstand and overcome stressors in a manner that enables sustained performance” (p. 177). Resilience functions at both the individual and team levels (Gucciardi et al., 2018), and can be described in terms of three categories of functions or strategies. Minimization strategies proactively and preemptively prepare individuals or teams for the experiences and exposures to risks they will ultimately face in-flight. Management strategies are employed while experiencing difficult or stressful situations with the aim of maintaining high levels of performance and health. Mending strategies occur after an acutely stressful situation and emphasize rest, repair, and return to normal functioning. In this section, we will discuss the range of countermeasures envisioned for LDSE using this three-part framework of minimizing, managing, and mending. This treatment is selective, rather than comprehensive, and focuses on countermeasures where technology advancements and industrial/organizational (IO) psychology have or can play an important role. Countermeasures are summarized in Table 5.2 and described below.

TABLE 5.2 Summary of Countermeasures Enabled by Technological Advances in IO Countermeasure Target Countermeasure Strategy

Individual Physical and Psychological Well-Being

Team Well-Being and Performance

Minimize. Selection. Unobtrusive measures can Crew composition. Unobtrusive measures Strategies to avoid complement traditional assessment can produce more precise measurement stressful situations methods to build more effective systems for individual attributes, and or minimize their systems to screen in candidates with more advanced predictive algorithms can impacts on health traits conducive to adapting to LDSE generate potential crews likely to form an and performance contexts and screen out those effective team on a given LDSE mission. before the candidates with risk factors for poor Team training. Simulated and virtual situations are physical or psychological adaptation. environments help to evaluate potential experienced. Training. Simulated and virtual crews for a given mission, develop environments improve the efficacy of mission or task specific coordination individual learning experiences for strategies, and general teamwork skills. critical skills like stress management Unobtrusive measures can help to provide and general social skills needed to structure feedback to guide team learning thrive on LDSE environments. and development. Manage. Strategies Virtual counseling and training. Crew management tools. Human agent to maintain health Immersive virtual environments to teaming to help diagnose team and performance provide training and counseling on performance issues, and support task levels while stress management or anxiety and execution and team adaptation. experiencing depression therapy. acutely or chronically stressful situations. (Continued)

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TABLE 5.2 (Continued) Summary of Countermeasures Enabled by Technological Advances in IO Countermeasure Target Countermeasure Strategy

Individual Physical and Psychological Well-Being

Mend. Strategies to Psychological assessment and return to baseline support. Agent-based, interactive, health and virtual assessment and therapy can performance after augment traditional ground-based exposure to support countermeasures for poor stressful psychosocial adaptation. situations.

Team Well-Being and Performance Team debriefings. Unobtrusive measures can support more effective team reflection, learning, and relationship management through interaction mirrors.

C

Strategies for minimizing risk can be implemented pre-flight as well as during a mission. The overall goal is to avoid or prepare people for different types of stressors and reduce the detrimental impact of these events or conditions. Pre-flight countermeasures include individual and crew-based selection and training interventions (Manzey, Scheiwe, & Fassbender, 1995). For individuals, efforts can be made to select candidates who are most likely to thrive in LDSE contexts into the astronaut corps. Precursors to individual adaptability and resilience have been identified (Suedfeld, 2005; Bartone, Krueger, & Bartone, 2018) and incorporated into the competency model for LDSE (Holland, Vessey, & Barrett, 2014) and astronaut selection procedures (Landon, Rokholt, Slack, & Pecena, 2017). IO psychology is pioneering the application of unobtrusive measurement methods described above to complement traditional assessment methods (e.g., self-, peer-, observer/expertratings; interviews, performance in simulated work samples) in the task of personnel selection (Scott, Bartram, & Reynolds, 2017; Salas, Kozlowski, & Chen, 2017). Individual training focuses on building resilient capacities through general social competence and stress management (Manzey et al., 1995; Flynn, 2008). At the crew or team level, composition or the configuration of traits among team members can be critical to team success as it affects social integration and team processes and emergent states (Bell, Brown, Abben, & Outland, 2015). Crew composition considerations include personality, cross-cultural competency issues, and role expertise diversity. Important conceptual advances are being made in multilevel selection and team composition (Mathieu, Tannenbaum, Donsbach, & Alliger, 2014), and the advancement of this work and development of algorithms to maximize the many differentially weighted dimensions of crew compatibility is a priority spaceflight research area (Landon, Vessey, & Barrett, 2015). Here, technological advances enable more precise measures of individual traits, as well as methods for building predictive models incorporating multi-level effects, the cutting edge of team composition research and practice (Bell, Brown, Colaneri, & Outland, 2018).

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After individual candidate crews have been composed for a given mission, these real teams can then be evaluated in a series of simulations and other team-level evaluations (Landon et al., 2015). Simulations will also serve a key role in team training activities designed to build resilient capacity through establishing coordination strategies for critical events, building general teamwork skills, and establishing trust and familiarity among crew members (Landon, Slack, & Barret, 2018). During mission countermeasures can be employed proactively to prepare for and protect against the effects of both chronic and acutely stressful environments on individual and team functioning. Strategies for minimizing the impact of chronic stressors on individual physical and psychological well-being include well-established methods of fatigue management and sleep hygiene (Avers, Hauck, Blackwell, & Nesthus, 2009; Rosekind et al., 1997), and stress management, e.g., through biofeedback (Lewis et al., 2015). The unobtrusive measures described above (e.g., activity tracking to detect latency in task performance) can potentially be used in day-to-day operations as well as in virtual reality (VR) simulations to assess the prevalence of fatigue states in crew members and trigger additional recommendations for changes to work and rest routines to address any performance issues. Additionally, VR applications have been developed to target psychological well-being during LDSE. For example, the Virtual Space Station (VSS) is an interactive training and treatment program targeting conflict management, stress management, and depression treatment (Anderson et al., 2016). The general growth in capabilities of virtual environments to support psychological state assessment and improvement indicate this is a powerful strategy for protecting against exposure to chronic stressful effects of LDSE (Smets, Abbing, Neerincx, Lindenberg, & van Oostendorp, 2008). At the team level, simulations can also be used to rehearse high-risk and stressful aspects of the mission (e.g., entering orbit, descending to planet), providing continued practice and elaboration of skills trained pre-flight (Landon & O’Keefe, 2018). These activities can have protective effects both in skill acquisition to enable high levels of performance and avoid errors that can make tough situations worse, as well as benefits from stress exposure or inoculation (Driskell & Johnston, 1998).

countermeAsures to Manage infligHt stressors When difficult situations cannot be avoided entirely, they must be managed. These strategies occur during missions and are designed to support individuals and teams maintain high performance capacities while enduring stressful situations. In LDSE, this can include acutely stressful performance episodes (e.g., a crisis situation such as a vehicle failure or medical emergency) as well as managing the cumulative effects of chronic stressors inherent in ICE. Inflight psychological support aims to maximize emotional well-being of astronauts, and prevent overload leading to exhaustion and performance decrements (Manzey et al., 1995). Individual wellbeing is addressed with entertainment, leisure and ground contact; regular crew meetings; and, ground-based monitoring of psychological states and supportive interventions. Automated and agent-based versions of psychological monitoring and support will be required to augment ground-support on longer duration missions with communication delays. Traditional countermeasures for crew-level

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acutely stressful situations include standard operating procedures defined to structure difficult tasks and ensure safe and appropriate actions are taken under conditions of crew member fatigue or other cognitive impairments. In military and aviation contexts, nootropics have been researched to manage performance decrements of various types, and even mild doses of common agents such as caffeine can help maintain performance levels under stressful conditions (Lieberman et al., 2002). Additionally, team-level resilient management strategies emphasize application of the communication and coordination skills and team task management protocols acquired in pre-flight or previously during mission preparatory training and simulation. There are few existing functional tools available to provide active real-time team support to teams (Maynard & Kennedy, 2016). However, systems such as the Mission Execution Crew Assistant (MECA; Neerincx et al., 2008) provide a vision of what these technologies may look like and how they may function as virtual team mates, using unobtrusive measures to adapt to the team and support coordination and task execution.

Countermeasures to Mend after Stressor Exposures Strategies for mending performance capacities occur after instances of acutely stressful performance or after mission completion, and focus on returning ­individuals and teams to their baseline status of health and performance. For individuals post-mission, this can include support and retraining of fundamental skills once back on earth. During mission mending strategies emphasize team debriefs (Tannenbaum & Cerasoli, 2013) to facilitate learning, conflict resolution, and ­relationship management. Technology can help support these collective and reflective conversations. For example, the use of team interaction mirrors (i.e., systems that use unobtrusive measures to provide a visualization of team communication and coordination activities) support team learning and adaptation (Jermann & Dillenbourg, 2008).

CONCLUSIONS AND FUTURE DIRECTIONS The use of sensors to measure individual and team states through physiology, communication, and geospatial data has increased in recent years. Sensor data can be used to improve individual and team well-being and functioning in LDSE missions. Analyzing patterns of activity and interaction by harnessing rich time-series data from sensors can be used to proactively alert crew members to indicators of declining health and team functioning. These data can also be used to customize the design of countermeasures to minimize, manage, or mend the adverse effects of stressors throughout various stages of the mission. In this way, sensors can be used to obtain a rich picture of individual and team health in LDSE environments.

ACKNOWLEDGMENT This work was partially funded by a grant from the National Aeronautics and Space Administration (Grant # NNX17AB55G; PI: Rosen).

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Computational Modeling of Long-Distance Space Exploration A Guide to Predictive and Prescriptive Approaches to the Dynamics of Team Composition Brennan Antone and Alina Lungeanu Northwestern University

Suzanne T. Bell KBR/NASA’s Lyndon B. Johnson Space Center

Leslie A. DeChurch and Noshir Contractor Northwestern University

CONTENTS Introduction ............................................................................................................ 108 Computational Modeling and Space Teams........................................................... 111 Motivating ABMs for Space Team Composition ................................................... 113 Developing an Agent-based Model for Crew Composition Effects ....................... 115 Model Construction........................................................................................... 116 An Integrated Model of Team Composition ..................................................... 117 Model Calibration ............................................................................................. 119 Model Validation................................................................................................ 121 Model Application............................................................................................. 123 Translating Science to Practice .............................................................................. 123 Conclusion ............................................................................................................. 126 Acknowledgments.................................................................................................. 127 References .............................................................................................................. 127

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INTRODUCTION Over the centuries, humankind has taken on many challenging explorations that require collaboration because their very survival relied on it. Humanity has been collectively exploring beginning with the agrarian and nomadic ages, followed by maritime explorations climaxing with the discovery of the ‘new world,’ scaling the peaks of our tallest mountains, diving to the deepest trenches of our oceans and standing-up bold polar expeditions. Finding individuals to engage in these daredevil adventures is not for the faint of heart – and spirit. An observation that Sir Ernest Henry Shackleton, a British Antarctic explorer who led three expeditions to the Antarctic, was acutely aware. Some sources recount that when Sir Shackleton tried to recruit a crew for one of his Antarctic expeditions, his classified ad in the newspaper reportedly read: ‘Men wanted for Hazardous Journey. Small wages, bitter cold, long months of complete darkness, constant danger, safe return doubtful. Honor and recognition in case of success’ (Huntford, 2013). This ad was not intended to appeal to the legendary glamorous swashbuckling sailors immortalized in fiction. Indeed, in a study of 25 personnel who spent the 9-month austral winter confined to two small, isolated research stations on the Antarctic ice cap, Biersner and Hogan (1984) found that the most positive peer nominations were received by those who scored low on self-reflection and emotional expressiveness. Relatedly, based on several studies of human responses to life at the US Amundsen-Scott South Pole station, Natani and Shurley (1974, p. 90) concluded that the Antarctic station had become ‘a haven for the technically competent individual who is deficient in social skills.’ It is within this much longer-term context that we must consider humanity’s 20th-century foray into space. It is but the latest ‘giant leap’ that is building on an arguably equally significant arc of achievements by our ancestors. Having explored and exploited most of the frontiers on Earth, space travel puts us on the brink of making humans an interplanetary species. The public’s interest in the rugged individualistic qualities that epitomized the very first astronauts in space – the Right Stuff – was captured in Tom Wolfe’s 1979 eponymous book. Tom Wolfe focused on the qualities of the Mercury Seven – Scott Carpenter, Gordon Cooper, John Glenn, Gus Grissom, Wally Schirra, Alan Shepard, and Deke Slayton – who were all part of the first (and last) solo Mercury missions into space. As we progressed through subsequent space programs – Gemini, Apollo, Skylab, Space Shuttle, and the International Space Station (ISS) – ‘The Right Stuff’ for astronauts demanded being a team player – an insight immortalized in the phrase ‘teamwork makes the dream work’ in the opening sentence of the Acknowledgment to the 2017 memoir Endurance by Astronaut Scott Kelly (2017), a veteran of the International Space Station who has spent more than 520 days in space. NASA and its international partners now acknowledge that crew members must not only be technically competent, but also effectively navigate interpersonal interactions in space. Crews have moved beyond the technically competent but socially deficient crews of the Antarctic. A diary entry (Stuster, 2016, p. 78) by a member of the ISS extolled the virtues of the then ISS commander:

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X is a master of good natured fun. I think when he leaves we will see a shift in the enjoyment of the people working the ground jobs. He is brilliant at knowing the perfect balance of fun with professionalism. I am in awe constantly. My love of joking around is immense but I am a mere child next to the talents of my commander. He is gifted.

But space travel is on the cusp of getting even more challenging. We are progressing from long-duration space exploration on the ISS (250 miles from earth), to long-distance space exploration (LDSE) returning to the moon (250,000 miles away) and then on to Mars (250 million miles away). The acronym LDSE has been used at various times to describe long-duration space exploration, long-distance space exploration, and by the Chinese National Space Administration as Lunar and Deep Space Exploration. We use LDSE here to refer to long-distance space exploration, since the challenges they present – and we seek to model – are beyond just a long-duration mission. It requires the crew to work with much greater autonomy. The days are numbered when we could quip that astronauts are the ‘eyes and ears’ but mission control on Earth remains the ‘brains’ of any mission. The fact that a radio signal can take up to 22 minutes one-way to travel from the Earth to Mars, significantly diminishes the likelihood of a successful resolution in response to a ‘Houston, we have a problem’ call by a Martian crew member. The first words uttered by Capsule Communicator at Mission Control, Charlie Duke, following Armstrong’s confirmation of the down-to-the-wire Apollo 11 landing of the Eagle on the moon was to tell the crew ‘You got a bunch of guys [at mission control] about to turn blue.’ Mission control will not have the luxury to ‘turn blue’ during a Mars landing. The crew will have to coordinate seamlessly on the complex task of landing with unparalleled levels of autonomy from mission control. Future LDSE missions will challenge the frontiers of human collaboration. Crews (representing diverse nations and cultures) are expected to live and work in isolated and confined spaces for up to 30 months, requiring a level of interpersonal compatibility that keeps conflicts between team members manageable and allows team members to rely on one another for support. While we ponder the substantial unknowns on how to compose dream teams for LDSE, we must leverage what we already know from prior research. We know from prevailing team effectiveness models that teams are best positioned for success when certain enabling conditions are in place (Hackman, 1987, 2012; Mathieu, Maynard, Rapp, & Gilson, 2008; Wageman, Hackman, & Lehman, 2005). Research on team composition, the configuration of attributes among team members, allows us to study the effects of who is selected for a space exploration crew on the future experiences and outcomes of that crew. Team composition models will consider the impact of crew member attributes (e.g. personality, relationships, demographics), but in this context are not just about selecting people for a crew and then washing your hands of the model. Team composition can also consider fluctuation in crew dynamics as they change through the mission as a consequence of crew member attributes. Team composition is a key enabling structure for teamwork (Bell, 2007). In fact, the composition of the space crew will perhaps be the largest leverage point for mitigating team risk. A vast body of research supports the importance of team

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composition (Bell, 2007; Mathieu, Tannenbaum, Donsbach, & Alliger, 2014). Team composition is empirically linked to outcomes such as cooperation (Eby & Dobbins, 1997), social integration (Harrison, Price, Gavin, & Florey, 2002), shared cognition (Fisher, Bell, Dierdorff, & Belohlav, 2012), information sharing (Randall, Resick,  & DeChurch, 2011), adaptability (LePine, 2005), and team performance (e.g., Bell, 2007). While it is widely acknowledged that team composition is a critical design feature for effective teams, much of what is known about effective team composition is from research within the confines of conventional workplaces (e.g., production plants). Less is known about how composition affects teams that operate in extreme environments such as those experienced by crews of future space exploration missions. But here we can draw upon insights gathered from teams that share some of the isolated, confined, and extreme (ICE) environments that will confront LDSE. Although they are not exactly comparable, we have learned from contexts such as polar stations, offshore drilling rigs, weather stations, nuclear submarines, and remote construction sites. While these field and case studies offer important general insights, the extreme environment within which LDSE crews will operate requires carefully designed experiments to study the impact of salient task, social, and physical contextual cues (e.g., isolation, confinement, sleep deprivation) on team functioning. Analog environments such as the Human Exploration Research Analog (HERA) at NASA’s Johnson Space Center in Houston, TX and the NEK facility at the Institute for Biomedical Problems in Moscow, Russia are designed to serve as isolated, confined, albeit controlled (ICC) – rather than extreme – environments to mimic some of the realities confronting future space exploration. A number of LDSE-analog studies have examined team composition factors in the LDSE-environment (see Bell et al., 2015 for a review). These studies implicate a number of team composition variables such as gender, national, professional and military background, values, personality, and specific abilities as factors tied to the social integration (e.g., subgrouping, isolation), team processes (e.g., conflict), and emergent states (e.g., shared team mental models) that can affect LDSE mission success. However, many of these studies were correlational, descriptive, and based on small team-level sample sizes. Further they only implicitly recognized that the impact of team composition on functioning was mediated by social network ties (such as advice, affect, hindrance, leadership) among crew members. Thus, although team composition is likely to play a critical role in crew social integration, processes, and emergent states for future LDSE crews, the critical team composition factors and the particular patterns of emergent network ties and subsequent outcomes associated with different compositions remain elusive. The purpose of our chapter is to outline a novel application of computational modeling – and more specifically agent-based modeling – to describe, predict, and prescribe the impact of team composition on team functioning. We report on our use of agent-based modeling to facilitate the study and improvement of crews simulating LDSE as part of a NASA-funded project titled Crew Recommender for Effective Work in Space (CREWS). Specifically, in the next section, we begin by discussing what motivates modeling of social systems and agent-based models

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(ABMs) of space teams. We trace the use of models in the hard sciences and delineate its use in the social sciences. In subsequent sections, we describe the steps in developing an ABM. We begin by specifying the factors (variables) that influence the construction of an ABM to explore the impact of team composition on crew functioning. Next, we describe how we calibrate these models using empirical data. This requires substantial efforts to instrument the contexts in order to capture all the data needed to calibrate these models. We describe a fairly novel approach to use the data to estimate parameters indexing the effect of various factors in the ABM. Having calibrated an ABM model, we next discuss how to validate the efficacy of the model’s predictions. Once validated we demonstrate how these models can be utilized. Finally, we envision how the science described here will translate into action via implementation of a dashboard (or, more accurately, a do-board) to assist decision makers at space agencies such as NASA to anticipate functioning of hypothetical crew configurations prior to a mission, as well as predict – and mitigate – crew functioning post-launch.

COMPUTATIONAL MODELING AND SPACE TEAMS We begin this section with a brief overview of what we mean by models, and how we use them to aid in composing space teams. A model is a formal representation of a system, real or hypothetical. A simple example of a model would be any mathematical function intended to describe reality, such as a formula from physics describing how a projectile dropped with a certain velocity will change its velocity as it approaches the earth. This model was constructed by physicists in order to detail how existing factors (e.g. the gravity of the earth) and existing theories (e.g. Newton’s equations of motion) come together to produce some outcome (e.g. the future speed of the projectile). A model is a way, in very precise language, to describe the process through which some input (the original speed with which the object was dropped) becomes translated to some output (current velocity of the projectile), as a function of some other parameters (e.g. acceleration due to gravity and time elapsed). Cast in this light, the concept of a model is actually quite broad. A model is any sort of precise, reproducible simplification of reality. Methods such as regression, or a hypothesis being tested in a factorial experimental design, are models, albeit simple ones. There are various ways in which a physicist or an engineer may try to leverage a model. First and foremost, in creating a model, a researcher is required to be precise. They are required to derive an exact, mathematical specification of how they believe each of the variables interacts. As a result, their model is a precise encapsulation of their beliefs that can then be tested, or easily shared with others. Physicists and engineers rarely stop at simply creating a model. Models are meant to be applied in various ways to explore implications for the phenomenon being modeled. There are three main types of analytics carried out with a model: descriptive analytics, predictive analytics, and prescriptive analytics (Delen & Demirkan, 2013). Descriptively, a model can provide a lens to describe, understand and/or explain what is observed. In the example, scientists examine how projectiles behave

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according to the model and begin to test the model using experiments. They estimate realistic values for the parameters, such as the effect of gravity, in order that their model will describe what they observe in real-world experiments. In addition, scientists often leverage the model predictively, guessing the future speeds of a hypothetical projectile (even if it was dropped at an initial velocity not previously observed empirically). They speculate on interesting scenarios to test experimentally in the future, and later conduct these experiments to validate whether their model was correct, or how they might revise their model accordingly. Forecasting is often a valuable end goal of predictive analytics. Consider the case of weather forecasting. However, in many instances, prediction while being a necessary step is not sufficient. While in most instances we tend to grudgingly accept a weather forecast, there are instances where we might want to do something to change  it. Consider the case of high profile sporting events such as the Winter Olympics where airplanes are sent to ‘cloudseed’ a noncompliant weather system to trigger an artificially created ‘prescribed’ snowfall over the ski routes. Clearly the rarity of this event suggests that weather forecasting doesn’t routinely lead to prescriptive analytics. However, in many other areas, once scientists are reasonably comfortable with the performance of their model, they begin to leverage it prescriptively in order to make decisions or generate recommendations: how should the inputs (timing or initial velocity) of a projectile be changed in order to obtain a desired outcome (final velocity)? All of these uses – learning about the world, predicting the future, and making the best decision – are jointly tied back to one integrated model that researchers develop. While these approaches have long been leveraged to understand and enable the physical world, there have been repeated calls to apply these to social systems (see, for example, Pentland, 2014). However, two major hurdles need to be overcome along the way. First, unlike most physical systems, social science theory has often not been able to unequivocally identify or decompose the key factors that influence the functioning and outcomes of social phenomena. In the social sciences we do not have – nor are we close to having – the equivalent of an equation that says, given the speed with which a projectile is dropped, the time elapsed and the universal gravitational force of earth, one can instantly predict with high precision the speed of the projectile at any future point in time. Further the distinction between inputs and outputs are often muddied within social systems where they may be interrelated and influencing one another. Our beliefs can influence who we choose to interact with – and who we choose to interact with can influence our beliefs. In modeling parlance, it is very unlikely that rich and complex social phenomena can be adequately modeled using equations that have elegant ‘closed form analytical’ solutions. Hence the example model from physics we discussed entailed only a deterministic, mathematical calculation, which is largely irrelevant to the social science. When we build models about social processes, it is only natural to incorporate stochastic processes and chance occurrences into our models. After all, not all humans think or interact in the same way each time, and not all influences upon a social process can be perfectly captured by a single model. (See Macy & Tsvetkova, 2015 for an elaboration on the importance of randomness in social science models). In many such cases where we don’t really ‘know’ the model, we need to rely on messier simulation techniques where

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we have to ‘grow’ the model. That is, use simulations to model what happens in the system one time step at a time to discover the emergent states of the system at subsequent time points. The second hurdle to building predictive and prescriptive models for social science phenomena is closely related to – and indeed an extension of – the first. Even if we were to know the factors that shape a social phenomenon, unlike in the hard sciences, we typically do not have solid evidence about the relative importance of each of these factors. In modeling parlance, we do not know the values of the parameters that provide a quantitative metric by which each factor influences a social outcome. The gravitational constant for acceleration is an example of such a parameter well established in the hard sciences. We argue that overcoming these hurdles is doable and effective when focused on the effects of composition on space teams. It is able to help us answer questions such as what social networks emerge among crew members? How do crew relationships evolve and change over time? How does one anticipate potential problems that the crew is likely to encounter and what strategies can we prescribe to preempt or mitigate against those problem predictions? Given a pool of potential crew members and role constraints that need to be met, how does one evaluate and rank order the merits of top crew configurations on different dimensions of crew functioning or ability to manage conflict when it occurs? Our preliminary efforts at building and validating these agent-based models of teamwork during simulated space missions to answer the aforementioned questions have been promising. This leads us to believe that further advances with these agent-based models are poised to inform NASA’s crew composition questions as it prepares for the Artemis mission that will take the first woman and the next man to the moon in the near future, build the Lunar Gateway, and prepare for a mission to Mars. This section has outlined the merits of employing models to describe, predict, and prescribe social phenomena. Unlike in the hard sciences, we recognized the limitations for us to ‘know’ closed-form analytic models to characterize rich social phenomena. Instead we argued for an effort to ‘grow’ computational models that simulate future states by traversing through time one step at a time. We noted that utilizing these models effectively requires us to overcome two major hurdles – identifying key factors (variables) that influence the social phenomena of interest and estimating the magnitudes of those influences (parameters). Past efforts to overcome these hurdles have relied on expert opinions rather than empirical estimation. But these have limits in situations where experts have divergent opinions on the factors and the magnitude of their impacts. In the next section we delve deeper into how ABMs can help describe, predict, and prescribe interventions for LDSE. We also outline the steps to build, calibrate, validate, and make these agent-based models actionable.

MOTIVATING ABMS FOR SPACE TEAM COMPOSITION To start developing models of large and complex social systems, we first characterize entities within the system as agents. In our case the agents are crew members. The model is a set of probabilistic rules (or equations), which specifies how each agent will update their attitudes (about themselves and other agents) and engage in

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behaviors (actions and interactions with others). These models often result over time in complex emergent patterns that are not easy for the human mind to intuit although they are entirely derived from probabilistic rules specified by humans. Agent-based modeling (ABM) is a perspective on modeling that embraces these ideas to tackle complex problems and understand emergent states. For those studying teams, ABMs offer an opportunity to examine dynamic team processes. Traditionally, team functions have been studied using Input–Process– Output (IPO) models that focus on how simple main effects result in some sort of outcome in teams. However, there have been increasing calls to move to more nuanced models that incorporate the complex interactions of multiple factors, incorporate emergent states that may form in a team, and incorporate temporal changes in team processes (Grand et al., 2016; Ilgen, Hollenbeck, Johnson, & Jundt 2005; McGrath, Arrow, & Berdahl, 2000). ABMs offer a promising way to bridge this gap. Traditional modeling approaches (regression, factorial design, structural equation models) require researchers to make certain assumptions and test hypotheses that follow a certain structural form. In contrast, agent-based models empower researchers to develop structural patterns of potentially mutual and/or nonlinear influences based on their assumptions. It empowers team researchers to build a more flexible model of the world as they see it. In the context of space, we have an outstanding opportunity to build a descriptive understanding about how various factors (attributes of team members, scheduling of tasks, sleep deprivation, communication delay, lifestyle during LDSE) systemically influence the ability of a crew to collaborate with one another and perform effectively. ABMs of team composition provide a mechanism for researchers to integrate multiple existing theories about team composition, calibrate them with empirical data, and explore the implication of these results. ABMs are especially well-suited for research in areas, such as LDSE a nalogs, where we are only able to study a limited number of crews but can collect voluminous amount of data about each of these individual crews, their network relations with one another and how they perform over time. These types of data have traditionally been more amenable for a qualitative, case-driven research approach than quantitative work. Inferential methods often assume a sufficiently large and independently distributed sample that is challenging to gather in LDSE analogs. Furthermore, inferential methods only work toward making ‘in sample’ claims: data from a 45-day analog mission only describes what to expect from the first 45 days of an LDSE analog, with no strong mechanism to speculate about future trends occurring beyond these 45 days. ABMs address these limitations: They provide an opportunity to build models that can be validated based on highresolution temporal data collected in other LDSE analogs and projected over longer time spans. Once a model of how different factors influence crew outcomes in LDSE is constructed, calibrated, and validated, it is now ready to be employed predictively. For instance, ABMs allow researchers to conduct in silico virtual experiments, in which hypothetical inputs (not previously observed in the real-world) are provided to an ABM to predict what outputs the model will produce. A model that is fed data about crew members’ characteristics and their upcoming task schedules can predict

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potential risks (e.g., interpersonal conflict, high workload) that members of the crew may encounter, paving the way for mission support to plan future countermeasures aimed at mitigating these risks. Finally, ABMs have prescriptive uses that can help mission support to plan those future countermeasures aimed at mitigating those aforementioned risks. Prescriptive analytics will evaluate the efficacy of these options. Relatedly, given the state of the crew, ABMs can recommend (or prescribe) how tasks can be scheduled, based on workload, sleep deprivation, or other factors, in a way that will help astronaut crews operate at their optimal performance. As such ABMs will be a potentially valuable tool to help researchers offer operational assistance to shape the effectiveness of team processes in LDSE.

DEVELOPING AN AGENT-BASED MODEL FOR CREW COMPOSITION EFFECTS The development of an agent-based model is a complicated and iterative process, in which researchers apply many different techniques to create, improve, and learn from their model. We outline steps we used to develop an agent-based model of team composition by describing four key processes we carried out: model construction, model calibration, model validation, and model application (Figure 6.1). While we apply this approach to team composition, it can be applied to other dynamic phenomenon in LDSE analog research. In model construction, we specify the system of interdependent variables of interest that capture the social phenomena we want to explain. We relied on theory, prior empirical research, and meta-analyses in order to select variables to include in our model and to specify potential mechanisms by which these variables may influence one another. The model calibration stage is where the empirical data collected in analogs are used to estimate the parameters of the model. In the model validation stage we evaluate the extent to which the model is valid in terms of fitting the observed data on which it was trained as well as on new test data. Finally in the model application stage, we conduct virtual experiments to predict what might happen in a hypothetical team as well as evaluate various prescriptive actions to mitigate potential problems that are predicted. We hasten to add that there is no single ‘correct’ approach to developing an agent-based model. Despite its linear

FIGURE 6.1 Flowchart for the steps that may be used in developing an emulative ABM.

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representation, in practice, model development is an iterative process of refinement and extension – moving through each process multiple times and adapting plans for the next step based on what happened in the previous ones.

model construction Defining Model Scope The first step in constructing an agent-based model is to describe the models’ scope: Who are the agents, what are the output metrics of the model that we seek to explain (e.g. team functioning, performance, viability) and what factors influence, and are perhaps in turn influenced by, these output metrics? These questions form the foundation of what the model will try to accomplish, and how it will go about doing it. Until recently, because of the paucity of dynamic empirical data, ABMs were more heavily utilized to develop simple, stylized models of social phenomena and were used primarily to explore how changes in inputs or mechanisms might impact emergent outcomes. For instance, a simple stylized model where new agents entering a network were more likely to connect with already well-connected nodes demonstrated the plausibility of preferential attachment as a theoretical mechanism to explain the widespread prevalence of scale-free ‘hub-and-spoke’ social networks (Wilensky, 2005). Models designed to puzzle through such thought experiments are often referred to as intellective computational models (Mavor & Pew, 1998). The parameters in these computational models are often arbitrarily chosen with little loss of generalizability. However, with the increasing availability of highresolution temporal data, there is greater interest in the development of emulative computational models (Carley & Hirshman, 2011). These much larger models seek to emulate in substantial detail the dynamic features and empirical characteristics of a specific team or organization (Carley, 2009). They often have, by comparison, a much larger number of inputs and outputs; however, the availability of large amounts of dynamic empirical data eliminate the need for modelers to a priori specify parameters for the impact of these variables on the phenomena of concern. Instead we use novel genetic algorithms and optimization techniques to empirically estimate these parameters (Stonedahl & Wilensky, 2010a; Sullivan, Lungeanu, DeChurch, & Contractor, 2015; Thiele, Kurth, & Grimm, 2014). Using empirical data to estimate the parameters in a computational model is a novel contribution to ABM research. The idea is somewhat analogous to a statistical (e.g., regression) model, in which empirical data is employed to identify whether, and to what extent, variables influence one another. Using empirical data to estimate parameters in ABM have the potential to blunt criticism that modelers face from theorists or empiricists who are wary of believing insights drawn from computational models which include, arguably, arbitrarily specified parameters – rather than parameters supported by empirical data. Having decided on the agents, the decision to design (in our case) an emulative (rather than intellective) model, and high-level categories of inputs and outputs, the next step is to develop the agent-based model.

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Theory, Prior Empirical Research, and Meta-Analysis In the first step, we used theory, prior empirical research, and meta-analyses for two purposes: (i) to identify a system of variables that are interrelated with the phenomena of interest, and (ii) create probabilistic rules that specify how agents’ attitudes and behaviors shape, and are shaped by, the system of variables. In our case the outcomes of interest are crew performance and viability. However, a central, arguably idiosyncratic, premise of our modeling effort is that the impact of compositional factors on crew performance and viability is completely mediated by crew members’ network relations (Figure 6.2). Indeed, a wide-body of extant literature (Balkundi & Harrison, 2006; Crawford & LePine, 2013; Mehra et al., 2006) have established significant connections between social relations and measures of team performance We identified four social relations that were relevant to analog research – task affect, task hindrance, leadership, and followership. In addition, our research on HERA crews has shown that properties of the task affect, task hindrance, leadership, and followership networks were all correlated with objective measures of performance on team tasks (Antone et al., 2019). Given our premise that social relationships mediate the effects of team composition on crew performance and viability, the remainder of the model is focused on compositional, network, and environmental factors that influence social relationships among crew members. Figure 6.2 provides a schematic of the factors in our ABM influencing social relationships among crew members. This model was based on a review of the theoretical and empirical literature on team composition, a smaller subset of case studies that looked at teams in isolated and confined environments and meta-analyses on team composition.

An integrAted model of teAm comPosition The factors shaping social relationships among crew members fall into five buckets: First, we consider the endogenous effects labelled ‘Social Network Trends’ in Figure 6.3. These include temporal patterns such as inertia – the likelihood of a crew member enjoying working with another in the future is often best predicted by the extent

FIGURE 6.2 Core networks predicting key outcomes of performance and viability.

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to which the crew members currently enjoy working with one another. Another common endogenous mechanism is based on reciprocity. If a crew member enjoys working with another, it is likely that the other will also report enjoying working with the former. Likewise, crew relation may be transitive, if A looks to B for leadership and B looks to C for leadership, A might also look to C for leadership. Finally, crew relations might exhibit the emergence of hubs. One crew member might draw hindrance ties from all other members. The two buckets on the right consider the compositional effects of individuals’ personality on crew relations. The bucket labelled ‘Personality’ considers the extent to which a crew member’s personality characteristics (Five Factor Model personality traits and facets, values, coping styles, psychological collectivism, and self-monitoring) make them more (or less) likely to report (or receive) specific social ties from other crew members. The bucket labelled ‘Personality Fit’ considers the extent to which the match (or mismatch) in personality characteristics between two crew members might increase or decrease the likelihood of a social relation between them. The two buckets on the left side of Figure 6.3 consider environmental factors that influence crew social relations. The bucket labelled ‘ICC’ refers to the impact of contextual factors (Isolation, Confinement, and Controlled conditions) on crew social relationships. Finally, the bucket on the bottom left labeled ‘Tasks and Scheduling’ considers how aspects of the tasks impact crew relations. Specifically, we modeled the extent to which crew relations were influenced by the workload, interdependence, situational strength, and duration of each task the crew carried out.

FIGURE 6.3

Factors integrated into our ABM of Teamwork in LDSE.

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Each of these potential influences were codified as a system of probabilistically driven rules that would update crew relations at each time point based on prior time points for the entire duration of the 30- or 45-day missions. Simplifying assumptions are made about the level of change during sleep periods. Time invariant factors such as personality and personality fit would have a baseline effect across all time periods while time variant factors such as days in isolation, variations in tasks and scheduling, and fluctuations in the social relations themselves had a more dynamic impact on future states of social relations. These systems of equations were then implemented in Netlogo (Wilensky, 1999), a widely used ABM platform. The ABM model was now ready to be calibrated as described in the following section.

model cAlibrAtion A distinctive feature of our deployment of ABM is to rely entirely on empirical data to estimate the magnitude with which each factor in our agent-based model influenced crew relations. This is in stark contrast with most prior ABM efforts (see Sullivan et al., 2015 for an exception) where the researcher uses some heuristic (a literature review of effect sizes or expert opinion) to specify the magnitude with which various factors impacted outcomes. As Smith and Rand (2017) argued, using data generated from real experiments is the ideal method to design and calibrate agentbased model’s rules and the mechanisms. Collecting high-resolution data for the study of long-duration space exploration is a major challenge. While it is not possible to intensely survey and monitor actual crews in space, we relied on data gathered in NASA’s Human Exploration Research Analog (HERA) at Johnson Space Center. HERA simulates long-duration space missions with a crew of four ranging for a period of 30–45 days. HERA places crews of individuals in conditions that simulate space exploration: completing simulated tasks, living in a small module for extended periods, experiencing communication delays with mission control as they ‘travel’ away from earth, as well as designated periods of extended sleep deprivation. Data collected in isolated and confined environments such as HERA is arguably the closest alternative for studying crews to actual space missions. That said, these long-duration space exploration analogs are also expensive and time-consuming to operate. Researchers are only afforded the opportunity to observe a handful of missions every year. The upside is that for the crews that are observed, we can observe many variables over time. For our model calibration, we obtained data from eight separate four-person crews completing 30–45 day missions in the HERA analog operated by NASA. To have our model estimate parameters based on what occurs in these HERA crews, we must collect data on all variables identified in Figure 6.2. Time invariant personality and personality fit variables only needed to be collected once using standard psychometric scales. Time variant variables needed to be measured at several points in time. The latter included social networks elicited from the crew via sociometric surveys at eight points in time over the course of a 30-day mission or 12 points in time over the course of a 45-day mission. In addition, we were able to collect data using pre-mission and post-mission surveys.

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As dependent variables in our model, we included measures of four relational networks: task affect, task hindrance, leadership, and followership. These four networks capture a long-standing distinction in the small group literature on task and social needs. The task affect and hindrance capture positive and negative working relationships among crew members. Task affect was measured with the prompt: ‘With whom do you enjoy working?’ Task hindrance was elicited with the prompt: ‘Who makes tasks difficult to complete?’ In addition to assessing manifest social relations, we also included two networks capturing behavioral and motivational aspects of teams: leadership and followership. Leadership was elicited by asking ‘To whom do you provide leadership?’ Followership relations were assessed by asking: ‘Who do you rely on for leadership?’ These four prompts yield four directed networks, each examined in relation to performance. We also coded task characteristics based on crew members’ perceptions of workload and we were also provided detailed minuteby-minute task schedules (nicknamed the ‘playbook’) for individuals working by themselves or in teams over the course of the entire mission. Finally, we were able to design our own tasks carried out by the HERA crews to gauge multiple measures of team performance across task types (Larson et al., 2019; Antone et al., 2020). To estimate the parameters of the ABM model, we used genetic search algorithms implemented in the BehaviorSearch tool for NetLogo (Stonedahl & Wilsensky, 2010c). The BehaviorSearch tool allows for the specification of an objective function that is minimized or maximized according to some set of constraints to ‘calibrate’ the model. Calibration simply describes the process of manipulating a model to get closer to a desired behavior (Calvez, & Hutzler, 2005; Stonedahl & Wilensky, 2010b). In this case, the desired behavior is matching as closely as possible the simulated social relations among crew members with the empirical observed social relations among crew members. The objective function we chose was the mean squared error between simulated crew relations and empirical crew relations. The BehaviorSearch software implements several search algorithms, which can be used to find a set of parameters that minimizes the mean SSE. To find the parameters for this model, each of the different search algorithms were tested. In our case, the standard genetic algorithm yielded the best results. Our results indicated, for instance, that crew members tend to enjoy working with individuals who are high on self-monitoring. Further, these individuals are less likely to be viewed as making tasks difficult to complete. Further, high workload schedules make crew members less likely to enjoy working with others. Turning to leadership relationships, our model estimates indicate that two crew members are not likely to claim leadership over one another. However, when crew members rely on one another for leadership, it is likely to be reciprocated. Unlike traditional statistical inferential techniques, estimates obtained from BehaviorSearch algorithms are not accompanied with standard errors and hence are not amenable to standard significance tests. However, to assess the robustness of the parameters estimated for, say, parameter P, we run the model fixing all the other parameters to the values estimated by BehaviorSearch, while letting the parameter P vary over its range (from −1 to 1) using enough replications to compute the mean fit error. For example, to test the significance of the finding that crewmembers tend to enjoy working with individuals who are high on self-monitoring, we ran the model

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500 times using the parameters determined by BehaviorSearch (e.g., self-monitoring parameter for the recipient of task enjoyment relations was 0.56). Then, we ran the model 500 times with all the same parameters except the self-monitoring parameter that could vary over its range (from −1 to 1). Finally, a one sample t-test was performed to determine whether the set of errors estimated with the fit parameter (0.56) are less than those estimated by allowing the focal parameter to vary (from −1 to 1). A negative and significant effect means that the focal (in this case, self-monitoring) parameter has a measurable and significant effect on reducing the error for crew social relations; as such it plays a significant role in matching the social relations in the simulated and empirical model. The procedure is repeated for all parameters estimated.

model vAlidAtion Having a calibrated model with parameter estimates begs the inevitable next question. How well did we do? The next phase is validation, in which our goal is to assess the extent to which simulation results from our agent-based model provides a useful reflection of observed data. There are three types of validation on which we focus: We confirm face validity, the extent to which the variables and mechanisms make intuitive sense for the phenomenon we are modeling, by relying on extant theory. Because we are producing a model to mimic reality, our goal is to check that the structure of our model is reasonable, before moving onto empirical approaches for assessing validity. For instance, we would expect that at least some of the parameter estimates for variables impacting crew relations have theoretical plausibility. Consider the result we reported in the previous section that workload schedules make crew members less likely to enjoy working with others. While not groundbreaking, results such as these help confirm the face validity of the model and open up the possibility for taking seriously, and puzzling over, some potentially counter-intuitive estimates. We next seek to confirm internal validity, the extent to which our model can explain what happens in the data we empirically observed. Specifically, we conduct direct comparisons between our predicted and simulated results for the same data set. Alongside face validity, these tests determine the extent to which the rules in the model are able to generate patterns in the simulated data that are aligned with the observed data. For instance, we examine plots of the number of relations for each crew in our simulations, in comparison with their observed values, as well as the predictive performance of our model at different points in time. Overall, we confirm that our model tends to mirror the aggregate trends in the data used to estimate it. Finally, we consider issues of external validity. A key question, for an emulative agent-based model in particular, is how well the model performs at making predictions for an unobserved crew? With a limited sample of crews, the best approach to estimating the predictive performance of our model is through cross validation. Given we have observed eight independent crews, we perform eight-fold cross validation: We select one crew to hold out as a test set, estimate our models’ parameters using data from the remaining seven crews, and then use this set of parameters to simulate the held-out crew. These simulated ties are compared with the empirically

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observed data to evaluate predictive performance. By repeating this process eight times, using each crew as the test set once, we obtain an estimate of how well our model would predict relations for a future crew. To evaluate our model, we examine the confusion matrix cross tab between presence or absence of predicted and observed ties, alongside summary statistics such as accuracy, precision, recall, F1 scores, ROC curves, and precision–recall curves (Davis & Goadrich, 2006; Fawcett, 2006). These summary measures provide a better understanding of model quality than accuracy alone, especially in the case where the relationship being predicted is either very frequently present, or very frequently absent. For instance, in our data, task affect relations are present 81.3% of the time, and task hindrance ties occur only 23.3% of the time. In this case, a trivial classifier predicting that all task affect ties exist and no task hindrance ties exist would obtain deceptively impressive but fundamentally useless accuracy scores of 81.3% and 76.7%, respectively. Such a classifier would not be useful practically in distinguishing who is likely to have a certain tie. Therefore other performance metrics, beyond accuracy, must be assessed. Specifically, we compute (1) Precision scores which indicate the percent of predicted ties that were observed in real crews, (2) Recall scores which indicate the percent of observed ties that were correctly predicted by our model, and (3) F1 scores, which use the harmonic mean of precision and recall as a measure of performance. Results of our model validation for predicting ‘who crew members enjoy working with’ achieved average F1 scores of 0.85 for internal validity (on the training data set) and average F1 scores of 0.81 for external validity (on a test data set). However, the results of our model validation for predicting who crew members cite as ‘making tasks difficult to complete’ (i.e. hindrance ties), our average F1 scores for internal validation fell to 0.56 and for external validation fell to 0.37. The disparity in validity between the two types of social relations is, at least in part, an artifact of the relatively sparse number of observed hindrance ties as compared with task affect ties, thus making it more difficult to capture that signal adequately. With small-sample data, cross-validation testing is critical to ensure we are not overfitting our model to nongeneralizable specifics of our observed crews. Additionally, such estimates of performance are necessary when assessing whether our model will be able to make predictions of sufficient quality to be used in practice. This type of validation, in particular, identification of uncertainty in predictions, has been considered critical by NASA in its published Standards for Models and Simulations (Steele, 2007; NASA Standard, 2009). The greatest challenge we will encounter, in modeling space exploration, however, is our reliance on analog data. What we observe in 30- to 45-day analog missions will not fully reflect the empirical realities of LDSE, and thus our findings may not completely generalize to these crews. Cross-validation testing cannot account for these issues. As we work toward building models usable for real-world decisionmaking, there is a need to start testing analog models outside of HERA – testing our models in scenarios involving longer missions, more extreme environments, different types of work, and multinational crews. Assessing generalizability in a varied ensemble of LDSE analogs (e.g. Antarctic studies, SIRIUS and HI-SEAS analogs) will be the best we can do prior to working on actual space missions.

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insights from our ABM accessible to decision makers without them requiring any knowledge of agent-based modeling. As such TEAMSTAR aspires to be both a dashboard – and a ‘do-board.’ TEAMSTAR is powered at the back-end by ABM and requires the administrator to upload relevant data (e.g., attributes of potential team members, prior relations, task schedules). Prior to the mission, TEAMSTAR provides decision makers with an easy to use interface to predict how a hypothetical team’s social relations are likely to evolve over the course of a mission. The decision maker selects a pool of potential crew members and then composes hypothetical teams by simply binning names of hypothetical teams (Figure 6.4). TEAMSTAR runs the virtual experiments in the background and provides decision makers with predictions about the relationships between crew members at any point in time over the upcoming mission (Figure 6.5). To be useful, a predictive team composition model needs to be flexible in terms of staffing capabilities, and its ability to estimate risks associated with different hypothetical crews. First, different staffing strategies can be used when composing teams. One strategy is for the compatibility of all crewmembers to be considered simultaneously. Another strategy is to first identify critical team members (e.g., the commander) and then assess the remaining crew members’ compatibility with those critical members. Because LDSE-crews are expected to be multinational, there may be little ability to influence the decision to select all team members, and instead the compatibility of a particular individual or set of individuals will need to be considered. Thus, a predictive team composition model needs to be flexible in its ability to inform different staffing strategies. The ABM powering TEAMSTAR will enable decision makers to evaluate composition scenarios for an entire set of teams, for single-member replacements, and/ or for subsets of teams. This will maximize its utility given that, in international missions, only some of the astronauts will be selected by NASA. TEAMSTAR

FIGURE 6.4 Selecting hypothetical crews to predict their dynamics.

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Predicting team dynamics for a hypothetical team pre-launch.

can also be useful in re-staffing teams should a member be replaced during premission training, recommending a best replacement member to NASA from a set of alternatives. Once in-mission, TEAMSTAR projects how the team is likely to evolve in terms of risk markers such as social integration, team processes (e.g., conflict), and emergent states (e.g., shared mental models). Since the ABM is both temporal and relational in nature, TEAMSTAR also produces detailed results on what social relations and overall crew cohesion looked like in the past and will look like in the future, with confidence intervals for these predictions (Figure 6.6). Second, because there may be constraints on the ability to influence the team’s composition as a whole,

FIGURE 6.6 Past and projected trends of a crew pathway into the mission.

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it is important to understand the risks associated with the team’s composition. With a predictive model of team composition, different risks (e.g., subgrouping, conflict, difficulty maintaining shared mental models) can be estimated for proposed or current crew compositions. Personalized medicine acknowledges that not all humans have the same needs; these individualized needs should provide the basis for countermeasures in human space flight (Schmidt & Goodwin, 2013). In the same way, not all crews will have the same needs. Estimated risks from the predictive model of team composition can be used to understand the training needs of a specific crew and guide the development and strategic application of countermeasures. In-flight countermeasures could be mapped to specific crew compositions and risks. For example, for a crew composition that has a high risk for subgroup conflict across national background, mission control could provide ‘critical’ work, specifically encouraging members from different subgroups to work interdependently, at key points in the crew’s life cycle.

CONCLUSION This chapter has sought to introduce how an agent-based modeling approach can be used to describe, predict, and prescribe the consequences of team composition: We have described the development of an emulative agent-based model of social relations in crews, illustrating the process of model construction, model calibration, model validation, and model application. We recommend the following resources for those interested in learning more about agent-based modeling processes (Wilensky & Rand, 2015; Gilbert, 2007; Heath, Hill, & Ciarallo, 2009), software for implementing agent-based models (e.g. Netlogo, Repast), and approaches for estimating and validating agent-based models (Thiele, Kurth, & Grimm, 2014). A future direction, for models such as ours, may be better quantification of the statistical uncertainty around model parameters. In particular, Bayesian approaches have been identified as promising for extremes team research, due to their ability to represent uncertainty and incorporate extant prior knowledge into these assessments (Bell et al., 2018). Our model is not without limitation. In developing a model for space exploration, we struggled with choices between constructing models that were more exhaustive, or more selective, in their scope. There is, naturally, a desire for researchers to build more integrative models. If more variables and mechanisms are included in a model, more nuances can be represented, and the influences of all these variables and mechanisms can fully be considered when using the models for prediction or decision-making. However, in the presence of a finite sample of data, including too many related or correlated variables can diminish our certainty about the ‘true’ or ‘best’ value of the model parameters for each one. This trade-off will be a key consideration for all models developed for space exploration teams. As an oft quoted statistical aphorism states, ‘all models are wrong but some are useful’ (Box, 1979). We will never have a perfect model for space crew composition, but hopefully we can keep building better models that are highly useful. Overall, we have demonstrated a proof-of-concept of the potential role that agentbased models could serve in helping prepare future crews for long-duration space

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exploration. We hope that this work lays the foundation for future researchers or practitioners interested in developing agent-based models for space exploration crews. As more and more data is gathered from space exploration analogs, progressively more nuanced agent-based models can be developed for space exploration. One final note: Over the past six decades, research conducted for space missions have had significant knowledge spillover in various sectors back on Earth. For instance, we have NASA to thank for the cordless drills originally designed to help astronauts drill on the surface of the moon. High-intensity LED (light emitting diodes) were developed for the NASA shuttles, but are now making great advances in power efficiency back on Earth. Astronauts needed something to keep their recycled water clean. NASA invented a filter with activated charcoal to neutralize pathogens. These technologies are used extensively around the world, including the Global South. Remarkably, all of these innovations have spun out of technological and health challenges faced in space. Today we are on the brink of an innovation that will have spun out of a social science challenge – anticipating and mitigating social dynamics in teams. Alongside important conversations about ethics and privacy, we are beginning to see interest in deploying advanced people analytics, especially relational analytics (Leonardi & Contractor, 2018) that will extend the models and methodology developed for space missions and apply them to the changing nature of work here on earth – and perhaps some day in interplanetary work contexts.

ACKNOWLEDGMENTS The material is based upon work supported by NASA under award No. NNX15AM32G. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NASA.

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Training Principles for Declarative and Procedural Tasks James A. Kole University of Northern Colorado

Alice F. Healy and Vivian I. Schneider University of Colorado Boulder

Immanuel Barshi NASA’s Ames Research Center

CONTENTS Introduction ........................................................................................................... 131 Principles that Benefit Declarative Memory Tasks ................................................ 132 Strategic Use of Knowledge .............................................................................. 132 Abstraction ........................................................................................................ 134 Distinctive Responding ..................................................................................... 135 Testing Effect .................................................................................................... 136 Note-Taking....................................................................................................... 137 Principles that Benefit Procedural Memory Tasks ................................................. 138 Mental Practice.................................................................................................. 138 Cognitive Antidote ............................................................................................ 139 Focus of Attention ............................................................................................. 140 Procedural Reinstatement.................................................................................. 141 Principles that Benefit both Declarative and Procedural Memory Tasks ............... 142 Variability of Practice........................................................................................ 142 Contextual Reinstatement ................................................................................. 143 Summary ................................................................................................................ 144 References .............................................................................................................. 146

INTRODUCTION The challenges in training astronauts for long-duration space flights, such as future exploration missions to Mars, are numerous. First, astronaut candidates must acquire an incredible amount of information over a fairly limited amount of time. This information includes learning about vehicle systems, procedures, and about specific roles 131

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and tasks. Even basic tasks that are usually taken for granted, such as food preparation or movement (as in extra-vehicular activities), are different during space flight than on Earth. Second, once acquired, this information must be retained over long time periods, given the expected length of the missions. Third, the skills learned on the ground will be deployed in a completely different physical environment, and situations are likely to arise during the mission for which astronauts were not specifically trained. Thus, astronauts will have to transfer the skills learned on the ground to novel situations. Research in experimental psychology has yielded a wealth of principles to maximize the retention and transfer of knowledge. However, there are few, if any, principles that generalize across the wide range of tasks that have been employed in the laboratory studies yielding these principles. For certain tasks, a given principle might benefit both retention and transfer, whereas for other tasks, the same principle might have no effect, or even a negative effect. Similarly, a principle that benefits retention might have no effect, or a negative effect, on transfer, whereas another principle that benefits transfer might have no effect, or a negative effect, on retention. Such contradictory findings raise problems for practitioners who wish to instantiate these principles into training regimens, as it is unclear whether doing so would help or hinder overall performance. In this chapter, we review 11 training principles that have been empirically demonstrated to increase the retention and/or transfer of knowledge and skills, and that could be applied to astronaut training for long-duration space missions. We organize these principles into those that benefit declarative memory tasks, those that benefit procedural memory tasks, and lastly those that benefit both types of tasks. Declarative memory includes knowledge for facts, events, and concepts of which one is consciously aware; procedural memory includes knowledge of skills that involve perceptual and motoric components, of which one is not consciously aware. Within each section, the principles are listed in no particular order, and their application should be considered in terms of relevance to trained tasks rather than their position on the list. Not all declarative and procedural memory tasks are the same. Thus, for each principle we also discuss generally the types of declarative and/or procedural tasks to which the principle applies, and also whether the principle has been demonstrated to benefit retention, transfer, or both. We also provide a short theoretical explanation of each principle. The provision of this additional information allows the practitioner to select the appropriate principle to implement in a training regimen based on both the similarity of the task to those described in this chapter and the desired outcome (maximal retention or transfer). In the case that a needed task does not closely match any of those described in this chapter, the practitioner may also use the theory underlying the principle in order to select the appropriate one to be used during training.

PRINCIPLES THAT BENEFIT DECLARATIVE MEMORY TASKS strAtegic use of KnoWledge Principle When acquiring new information, learners should relate that information to prior knowledge, regardless of whether or not that prior knowledge is related

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or conceptually similar to the new information (Kole & Healy, 2007, 2011; Van Overschelde & Healy, 2001). The strategic use of knowledge principle follows from classic research in cognitive psychology demonstrating expertise effects in memory. De Groot (1965) established that master-level chess players were able to recall plausible configurations of chess pieces shown for 5 seconds at a much higher rate (approximately 92%) than could novice chess players (approximately 18%). However, this advantage was not due to a superior memory ability of the master-level chess players, as there was no difference in memory between master-level and novice players for randomized configurations (Chase & Simon, 1973). The studies by de Groot (1965) and by Chase and Simon (1973) suggest that prior knowledge of one domain, such as chess, may be used to recall information related to that domain, such as chess configurations. Subsequent research has shown that individuals may also generalize their prior knowledge to learn domainirrelevant facts. Kole and Healy (2007) required subjects to learn a large number (144) of facts about people within a single, hour-long experimental session. In the first two experiments, subjects learned fictitious facts (such as the type of car owned) about either familiar individuals (e.g., family members) or unfamiliar individuals. In a third condition, subjects also learned facts about unfamiliar individuals, but did so while associating them with familiar individuals. At an immediate retention test, subjects who associated unfamiliar with familiar individuals could recall over twice the number of facts as those in the unfamiliar condition. In the third experiment, subjects used prior knowledge of individuals to learn either fictitious facts about unfamiliar individuals, as in the previous two experiments, or real facts about relatively unfamiliar countries. The advantage for using prior knowledge was equivalent when learning about individuals or countries, thus demonstrating that prior knowledge also transfers to the learning of conceptually unrelated information. Theoretical Explanation Knowledge of a domain may be conceptualized as a knowledge structure, or an integrated representation in long-term memory, consisting of a set of highly interconnected facts or concepts. When committing new information to long-term memory, that information becomes associated with retrieval cues, which in turn may be used to aid in the retrieval of that information. When connections can be made between new information and an existing knowledge structure, that new information becomes associated with many different facts or concepts, each of which may serve as a retrieval cue. By increasing the number of retrieval cues, the chance of successful retrieval in the future increases. Tasks Empirical demonstrations of the strategic use of knowledge principle have employed declarative memory tasks, specifically under the circumstance that subjects were required to learn a large number of facts over a brief time interval, and retention of that information was more important than transfer (i.e., subjects had to recall learned information rather than apply it). Thus, application of this principle is recommended

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under similar circumstances, although it is predicted to be effective even when learning a smaller set of facts over an extended time interval.

AbstrAction Principle Instructional methods should encourage learners to abstract general principles rather than memorize specific details. Abstraction of general principles allows for greater transfer than does memorizing information (McDaniel, Cahill, Robbins, & Wiener, 2013). Abstraction may be defined generally as the process of isolating commonalities from a series of examples, usually accompanied by forgetting specific examples. Abstraction, or a process similar to it, has been invoked as an explanatory process for a wide range of empirical findings from different areas of psychology. For example, in perception, prototype matching theory describes how people recognize and classify novel instances of known perceptual categories, with prototypes as an abstraction of previous experiences with these perceptual categories based on physical similarity (Posner & Keele, 1968). In numerical estimation tasks, metric information describes the numerical information (such as mean and standard deviation) that is abstracted from previous experiences with quantitative information, such as population sizes of different countries and caloric content (Brown & Siegler, 1996; LaVoie, Bourne, & Healy, 2002; Wohldmann,  2015). In memory, scripts may be considered abstractions of everyday sequences of events (Schank & Abelson, 1977), and in language, grammatical rules may be considered abstractions that are formed as a natural consequence of exposure to language (Reber, 1967). Other terms denoting a similar process include generalization and induction. A study by Kornell and Bjork (2008) illustrates abstraction in a perceptual task. Subjects in this study were presented with 6 paintings from each of 12 different artists. The artists were relatively unknown, thereby precluding prior knowledge from influencing learning, and the paintings dealt with similar subjects (landscapes or seascapes). During a study phase, subjects viewed each painting individually, along with the artist’s name. Following this study phase was a test phase when subjects were shown 48 new paintings by the same 12 artists, and for each painting, subjects selected which of the 12 artists had created the painting. During the test phase, subjects were able to identify the correct artist at a rate significantly above chance, even though each of the test paintings had not been viewed previously. This finding suggests that subjects were able to abstract a given artist’s style based on exposure to previous examples of paintings by that artist, and to apply that knowledge to new examples to aid identification. Individuals vary naturally in the degree to which they are able to abstract (McDaniel et al., 2013), although any experimental manipulation that encourages the forgetting of specific examples may encourage abstraction (Vlach & Kalish, 2014). Introducing a temporal delay between study and test allows for the forgetting of specific examples, which thereby encourages abstraction. Within a single experimental session, spacing, or the introduction of temporal gaps between related exemplars, also promotes abstraction (Kornell & Bjork, 2008).

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Theoretical Explanation Individuals may adopt different strategies to perform a task such as artist identification. The first is a memory-based strategy that entails committing specific examples to long-term memory, then comparing novel instances to those examples. The second is an abstraction-based strategy that entails extracting a rule from specific examples, then comparing novel instances to the rule. Rules are more useful when encountering novel instances because only core features, rather than unimportant details, become part of the abstracted rule. Tasks As described previously, abstraction is thought to play a role in many different cognitive processes (e.g., perception, memory), and thus may be applied to a wide range of tasks. The task, however, must be amenable to the presentation of multiple examples to illustrate a concept. Because abstraction is usually accompanied by the forgetting of examples, manipulations that promote abstraction should not be employed when the retention of specific examples is the primary learning objective. Rather, the primary benefit of abstraction is transfer, such as when trying to classify novel examples into known categories. Application of this principle is recommended when transfer is of greater importance than retention.

distinctive resPonding Principle For information that is particularly important to remember, associate a distinctive response to that information. Distinctive responses protect the information associated with it, making that information less susceptible to the inevitable forgetting that occurs over time (Bourne, Healy, Bonk, & Buck-Gengler, 2011). The principle of distinctive responding is based on prospective memory, which involves remembering to execute in the future a given act (either physical or psychological, such as to stop by the store on the way home from work) that is often out of the norm. This type of memory involves the formation of an intention to act, as well as an association between that intention and a cue (either internal or external) that can draw into working memory the intention at a future time. Prospective memory allows for future, goal-directed, and adaptive behavior. Also of interest is the effect of forming an intention on associated memories (e.g., what to buy at the store) and, in particular, if those associated memories exhibit the same characteristics (e.g., forgetting) as those that are not associated with a distinct intention. This question was investigated by Bourne et al. (2011) using a memory updating task. For this task, subjects saw a name in 1 of 4 colors in the center of the screen, and surrounding the name were two rings (an inner ring and an outer ring) of four color patches. For study trials, subjects simply had to respond by clicking on the matching color patch using the inner ring. For test trials, the name was in black font, and subjects had to recall the font color of the name the last time it had been presented. For most test trials, subjects also clicked on the inner ring of colors, but for distinctive test trials, subjects had to remember to use the outer ring of colors to

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respond. Subjects knew beforehand if they would have to use the outer ring (at study the name had occurred in all capital letters rather than in mixed case with only the initial letter capitalized), thereby creating an intent to respond in the future in a different manner than usual. The intention to respond distinctively improved memory for font colors: Accuracy in memory for font colors was better when subjects were to respond using the outer ring than the inner ring. Further, accuracy for distinctive responses was equally high whether there was one intervening trial between study and test or seven intervening trials, indicating that memory for font colors of distinctive responses was not susceptible to forgetting over time as are normal memories. Theoretical Explanation At present, the theoretical foundation of the distinctive responding principle is not fully understood. Bourne et al. (2011) originally speculated that holding an intention to respond in a distinctive manner might preserve the associated information (like font color) in working memory. However, in a follow-up study, Healy, Schneider, Buck-Gengler, Kole, and Barshi (in press) found that disrupting working memory via a concurrent secondary task (counting backwards by three’s) did not eliminate the advantage for information associated with an intent to respond in a distinctive manner. Thus, it appears that mechanisms apart from working memory underlie this principle. Tasks The distinctive responding principle may be instantiated in tasks for which different responses are allowable. This principle has only been tested with a declarative task involving retention, not transfer.

testing effect Principle Learners should be tested over, or actively try to retrieve, the material they are trying to learn rather than engaging in other more passive methods such as restudying (Carpenter & Yeung, 2017; Roediger & Karpicke, 2006). The testing effect is the phenomenon in which testing (or the active retrieval of information) leads to better performance on a subsequent memory test compared with re-studying (Carpenter & Yeung, 2017). The testing effect is a particularly robust finding, having been demonstrated with many different types of learning materials including simple verbal stimuli such as word pairs (Carpenter & Yeung, 2017) and more complex verbal stimuli such as text passages (Hinze, Wiley, & Pellegrino, 2013), as well as for spatial learning tasks (Carpenter & Kelly, 2012). Testing effects have been demonstrated in multiple settings, such as laboratories and classrooms (Carpenter, Pashler, & Cepeda, 2009), and with multiple types of memory assessments such as free recall (Carpenter & DeLosh, 2006; Roediger & Karpicke, 2006) and cued recall (Carrier & Pashler, 1992). In a typical testing effect study, subjects complete multiple learning rounds by studying (e.g., reading) the material to-be-learned, and are then either tested over the material or given an additional restudy opportunity. Following testing or restudy

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is a final test in which subjects are tested over the material. In general, performance on the final memory test is better when material is actively recalled via testing than when it is simply restudied. Additional research has provided evidence for a “pretesting effect,” in which memory is enhanced when subjects are tested over the material before actually learning it (Hartley, 1973; Little & Bjork, 2016). Theoretical Explanation Many studies attribute the testing effect advantage to elaborative retrieval (Carpenter & Yeung, 2017), which is the idea that while encoding target information subjects also encode other information surrounding the target. When they are asked to retrieve the target information, subjects might retrieve the contextual information first, which provides clues that assist in the retrieval of target information (Carpenter & DeLosh, 2006). Similarly, some research suggests that mediators play a role in helping subjects retrieve target information (Carpenter, 2009; Pyc & Rawson, 2010). Mediators are words that can connect cue words to target words such that seeing the mediator may assist with recalling the cue and target words; that is, seeing the cue could elicit the mediator, which in turn could elicit the target (Carpenter, 2009). These explanations apply less to the pre-testing effect, which is usually explained in terms of orienting attention to important facts, as well as increasing motivation (Hartley, 1973). Tasks Given that the testing effect has been demonstrated with many types of declarative memory tasks, it is reasonable to assume that additional testing may be incorporated into most training regimens without detriment. Most testing effect studies have examined the retention of declarative knowledge, whereas fewer have examined transfer. There is some evidence that the testing effect transfers across temporal contexts, types of memory assessments, and within and across domains of knowledge (for a brief review, see Carpenter, 2012).

note-tAKing Principle When presented with declarative information, learners should attempt to write notes long-hand rather than taking no notes or using an external device such as a laptop. Long-hand note-taking promotes conceptual understanding (Mueller & Oppenheimer, 2014). In a study by Mueller and Oppenheimer (2014), subjects watched short videotaped lectures and were asked to take notes over the content either using a laptop or long-hand. After a brief delay, subjects were tested over the content of the lecture without use of their notes. The test involved factual recall questions, which involved retention of specific facts from the lecture, as well as conceptual application questions, which involved transfer, or the application of facts learned during the lecture. There was no difference in performance on factual recall questions between laptop and long-hand note-taking conditions; however, for conceptual questions, there was an advantage for long-hand note-taking over the laptop condition.

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Theoretical Explanation The advantage of long-hand note-taking may be due in part to two well-established principles: depth of processing (Craik & Lockhart, 1972) and the generation effect (Slamecka & Graf, 1978). Because long-hand note-taking is a slower process than taking notes using an external device, subjects in the long-hand condition write fewer notes than do those in the laptop condition, who instead record verbatim notes that are a transcript of what was heard during lecture. Thus, subjects in the long-hand condition might be required to more deeply process material (i.e., for meaning) in order to write concise summaries, whereas subjects in the laptop condition might process the material more shallowly; deep processing leads to better memory than shallow processing by the depth-of-processing framework. If subjects are indeed generating more concise summaries of lecture content, then the material should also be rendered more memorable by the generation effect, which is the finding that selfgenerated information is more memorable than information that is passively read. The most recent evidence suggests that summarizing, regardless of medium, and deeper processing underlie the effect of note-taking (Lalchandani, 2018). Tasks Although this principle was developed using a laboratory simulation of a collegelevel lecture, this principle seems applicable to a wide range of tasks, but especially those that require conceptual understanding or transfer of learned material for which subjects take notes.

PRINCIPLES THAT BENEFIT PROCEDURAL MEMORY TASKS mentAl PrActice Principle The mental practice of procedural skills (also called motor imagery) can aid in the acquisition and retention of these skills over long retention intervals, and may also promote greater transfer than physical practice (Wohldmann, Healy, & Bourne, 2007, 2008). In a study by Wohldmann et al. (2007), subjects practiced a simple data entry task that involved typing four-digit numbers presented on a computer screen. Some subjects engaged in physical practice by typing the numbers using their hands, whereas others engaged in mental practice by imagining typing the numbers without making any overt movements. In a third condition, subjects simply viewed numbers without practicing either physically or mentally. On an immediate test, subjects were presented with old numbers that had been practiced or viewed, as well as new numbers that had not been practiced or viewed earlier. Subjects in the physical and mental practice conditions were faster to type old than new numbers, indicating that mental practice was as effective as physical practice. In contrast, the view-only condition showed no advantage for previously viewed numbers. Mental practice was also as effective as physical practice in maintaining these specific digit sequences over a 3-month period, and both types of practice resulted in general skill learning indicated by a general speed-up in typing new numbers.

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Theoretical Explanation All tasks, even relatively simple perceptual motor tasks such as data entry, include both cognitive components, such as perception and response planning, and motoric components, such as enacting a response. There are two primary hypotheses to explain mental practice effects, each of which focuses on these different task components. The first hypothesis is that mental practice improves the cognitive requirements of a task rather than the motoric (Driskell, Copper, & Moran, 1994), perhaps by increasing perceptual fluency while processing stimuli or by increasing the speed with which response planning is achieved. The second hypothesis is that mental practice improves the motoric requirements of a task because it involves covert physical practice such that the muscles required to enact the skill are innervated even if no overt movements are observed (Driskell et al., 1994). There is evidence to support both hypotheses; neuroscientific evidence shows an overlap in the neural substrates underlying physical and mental practice (Lotze et al., 1999). However, mental practice also appears to specifically strengthen non-motoric (e.g., perceptual) aspects of a task (Wohldmann et al., 2008). Tasks An advantage of mental practice over physical practice is that it can be done anywhere and at any time, and does not require access to the instruments required to perform the task. However, the complexity of the task and the expertise of the subject must also be considered when deciding whether or not to implement mental practice into a training regimen. Some research has shown that for simple skills mental practice may benefit novices, whereas for more complex skills, subjects should have some previous experience with the task (Simonsmeier, Frank, Gubelmann, & Schneider, 2018). Mental practice promotes both retention and transfer of the motor skill.

cognitive Antidote Principle The addition of cognitive complications to routine tasks performed over a prolonged time period may counteract performance decrements (Chapman, Healy, & Kole, 2016; Kole, Healy, & Bourne, 2008). When performing a task over a prolonged period of time, a speed-accuracy tradeoff may occur whereby the speed with which the task is performed increases, but the accuracy with which the task is performed decreases (e.g., Healy, Kole, BuckGengler, & Bourne, 2004). From an applied standpoint, these findings raise the issue of how to counteract the accuracy decrements that occur with increased time on task. Kole et al. (2008) provided evidence that the speed–accuracy trade-off is due more to cognitive factors, such as cognitive fatigue or boredom, than to motoric factors, such as physical fatigue. In this study, subjects completed a simple data entry task (typing four-digit numbers presented to them); for some subjects, the (cognitive) task demands were increased by requiring mental multiplication or by simply alternating the concluding keystroke. Introducing these cognitive complications allowed subjects to sustain or improve their performance with practice, such that they did not demonstrate a

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speed-accuracy trade-off. More generally, an increase in working memory load while performing a simple task serves as a cognitive antidote (Chapman et al., 2016). Theoretical Explanation Several different cognitive constructs may underlie the speed–accuracy trade-off that occurs with prolonged time on task. First, subjects may become cognitively fatigued such that no additional mental resources are available to sustain performance. Second, subjects may become disengaged with the task and adopt a loweffort strategy such that more mental resources are available but are not used. Third, subjects may become physically fatigued such that no additional physical resources are available to sustain performance. Using a computational modeling approach, Gonzalez, Best, Healy, Kole, and Bourne (2011) argued that a combination of factors including decreased arousal (disengagement) and skill acquisition explains the speed-accuracy trade-off. Tasks The cognitive antidote principle has not been tested using a more complex perceptual-motor task than data entry, or for more extended temporal intervals; thus, its application to such situations requires further study. However, this principle has recently been used to explain the effectiveness of classroom response systems in improving student learning outcomes (i.e., more complex declarative memory tasks; Healy, Jones, Lalchandani, & Tack, 2017).

focus of Attention Principle When performing a motor task, adopt an external focus of attention (focusing on the outcomes of one’s actions) rather than an internal focus (focusing on one’s body movements) to improve acquisition, retention, and transfer (Lohse, Sherwood, & Healy, 2010). Several studies have shown that when performing a motoric task, if the actor focuses attention on the outcome of his or her actions, rather than focusing explicitly on trying to control the component movements of the skill, performance is improved. For example, Lohse et al. (2010) had subjects perform a dart-throwing task while either focusing internally or externally by instructing subjects to attend to their arms or to the target, respectively. Subjects were more accurate (i.e., were closer to the bull’s eye) when they focused externally. This finding has been replicated with several types of tasks, including golf (Beilock & Carr, 2001) and musical performance (Wan & Huon, 2005). In fact, for highly skilled motor tasks such as those required in professional sport, ‘choking’ or failures in performance that occur under high pressure situations have been attributable to changes in focus of attention from external to internal (Pijpers, Oudejans, & Bakker, 2005). A caveat to the focus of attention principle is that some studies have found that for novices, initial acquisition of a motor skill is better facilitated by maintaining an internal focus of attention (e.g.,  Lawrence, Gottwald, Hardy, & Khan, 2011), although others have found an external focus of attention improved performance regardless of experience level.

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Theoretical Explanation By the constrained action hypothesis (Wulf, McNevin, & Shea, 2001), when motor skills are highly practiced or well learned, their execution becomes automatic; they are executed without the need for conscious control processes. The engagement of such control processes, which occurs with a switch to an internal focus of attention, interferes with the automatic execution of these skills resulting in performance decrements. Tasks Given the breadth of tasks that have been employed in empirical studies on focus of attention, it is assumed that this principle may be applied to most motoric tasks to enhance the acquisition, retention, and transfer of the skill. However, this principle might be restricted to those individuals who have at least some practice with the motor skill to the extent that the skill has become automatic.

ProcedurAl reinstAtement Principle To maximize performance at test, the procedures required during training should be reinstated at test (Healy & Bourne, 1995; Healy, Wohldmann, & Bourne, 2005). In a study using a data-entry task (described earlier), Fendrich, Healy, and Bourne (1991; Experiment 2) trained subjects on 1 of 2 keypad configurations. The first was a descending layout as on a calculator, and the second was an ascending layout as on a phone. During a test session held 1 week later, subjects typed the trained numbers as well as new numbers; however, half of the subjects completed the task using the same configuration as during training and the other half used the alternate configuration. Further, for the subjects who alternated keypad configurations, half of the old numbers presented were identical to those presented at training and thus required a different motor response (old-digit), and half were different but required the identical motor response as at training (old-motor). For example, if the subjects saw the number 2147 at training, they were shown either the number 2147 (old-digit) or 8741 (old-motor). At test, response times for both old-digit and old-motor numbers were both significantly faster than for completely new numbers, thus suggesting that the reinstatement of either motoric procedures (old-motor) or perceptual procedures (old-digit) facilitated test performance. Theoretical Explanation The procedural reinstatement principle is related to several classic empirical findings that demonstrate retention is successful to the extent that the conditions under which information was acquired match those during which it is tested. Encoding specificity (Tulving & Thomson, 1973) is the idea that information is encoded within a context, and so the provision of contextual elements at the time of recall benefits retrieval. For example, if a subject studies word pairs, then providing one member of the word pair will aid the retrieval of the associated word. Similarly, transfer appropriate processing (Morris, Bransford, & Franks, 1977) is the idea that memory is improved

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if the processes evoked during learning are reinstated at test. Encoding specificity and transfer appropriate processing apply to declarative memory tasks; the procedural reinstatement principle applies to procedural memory tasks, and states that performance at test is facilitated when there is a match between learning and test procedures. Tasks The procedural reinstatement principle was established using simple perceptualmotor tasks, so its generalizability to more complex tasks requires further study. This principle applies to both retention and transfer; transfer is expected to the extent that training procedures may at least be partially reinstated at test.

PRINCIPLES THAT BENEFIT BOTH DECLARATIVE AND PROCEDURAL MEMORY TASKS vAriAbility of PrActice Principle To promote the retention and transfer of procedural or declarative knowledge, vary the conditions of training such that the task is not always practiced in the same manner (Schmidt & Bjork, 1992; Schneider, Healy, Barshi, & Bourne, 2015). The literature demonstrates many different ways in which variability may be introduced to a task. At the simplest level, variability may be introduced through the inclusion of a greater variety of items within the learning set (i.e., the set of items that are to be learned or trained). For example, Kerr and Booth (1978) trained subjects on an aimed throwing task (procedural), with some subjects trained on multiple distances (2 and 4 feet) and others on only a single intermediate distance (3 feet). Those subjects who trained on multiple distances performed better on a subsequent test than those who trained on a single distance, even though the test distance was the same as that trained in the single distance condition (i.e., 3 feet). Schneider et al. (2015) trained subjects on a (declarative) navigation task in which subjects heard and had to repeat back and execute navigation instructions. For some subjects, the navigation instructions included 1–6 commands, but for others, the navigation instructions included only fewer (1–3) or more (4–6) commands. On a subsequent test, subjects who practiced all command lengths were able to repeat back the instructions with greater accuracy than did those who practiced with either only fewer or more commands. Similar results have been found with other procedural memory (e.g., Wulf & Schmidt, 1997) and declarative memory (e.g., Goode, Geraci, & Roediger, 2008) tasks. Even if the learning set size is held constant, variability may also be introduced by randomizing the order of items in the learning set rather than blocking them (i.e., contextual interference; Battig, 1972; Schneider, Healy, & Bourne, 2002; Shea  & Morgan, 1979). When training multiple tasks, variability may be introduced by interleaving the tasks rather than practicing one task at a time (Szpiro, Wright, & Carrasco, 2014).

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Theoretical Explanation For procedural memory tasks, variability of practice is often explained in terms of schema theory (Schmidt, 1975; see also the Abstraction section above); the theory is that variability of practice promotes the formation and use of a rule (schemata) that relates task requirements to internal movements. For example, in the aimed throwing task, subjects might form a general rule as to how much force to use in order to throw an object a certain distance. For declarative memory tasks, randomizing the order of items in the learning set requires effortful retrieval of items from long-term memory on subsequent trials, whereas blocking items simply requires holding a single item in working memory. As mentioned in the Note-Taking section, generating information makes that information more memorable. Further, retrieving items from longterm memory during practice more closely matches the procedures required during declarative memory tests (see the Procedural Reinstatement section). Tasks Variability of practice is a particularly powerful principle as it applies to both procedural and declarative memory tasks; however, in both cases it is shown to slow the acquisition process, although it does benefit both retention and transfer. Thus, this principle is recommendable if acquisition speed is not of utmost importance. Further, for complex motor skills (e.g., volleyball or tennis serve; tennis forehand), the evidence is mixed as to whether variability of practice improves learning (Wulf & Shea, 2002).

contextuAl reinstAtement Principle The retrieval of declarative and procedural knowledge is enhanced when the training context is reinstated at test. The contextual reinstatement principle statement is similar to the procedural reinstatement principle in that both focus on the similarity between training and test conditions. However, context is broadly defined as the elements present in the training environment, but incidental to the task (Murnane & Phelps, 1993). In other words, the procedural reinstatement principle applies to procedural knowledge that is endogenous to a task, whereas context applies to elements that are exogenous to a task. Context has been considered primarily as environmental/physical factors (e.g., Bjork & Richardson-Klavehn, 1989), although it has also been interpreted to include emotional factors (Bjork & Richardson-Klavehn, 1989) and non-essential perceptual features of the task (Kole, Healy, Fierman, & Bourne, 2010). In a classic study, Godden and Baddeley (1975) had subjects (who were all divers) learn word lists in two different locations: on land or under water. After a short distractor task, subjects were tested via free recall over the lists in either the same environment (land–land, water–water) or the alternate environment (land–water, water–land). Testing in the same environment increased recall of the word lists by almost 50%, thus demonstrating that reinstating contexts benefits memory. Interestingly, other studies have found that when the environmental context changes between training and test, mentally imagining the training context can improve recall (Smith, 1984; see also the

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Mental Practice section). Contextual reinstatement has also been found for simple motor skills tasks, such as sequential keystroke learning (Wright & Shea, 1991). Theoretical Explanation The theoretical explanation of contextual reinstatement effects is similar to that for the strategic use of knowledge principle. That is, new information becomes associated with (contextual) retrieval cues, and the provision of those cues during test facilitates recall. However, contextual retrieval cues are the physical features of an environment rather than the concepts stored in long-term memory, and are often incidentally encoded rather than consciously selected by the learner. Tasks Contextual reinstatement effects have been found for simple perceptual motor tasks and for list learning tasks that involve free recall at test. Many studies have not found contextual reinstatement effects when memory is assessed through recognition (e.g., Skinner, Manios, Fugelsang, & Fernandes, 2014). Both retention and transfer are enhanced when context is reinstated.

SUMMARY Future exploration missions to Mars will require crew members to acquire a vast amount of both declarative and procedural knowledge, retain over missions that will last years, and transfer to unfamiliar environments and to novel untrained situations. Perhaps the latter consideration is most critical, as crew members will operate fairly autonomously, with increasingly long communication delays with Mission Control or due to communication blackouts, and in high consequence environments. In this chapter, we reviewed 11 training principles (for summary, see Table 7.1) that have been empirically demonstrated to increase the retention and/or transfer of knowledge and skills. As the organization of this chapter suggests, we believe it is most TABLE 7.1 Summary of Training Principles Principle

Summary

Tasks

Outcome

Principles that Benefit Declarative Memory Tasks Strategic use Relate new information to of knowledge prior knowledge

Memory tasks for which a large Retention amount of information must be learned over a brief time interval Perceptual, language, memory, or Transfer quantitative tasks in which many examples of a concept are presented Prospective memory tasks, or tasks Retention that allow for different responses

Abstraction

Learn general principles rather than specific examples

Distinctive responding

Associate information to-berecalled with distinctive responses Test or actively recall information Language, memory, and spatial rather than restudy it learning tasks

Testing effect

Retention, transfer (Continued)

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TABLE 7.1 (Continued) Summary of Training Principles Principle

Summary

Note-taking

Write notes long-hand rather than use an external device

Mental practice

Imagine executing movements rather than physically performing them

Tasks General memory tasks

Outcome Transfer

Principles that Benefit Procedural Memory Tasks

Cognitive antidote

Add cognitive complications to simple perceptual-motor tasks performed over a prolonged time interval Focus of Focus on the outcomes of attention physical actions rather than physical actions themselves Procedural Ensure that training and test reinstatement procedures match

Simple perceptual-motor tasks, or Retention, complex perceptual-motor tasks for transfer which the trainee has some familiarity Simple perceptual-motor tasks; has General also recently been applied to a performance declarative memory task Simple perceptual-motor tasks; tasks Retention, for which trainee has some transfer familiarity Simple perceptual-motor tasks Retention, transfer

Principles that Benefit both Procedural and Declarative Memory Tasks Variability of practice

When practicing a task, increase Simple perceptual-motor tasks; many Retention, the size of the learning set, vary declarative memory tasks (e.g., transfer the order of items in the vocabulary learning, anagram learning set, or interleave tasks solution, spatial cognition) Contextual Ensure that training and test Declarative memory tasks involving Retention, reinstatement contexts match free recall retention tests; simple transfer perceptual-motor tasks

important for trainers and curriculum developers to identify the nature of the task, that is, whether the task requires mastering factual/declarative knowledge or motorbased skill/procedural knowledge, as most principles apply to only one of these forms of knowledge. We also believe it is equally important to identify the primary goal of training; that is, whether the ability to retain knowledge over time or the ability to apply knowledge to different circumstances is of utmost importance. Some principles maximize retention without benefit to transfer, and others maximize transfer without benefit to retention. We believe that these training principles may be applied to any training regimen, including training astronauts for long-duration missions (for a discussion on applying some of these principles to specific tasks involved in astronaut training, see Chapter 4 by Dempsey and Barshi, in Volume 2 of this book). Astronauts work together in crews, and therefore some part of training necessarily involves learning team-based skills, such as leadership and communication. But this is not addressed in this chapter as the principles reviewed here were derived from laboratory studies focused on individual learners. It is reasonable to expect that some of these principles would also apply to team-based skills, but such application must be tested directly and is a fruitful avenue for future research.

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8

Team Adaptation and Resilience Where the Literature Currently Stands and How It Applies to LongDuration Isolated, Confined, and Extreme Contexts M. Travis Maynard Colorado State University

Deanna M. Kennedy University of Washington Bothell

Scott I. Tannenbaum The Group for Organizational Effectiveness (gOE)

John E. Mathieu University of Connecticut

Jamie Levy The Group for Organizational Effectiveness (gOE)

CONTENTS Introduction ............................................................................................................ 152 Definitional Model of Team Adaptation................................................................. 152 Integration of Team Resilience into the Team Adaptation Nomological Network........................................................................................ 153 Literature Review of Team Adaptation and Resilience from Analogous and ICE Settings............................................................................................................ 155 Antecedents Considered in Analogous and ICE Settings.................................. 156 Processes Considered in Analogous and ICE Settings...................................... 157 151

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Mediators Considered in Analogous and ICE Settings ..................................... 157 Outcomes Considered in Analogous and ICE Settings ..................................... 158 Interventions and Countermeasures ....................................................................... 158 Pre-Mission Interventions and Countermeasures.............................................. 159 During-Mission Interventions and Countermeasures........................................ 160 Recommendations for Future Research ................................................................. 162 Conclusion ............................................................................................................. 164 References .............................................................................................................. 165

INTRODUCTION Given the prevalent use of teams to deal with dynamic and complex organizational contexts, the topic of team adaptation has gained prominence over the past decade (e.g., Burke, Stagl, Salas, Pierce, & Kendall, 2006). In particular, researchers have paid increasing attention to the environment in which teams operate and noted that some contexts are more extreme than others (e.g., Maynard, Kennedy, & Resick, 2018). In isolated, confined and extreme (ICE) environments, it is particularly important to consider the factors that shape team adaptation. One example of an ICE environment will be the long-duration missions that NASA is currently preparing for in the not too distant future (e.g., Salas et al., 2015). While team adaptation has been salient for past NASA missions, the ability to adapt and to bounce back from challenges will be of even greater importance in future deep space missions. On deep space missions, the flight crew will have increased autonomy as mission control will have limited opportunities to assist them (e.g., Neerincx et al., 2008). Accordingly, the purpose of this chapter is to review and integrate team adaptation research with an emphasis on NASA analog and ICE contexts, develop new insights from the findings and gaps identified, and propose directions for future research. To set the stage for the literature review, we begin with a definitional model of team adaptation (Maynard, Kennedy, & Sommer, 2015a) and then integrate team resilience (Kennedy, Landon, & Maynard, 2016) into the nomological network. We structure our definitional model to address the antecedents that create the capacity to adapt (input), the adaptation process (mediator), and the consequences of adaptation (outcome). We then wrap in the development of resilience from an individual to team level construct and review how team resilience factors can be integrated into the nomological network introduced here. We will focus on the team adaptation and resilience research conducted in NASA-specific analogs and other ICE environments. In addition to providing this literature review, we provide evidence and support for potential interventions and countermeasures, as well as future research directions.

DEFINITIONAL MODEL OF TEAM ADAPTATION The topic of team adaptation is growing in popularity in recent years. Early taxonomies of adaptation viewed it as a multifaceted individual-level construct (e.g., Pulakos, Arad, Donovan, & Plamondon, 2000). In contrast, Maynard and

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colleagues (2015a) advanced a team-level conceptualization of adaptation. They leveraged an input–mediator–outcome (IMO) framework that is central within the organizational team literature (Ilgen, Hollenbeck, Johnson, & Jundt, 2005). Many definitions of adaptation include the process of change (e.g., Burke et al., 2006; Marks, Zaccaro,  & Mathieu, 2000), yet the definition we apply goes further in describing what is changed. The team processes framework proposed by Marks, Mathieu, and Zaccaro (2001) delineates the types of activities that teams engage in that may be subject to adaptation. Their framework includes action (activities regarding the accomplishment, coordination, and monitoring of progress, systems, and backup behaviors); interpersonal (activities involved in managing conflict, affect, motivation, and confidence); and transition processes (that encompass activities about mission analysis, planning, goal specification, and formulating strategies). Accordingly, following the definition by Maynard et al. (2015a, p. 5), we view team adaptation as: Adjustments to relevant team processes (i.e., action, interpersonal, transition) in response to a disruption or trigger.

An antecedent to the adaptation process includes the team’s own inherent capacity to adapt (i.e., team adaptability). Indeed, researchers suggest that team members may have certain abilities and skills that contribute to team adaptation (e.g., Pulakos et  al., 2002). However, in our conceptualization, team members’ individual tendencies toward adaptation are quite different from the actual team-level collective process of adapting. This is evident in the definition by Maynard et al. (2015a, p. 4): Team adaptability is the capacity of a team to make needed changes in response to a disruption or trigger.

Finally, team adaptation can lead to various consequences such as team performance and team member affective reactions (i.e., team adaptive outcomes) and we reviewed these extensively to better understand the relationship team adaptation has with various team outcomes. In keeping with the definition by Maynard and colleagues (2015a, p. 3), we envision team adaptive outcomes as: The consequences of the adaptation process, which may include constructs such as: various emergent states such as team cognition, team member affective reactions such as willingness to work together again, team effectiveness, and team performance.

integrAtion of teAm resilience into tHe teAm AdAPtAtion nomologicAl netWorK Although we feel that the distinctions made above regarding adaptability, adaptation processes, and adaptive outcomes are central to clearing up some of the confusion present within the team adaptation literature, we also believe that integrating resilience into the adaptation definitional framework is appropriate given its conceptual overlap with team adaptation. The overlap between these constructs is seen, for example, in the way Luthar, Cicchetti, and Becker (2000) defined resilience

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as “a  dynamic process encompassing positive adaptation within the context of significant adversity” (p. 543). Yet, van der Kleij, Molenaar, and Schraagen (2011) acknowledge that although resilience “has many commonalities with adaptation” (p. 2158), they are distinct constructs. We argue that resilience is salient in considerations of team adaptation. While resilience has largely been studied as an individual-level phenomenon, teams must also be resilient. Envisioning how individual-level resilience might translate into team resilience, Bartone (2006) suggested that if the team leader has a “hardy” personality, that should help the team be more resilient. More recently, researchers have argued that a dedicated team-level consideration of resilience is needed (e.g., Alliger, Cerasoli, Tannenbaum, & Vessey, 2015). For example, in considering how resilience is shaped in a military context, research suggests that interpersonal conflicts, team morale, and cohesion matter (Boermans, Delahaij, Korteling, & Euwema, 2012). Research has also suggested that resilient teams are better at coordinating and responding when faced with crises (Gomes, Borges, Huber, & Carvalho, 2014). However, there is much confusion about how to conceptualize team resilience and therefore, how to study it. A main point of confusion regarding team resilience is whether it exists inherently, is developed, or produced after-the-fact. Likewise, researchers have questioned whether team resilience is a trait of team members or a dynamic team process (e.g., Fletcher & Sarkar, 2013). Recent research by Kennedy et al. (2016) conceptualized team resilience as an emergent state. An emergent state describes “constructs that characterize properties of the team that are typically dynamic in nature and vary as a function of team context, inputs, processes, and outcomes” (Marks et al., 2001, p. 357). This view of resilience as an emergent state is also embedded within the definition provided by Kennedy and colleagues (2016; p. 468) which we also adopt here: Team resilience is a shared belief held by the team that it can respond to disruptive and challenging events, recover from setbacks, and thrive as a team under these conditions.

Treating team resilience as an emergent state as compared with other treatments (e.g., as a trait or process) is appropriate given that researchers suggest resilience is dynamic (e.g., Luthar et al., 2000) and is influenced by adaptation and other team processes (e.g., Moran & Tame, 2012). Reich, Zautra, and Hall (2010) suggested that resilience is the outcome of successful adaptation to hardships, which is consistent with our view of team resilience as an emergent state impacted by various input and process variables including adaptation – see Figure 8.1. Likewise, treating team resilience as an emergent state is consistent with those who have defined it as “a team’s belief that it can absorb and cope with strain, as well as a team’s capacity to cope, recover and adjust positively to difficulties” (Carmeli, Friedman, & Tishler, 2013, p. 149) and the “capacity to bounce back from an adversity-induced process loss” (Stoverink, Kirkman, Mistry, & Rosen, 2020, p. 7). Given the conceptual overlap that exists (as depicted in Figure 8.1), we view adaptation processes and team resilience to be a part of a reciprocal relationship that is central to the team adaptation nomological network. Such a relationship is consistent with those who have previously suggested a link between adaptation

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FIGURE 8.1 Team adaptation nomological network.

and resilience (e.g., Sutcliffe & Vogus, 2003). Lopes (2010) suggests that resilience increases each time a team (or individual) successfully overcomes an obstacle or adapts. Alliger and colleagues (2015) describe a framework of behaviors that their research tied to resilient teams and include actions taken before the arrival of the problem (i.e., to minimize the impact of disruptions), actions taken during the challenge (i.e., to manage them), and actions taken after the disruption (i.e., to mend from them). As such, a connection between team adaptation and resilience has been tacit, if not explicitly pronounced.

LITERATURE REVIEW OF TEAM ADAPTATION AND RESILIENCE FROM ANALOGOUS AND ICE SETTINGS We reviewed the literature about NASA analog and ICE environments to extract lessons about team adaptation and resilience as potentially related to future long-duration space missions. Research conducted in analog settings is quite valuable, as there are very few teams from which to gather data in the International Space Station, and no teams currently engaged in deep space exploration. The work described here will include missions conducted in Mars500, NASA Extreme Environment Mission Operations (NEEMO), Human Exploration Research Analog (HERA), the Hawai’i Space Exploration Analog and Simulation (HI-SEAS) and other similar contexts which provide “a good arena to test psychological aspects of long duration missions before real interplanetary missions are pursued” (De La Torre et al., 2012, p. 588). Teamwork has clearly been seen as important in such settings and sustained collaboration in such environments can be challenging. For instance, Lapierre,

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Bouchard, Martin, and Perreault (2009) discuss a physical altercation that occurred with team members within a State Biomedical Institute of Russia (IBMP) isolation chamber. Although publications about analog settings discuss team adaptation and resilience, there has not been a great deal of research that measured those constructs. Below we integrate relevant research conducted within analogous settings that informs our current discussion concerning team adaptation.

Antecedents considered in AnAlogous And ice settings While adaptation has not been the primary focus of most of the studies within analogous settings, we did find some relevant studies. A few of those studies would suggest that specific constructs may increase team adaptation within analog settings. For instance, team members’ trait configurations may play a salient role in subsequent team adaptation. Support for this contention comes from Bishop, Kobrick, Battler, and Binsted (2010) who suggest that having experience in the arctic Mars simulation is a relevant antecedent for team adaptation. Likewise, Suedfeld, Brcic, Johnson, and Gushin (2015) conducted interviews of 20 retired Russian cosmonauts and found that those with more experience tended to report a higher level of active coping. Beyond experience, there have been other team composition variables that have been examined within analogous settings that may be relevant to team adaptation and resilience. Leon, Sandal, Fink, and Ciofani (2011a) studied a North Pole Expedition team and found that compatibility between members allowed for each member to better cope with the challenges that the team faced. In terms of personality, research would suggest that those with high levels of instrumentality and expressivity coupled with lower levels of interpersonal aggressiveness may be ideal for ICE settings (e.g., Sandal, Bergan, Warnche, Vaernes, & Ursin, 1996; Sandal, Endresen, Vaernes, & Ursin, 1999). Some researchers have questioned whether personality characteristics will maintain salience as mission duration is extended. Ursin, Comet, and Soulez-Lariviere (1992) suggest that moderate levels of motivation, flexibility, and empathy and low levels of aggressiveness and vitality may be more beneficial for longer missions. Additionally, research has evidenced that possessing stronger and similar coping strategies (e.g., Leon, Atlis, Ones, & Magor, 2002; Leon et al., 2011a) may help individuals deal with challenges during missions (e.g., Atlis, Leon, Sandal, & Infante, 2004). A survey of 576 employees of the European Space Agency suggests that cultural diversity plays a role in the ability of people to interact effectively with teammates (e.g., Sandal & Manzey, 2009) and therefore, may impact team adaptation and resilience. Other articles have considered variables that extend beyond initial compositional factors. For instance, Urbina and Charles (2014) provide a description of the Mars500 mission and noted that the crew had to address several challenging events. For example, the crew had to address a scrubber that impacted CO2. Although team adaptation or resilience was not explicitly measured in this study, the authors noted that the crew training program may have played a role in the team being able to adapt in this mission. The training program was conducted as part of the final selection process and taught psychological coping techniques through exposure to stressful

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situations and may have also afforded the team an opportunity to build a bond. Such a bond may be particularly salient within ICE contexts, as Lapierre and colleagues (2009) echoed the importance of creating a bond as a foundation upon which adaptation can occur. While perhaps less important in other contexts, these authors suggest that activities such as spending free time together may assist in developing relationships needed for adaptation and resilience.

Processes considered in AnAlogous And ice settings Research conducted in analog settings provides evidence that team process adjustments made in response to environmental disruptions are related to effectiveness (i.e., team adaptation process). For instance, Urbina and Charles (2014) discussed an episode in which the Mars500 crew had to address a power malfunction and work collectively to assess the magnitude of the disruption and preserve food supplies. Although the authors do not describe which type of team processes were adapted during the episode, they do suggest that the crew realized “the importance of the implementation of a full culture of transparency in the communication from mission control” (p. 381). Gushin and colleagues (2012) found that in a MARS105 experiment, one group changed their communication strategy after receiving more autonomy while the other subjects maintained their communication strategy. There is a need to more fully understand how autonomy influences team adaptation and resilience, given that a key feature of long-duration missions will be heightened crew autonomy. Suedfeld and colleagues (2015) examined coping strategies utilized by Russian cosmonauts and found that problem-oriented strategies were used significantly more than emotion-oriented strategies. This may also signal the need to consider cultural preferences of crew members. With long duration missions being international efforts, researchers should explore cultural effects in coping that would affect team adaptation and resilience over time. Palinkas (2003) suggests that individuals are able to adapt to ICE environments more quickly if they do not rely on their crew members for social support – a finding also evidenced by Sandal and colleagues (1998). Leon and colleagues (2002) provided an interesting contrast in their study of a three-couple expedition to the Arctic; although crew members did not go to other teammates for support, they did rely on their partner for assistance and support. Accordingly, it may be fruitful for future research to examine whether team interpersonal processes are managed differently within such long-duration missions, or whether certain dimensions of team processes compensate for others that receive less attention given the nuances of such missions.

mediAtors considered in AnAlogous And ice settings Team mediators are intervening variables that help explain variation in the relationship between antecedents and outcomes. In NASA analog and ICE settings, we find that mediators may play a role between team adaptation and adaptation outcomes. While adaptation was not the focus of their study, Wu and Wang (2015) found that cohesion scores of the three Chinese crew members who were part of the simulated

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experiment within an analog space station at Beihang University were lower at the beginning of the experiment as compared with later time periods. The authors suggest that this may “indicate that when the crewmembers were trying to adapt to their new environment and crewmates…, they were confronted with some issues or conflicts” (p. 4). In contrast, research on Mir missions found that individual crew members exhibited increasing levels of cohesion at the beginning of the mission as they were adapting to one another and their new environment (e.g., Ritsher, Kanas, Ihle, & Saylor, 2007). Accordingly, adaptation (at least at the individual level) has been tied to cohesion, but future work should examine teams in analog situations and track the development of their cohesion and other emergent state constructs over time to ascertain the impact that such development has on their adaptation processes. Urbina and Charles (2014) reported that constancy of work played a key role in maintaining team motivation within the Mars500 mission. Likewise, the authors mention that while conflict resolution was noted, it seemed to be very subtle, as team members relied on implicit compromises so that frictions never escalated to extreme levels. Additionally, each team member played a videogame in their free time to partially ameliorate the impact of sensory deprivation as well as to provide the team with a means to maintain their collective bonds.

outcomes considered in AnAlogous And ice settings Although scholars often mention team adaptation and resilience within studies in NASA analog and ICE settings, the constructs have rarely been measured and connected to outcomes. However, in a study of a seven-person crew who spent four months in a simulated Mars habitat, Bishop and colleagues (2010) found that negative moods declined for most participants, which they suggested was an indication of individual-level adaptation. Similarly, a three-person crew in a Mir space station simulator demonstrated improved mood and social climate and the authors suggested this was an adaptation effect (e.g., Kanas, Weiss, & Marmar, 1996). More recently, Mathieu, Tannenbaum, Thayer and Salas (working paper) associated measures of team resilience to team performance in a student laboratory environment, in HERA, and qualitatively with two NEEMO crews.

INTERVENTIONS AND COUNTERMEASURES Given that future NASA missions will be longer and involve more heterogeneous, autonomous, and multicultural crews, countermeasures to prepare crews to adapt will be even more critical than in the past (e.g., Leon, 1999). Unfortunately, systematic assessments of potential interventions to enhance levels of adaptation and resilience have not received significant empirical consideration. That said, the literature suggests some support for the following interventions as being potentially valuable to enhancing team adaptation and resilience, especially for teams that will work for long durations in ICE contexts. While there may be some flexibility around when such interventions and countermeasures are implemented, given the long duration of the missions that NASA is considering, we will initially categorize them into premission and during-mission interventions and countermeasures.

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Pre-mission interventions And countermeAsures Team Composition The selection of team members will likely shape team adaptation and resilience. Researchers have focused on the way performance is influenced by dispositional characteristics (Van Knippenberg, Kooij-de Bode, & van Ginkel, 2010) and personality characteristics which were identified using the five characteristics of emotional stability, extraversion, openness, agreeableness, and conscientiousness (e.g., Neuman & Wright, 1999). Sandal, Leon, and Palinkas (2006) synthesized work which examined the impact of personality characteristics on individual outcomes within ICE settings including the idea of “absorption” or a person’s ability to become engrossed in a particular activity and as a result not attending to other events in one’s life (Atlis et al., 2004). Future research should consider the personality and other individual characteristics that exist within the team to ascertain the impact that such factors have on team adaptation and resilience. For example, given the tight quarters that team members will inhabit together, it may be of value to consider team members’ living preferences and whether pre-mission exercises can bring living preference issues to the forefront prior to the mission. Chartering A team charter defines the boundaries for a team. It often contains basic information about the team’s purpose, resources, authority, period of performance, and expected results. While there has been a robust literature on the value of planning for teams (e.g., Weingart, 1992), there has not been as much empirical consideration of team charters. Instead much of the merits behind team charters are embedded within the practitioner literature (e.g., Wilkinson & Moran, 1998). In response, Mathieu and Rapp (2009) conducted a study of team charters and evidenced that teams with the highest sustained performance were those that had team charters with effective teamwork and task work components. While charters have not been examined extensively within the long-duration mission context, we believe they hold great promise and could be particularly salient in assisting teams to be able to adapt and build up resilience. However, in order to impact such constructs, team charters should be developed to include sections about how the team intends to handle daily challenges that could drain resilience. For instance, teams could discuss what they would do if: a crew member is annoying them, they have privacy or sleep concerns, they see a crew member isolating themselves, etc. Pre-Mission Training The US Military has relied heavily on training to enhance individual and team resilience. For instance, the US Army Master Resilience Trainer course centers on providing noncommissioned officers resilience skills and trains these individuals to train the rest of their unit to enhance overall unit resilience. Part of this training is based upon the Penn Resilience Program, which has been empirically validated (e.g., Seligman, Ernst, Gillham, Reivich, & Linkins, 2009). As well, Morie, Verhulsdonck, Lauria, and Keeton (2011) propose a training approach that may help crew members overcome the adverse effects of stress and trauma by attending to

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personal well-being and positive group interactions. Their approach leverages the use of virtual worlds to provide formal and fun simulations (Morie et al., 2011) and their report presents several capabilities that may be provided or developed. In particular, the authors suggest that paramount for long-duration space flights will be training for resiliency, “the ability to withstand hostile conditions and long-lasting adversity by using techniques to maintain a strong and positive outlook on a group and individual level” (Morie et al., 2011, p. iv). Training that educates crew members about the team adaptation process, in particular, how both acute and chronic stressors may trigger the need for adaptation, can help enhance awareness. Acute stressors can be characterized as unexpected events (Weick & Roberts, 1993), whereas chronic stressors may be on-going or frequent (Salas et al., 2015). Yet, as chronic stressors continue, they may precipitate into an acute stressor that triggers the need for an immediate acute response. In addition, such training could teach the crew about the types of behaviors that effective teams demonstrate to minimize, manage, and mend from stressful challenges as well as any adaptation pitfalls they will want to avoid, such as overreacting to a challenge or catastrophizing. Scenario-based training can also be used to build a crew’s readiness to adapt prior to a mission. In this approach, a series of hypothetical events and challenges would be presented to the crew. As each mission challenge is described, the team discusses any adjustments they would make and why. For example, they would discuss when to shift from normal operating procedures to problem-solving or even emergency-mode. One key to any pre-mission adaptation training is to ensure that adequate focus is given to the team and not just to individuals. Individual adaptation and resilience is certainly important, but because crew members will almost always be in the presence of others during a long-duration space mission, team adaptation and resilience is more salient than in almost any other context. A second key is to consider pre-mission countermeasures as a form of inoculation. They are intended to reduce the deleterious effects of challenges that emerge during the mission, but actions must also be taken during the mission to adjust to and learn from those challenges.

during-mission interventions And countermeAsures Team Debriefs A prominent approach for enhancing individual resilience and team performance is team debriefing (e.g., Boermans et al., 2012). During a team debrief, team members reflect upon a recent team experience, discussing what went well and what could have been done differently, and establish agreements about how they intend to work (and perhaps live) together in the future (Tannenbaum, Beard, & Cerasoli, 2013). A debrief is ideally suited to help a team learn from challenging situations and promote ongoing adaptation. Research has consistently shown that team debriefing improves team performance (Tannenbaum & Cerasoli, 2013). In future space missions, debriefs should examine both task and team-related challenges, either of which might serve as triggers requiring adaptation. Pre-mission debriefs are often

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led by a facilitator, but during a mission the crew will need to self-manage and run their own debriefs. Therefore, we recommend that crews be taught how to run effective debriefs, including what works and what to avoid (Reyes, Tannenbaum, & Salas, 2018). Unstructured debriefs are not as effective as structured ones (Eddy, Tannenbaum, & Mathieu, 2013), so crews should be taught how to structure their debriefs and have the opportunity to practice running their own debriefs on Earth (for example during training expeditions), so they are comfortable using debriefs in space. Mathieu et al. (working paper) tested the efficacy of a crew self-administered team resilience debriefing intervention in three environments. Importantly, the debriefing countermeasure could be administered without ground participation (e.g., a facilitator or mission control), repeatedly as desired, and was totally under the control of the crews. The debriefing intervention was found to enhance team performance in both a student laboratory simulation environment and with eight HERA crews. In addition, two crews of astronauts participating in NEEMO training missions responded positively to the countermeasure. Resilience Check-ins If a crew has established a charter and been trained to know the types of behaviors that teams can exhibit to address challenges to resilience, then they could conduct a “resilience check-in” during the mission. We envision this as a team-led discussion where they identify situations where they have been operating in a manner that is consistent with (and in conflict with) their charter and reach agreements about future actions. They can also use this as an opportunity to update their charter as needed. In addition, they can review a list of behaviors that effective teams do to minimize, manage, and mend when challenges emerge, noting which ones they are doing and which ones they want to emphasize more going forward. During-Mission Training While we believe that pre-mission training will be essential, given that future space teams are likely to have a long transit time, we also see great value in having such teams engage in during-mission training activities as well. In part, the benefits of such training initiatives can be attributed to the development of shared mental models which are often the by-product of such training programs. Mental models are basic cognitive structures used by team members to explain what is going on in the world, draw inferences, and make decisions (Cannon-Bowers, Salas, & Converse, 1993). Over time the content of team members’ mental models evolve (McComb, Green, & Compton, 2007). This evolution continues until the content of the team members’ mental models has become similar. These resulting cognitive structures are often called shared mental models (Cannon-Bowers et al., 1993) and have a positive relationship with team performance (DeChurch & Mesmer-Magnus, 2010). Burke, Wilson, and Salas (2005) propose that creating resilience may take compatible cognitive frameworks, such as the form of shared mental models. As such, team interactions that build shared mental models may help members prepare for routine and nonroutine situations.

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Problem Solving Aids/Technological Assistance During long duration space missions, the expected increase in autonomy and communication delay will make decision-making through mission control laborious and time prohibitive. As such, teams may benefit from having a mechanism to evaluate adaptation triggers and receive input and alternative approaches for adaptation. As detailed by Neerincx and colleagues (2008), tools such as the Mission Execution Crew Assistant are being developed to add to the cognitive capacities of human-machine teams during planetary exploration missions. Such tools could be invaluable when the mission team faces disruptions as such tools can help the team assess the situation and determine viable solutions. Likewise, mission teams could use these tools to train during the mission to maintain performance levels needed to accomplish mission objectives. These tools could be used during the mission crew’s down time such as during the flight to Mars which could allow for adaptation and resilience to be developed during flight, not just during the training activities on ground.

RECOMMENDATIONS FOR FUTURE RESEARCH While there has been a fair amount of research attention given to possible antecedents and outcomes of team adaptation, the majority of such work has been conducted in non-ICE-type settings. There has also been relatively little work focused on the relationship between team adaptation processes and various team emergent states. For instance, while crew autonomy will likely be higher on long-duration space missions (e.g., Neerincx et al., 2008), the relationships between autonomy as well as team empowerment and adaptation constructs (adaptability, adaptation processes, and adaptive outcomes) have not been fully examined. Similarly, while cohesion has been linked to individual resilience, stress, and trauma in various contexts (e.g., Eid & Johnsen, 2002), some have argued that too much team cohesion can be detrimental (Maynard, Kennedy, Sommer & Passos, 2015b). Thus, there is a need to examine the relationship between adaptation and cohesion by teams within ICE settings. Likewise, while some have suggested a link between individual confidence and adaptation (e.g., Palinkas & Suedfeld, 2008), research has yet to consider such relationships at the team level. Accordingly, future research should examine the links between team adaptation and group potency and collective efficacy. Beyond the need to more fully consider emergent states within the team adaptation nomological network, there are significant gaps in the team adaptation literature involving moderating influences. For instance, team interdependence is considered by many to be a defining characteristic of teams (e.g., Ilgen et al., 2005). However, research on team adaptation has yet to fully consider interdependence. Future research could examine whether teams are able to adapt when they are more (or less) interdependent and whether such relationships are also influenced by the type of interdependence. Research has also not devoted sufficient attention to the nature of environmental events that create the need for team adaptation and their implications for effective countermeasures (cf., Morgeson, Mitchell, & Liu, 2015). For instance, the criticality and novelty of environmental challenges generate far different demands on team adaptation than routine disruptions.

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Additionally, Maynard and colleagues (2015a) suggest that whether a team is facing task- or team-based triggers may result in different team processes needing to be adjusted. However, discussion of task- and team-based triggers and their differential effects have only been theoretical thus far. Therefore, future research should empirically consider different types of events and disruptions to better understand their impact on team adaptation. Future research also needs to actually measure team adaptation and resilience within analog settings so that lessons learned can be more directly generalized to ICE missions. In so doing, it will be essential to not only consider how teams adapt their processes, but also what impact such adaptations have on various team emergent states and outcomes. It would be particularly valuable if researchers leveraged digital trace and other nonobtrusive measures (e.g., Tafforin, Vinokhodova, Chekalina,  & Gushin, 2015) in such pursuits. Likewise, capturing objective measures of team performance and assessing emergent states from outside of the team (i.e. team managers) may assist in reducing same source biases (e.g., Podsakoff & Organ, 1986) and thus strengthening the overall study design (Luciano, Mathieu, Park, & Tannenbaum, 2018). Given that we suggest the need to track team members and entire teams over time (see point below), future projects in this domain will also need to assess individual and team adaptability in order to quantify how such constructs change following instances such as training interventions as well as having to deal with various types of disruption triggers. The only study that we found that comes close to our suggestion here was the study of Japanese residents in the Antarctic who were found to demonstrate a decline in hardiness by the end of winter (i.e., Weiss, Suedfeld, Steel, & Tanaka, 2000). However, this study was at the individual level of analysis while we are advocating for a similar examination at the team level. We also believe that studying these constructs longitudinally is important given that temporal and dynamic factors may affect both team adaptation and resilience. An opportunity lies in assessing the impact of other team adaptation process antecedents, especially those that are dynamic such as emergent states like psychological empowerment. Also, given that team resilience is an emergent state, team resilience levels may fluctuate over time. For instance, Mathieu et al. (working paper) assessed team resilience after each of three performance episodes in their laboratory investigation, and nightly during their study of single and twoweek HERA missions. They then employed longitudinal growth modeling analyses of resilience – performance trajectories as impacted by debriefing countermeasures. By conducting examinations of team adaptation over time, future researchers would also be able to test evolutionary, episodic, and event-based models of temporal processes (e.g., Kanas et al., 2006; Luciano et al., 2018). Adopting a longitudinal perspective seems appropriate as adaptation and resilience are likely more salient in certain phases (e.g., approach and landing to Mars and Earth) as compared with others (e.g., transit). Our review of the literature identified initial attempts to understand the impact of training on team adaptation and resilience, yet numerous questions remain. For instance, is it more beneficial to train members individually or collectively as a team? (e.g., Kanki et al., 2009). There is a lack of systematic evidence regarding the impact that such training sessions may have on NASA teams and those performing in analogs.

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By collecting such information and tracking these teams over time, researchers would be able to understand whether the adaptation gains during such training programs erode or build over time. There is also evidence that training-performance intervals can contribute to errors. The collision of the Russian spacecraft Progress 234 with the Mir space station was attributed, in part, to the fact that the crew last received training four months before the incident (Shayler, 2000). Understanding such timing effects can guide decisions regarding whether, how much, when, and what types of training teams need to develop and have the knowledge and skills required (e.g., Urbina & Charles, 2014). Given the long duration of the transit that LDSE missions will require, it begs the question of how best to use time and training programs before and during flight. Likewise, research should also seek to better understand the specific facets that should be included within such training programs. We see great value in future team adaptation and resilience research occurring in field settings. Bishop and colleagues (2010) recently noted that most examinations of groups in ICE contexts have been conducted in “pure simulation conditions” (p. 1353) such as the Mars Desert Research Station (e.g., Bower, 2014), NEEMO (e.g., Todd & Reagan, 2004), and HI-SEAS (e.g., Kizzia, 2015). Such research environments are certainly needed as they allow researchers to manipulate factors that may be salient to consider within long-duration missions. As such, we suggest that these analogs continue to be used as they allow researchers to isolate certain aspects of long-duration missions that are difficult to replicate in field settings. While research within such simulated environments is beneficial, some have argued that given that such settings cannot replicate the life-threatening environments that future space missions will encounter, there should also be research conducted in other settings (e.g., Leon et al., 2002). For example, there have been studies of groups in other analogous settings such as submarines (e.g., Sandal, Endresen, Vaernes, & Ursin, 2003), Antarctica missions (e.g., Lugg & Shepanek, 1999), and a variety of expedition groups (e.g., Leon, Sandal, & Larsen, 2011b). While each of these settings provide an excellent opportunity to learn more about groups in such contexts, it is almost impossible to mimic all team composition factors (e.g., team size, team member backgrounds, etc.) as well as contextual challenges that a mission to Mars may present (e.g., Leon et al., 2011b). Likewise, some of these contexts do not provide large samples, which restricts the research questions and methodologies that can be leveraged. Accordingly, we suggest that future work in the area of team dynamics in long-duration ICE contexts use a more diverse sampling of populations to triangulate the nuances that are context-specific and those that will likely translate to long-duration space missions. For instance, we recommend that research considering team adaptation and resilience do so within unique real-world ICE settings where the need for such constructs may be amplified such as in deep-sea diving and military contexts.

CONCLUSION While there has been a great deal of momentum around the topics of team adaptation and resilience over the past decade, there still remain numerous constructs that have not been examined within the team adaptation nomological network.

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Likewise, some of the under-examined constructs are those that will be of increasing importance within the type of long-duration missions that NASA envisions. Our hope is that this work provides a nice synthesis of what is known about team adaptation and resilience in extreme contexts and can provide the foundation for work in this literature for decades to come and thereby provide some value to long-duration space missions in the future.

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Mathieu, J. E., Tannenbaum, S. I., Thayer, A. L., & Salas, E. (working paper). Managing team resilience for sustained performance in extreme teams through staffing and debriefing: A mixed method approach from multiple environments. UConn, Storrs, CT. Maynard, M. T., Kennedy, D. M., & Resick, C. (2018). Teamwork in extreme environments: Lessons, challenges, and opportunities. Editorial. Journal of Organizational Behavior, 39(6), 695–700. Maynard, M. T., Kennedy, D. M., & Sommer, A. S. (2015a). Team adaptation: A fifteenyear synthesis (1998–2013) and framework for how this literature needs to “adapt” going forward. European Journal of Work and Organizational Psychology, 24(5), 652–677. Maynard, M. T., Kennedy, D. M., Sommer, S.A. & Passos, A. (2015b). Team cohesion: A theoretical consideration of its reciprocal relationships within the team adaptation nomological network. In E. Salas, W. B. Vessey, & A, Estrado. (Eds.), Research on managing groups and teams (Vol. 17, pp. 82–111). Bingley, UK: Emerald Publishing. McComb, S. A., Green, S. G., & Compton, W. D. (2007). Team flexibility’s relationship to staffing and performance in complex projects: An empirical analysis. Journal of Engineering and Technology Management, 24(4), 293–313. Moran, B., & Tame, P. (2012). Organizational resilience: Uniting leadership and enhancing sustainability. Sustainability: The Journal of Record, 5, 233–237. Morgeson, F. P., Mitchell, T. R., & Liu, D. (2015). Event system theory: An event-oriented approach to the organizational sciences. Academy of Management Review, 40(4), 515–537. Morie, J. F., Verhulsdonck, G., Lauria, R. M., & Keeton, K. E. (2011). Operational assessment recommendations: Current potential and advanced research directions for virtual worlds as long-duration space flight countermeasures (Publication No. NASA/TP-2011216164). Hanover, MD: NASA Center for AeroSpace Information. Neerincx, M. A., Bos, A., Olmedo-Soler, A., Brauer, U., Breebaart, T. G., Smets, N., … Wolff, M. (2008). The mission execution crew assistant: Improving human-machine team resilience for long duration missions. In Proceedings of the 59th International Astronautical Congress (IAC2008), Glasgow, Scotland. Neuman, G. A. & Wright, J. (1999). Team effectiveness: Beyond skills and cognitive ability. Journal of Applied Psychology, 84(3), 376–389. Palinkas, L. A. (2003). The psychology of isolated and confined environments: Understanding human behavior in Antarctica. American Psychologist, 58, 353–363. Palinkas, L. A., & Suedfeld, P. (2008). Psychological effects of polar expeditions. The Lancet, 371, 153–163. Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12, 531–544. Pulakos, E. D., Arad, S., Donovan, M. A., & Plamondon, K. E. (2000). Adaptability in the workplace: Development of a taxonomy of adaptive performance. Journal of Applied Psychology, 85(4), 612–624. Pulakos, E. D., Schmitt, N., Dorsey, D. W., Arad, S., Hedge, J. W., & Borman, W. C. (2002). Predicting adaptive performance: Further tests of a model of adaptability. Human Performance, 15(4), 299–323. Reich, J. W., Zautra, J. W., & Hall, J. S. (2010). Handbook of adult resilience. New York: Guilford. Reyes, D. L., Tannenbaum, S. I., & Salas, E. (2018). Team development: The power of debriefing. People + Strategy, 41(2), 46–51. Ritsher, J., Kanas, N., Ihle, E., & Saylor, S. (2007). Psychological adaptation and salutogenesis in space: Lessons from a series of studies. Acta Astronautica, 60, 336–340.

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Toward an Understanding of Training Requirements for Multicultural Teams in Long-Duration Spaceflight C. Shawn Burke and Justine Moavero University of Central Florida

Jennifer Feitosa Claremont McKenna College

CONTENTS Cultural Diversity: Scoping Boundaries ................................................................ 172 State of the Science: Cultural Diversity in Teams.................................................. 173 Team Cognition.................................................................................................. 173 Team Processes.................................................................................................. 174 Emergent States ................................................................................................. 178 State of the Science: Interventions and Countermeasures ..................................... 180 Cognitive Outcomes .......................................................................................... 180 Behavioral Outcomes ........................................................................................ 180 Attitudinal Outcomes ........................................................................................ 180 Training Methods .............................................................................................. 181 Training Elements ............................................................................................. 182 An Integrated Perspective ...................................................................................... 182 Information Provision........................................................................................ 183 Skill Acquisition ................................................................................................ 184 Application and Practice ................................................................................... 185 Concluding Comments........................................................................................... 186 References .............................................................................................................. 187

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Human interactions onboard a spacecraft, isolated in time and space from Earth, may well be one of the more serious challenges to exploratory missions by humans (Ball & Evans, 2001, p. 3).

The challenges facing spaceflight crews engaging in exploration missions, especially those targeting exploration beyond low-earth orbit, have been repeatedly argued. However, the so-called human element remains one of the most complex components in the design of long-duration space missions (Ball & Evans, 2001). Forming the core of many of NASA’s current and future spaceflight missions are crews who are culturally diverse in terms of their nationality and corresponding beliefs and values. The diversity present in such teams may manifest in numerous ways, all with the potential to influence team processes, emergent states, and corresponding team outcomes. While cultural diversity can clearly impact teams, “the challenge in managing multicultural teams effectively is to recognize the underlying cultural causes of conflict, and to intervene in ways that both get the team back on track and empower its members to deal with future challenges themselves” (Brett et al., 2006, p. 89). Therefore, this chapter reviews the stateof-the art regarding the impact that cultural diversity has on team interaction, with an emphasis on the context of spaceflight. In doing so, we seek to document what is known as well as what the future holds for research. As research explicitly examining the impact of cultural diversity on team interaction within the context of spaceflight is limited, we also leverage research conducted outside spaceflight and consider the characteristics of long duration, long distance exploratory missions to make predictions.

CULTURAL DIVERSITY: SCOPING BOUNDARIES Prior to reviewing the scientific evidence, we first set our conceptual boundaries by defining multicultural teams, and the corresponding cultural diversity. Although there are a variety of definitions of multicultural teams, for the purposes of this chapter, we rely on the following two definitions, as they seem to encapsulate the predominant points argued for by most researchers. Specifically, a multicultural team is, a collection of individuals with different cultural backgrounds, who are interdependent in their tasks, who share responsibility for outcomes, who see themselves and are seen by others as an intact social entity embedded in one or more larger social systems, and who manage their relationships across organizational boundaries and beyond” (Halverson & Tirmizi, 2008, p. 5).

The definition put forth by Earley and Gibson (2002) is also informative and states, “three or more individuals who interact directly or indirectly for the accomplishment of a common goal and whose members must come from two or more different national or cultural backgrounds” (p. 3). While multicultural teams set an initial boundary condition for what follows, primary interest is not only in multicultural teams, but those where cultural diversity

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exists. While not common, it is possible to have a multicultural team whose members, although originating from different cultures, have alignment in their values and thereby are not culturally diverse. The primary focus of the current chapter is on cultural diversity as manifested through individual values, beliefs, and attitudes as driven by national differences. This focus primarily reflects an interest in deep-level diversity or the unobservable psychological characteristics of an individual that are inherently latent (i.e., personality, values, attitudes, Harrison, Price, & Bell, 1998). These differences are typically only learned through extended interaction and have been argued to be more subject to interpretation as compared with surface-level diversity (Jackson, Joshi, & Erhardt, 2003; Jackson, May, & Whitney, 1995). A primary reason for focusing on the impact of deep-level features of cultural diversity is that research has suggested that it is this type of cultural diversity that can lead to the greatest challenges for effective team interaction over time (Harrison, Price, & Bell, 1998; Harrison, Price, Gavin, & Florey, 2002).

STATE OF THE SCIENCE: CULTURAL DIVERSITY IN TEAMS Now that we have set our boundary conditions, next we describe the state of the science in terms of knowledge pertaining to the impact of cultural diversity in teams, with an emphasis on how these may impact spaceflight crews. In presenting our findings, we organize our results in terms of the impact of cultural diversity on team knowledge, behaviors, and attitudes.

teAm cognition Team cognition has consistently been argued to be a prominent factor in a team’s ability to coordinate their actions within complex environments, such as space exploration (Langan-Fox, Anglim, & Wilson, 2004; Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000; Rentsch & Woehr, 2004). However, little research was uncovered that directly examined the impact of culture or cultural diversity on team cognition. Furthermore, none of the research uncovered was explicitly conducted in the context of spaceflight teams. Below we leverage extant research to indicate how cultural diversity may impact team cognition in space exploration teams. Limited work has been conducted to examine how mental models regarding team functioning vary across culture. In this regard, work by Gibson and Zellmer-Bruhn (2001, 2002) is instrumental in that they examined the concept of teamwork across six pharmaceutical firms from four geographic areas (i.e., Europe, Southeast Asia, Latin America, and the United States). Results of their analyses indicated that individuals used one of five different metaphors to describe teamwork (i.e., family, sports, community, associates, military). Encompassed in the use of each metaphor are expectations regarding the team’s objectives/purpose, scope, and roles. For example, cognitive schemas pertaining to the structuring of team roles can vary from explicit (i.e., sports, military, family metaphors) to informal (i.e., community, associate metaphors) as well as hierarchical (i.e., military, family) to flat and/or shared (i.e., sports, community). Cognitive schemas pertaining to the team’s scope were found to range

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from expectations of teams having a limited scope (i.e., sports, military, associates) to broad in scope (i.e., family, community). Encompassed in schemas related to team scope are the type of domains in life the team may encompass (e.g., sports – physical, social; military – professional, physical, educational; family – psychological, physical, social, entertainment, professional, etc.). Finally, the metaphors that individuals used also revealed their cognitive schemas regarding team purpose, ranging from clear and salient to evolving, ambiguous and sometimes non-task orientated. For example, use of sports and military metaphors were indicative of cognitive schemas that encompassed the team having clear, salient outcomes or objectives. In contrast, individuals using family metaphors had cognitive schemas whereby outcomes were typically nonexistent or ambiguous. Community and associate metaphors were indicative of schemas whereby expectations regarding team objectives were less task-related and often evolving. This work not only indicates that individuals vary in their schemas regarding teams (and correspondingly their expectations regarding team dynamics), but findings indicate that national culture predicts the type of metaphors that were used to describe teams. Individuals from cultures with individualistic values were found to use metaphors whereby teams were limited in scope. In contrast, individuals from more collectivist cultures had expectations whereby team scope encompassed more domains in life. Moreover, cultures high in power distance tended to use metaphors related to the military thereby revealing their expectations that teams had the following characteristics: clear objectives, clear and hierarchical roles, and expectations that the team had a fairly limited scope (Gibson & Zellmer-Bruhn, 2002). This work illustrates, at a high level, how member mental models regarding the team may vary with respect to core expectations concerning team scope, roles, and objectives. These differences in mental models, in turn, may explain part of the impact that culture has on team interaction (Gibson & Zellman-Bruhn, 2001). According to Marks, Zaccaro, & Mathieu (2000), “sharing a similar mental model is important for team performance” (p. 973). This suggests that cultural orientation may drive differences in team member cognition which, in turn, impacts the manner in which the team works to respond to events.

teAm Processes Cultural diversity research has established that differences in beliefs, values, and preferences for action impact interdependent team operation (e.g., Stahl, Maznevski, Voigt, & Jonsen, 2010; Shaw et al., 1981; Elron, 1997; Chatman & Flynn, 2001; Gudykunst, 1997; Watson, BarNir, & Pavur, 2005). While arguments can be made with respect to how cultural diversity impacts the predominant number of team processes seen in the literature (e.g., Marks et al., 2001; Salas, Shuffler, Thayer, Bedwell, & Lazzara, 2015), here we briefly highlight the most prominent areas. Further detail on those team processes not covered may be found in Burke and Feitosa (2015). Communication Culture has the potential to affect the ways in which individuals communicate and may produce challenges including languages, miscommunication, and norms regarding interaction (Ji, Zhang, & Nisbett, 2004; Adler, 1997; Humes & Reilly,

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2007; Kealey, 2004). Communication has been defined as, “a reciprocal process of team members’ sending and receiving information that forms and re-forms a team’s attitudes, behaviors, and cognitions” (Salas et al., 2015, p. 603). Cultural differences in communication are likely to lead to miscommunication within spaceflight crews as well as between crews and mission control (Kealey, 2004; David, Rubino, Keeton, Miller, & Patterson, 2011). Research onboard the International Space Station illustrated the following differences in communication: (1) Russian crew members were less comfortable interacting in conversations involving large groups than American crew members, and (2) Russians were found to be more likely to make eye contact and to be more agreeable while Americans were direct, dominant, and opinionated (David et al., 2011). Further findings suggest that cultural dimensions of individualism and collectivism influence individual recognition practices, as well as differences in methods of direct and indirect communication (David et al., 2011). Information exchange is another way that cultural diversity may impact communication. Due to the increased effort required to interpret meaning based on differing cognitive styles and cultural values, information exchange may be slower in multicultural teams (see Adler, 1997). Additionally, collectivist team members are more likely to engage in direct communication with the leader than individualists, and though at first more hesitant to share information, collectivists are more likely to speak for longer periods of time when sharing (Conye, Wilson, Tang, & Shi, 1999). Furthermore, in contrast to individualists, collectivists arguably prefer an indirect communication style (Gudykunst, Matsumoto, Ting-Toomey, Nishida, Kim, & Heyman, 1996). The preference for an indirect communication style could impact both the sharing of relevant information and the likelihood of information being passed to the appropriate person. Information exchange may also be influenced by power distance and uncertainty avoidance. Cultures with high power distance could face challenges in information exchange as subordinates may fail to provide crucial information to higher status team members because they are afraid to question or critique a higher ranking member’s performance. Sutton and Pierce (2003) found that “If team members are high power distance, they may not share information that could alter a decision, believing that it is the leader’s responsibility to make decisions” (p. 14). As evidenced within aviation and other contexts, this dynamic may have detrimental consequences (Helmreich, 2000; Ilgen, LePine, & Hollenbeck, 1997). With regard to uncertainty avoidance, cultures which possess a low tolerance for uncertainty may be less likely to notice and report situations that deviate from the original plan (Ilgen et al., 1997). Moreover, “If team members have a high need for certainty, they may ask for so much guidance and information that they no longer provide unique contributions to the task” (Sutton & Pierce, 2003, p. 14). Therefore, a team culturally diverse in uncertainty avoidance may help facilitate an awareness and corresponding reporting of such information. Coordination Coordination has been defined as, “the enactment of behavioral and cognitive mechanisms necessary to perform a task and transform team resources into outcomes” (Salas et al., 2015, p. 603). Limited research has been conducted into

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whether culture, and specifically diversity of national culture, may impact coordination directly. However, as shared mental models have been a primary mechanism argued to facilitate coordination (Langan-Fox, Anglim, & Wilson, 2004; Rentsch & Woehr, 2004), the manner in which culture may impact the development and maintenance of shared mental models may be informative to the impact of cultural diversity on coordination. The challenge with multicultural teams lies in the differences in root cognition among team members, which, in turn, drives the development and maintenance of shared mental models. As these differences are deep-level they are often unknown and can prompt incorrect assumptions about fellow members’ views regarding the team or team interaction. Furthermore, Sutton and Pierce (2003) have shown that multinational team leaders with a high power distance may fail to utilize subordinate team members’ best skills, resulting in a lack of coordination. Monitoring Behavior In thinking about monitoring within the context of culturally diverse teams, there are at least three foci that can be considered: monitoring progress toward goals, systems monitoring, and team monitoring and backup (see Marks et al., 2001). Next, we briefly highlight a few exemplar findings which indicate how cultural diversity may impact monitoring within teams. With respect to monitoring progress toward goals, cultural differences in time orientation and tolerance for uncertainty may cause challenges for the team. For example, research has argued that those whose cultural alignment perceives time as being in short supply would undertake more monitoring of progress toward goals (Arman & Adair, 2012). One could also imagine that differences in the degree to which one perceives time as discrete or continuous may impact the interpretation of goal progress. Additionally, when tolerance for uncertainty is low goal progress may be monitored at a higher frequency in an effort to sustain acute awareness of the circumstances. In turn, the team may circumvent uncertainty to the degree that the environment will allow. Systems monitoring may also be influenced by cultural differences such as time orientation and tolerance for uncertainty, field dependence/independence, and collectivism/individualism, with the latter two potentially modifying the type of system cues which prompt team attention and, in turn, impacting the frequency with which the system is monitored. Cultural diversity may also have an impact on the speed with which problems are recognized; teams with lower levels of power distance and uncertainty avoidance may take more time to acknowledge problems. Though cultures with lower power distance may react more slowly, they will be more effective at adaptation and recovery when communication is lost. With respect to team monitoring and backup behavior, research indicates that in-group members are evaluated more positively in collectivist cultures as compared to individualistic cultures (Gomez, Kirkman, & Shapiro, 2000). Furthermore, in collectivist cultures higher cooperation among in-group members and lower cooperation among out-group members is seen. (Triandis, 2018; Triandis et al., 1988). This may, in turn, not only impact the degree to which team monitoring is likely to occur (versus only monitoring a subset of the team), but how that monitoring is likely to be interpreted.

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While not specifically concentrating on teams, other research can also be leveraged to uncover the manner in which culture may impact team monitoring and backup behavior. For example, regarding feedback, it has been shown that differences in the manner that a culture makes attributions could influence whether team members are willing to provide and accept feedback. Schweder and Bourne (1992) established that cultures that adopt a root cause orientation when making attributions have a tendency to assign personal responsibility; conversely, cultures with a systems orientation utilize context-based attributions. Team members with a root cause orientation, according to Klein (2004), often pursue (and expect) feedback because they identify it as a routine component of the learning process. To the contrary, those with a systems orientation may not see feedback as an evaluation of a discrete capability, but as an individual encroachment. This leads to the argument that attribution assignment methods, including root cause orientation and systems orientation, may disturb teamwork in multinational teams (Klein, 2004). Another cultural orientation which may impact team monitoring and backup behavior is power distance. Team members from cultures with higher power distance are less likely to accept subordinate assistance, nor will subordinate team members be expected to supply backup behavior to team leaders because the leader is expected to provide the solution. The reverse is likely to be true for those members with lower levels of power distance. Specifically, these members will be more likely to accept and offer assistance regardless of member status. Coaching In the context of spaceflight, researchers have indicated the importance of leadership with regard to the operation of culturally diverse spaceflight crews (Kanas & Ritsher, 2005). Salas et al. (2015) defined coaching as, “the enactment of leadership behaviors to establish goals and set direction that leads to the successful accomplishment of these goals” (p. 603). Leadership research has indicated that culturally diverse teams emphasized interpersonal leadership to expedite working through different viewpoints, while culturally similar teams demonstrated task-related leadership (Watson, Johnson, & Zgourides, 2002). Furthermore, once culturally based interpersonal concerns were resolved, culturally homogeneous teams were able to operate more effectively than the homogeneous teams on a problem-solving task. Findings by Kanas and colleagues also have implications for culturally diverse spaceflight crews. Kanas et al. (2007) found that within spaceflight crews socialbased leadership characteristics connected primarily to crew cohesion, while both task- and social-based leadership characteristics were related to cohesion with mission control. It was suggested that these differences were a result of a small crew necessitating less need for task-related leadership behaviors. Moreover, proficiency and comfort in engaging in key leadership behaviors (e.g., managing relationships, building networks), have been shown to vary across cultures (House, Hanges, Javidan, Dorfman, & Gupta, 2004). Cultures also differ in their orientation toward being and doing (Kirkman & Shapiro, 2001). Researchers have argued that cultures that possess a doing orientation value action, while those with a being orientation value reflection and understanding (Kluckhohn & Strodbeck, 1961). These differences will drive team dynamics including transition (e.g., mission analysis, planning, strategy

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formulation, goal setting), action (e.g., coordination and monitoring activities), and interpersonal related behaviors (e.g., management of relationships, conflict management) (see Marks et al., 2001).

emergent stAtes Emergent states are described as “constructs that characterize properties of the team that are typically dynamic in nature and vary as a function of team context, inputs, process, and outputs” (Marks et al., 2001, p.357). While there are a number of emergent states, perhaps the ones most likely to impact spaceflight crews include trust (e.g., Brown, Adams, Famewo, & Karthaus, 2008), cohesion (e.g., Arman & Adair, 2012), attitudes (e.g., Boyd et al., 2009), and moods (e.g., Kanas et al., 2009). Next, we briefly highlight work done in these areas and the potential impact on culturally diverse spaceflight crews. Trust Culture may impact the development and maintenance of trust in several ways. For example, Rockstuhl and Ng (2008) found that culturally diverse team members engendered lower interpersonal trust than culturally similar members. The cues upon which trust development is based can also vary across cultures. As an example of this, the following statement was made in an interview with a NASA flight director, The Russians don’t tend to put a lot of stock in your position within an organization. What they care about is knowing you personally, and knowing if they can trust what you say because of your personal history with them (personal communication, May 2014).

A third way that cultural diversity may impact trust pertains to team compositional issues. Specifically, according to Thagard (1997), “majority crew members are typically quite positive about their foreign colleagues’ personality and ability to get along with the rest of the crew, but the distrust in their competence within the “home team’s” spacecraft (and/or with the home team’s language) persisted nonetheless” (as cited in Suedfeld et al., 2013). This, in turn, may result in members of the minority culture performing low-status responsibilities or tasks, not being designated assignments, and commonly inhibited from being effectively utilized. Cohesion Cultural diversity can impact cohesion of the team as “diversity creates social divisions that, in turn, create poor social integration and cohesion, resulting in negative outcomes for the group” (Mannix & Neale, 2005, p. 34). Moreover, other types of diversity can interact with cultural diversity to form faultlines that may reduce social integration, information exchange, and team affect (Harrison & Klein, 2007). Similarly, Kanas et al. (2001), determined that culturally diverse crews onboard MIR were impacted by interpersonal problems (e.g. disagreements or conflict) as well as perceived lack of support. Cultural differences in time orientation (e.g., pace of life, punctuality), may negatively impact team processes and lead to decreased cohesiveness (Arman & Adair, 2012). While cultural differences in spaceflight crews have been found to have a

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negative influence on cohesion (Kanas et al., 2009), selection and team training have been found to mitigate potential negative effects (Sandal, Vaernes, & Ursin, 1995). Dispersion within culturally diverse teams has also been found to result in less conflict and higher levels of cohesion as compared to collocated teams (Stahl et al., 2010). Conflict Research has generally indicated that conflict is a negative state for teams, especially culturally diverse teams (e.g., Adler, 2005; Jehn, Northcraft, & Neale, 1999; Watson, Johnson, & Merritt, 1998; Milliken & Martins, 1996; Pelled, Eisenhardt, & Xin 1999; Brickson, 2000). For example, misunderstandings, miscommunication, and interpersonal conflict have been reported among international space shuttle crew members (Santy, Holland, Looper, & Macondes-North, 1993). More specifically, research has shown that cultural dimensions (e.g., uncertainty avoidance, individualism/collectivism) impact the manifestation of team conflict. While collectively orientated cultures were shown to have fewer conflicts than individualistic cultures (Oetzel, 1998), diversity in the degree of uncertainty avoidance was positively related to issue-based conflict (Elron, 1997). Similarly, meta-analytic work found that cultural diversity was positively related to task conflict, but unrelated to relationship and process conflict (Stahl et al., 2010). Mood Highly related to conflict, negative affect has been shown to appear as a result of cultural diversity within the context of spaceflight, emerging as instances of crew tension and frustration, leadership conflicts, and occasional ostracization of crew members (Kanas, Weiss, & Marmar, 1996; Sandal, 2001, 2005; Kanas, 2004). Both spaceflight crews and mission control experience negative affect while working in teams (Sandal, 2005). For example, crew disintegration and intra-group tensions were shown to emerge during a 135-day, isolated simulation mission in the MIR space station (Gushin, Kholin, Ivanovsky, 1993; Gushin et al., 1998). Furthermore, anger/hostility, tension/anxiety, work pressure, and total mood disturbance were significantly greater for crew members onboard the ISS as compared with mission control (Kanas et al., 2007). Cultural differences may not only influence mood, but the ways that emotions are communicated. For example, cultures vary in the degree to which emotional expressiveness is viewed as appropriate (Matsumoto, 2009; Kanas et al., 2009). Within spaceflight, variations in rules of conveying emotion may make it more complicated for team members to manage the potential stressors, disagreements and/or misunderstandings that can occur during missions. As an example, Palinkas et al. (2004) found that culture impacted the degree to which negative affect was exhibited within spaceflight. Specifically, Russian crews had a tendency to exhibit fewer negative states (i.e., depression, anxiety, confusion), while American crews were more likely to manifest negative states (i.e., anxiety and fatigue). Kitayama and Markus (1994) argued that, on average, Russians are relatively more expressive than people from Western countries. Research has also shown that minority crew members may experience frustration and dissatisfaction (Suedfeld et al., 2013). Therefore, it

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is possible that cultural differences in expression of affect may have interfered with team composition to impact the degree of negative affect felt.

STATE OF THE SCIENCE: INTERVENTIONS AND COUNTERMEASURES With respect to cultural diversity, most training programs focus on preparing individuals to work within another culture or work with someone from another culture, but do not emphasize working within multicultural teams. In essence, these programs neglect what cultural diversity means for interdependent, coordinated action. Prior to highlighting the type of content cultural training programs focus upon, a definition of cultural competence is provided to set the context. While a multitude of definitions of cultural competence exist, definitions converge on the idea that cultural competence equates to being effective in different cultural situations. As an exemplar, the definition put for by Hammer, Bennett, and Wiseman (2003) is provided, “the ability to think and act in interculturally appropriate ways” (p. 422). Programs targeted at training cultural competence tend to focus on cognitive, behavioral, or attitudinal changes.

cognitive outcomes Programs whereby the ultimate training goal is the development of cultural knowledge tend to target an increased awareness of cultural differences between the home and host culture and/or fostering the accurate analysis of cross-cultural behavior. This type of training often includes information on how social divisions (i.e., ingroup, outgroup) impact the manner in which individuals process information. An example of cognitive-focused training would be training fostering the accurate analysis of crosscultural behavior by highlighting cultural differences in how members view teamwork and taskwork (see Gibson & Zellmer-Bruhn, 2001). Based on a review of the existing literature, Mendenhall et al. (2004) found that 60% of the programs targeting cognition had a significant positive change in knowledge. Additionally, this work found that cognitive outcomes are rarely the sole desired outcome, but are prominently targeted.

beHAviorAl outcomes Cultural training programs that focused on promoting behavioral change tended to focus on the display of cultural sensitivity, cultural competence, problem-solving ability, and an ability to deal with misunderstandings (Mendenhall et al., 2004). Cultural programs targeting behavioral change were much less effective than those focusing on cognition, with 57% failing to show significant behavioral changes.

AttitudinAl outcomes Cultural training programs whose primary focus was attitudinal change targeted reductions in stereotyping and trainee ethnocentrism and promotion of a positive attitude toward other cultures (Mendenhall et al., 2004). An example of training

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highlighting the affective components is the stress and coping approach (Stahl & Caligiuri, 2005). This approach often targets situational and contextual characteristics impacting cultural adjustment. As such, attitudinal competencies often targeted in cultural competence include: charisma, empathy, and management of anxiety under conditions of uncertainty (Gudykunst, 1997). With respect to effectiveness, programs targeting attitudes fell between cognitive and behavioral-based training programs. Specifically, Mendenhall et al. (2004) found that less than half of the programs (47%) produced a significant change in attitudes, whereas approximately that same amount did produce a significant change. Programs that targeted a mixture of attitudinal and cognitive outcomes were more likely to be successful than those solely targeting attitudes.

trAining metHods While the cultural training literature does not provide a succinct breakdown of the elements to be included in training to foster cultural competence, training methods that commonly appear can be categorized as intellectual or experiential (Bennett, 1986). While each of the two training methods are briefly reviewed below, we believe that effective training should use intellectual and experiential components (see also Bennet, 1986; Brislin & Yoshida, 1994; Paige, 1986), be cognizant of cultural expectations, and be ready to adapt to the audience (Fowler & Blohm, 2007). For example, Ritsher (2005) found that Russian spaceflight crews were more likely to be trained using intellectual methods, whereby American crew members were more likely to utilize experiential methods. These differing expectations may, in turn, impact the mental models that are developed concerning training and the acceptability of particular training practices. Intellectual training or didactic methods are perhaps the most common and provide trainees with declarative knowledge regarding a culture (Celaya & Swift, 2006; Brewster, 1995; Kealey & Protheroe, 1996). The content within this method often focuses on increasing knowledge concerning cultural dimensions and tendencies, language practices, and other country-specific information. The knowledge imparted through intellectual methods tends to be delivered through information briefings, formal activities (e.g., lectures, computer-based instruction, and/or slide presentations), cultural assimilators, and area studies (Brewster, 1995, Johnson, Lenartowicz,  & Apud, 2006; Fowler & Blohm, 2007; Kealey & Protheroe, 1996; Morris & Robie, 2001). The premise underlying intellectual methods is that the cognitive knowledge gained in training will assist in sensemaking within cultural situations by serving as a frame of reference. However, research has suggested that the use of intellective methods may be less effective than behavior-oriented approaches (Calaya & Swift, 2006). In contrast to intellectual methods, experiential methods tend to be more behaviorally orientated and emphasize the “learn by doing” philosophy (Littrell & Salas, 2005). Experiential methods provide opportunities for individuals to practice cultural interactions within a “safe” environment where instructor guidance and feedback is available. While experiential methods lend themselves to the targeting of many of the behavioral skills argued for within cultural competence, cross-cultural

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communication skills are a common focus (Kealey & Protheroe, 1996; Morris & Robie, 2001). Techniques commonly utilized in experiential methods include, but are not limited to: role-playing, simulations, look-see visits, and intercultural workshops (Grove & Torbiörn, 1985; Kealey & Protheroe, 1996; Morris & Robie, 2001). Given the focus and structure of experiential methods, many have argued for their efficacy in training cultural behaviors (Bhawuk & Brislin, 2000; Fowler & Blohm, 2007).

trAining elements Four basic elements of any training program (including cultural training programs) are information, demonstration, practice, and feedback (Salas, Tannenbaum, Kraiger, & Smith-Jentsch, 2012), Leveraging the science of training, cultural training designed for spaceflight exploration crews should ensure that the instructional strategies comprising the program allow: (1) presentation of the information to be learned (i.e., didactic, information-based methods), (2) presentation of the KSAs to be learned (i.e., demonstration-based methods), (3) creation of guided practice opportunities (i.e., experiential methods), and (4) delivery of constructive criticism during practice (i.e., feedback). Burke and Feitosa (2015) conducted a review of the cultural training literature and found some of the above principles present; others were nearly nonexistent with less than half of the common training types including all four elements (IDPF). Despite the growing focus on cultural training, programs often lack strong theoretical grounding (Deardorff, 2011; Mesmer-Magnus & Viswesvaran, 2007). Programs were found to be often based primarily on anecdotal examples without effort to tie key examples back to the broader theoretical literature. This is not to say that anecdotes are not useful as they can provide an aspect of face validity for training and may form a basis for scenario development; however, trainers should verify their tie to findings in the scientific literature (Ptak et al., 1995).

AN INTEGRATED PERSPECTIVE Cross-cultural competence training was found to be the most common intervention, but other techniques were also identified (i.e., interaction training/socialization, creation of superordinate identity, application of team metaphors, immersive simulation, team debriefing, team composition, and meaningful work). Within this section, we present a subset of these techniques within the overarching context of stress exposure training (see Burke & Feitosa, 2015 for a more detailed description). Stress exposure training (i.e., SET) has been shown to be effective in similar complex environments (Saunders, Driskell, Johnston, & Salas, 1996). The goal of this technique is to provide familiarity with the stressful environment (and therefore expand mental models) as well as build the skills and self-efficacy required to handle the stressors by scaffolding training through the provision of practice opportunities (Driskell  &  Johnston, 1998). We chose this overarching framework as we believe that cultural diversity can be viewed as a potential stressor for spaceflight crews due to the challenges it often presents for team interaction. Next, we expand on the stages

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within stress exposure training, highlighting how each stage can be mirrored to key training elements and assist in the mitigation of cultural diversity on team interaction in long duration spaceflight crews.

informAtion Provision The primary goal within the first stage of stress exposure training, information provision, is to facilitate knowledge of the stress environment within the trainee. Herein information is provided regarding stress, stress symptoms, and the likely impact of stress effects in context. This type of information serves not only as preparatory information for later stages, but serves to create an advanced organizer. Didactic methods that focus on informational elements of training are techniques commonly used during this stage. Next, a set of mitigation strategies that could occur within this stage are highlighted. Informational Elements of Cultural Training Programs Many of the training techniques used to facilitate cultural competence can be fit into this stage (e.g., cultural awareness training and attribution training). For example, cultural awareness training is often didactic in nature and highlights one’s own cultural orientation and cultural biases (Bennett, 1986). The initial stages of attribution training may also fit here due to the focus on didactic methods and informational elements which provide members with the knowledge to help them interpret cultural nuances in behavior. In essence, the knowledge portions of these two types of training programs serve to impart knowledge concerning how cultures vary in their beliefs, values, and attitudes. In turn, this highlights the nature of cultural diversity (the stressor) and could also be utilized to highlight the manner in which cultural differences may be manifested in team interaction. Interaction Training Interaction training is based on the premise that adjustment to a new or different culture will be most successful when it involves orientation from someone who has already been through the experience (Befus, 1988). Therefore, this training is often primarily didactic in nature, and in space exploration crews might be operationalized as briefings or informational presentations whereby former astronauts share experiential knowledge and help new crew members to orient to the work environment. The passing of first-hand knowledge and workarounds paired with behavioral modeling can serve to quickly promote cultural understanding. By facilitating awareness of existing expectations and norms (Moreland & Levine, 1982), it can provide insight into how to manage cultural diversity in space. Interaction training in its entirety goes beyond the type of content that would be presented in the first stage of SET, but does contain portions which would fit here. This follows recommendations that knowledge of cultural differences and their potential impact should be provided to crews prior to mission deployment (Ritsher, 2005). With that in mind, the socialization period prior to the mission can facilitate interaction training. Analog environments (e.g., NEEMO, HERA, and NOLS) and spending

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time in the host country may also provide opportunities for socialization and allow crew members to build mental models regarding team members prior to the mission (Noe, Dachner, Saxton, & Keeton, 2011). This socialization can diminish the culture shock often experienced when cultural diversity is present (Smith, Bond, & Kag ̆itçibasi,̧ 2006). Metaphors Work on team metaphors (Gibson & Zellmer-Bruhn, 2002) can also be infused within this stage. Discussions around team metaphors can be used as a method to make implicit assumptions about team functioning explicit and provide the crew with the building blocks to move forward towards a hybrid culture with a common set of agreed upon norms for interaction. Moreover, discussion of team metaphors can be combined with information regarding triggers that may cause interpersonal friction due to cultural differences. Given that culturally diverse members are likely to have varying team metaphors (Gibson & Zellmer-Bruhn, 2001), the formation of similar, accurate team mental models is bound to be an initial challenge. Therefore, we highlight the discussion of metaphors as one mechanism to foster an understanding of team tasks, team interaction, and cultural differences.

sKill Acquisition Skill acquisition, the second stage of SET, aims to teach crew members the skills needed to mitigate the impact of cultural diversity and provides an opportunity to practice the newly learned skills. The types of skills fostered within this stage are often those argued to make crew members more resistant to the stressor (e.g., cultural diversity) or allow them to compensate for the loss of team synergy presented by the stressor (Driskell & Johnson, 1998). Mitigation techniques identified in the literature that might best fit the skill acquisition stage of training are described next. Superordinate Identity Faultlines, or hypothetical divides, within the crew are a problem that often arises in multicultural teams (Lau & Murnighan, 1998, 2005). The many perspectives and differences in values that often appear in multicultural teams may make it more difficult to build a cohesive team identity. Social identities are multifaceted and negotiated (Markus & Wurf, 1987; Swann, 1983), and research has shown that subcultures within spaceflight crews may form due to cultural differences in backgrounds and class membership as well as values and goal (Bishop, 2010). Training that focuses on creating a team identity to avoid existing faultlines from being activated is important (Dion, 2004). The impact of sub-groups can be mitigated when people identify with an overarching collective (Gaertner & Dovidio, 2000). Having a sense of common identity can reduce the impact of degraded interpersonal affect and bias toward outgroup members (Gaertner, Mann, Murrell, & Dovidio, 1989). In this vein, it has been argued that promoting commonality that makes the differences less salient can facilitate interaction among members of culturally diverse spaceflight crews (Sandal & Manzey, 2009). In line with the above, there is some evidence that

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the isolation that spaceflight crews experience may increase the tendency for a third integrated culture to emerge as compared with subgroups (David et al., 2010). This emergent culture can help mitigate differences and provide a new negotiated set of norms for the crew. Therefore, focusing on the superordinate identity is a feasible and desirable approach within spaceflight. Perspective Taking Training in perspective taking builds from several tenets within cultural attribution training. Multicultural perspective taking involves the ability to extract, understand, and interpret cultural information (Rentsch, Gunderson, Goodwin, & Abbe, 2007). Perspective taking can facilitate intercultural coordination (Mor, Morris, & Joh, 2013), reduce stereotyping, encourage backup and prosocial behavior as well as facilitate the anticipation of points of disagreement (Galinsky, Wang, & Ku, 2008; Sessa, 1996). Furthermore, Sessa (1996) argues that perspective taking can facilitate conflict management by promoting an understanding of the other person. Rentsch et al. (2007) argues for a set of three skills that form the foundation for perspective taking: self-awareness, interpersonal skills, and regional expertise. While the exact manner in which perspective taking would be trained will be driven by the targeted subcomponent, it is expected that experiential methods combined with elements of information, demonstration, practice, and feedback would form the foundation.

APPlicAtion And PrActice Within the final stage of training, crewmembers should apply the knowledge and skills learned in the firsts two phases under conditions that gradually approximate the stressful environment. Immersive Simulation With respect to cultural training, simulation allows crewmembers to practice skills to mitigate the impact of cultural diversity on team interaction in a “safe” environment. NASA has already started to utilize simulation and identified benefits include, the ability to: (1) highlight specific features about a culture, (2) conduct a variety of scenarios (and runs of those scenarios) that would be improbable without simulation but may appear in reality, (3) customize and modularize scenarios allowing for an easier adaptation to constraints (temporal, contextual), and (4) draw from a multitude of training techniques as well as culturally specific and generic scenarios (Draguns & Harrison, 2011). While research has begun to show the effectiveness of simulation, it is only a tool and it must be designed properly in order to yield maximum benefits (Tannenbaum & Yukl, 1992). In this vein, Salas and Burke (2002) identified several key elements that should be embedded when designing simulations for training in order to maximize the efficacy of the simulation in terms of learning. Example features that should be embedded include: instructional features, carefully crafted scenarios, opportunities for assessment and diagnosis of performance, guided learning, and match of simulation/scenario fidelity to training objectives and goals.

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Team Debriefing Debriefing can be defined as a discussion with respect to a targeted experience with the purpose to learn from it (Lederman, 1992). While the exact form of team debriefs varies, most include the following: an introduction, explanation of objectives, sharing individual reactions regarding an event or process, examination of factors that contributed to key outcomes, and discussion around lessons learned (e.g., what and how improvements can be made) (Adler, Castro, McGurk, 2009; Allen, Reiter-Palmon, Crowe, & Scott, 2018). While team debriefs originated within the military context, adaptations have allowed expansion of this technique far beyond the military (e.g., Sawyer & Deering, 2013). While research has shown that debriefs can improve performance by over 20% (Tannenbaum & Cerasoli, 2013), they must be designed with the science of learning in mind in order to maximize gains (Gardner, 2013). Debriefing has been argued by some to be one of the most important training components due to its potential impact on individual and team outcomes (Rall, Manser, & Howard, 2009). Guided team self-correction is one form of debriefing which can be utilized to mitigate decrements in team interaction due to cultural diversity (Smith-Jentsch et al., 2008). Team self-correction takes advantage of a team’s natural desire for self-review. Team self-correction is a structured technique whereby prior to training targeted learning outcomes are described to the team. At the conclusion of training a debrief session is guided by a facilitator and the team is actively involved, doing most of the diagnosis. In essence, the facilitator recaps key events to trigger member recall. The debrief then proceeds with the facilitator working through each of the targeted learning outcomes asking for concrete examples of effective and ineffective performance with respect to the targeted learning outcome. Finally, a discussion is facilitated regarding an action plan for improvement in each area. This strategy, although not yet utilized to mitigate team decrements caused by cultural diversity, has clear applicability.

CONCLUDING COMMENTS Spaceflight crews are often culturally diverse and operate in mission-critical environments. As NASA moves toward longer duration, exploration class missions the isolation and confinement crews face is only expected to increase. Therefore, while the impact of cultural diversity on team interaction has not traditionally been a heavily researched area at NASA, it is beginning to gain increased attention. Within the current chapter, we began by providing a high level overview of the many ways in which cultural diversity may impact the manner in which interdependent, coordinated action occurs within long duration spaceflight crews. Given the scarcity of literature that examined the impact of cultural diversity on team interaction within the context of spaceflight crews, the broader literature on cultural diversity and teams was heavily leveraged, pulling in spaceflight literature where it existed. In general there is a myriad of work which either directly or indirectly suggests that cultural diversity can create challenges for team interaction along a number of emergent states as well as behavioral and cognitive processes. There is also a fair amount of information in the cultural diversity literature that may be extrapolated to begin to understand how cultural diversity may impact the various components of team interaction, but these

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extrapolations remain to be tested. Lacking, however, is work that focuses on team composition with regard to how patterns of cultural diversity and/or cultural values may differentially impact team interaction (i.e., processes, states) and performance. This is an area where future research is needed. Mitigation strategies are another area that should be further explored. While there is an accumulation of research supporting cultural diversity training to varying degrees, training often does not incorporate what is known regarding the science of training. Additionally, most programs focus on developing expatriates for work abroad, and not on preparing individuals to work as a member of a culturally diverse team. This is an important gap for long duration space exploration given that crews will be culturally diverse and expected to interact in a coordinated manner, sometimes over a long duration on mission critical tasks. The predominant number of identified programs not only fail to account for the unique requirements of working in a team (i.e., coordinated, interdependent action), but little has been published within the context of spaceflight. While the dominant focus within the cultural diversity literature has been on training as a mitigation mechanism, other types of mitigation strategies were found. For example, the formation of hybrid team cultures and superordinate identities, provision of socialization opportunities, understanding of culturally-driven team metaphors, and team composition have all been argued to directly mitigate decrements in interaction when working within culturally diverse teams or environments. Other mitigation strategies and corresponding frameworks were identified from outside the literature on cultural diversity that can be leveraged to mitigate challenges to team interaction with regard to cultural diversity (i.e., team debriefing, stress exposure training, event-based training). We concluded the chapter by placing these findings into an overarching framework that we feel can be used as an integrative approach to the training of culturally diverse spaceflight crews – stress exposure training. However, this is an area in need of future research. While research should examine the degree to which the strategies put forth generalize to the spaceflight context and the unique stressors that crews face. There also needs to be investigation of training focused on the content required to create cultural synergy within a team environment. Research in this area offers many opportunities and there seems to be tremendous potential in linking the literatures on culture, teams, and spaceflight to maximize the benefits of cultural diversity.

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10

Teamwork in Space Exploration Jensine Paoletti, Molly P. Kilcullen, and Eduardo Salas Rice University

CONTENTS Purpose of the Chapter ........................................................................................... 196 Goals of Astronaut and Flight Controller Teams ................................................... 197 Teamwork ABCs .................................................................................................... 197 Attitudes ............................................................................................................ 198 Psychological Safety .................................................................................... 198 Mutual Trust ................................................................................................. 198 Collective Orientation .................................................................................. 201 Behaviors........................................................................................................... 201 Conflict Management ................................................................................... 202 Coordination ................................................................................................. 203 Team Care..................................................................................................... 205 Problem-Solving........................................................................................... 206 Cognitions .........................................................................................................207 Team Learning ..............................................................................................207 Team Knowledge Outcomes......................................................................... 208 Future Directions and Applications ....................................................................... 209 Acknowledgments.................................................................................................. 210 References .............................................................................................................. 211 Teamwork can now suddenly be part of the approach to the entire day. This is a huge difference, which enables synergy that makes it possible to get more than twice the work done. (Stuster, 2016, p. 81) This day stands out because we accomplished everything on the schedule, even a little early. It is pretty cool to see how well we work together as a team, not only up here but also with the ground. (Stuster, 2016, p. 23) Teamwork skills are an important, yet less emphasized, aspect of an astronauts’ and flight controllers’ jobs (Salas et al., 2015). Several examples (e.g., above) from astronauts’ journals mention the role of teamwork and team interaction in space 195

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as either (1) enhancing astronauts’ performance and well-being or (2) at times detracting from their experience in spaceflight (Stuster, 2016). Like many other organizations, NASA relies on teams to complete work; most notably, both astronauts and Mission Control personnel work in teams (Landon, Slack, & Barrett, 2018). According to NASA’s astronaut job analysis, teamwork is a required competency for astronauts currently flying for the International Space Station (ISS) as well as for future Mars-like missions (Landon et al., 2018). Indeed, teamwork competencies, or teamwork skills, are required for astronauts ‘at hire’; these team competencies only increase in importance, due to increasing communication delays, as the mission lengths and distance from the Earth continue to increase (Landon et al., 2018). These increasing delays in communication necessitate selecting and training competent astronaut teams that can function independently. Spaceflight has been characterized as an extreme work environment because it requires ‘technological, social, and physical components that require significant human adaptation for successful interaction and performance’ (Barnett & Kring, 2003, p. 961). Therefore, astronaut teams are considered extreme teams, as they work in environments with atypical contextual factors (e.g., extreme time pressure, confinement, danger) face serious health and well-being consequences in cases of ineffective performance (Bell, Fisher, Brown, & Mann, 2018). Our understanding of the teamwork attitudes, behavior, and cognition (ABCs) needed in spaceflight come from applications of team science to four main populations. These populations include (1) astronauts in orbit on the ISS, (2) ground control teams that support astronauts, (3) teams that work in analogous environments to space, called isolated, confined, and extreme (ICE) environments, and (4) teams that work in analogous study environments (e.g., such as the Human Exploration Research Analog, or HERA) designed to research humans in isolated, confined, and controlled (ICC) environments for the expressed purpose of preparation for spaceflight. Thus, astronauts on the ISS view their role as field-testing procedures, technology, and life in space for future exploration missions (personal communication, Slack). However, most of the data on teams in ICE environments come from non-space ICE teams. From these samples, data demonstrated differences between team members in typical work environments and ICE environments; specifically, nascent research has suggested that social support may be a job demand for ICE environments and negative emotions may play a nuanced role in team interactions, among other early findings (see Golden, Chang, & Kozlowski, 2018, for a review).

PURPOSE OF THE CHAPTER ICE environment-specific team research is still in its early stages, so this chapter discusses all relevant team research for spaceflight, including some seminal articles. This chapter focuses on teamwork attitudes, behaviors, and cognitions effective in traditional and extreme teamwork environments. Overall, the purpose of this chapter is to highlight specific constructs from the psychological science relevant to spaceflight. Specifically, we will discuss constructs relevant for astronaut selection and training, some topics which have robust literatures, as well as some nascent

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constructs with promising ongoing research. We also discuss opportunities for team science applications and future directions.

GOALS OF ASTRONAUT AND FLIGHT CONTROLLER TEAMS Mission Control Center (MCC) at NASA’s Johnson Space Center is a control hub that oversees crucial aspects of every American human spaceflight (Dunbar, 2009). This involves planning, organizing, and controlling missions. Mission Control helps astronauts in any situation that could arise during their mission. They are divided into flight control and ground-team workers. The ground team gathers data from spacecraft and launch facilities. The flight control team analyzes this data to make decisions on the best way to proceed. Therefore, these teams work together in a multiteam system, discussed more in Chapter 12. Just as Mission Control has varying tasks and goals, astronauts also have to manage their personal and mission goals; each astronaut has their own roles and responsibilities based on their technical expertise (e.g., science experiments, vehicular maintenance). Astronauts are required to shift to highly interdependent tasks after long periods of autonomous work, which is believed to risk performance decline (Smith-Jentsch, 2015). Teamwork research can be used to address the risks of performance reductions. Goals for astronaut and flight controller teams vary by mission, but the superordinate goal for all missions is to maintain the safety, and physical and psychological health of astronaut crews; sometimes just referred to as the ‘safety goal.’ Astronaut crews are routinely subjected to high stress situations during their missions, given their extreme environment, which can lead to decreased individual and team performance (Ellis, 2006; Hunziker et al., 2011). This high level of stress can also lead to decreased well-being, cohesion, trust, as well as increased conflict. Astronauts operating in these stressful, risky environments, require them to mitigate, if not prevent risks prior to accidents (Leveson, 2015). NASA’s risk-management strategy involves identifying, analyzing, planning, tracking, controlling, communicating, and documenting risks (Perera & Holsomback, 2005). For other organizations, improper risk management can lead to unfortunate financial consequences, but for NASA, poor risk management can lead to incidents such as the Space Shuttle Columbia accident. For astronauts, risk varies from low to high with threats coming from a variety of different environmental factors. Additionally, threats can also arise from physiological and psychological sources in the flight crew (e.g. error caused by fatigue, stress, or poor communication). Not only astronauts, but teams across NASA must engage in effective risk management to prevent significant financial and safety incidents. Team science may provide the trainable knowledge and tools to support NASA’s mission-related and safety-related goals.

TEAMWORK ABCs For the bulk of the chapter, we will discuss some of the key teamwork attitudes, behaviors, and cognitions in the literature and cover the seminal articles and major findings in each of the content areas. We further organize the chapter into attitudes, behaviors, and cognitions (ABCs) needed for teamwork in space.

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While  these  distinctions are made in the chapter organization, it is important to note that many team ABCs are interrelated (e.g. team cognition variables affect team member behavior, etc.). These components allow us to expand toward other important constructs for effective astronaut teams. Additionally, we describe team ABCs according to their relative importance across the course of a space exploration mission in Table 10.1.

Attitudes Team attitudes, or team affect, are the internal emotional state(s) associated with a team and the corresponding effect on the team’s interaction processes (Salas, Rosen, Burke, & Goodwin, 2009). This includes, but is not limited to, psychological safety, collective orientation, and mutual trust. Psychological safety, collective orientation, and mutual trust are considered important for outcomes such as team learning and team effectiveness (Salas, Grossman, Hughes, & Coultas, 2015; Salas et al., 2009, 2015). Psychological Safety For teams to be able to effectively function, there must be a psychologically safe team climate. Team psychological safety is ‘a shared belief that the team is safe for interpersonal risk taking’ (Edmonson, 1999, p. 354), a team climate that is characterized by mutual respect such that members of the team are comfortable being themselves. Therefore, psychological safety is a team-level attitude, also called an attitudinal emergent state, which emerges via individual team members’ attitudes and interactions (Salas et al., 2009). Psychological safety facilitates team exploratory learning through the creation of an environment that is conducive to critical thinking and open discussion of potentially sensitive issues, thus encouraging the development of novel ideas through challenging preexisting knowledge and presumptions (Kostopoulos & Bozionelos, 2011). Indeed, research, including a recent meta-analysis, has found that psychological safety is related to performance (Baer & Frese, 2003; Frazier, Fainshmidt, Klinger, Pezeshkan, & Vracheva, 2017), such that it increases learning behavior and subsequently team learning (Edmonson, 1999; Frazier et al., 2017). Psychological safety is important for NASA teams, as team members must feel psychologically safe to voice concerns when unacceptable safety risks are identified. Psychological safety also supports such team behaviors as problem-solving and decision-making, where team members should feel safe to voice their opinion and present new ideas. In fact, low psychological safety among NASA engineers has been linked to such tragedies as the Columbia and Challenger disasters, due to engineers feeling disempowered to discuss potential risks (Starbuck & Farjoun, 2009; Vaughan, 1997). Learning from these disasters, NASA has embraced a culture of safety, including working to foster psychological safety among its employees. Mutual Trust Mutual trust refers to the ‘shared belief that team members will perform their roles and protect the interests of their teammates’ (Salas et al., 2005, p. 561).

Verbal communication (e.g., Marlow et al., 2018): While this is important throughout the mission, there will be substantially more verbal communication in the beginning, until shared mental models are developed, and the crew can communicate and coordinate in an implicit manner.

Problem-solving (e.g., Burke et al., 2018) and decision-making (e.g., Klein, 2008): as the crew encounters off-nominal events and experience increasing communication delays with ground-control, the crew will have to solve unforeseeable problems and make decisions primarily on their own, as a team. Nonverbal communication: once shared mental models develop, the crew will engage in more nonverbal communication to relay information and coordinate actions.

Coordination (e.g., Zalesny, Salas & Prince, 1995): this will be particularly important during this time period, as the crew will have to coordinate their actions to achieve tasks that are a result of these decisions being made.

Mid-Flight Team learning (e.g., Schippers, West, & Dawson, 2015): this involves the crew building their collective knowledge and learning to solve problems and reflect upon their methods. Crews should engage in team learning during pre-flight training.

Collective orientation (e.g., Eby & Dobbins, 1997): as this is a relatively stable attitude, it is primarily important for astronaut selection pre-flight.

Pre-Flight

Problem-solving (e.g., Burke et al., 2018) and decision-making (e.g., Klein, 2008): as the crew encounters off-nominal events and experience increasing communication delays with ground-control, the crew will have to solve unforeseeable problems and make decisions primarily on their own, as a team. Nonverbal communication: once shared mental models develop, the crew will engage in more nonverbal communication to relay information and coordinate actions.

Conflict management (e.g., De Dreu et al., 2001): crew will likely experience more conflict toward the end of the mission, as physical and psychological strain builds up, thus they will have to increasingly engage in 1 of 5 conflict management styles.

Return-Flight

TABLE 10.1 When the ABCs of Teamwork Are the *Most* Important across the Mission Timeline Foundational

(Continued)

Mutual trust (e.g., Salas et al., 2005): while mutual trust is important throughout the mission, it should be established before the mission begins to ensure optimal teamwork.

Psychological safety (e.g., Edmonson, 1999): during these critical, decision-making, and problem-solving times during the mission, it is critical that the crew is psychologically safe, and members can voice their concerns freely to ensure the best possible safety outcomes Team care (e.g., Adler et al., 2017): as conflict arises between crew members, it will become increasingly important for the team to provide support for each other.

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Return-Flight Shared mental models and transactive memory systems (Kozlowski & Chao, 2012): SSMs and TMS are developed over time as the crew interacts and builds a collective understanding of individuals’ roles and unique knowledge. Coordination (e.g., Zalesny, Salas, & Prince, 1995): this will be particularly important during this time period, as the crew will have to coordinate their actions to achieve tasks that are a result of these decisions being made.

Mid-Flight Shared mental models and transactive memory systems (Kozlowski & Chao, 2012): SSMs and TMS are developed over time as the crew interacts and builds a collective understanding of individuals’ roles and unique knowledge.

Team cohesion: this construct is important throughout the mission. It is an emergent state that develops as team tenure increases.

Foundational

Note: Constructs were categorized into three stages of spaceflight based on the characteristics of those stages (i.e., training, landing on Mars, length of time of mission). Additionally, a fourth category (foundational) indicates constructs that will likely be vital across mission stage. While most of these constructs are relevant throughout the mission, they were categorized based on when they are most important. For example, conflict management was deemed the most important for the return trip, since teams will likely experience increased interpersonal conflict as team interaction within the confined space increases. In contrast, collective-orientation was placed in the pre-flight category, since it is a disposition (i.e., not trainable) and needs to be considered mostly during the astronaut selection phase before flight.

Pre-Flight

TABLE 10.1 (Continued) When the ABCs of Teamwork Are the *Most* Important across the Mission Timeline

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Mutual trust differs from psychological safety (above) as it is an others-focused attitudinal emergent state, while psychological safety is a self-focused attitudinal emergent state. Theory refers to two categories of team trust: cognition-based trust (the appraisal of teammates’ reliability and dependability) and affect-based trust (the reciprocated interpersonal care and concern); (McAllister, 1995). Both affectbased and cognition-based team trust are positively related to team performance according to meta-analytic research (De Jong, Dirks, & Gillespie, 2016). Mutual trust is a shared attitude that promotes team member satisfaction and performance by reducing wasted time on unnecessary, excessive performance monitoring (Salas et al., 2005, 2009). Mutual trust is particularly important for astronaut teams, where failure can result in life-threatening consequences, as members must focus on their individual duties and not waste unnecessary cognitive resources on monitoring teammates’ actions. Collective Orientation Collective orientation is a selectable, stable trait defined as ‘the attitude or preference to work in a collective manner in team settings’ (Driskell, Salas, & Hughes, 2010). This construct is typically viewed as being relatively stable within-person attitude, but able to change over time through experience (Eby & Dobbins, 1997). Eby and Dobbins (1997) proposed that an individual’s preference for working collectively may be due to a set of beliefs about working with others: self-efficacy for teamwork, external task locus of control, positive past experience working in teams, and meeting needs for affiliation and approval. Salas, Sims, and Burke (2005) laid out the ‘Big 5’ of teamwork and named team orientation as essential for successful team performance. Indeed, previous research has found that teams with more collectivistic orientation perform better (Jackson et al., 2006) through enhanced cooperation (Eby & Dobbins, 1997) and problem-solving (Saleh & Wang, 1993). Conversely, low team orientation in pilots may lead to riskier behavior and decreased openness to team members’ input during times of stress (Berg, Moore, Retzlaff, & King, 2002; Salas et al., 2009). Collectively oriented astronauts and flight controllers may perform better as they may be less likely to experience team input loss.

beHAviors Team behaviors have been defined as observable competencies needed to perform teamwork and team tasks (e.g., adaptation, communication); (Cannon-Bowers, Tannenbaum, Salas, & Volpe, 1995; Salas & Cannon-Bowers, 2000). We focus on the demonstrated skills and behaviors beyond technical expertise that allow them to function effectively in teams. Teamwork skills are an integral part of team effectiveness and teamwork performance management, as team skill measurement combined with task skill measurement can provide insight to a member’s performance on the team (Baker & Salas, 1992; Cannon-Bowers & Salas, 1998; Hartenian, 2003; Salas et al., 2004). Here, we discuss a few important teamwork skills, namely, conflict management, coordination, team care, and problem-solving.

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Conflict Management Astronauts have unique experiences as employees because they live and work with the same group of people. See Chapter 11 for a more in-depth discussion of the experiences of astronauts living together, termed ‘group living.’ The dearth of social interaction partners has been cited as a reason for astronauts’ conflict avoidance, who ‘recognize that over a long period of time they are likely to get into conflict with another crewmember, and before getting into a serious argument ask themselves “Is it worth it?”’ (Williams et al., 2009, p. 11). While the lack of interaction partners seems to be a deterrent to interpersonal conflict in space, astronauts still receive conflict-management training as part of their teamwork-skills training before flying in space (Sipes & Vander Ark, 2005), as conflict has been described as inevitable by astronauts. Well the day has finally arrived. I am now somewhat frustrated with my crewmates. Maybe it happens to everybody, but one of them continues not to do what they are supposed to do. Small things that wind up being big things-not vacuuming the razors when it is their time to do it, leaving stuff open on the computer, changing camera settings in the cupola. (Stuster, 2016, p. 35)

This astronaut writes about the small irritants of living with others becoming sources of negative conflict, consistent with the experiences of roommates and family members (Stuster, 2016). Importantly, conflict is not universally negatively related to team effectiveness. Types of Conflict Meta-analytic evidence demonstrates that (1) process conflict, or disagreements on the team’s work, should be accomplished and (2) relationship conflict, or interpersonal tension and friction, are negatively correlated to team performance (O’Neill, Allen, & Hastings, 2013). However, task conflict, or disagreements on ideas or perspective about the work, is a mixed bag; it is sometimes positively related to team performance when paired with psychological safety (O’Neill et al., 2013), such as product development teams who use conflict to innovate, but other times negatively related to team performance (e.g., De Dreu & Weingart, 2003; Khan, Breitenecker, Gustafsson, & Schwarz, 2015). Research suggests contingencies on positive relationships between task conflict and team performance, i.e., teams with high levels of emotional-regulation skills, may benefit from task conflict (Jiang, Zhang, & Tjosvold, 2013). Task conflict may promote team innovation in an inverted U-shape so that moderate amounts of conflict are effective (Xie, Wang, & Luan, 2014). This indicates further distinction in team conflict management skills, as astronauts should understand which types of conflict are the most detrimental, i.e. relationship and process conflict, and which conflict types may sometimes be beneficial to the team, i.e. task conflict. Conflict-Management Strategies How do teams actively manage conflict when it arises? According to the Dual Concern Theory, individuals manage conflicts by balancing concern for others and

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concern for self (Pruitt & Rubin, 1986). The interaction of these two variables creates five conflict-management strategies, avoiding (low levels of concern for self and others), forcing (high levels of concern for self, low levels of concern for others), yielding (low levels of concern for self, high levels of concern for others), compromising (moderate levels of concern for self and others), and problem-solving (high levels of concern for self and others) (De Dreu, Evers, Beersma, Kluwer, & Nauta, 2001). Other research suggests that top-performing teams manage conflict by targeting interaction content rather than interpersonal style, discussing rationale for work assignments, and assigning work based on individual expertise (Behfar, Peterson, Mannix, & Trochim, 2008). These healthy conflict-management techniques may take place in the context of team-building intervention, which is considered appropriate for developing constructive conflict-management behaviors (Edelmann, 1993). Further, Lacerenza and colleagues (2018) propose that team-building is effective for promoting improvements to interpersonal processes such as conflict management. These team-building interventions have been shown to be effective for conflict management and often also boost team trust and provide opportunities for open conversation (Argyris, 1962). Overall, astronauts should consider the importance of conflict management in their role, the types of conflict they experience, and the evidencesupported conflict-management techniques presented here. Coordination Teams are thought to be better suited to handle complex tasks because they are able to transfer the workload between members, a capability which is considered to be a part of team coordination (Ellis, Bell, Ployhart, Hollenbeck, & Ilgen, 2005; Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000). Team coordination refers to shared ‘team task,’ behaviors performed by more than one member of the team, and sequential taskwork, or work that is completed in a specified order (Zalesny, Salas, & Prince, 1995). According to Salas and colleagues’ taxonomy of teamwork components, there are three main coordinating mechanisms for teams. These mechanisms include (1) closed-loop communication, (2) shared mental models, and (3)  mutual trust (Salas, Sims, & Burke, 2005). Mutual trust (see Attitude section) and shared mental models (see Cognition section) are both considered implicit coordination. Although these are not behaviors themselves, trust and shared mental models ensure team members organize their actions in a sequential and effective manner. Despite their reliance on individual work, coordination frequently occurs between astronauts in space, as crews on the ISS work together to finish their tasks during the full workdays, as described in an astronaut’s journal, X [crew member name] was all over the place helping out, we switched the order of the ultrasound to utilize the available crew members, I helped out Y [second crew member name] with the suits and Y helped me out with the hatch ops. (Stuster, 2016, p. 34)

Astronauts and flight controllers should monitor their team’s coordination, because it becomes more important for teams as their work becomes more interdependent (Marks et al., 2001).

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Mutual Performance Monitoring and Back-Up Behavior Mutual performance monitoring refers to when team members keep track of each other while still completing their own workload, thus allowing team members to keep each other accountable while working interdependently (McIntyre & Salas, 1995). Mutual performance monitoring is thought to promote team effectiveness through spurring back-up behavior, discretionary help from team members including (1) giving feedback, (2) giving assistance for a task, or (3) completing a task for another teammate (Salas, Sims, & Burke, 2005). Shared mental models, or the shared understanding of the team’s goals and tasks, may be important for teammates to understand the roles and responsibilities of their teammate, therefore enabling mutual performance monitoring and back-up behavior (Salas et al., 2005). Likewise, research suggests that mutual performance monitoring promotes future team learning (Albon & Jewels, 2014; Fransen, Kirschner, & Erkens, 2011). In fact, mutual performance monitoring and back-up behaviors have been used as behavioral markers of underlying team cognitive processes (Salas, Rosen, Burke, Nicholson, & Howse, 2007). At NASA, this may be important for astronaut teams, as their tasks are high-risk and may therefore require more intensive monitoring to reduce the chance of error. Additionally, back-up behavior may be particularly important as astronauts’ missions continue and teammates may suffer from sleep deficit and require more assistance in maintaining performance. Both components of team coordination promote team performance (Burke et al., 2006). Together with other team characteristics, mutual performance monitoring and back-behaviors encourage team adaptation, team innovation, and team modification (Burke et al., 2006). Communication Teams with poor coordination may also experience the so-called ‘process loss’ associated with working in a team, which was historically considered a major concern for teamwork (Bowers, Baker, & Salas, 1994; Shiflett, 1979). At times, organizations will refer to this process loss by discussing ‘communication breakdowns’ or being ‘out of sync’ (Marks et al., 2001). Indeed, communication was previously 1 of the 7 behavioral outcomes for team coordination (Bowers, Salas, Prince, & Brannick, 1992). No longer considered an outcome, now team communication is a key coordination process. Now, team communication is frequently called information exchange; robust evidence delineates information exchange as vitally important for team performance, cohesion, decision satisfaction, and knowledge integration (Mesmer-Magnus & DeChurch, 2009). Additionally, meta-analytic evidence demonstrates that team communication quality is more positively related to team performance than team communication frequency (Marlow, Lacerenza, Paoletti, Burke, & Salas, 2018). One common, trainable technique for effective team communication is called closed-loop communication; it involves one party sending information, and the other party receiving the information and confirming its receipt. Closed-loop communication, along with planning behaviors, is considered to be a form of explicit coordination, and therefore must be purposely and intentionally enacted by team members

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to coordinate (Rico, Sánchez-Manzanares, Gil, & Gibson, 2008). Closed-loop communication is practiced by the MCC and astronaut crews. Explicit coordination via communication and planning is found within astronaut crews on the ISS, as they enact plans to help each other accomplish work, ‘I suggested that we have a policy to get all your stuff done and if somebody else is working after [the daily planning conference] it’s all hands on deck to help them get through their thing’ (Stuster, 2016, p. 34). Thus, explicit coordination (i.e., planning and communication) helps astronaut teams work effectively together and accomplish their goals. Team Care Team care, an empirically derived construct, is an immature but promising secondorder team process that builds the well-being of the team, and has been defined as ‘the degree to which individuals provided support to fellow unit members’ (Adler et al., 2017, p. e1671). Although this construct is still in its nascent stage, astronauts have identified team care as important for current and future astronauts’ jobs (Barrett, 2016). Specifically, team care is a social process that involves monitoring, supporting, adapting, and coordinating. For example, if a crewmate gets bad personal news from home, then the rest of the crew may choose to care for their crewmate by coordinating with each other and adapting their routine to complete his or her taskwork and chores while he or she processes the news. We argue that engaging in team care includes monitoring the team social dynamics, workload, stress, and psychological and physical health for the purpose of team member well-being. Monitoring behavior includes ‘Look[ing] out for buddies in trouble’ and ‘Engag[ing] someone in conversation to find out how they are doing’, which allows team members to identify when support is needed and adapt and coordinate to provide support (Adler et al., 2017, p. e1672). These supporting behaviors may include any of the four manifestations of support including (1) emotional support or empathy and love, (2) appraisal support or feedback, (3) informational support or advice, and (4) instrumental support or helping behaviors (House, 1981). Adapting and coordinating behaviors are useful for team care to organize workflow changes. As mentioned earlier, team care is a second-order team process built from firstorder team processes rather than functioning as an individual-level skill. Team care differs from team cohesion because it is a team process that focuses on well-being rather than an emergent state and differs from organizational citizenship behaviors because it is not an individual behavior and may be a job requirement for astronauts. While research is currently underway to establish team care in organizational science as an important process for team well-being, the topic area is nascent and deserves more attention. Outside of astronaut teams, team care could be especially useful for military teams, rescue teams, and other extreme teams which require members to spend extended periods of time together under stress. In fact, firefighting teams discuss a culture of friendly love or emotional culture that is important for their well-being, which may be related to team care (O’Neill & Rothbard, 2015). Further, team care (i.e., preparing meals, monitoring team member’s behavior for well-being declines) may be considered a proactive behavior to prevent conflict. Astronauts could be trained on team care norms and behaviors preflight, emphasizing the increasing

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importance of team care as missions are in their later stages. Currently, astronauts receive conflict management training, but this could be expanded to incorporate aspects of team care. Problem-Solving Problem-solving involves choosing issues that require attention, setting goals, and finding or designing appropriate courses of action (Simon et al., 1987). It precedes decision-making, which involves evaluating and choosing among various courses of action. Problem-solving ‘relies on large amounts of information … stored in memory and … retrievable whenever the solver recognizes cues signaling its relevance’ (Simon et al., 1987, p. 21). Therefore, it is logical that problem-solving efforts would be more successful when performed in groups, where there is a larger bank of knowledge to access, than for individuals working alone. Issues with Early Problem-Solving Research Early research regarding group problem-solving yielded beguiling results such as ‘individuals produced more ideas than groups on problem-solving tasks’ (Dunnette, Campbell, & Jaastad, 1963). These studies also indicated that group solutions tended to be of significantly lower quality, as well as quantity, and did not improve individual problem-solving efforts (Taylor, Berry, & Block, 1958; Campbell, 1968). Likewise, Rotter and Portugal (1969) found that individual production of ideas was superior to both (1) group production and (2) the combination of group and individual production. They deduced that idea production was directly proportional to the time that individuals work alone. Some researchers suggest that early research found that group problem-solving inferior to individual problem solving is because of group-think or low psychological safety, so members would share ‘only those ideas which are socially acceptable will be voiced within a group context’ (Rotter & Portugal, 1969, p. 341). Additionally, early group research typically only focused on ad hoc teams, which does not imbibe trust and psychological safety to build over time for effective group problem-solving. Current Research As team problem-solving research has progressed, studies examining differences between individual and team performance have shown that the synergy, or the interaction between two or more agents or the combined effort is greater than the sum of their individual efforts (Hardy & Crace, 1997), and shared experiences that team members contribute to problems-solving within teams leads to higher performance (Jordan & Troth, 2004). In addition, it has been found that high-interaction orientation (the degree to which an individual is concerned with maintaining harmonious relationships) groups tend to perform better than low-interaction orientation groups when asked to reach a consensus on a team problem-solving task (Campbell, 1968). Previous research has found that teams in a collaborative problem-solving setting outperform individuals working alone, such that they generate more accurate planning and quantitative solutions (Barron, 2000). Indeed, collaborative problemsolving is related to team productivity when teams are small, not very cohesive, task

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certainty is lower, and members are not intimately connected in terms of work flow (Dailey, 1978). Effective team problem-solving may be particularly important for NASA contexts, especially for astronaut teams and ground control, as they ‘operate in environments that are complex, dynamic, and often unpredictable, and therefore, engaging in problem solving in situ such that adaptive action can be taken to an unexpected event is critical’ (Burke et al., 2018, p. 720). Role of Leader in Team Problem-Solving Team leaders play a critical role in facilitating group problem-solving. It is important for leaders of innovation teams to possess creative problem-solving skills to effectively manage teams, particularly during the idea-generation phase (Thayer, Petruzzelli, & McClurg, 2018). However, leaders should also support the team throughout all phases of problem-solving, including aiding the team in the assessment, development, and implementation of solutions (Burke, Shuffler,  & Wiese, 2018). Additionally, team leaders should help to build the shared affective, behavioral, and cognitive capacity within teams that facilitate the coordinated interaction that is necessary for effective problem-solving (Burke et al., 2018). Therefore, it is critical that as problems arise in NASA astronaut teams, team leaders within astronaut crews and flight control actively support essential team problem-solving behaviors.

cognitions Astronaut teams must operate in high-risk environments, where emergencies arise and decisions, or choices among alternatives, carry weighty consequences such as mission success costing billions of dollars or even are a matter of life and death (Landon et al., 2018). In other high-reliability environments (i.e., places where decisions are costly), such as Antarctica, teammates have expressed the utmost importance of ‘mutual decision-making’ so that members are in agreement before enacting their shared choices (Atlis, Leon, Sandal, & Infante, 2004). These extreme teams express the importance of team cognition as the basis of decisions (Atlis et al., 2004). Team cognition is the multilevel study of team-learning processes and knowledge outcomes (Fiore et al., 2010; Kozlowski & Chao, 2012). The study of team cognition was born from shared mental models (Cannon-Bowers, Tannenbaum, Salas, & Volpe, 1995), although the field has expanded to include transactive memory systems and various learning-related constructs that support team performance (Kozlowski & Chao, 2012; Salas et al., 2009). Team Learning Team learning includes knowledge building, macrocognition, and team reflexivity. Individual knowledge building occurs when a team member learns information about a problem or domain relevant to their work. Behavioral processes like collaboration and communication allow individual knowledge building to translate to team knowledge building, which is one reason that team learning is considered an outcome to team adaptation (Burke et al., 2006; Kozlowski & Chao, 2012). Macrocognition refers to the process of building team knowledge in support of problem-solving outcomes

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(see Fiore et al., 2010). Team reflexivity, an essential component of team learning, is the ‘extent to which teams collectively reflect upon and adapt their working methods and functioning’ (Schippers, West, & Dawson, 2015, p. 769), and is important for teams to prepare for future cognitive tasks (Schippers, Edmondson, & West, 2014). Teams that are high on reflexivity are able to reflect upon errors and change their future behavior to prevent the same errors from recurring, thereby reducing risk in future operations. Reflexivity allows teams the cognitive space for learning and may promote the self-awareness necessary for them to recognize their work demands and understand the team’s capacity to respond to such demands (Schippers, West, & Dawson, 2015). While team learning can be conceptualized as an outcome, according to the IMOI model, these outcomes can be an input and part of the team process for predicting future outcomes (Ilgen, Hollenbeck, Johnson, & Jundt, 2005). For instance, NASA astronaut teams must build their individual knowledge, engage in macrocognition, and be high on team reflexivity to effectively problem-solve during missions. Team Learning in Decision-Making One example of a decision-making model for extreme environments is the SAFE-T model, which urges trainees to focus on situational awareness, SA, plan formulation, F, plan execution, E, and team learning, T (Power, 2018). Exploratory research supports this model, and found that high-performing teams on a decision-making task spent more time sharing information and a shorter amount of time deciding on the plan (Uitdewilligen & Waller, 2018). While the SAFE-T model emphasizes team learning, evidence shows that team learning goal orientation does not predict decision-making performance, rather, team performance proves goal orientation (i.e., focus on competition and performance) was seen to excel at decision-making due to higher levels of team planning (Uitdewilligen & Waller, 2018). However, we urge decision-making teams, such as NASA astronaut flight teams, to continuously learn from their experiences, so that they will build their team knowledge outcomes, and thus future decisions will be quicker and easier to make. Team Knowledge Outcomes Team knowledge outcomes include transactive memory systems and shared mental models, which both promote team problem-solving and decision-making (Kozlowski & Chao, 2012). Transactive memory states are a way for team members to hold unique knowledge and understand who holds what knowledge on a team (DeChurch & Mesmer-Magnus, 2010). They are built when teams interact with each other and as members train, particularly through role-specific training which is found to improve decision-making effectiveness (Linton, Critch, & Kehoe, 2018). In contrast to shared mental models, the focus of transactive memory systems is not on a shared understanding, but on the distribution of knowledge throughout a team’s social network (Kozlowski & Chao, 2012). Shared mental models, are defined as the ‘knowledge structures held by members of a team that enable them to form accurate explanations and expectations for the task’ (Cannon-Bowers & Salas, 2001, p. 228); they are considered 1 of 3 mechanisms

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that support team coordination behaviors (Salas et al., 2005). Cannon-Bowers and Salas describe four main types of shared mental models, task-specific knowledge, task-related knowledge, knowledge of teammates, and knowledge of attitudes or beliefs. A fifth type of shared mental model, added later, is called team situation models, which is dynamic and context-driven, includes trust, and considered a main driving force of coordination (Rico et al., 2008). Each type of shared mental models of a situation or a problem should include (1) shared understanding of the goal, (2) strategies, and (3) team members’ individual roles (Cannon-Bowers, Salas, & Converse, 1993). Shared mental models serve as a basis to understand the communication in decision-making contexts; relatedly, mental model similarity is thought to predict decision-making quality (Cannon-Bowers et al. 1993). Empirical evidence suggests that this may be partially true, but that a team’s norms around constructive confrontation (i.e., openness to cognitive conflict) attenuate the relationship between mental model similarity and decision-making quality (Kellermanns et al., 2008). Research suggests that shared mental models are more difficult to develop in extreme environments like spaceflight due to added stress, which taxes two components necessary for shared mental model development, individual cognition and social interactions (Driskell, Salas, & Driskell, 2018). However, because shared mental models are foundational for many team processes, they may be even more important to develop for astronaut teams. Team Knowledge Outcomes in Decision-Making Team knowledge outcomes are often considered the basis of decision-making, particularly for the naturalistic decision-making perspective, which focuses on how people actually make real-life decisions, particularly in the high-stakes situations. Naturalistic decision-making states that people do not curate a random list of solutions to a problem and evaluate each of the options rationally to determine which is the best (Klein, 2008). Recognition-primed decision-making, which is a type of naturalistic decision-making, states that people use their experiences to form a bank of patterns, based on knowledge outcomes like mental models, that are later recognized in decision-making situations, allowing for faster decisions, as the first generated solution is often satisfactory. Thus, decision-makers are partially relying on intuition derived from past experience as they recognize current situations that are similar to the past, check to see if the past solution will work, and enact the solution (Klein, 2008). Taken together, team cognition in all its forms is important for supporting key team functions such as decision-making, problem-solving, coordination, and goal attainment.

FUTURE DIRECTIONS AND APPLICATIONS Teamwork attitudes, behaviors and cognitions together result in team effectiveness (e.g., Salas, Burke, Fowlkes, & Priest, 2004). Thus, organizations such as NASA have a vested interest in selecting on stable team-relevant traits plus improving their teams’ behaviors and skills. Additionally, there have been calls for NASA to incorporate more team skills training into the astronaut’s technical training regimen, as much of the contemporary astronaut training happens individually

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TABLE 10.2 The Properties of the ABCs of Teamwork Selectable Collective orientation Learning ability Problem-solving ability

Trainable Communication behaviors Coordination Conflict management Problem-solving techniques

Emergent Psychological safety Team care Mutual trust Team learning Shared mental models and transactive memory systems

Note: Constructs were categorized into three stages based on the characteristics of those constructs (i.e., whether they are selectable, trainable, or an emergent state). For example, conflict management was deemed as trainable, since it is possible for individuals and teams to be trained in various conflict management strategies to both proactively avoid and manage interpersonal or task conflict when it arises.

(Salas et al., 2015). NASA’s Human Research Program has established a tool called the Human Research Roadmap identifying broad scientific gaps and specific tasks within each of the gaps as scientific goals for the space program. Therefore teamwork skills are targeted for improvement during team development interventions such as team training, team composition, and team debriefing (Shuffler, Diazgranados, Maynard, & Salas, 2018). While teamwork skills are needed at hire and should also be incorporated into the selection system (Landon et al., 2018), team skills can also be improved via training according to extensive evidence (e.g. Ellis, Bell, Ployhart, Hollenbeck, & Ilgen, 2005; Salas et al., 2008). However, there is still a need for the research to translate to practice. Specifically, there is room for research on teamwork to translate to astronaut selection. Based on a report written by NASA, collective orientation, a stable trait, may be important to include as selection criteria, as it may proceed behaviors and emergent states of interest like mutual trust, psychological safety, team care, and conflict management (Landon et al., 2018; Mohammed and Angell, 2004; Smith-Jentsch, 2015). Further, team-building and preflight socialization could be meaningful to build trust and establish shared mental models among the team, particularly if the team members do not have the opportunity to train together as a team. Therefore, broadening the content of team skills and integrating several team skills into astronaut training may provide astronauts with more easily transferable teamwork competencies (Table 10.2).

ACKNOWLEDGMENTS This work was supported in part by grants NNX16AP96G and NNX16AB08G from National Aeronautics and Space (NASA) to Rice University. This work was also supported by grant NNX17AB556 from NASA to Rice University via Johns Hopkins University (Michael Rosen, P.I.).

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11 Exploring Group-Living Skills Extreme Roommates

as a Unique Team Skill Area Lauren Blackwell Landon KBR/NASA’s Lyndon B. Johnson Space Center

Jensine Paoletti Rice University

CONTENTS Background ............................................................................................................ 218 What We Know from the Work–Nonwork Literature ............................................ 219 Border and Boundary Theories ......................................................................... 220 Work Recovery .................................................................................................. 221 Conservation of Resources Theory.................................................................... 223 Work-Nonwork Enrichment .............................................................................. 224 Summary ........................................................................................................... 224 Defining Group Living: NASA Astronaut Crew Office Expeditionary Skills ....... 225 Predictors and Outcomes of Group-Living Skills in Extreme Environments ........ 227 Group Living as an Astronaut Selection Factor ................................................ 227 Traditional Personality Predictors ..................................................................... 228 Developing Group-Living Skills ....................................................................... 229 Supporting Group-Living Skills ........................................................................ 231 Conclusion ............................................................................................................. 232 References............................................................................................................... 232 It’s different of course, and I knew it would be, living with people you didn’t choose. Just some basic, standard issues of life where you have a different outlook on life and the way you operate and the things you talk about. —astronaut’s journal entry (Stuster, 2016, p. 35) Extreme teams in isolated, confined environments differ from typical organizational teams in many ways, but no other characteristic sets these extreme teams apart like group living. That is, many extreme teams must be coworkers and roommates. Many organizations engage in team skills training in the context of performing tasks or maintaining a collegial work environment, but few organizations approach teamwork divorced

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from tasks. Extreme teams must consider all applications of team skills, in both work and nonwork situations. The boundary between work and home life becomes blurred as teams are deployed on missions together for months or years. In recognition of this unique aspect to extreme teams, organizations such as the military, fire departments, the National Science Foundation’s Antarctic Program, and space programs, have developed guidelines or codes of conduct to outline acceptable and unacceptable behaviors. Traditionally, these codes of conduct focus on ethical behaviors, respect for others, creating a safe workplace, and preparing oneself and the unit to do the job. While some of these guidelines naturally spill over into living together, they are not treated as a skill that must be trained and practiced; instead they are viewed as rules. Often, norms and guidelines related to living together in a deployed unit such as a submarine consists of an unwritten set of loose rules, which have developed organically over time and reflect the preferences of the commanders and personnel onboard. We break with this tradition and introduce living together as a skill that allows astronauts to adapt to the different rules, norms, and guidelines of different crews and function effectively in space outside their working hours. In  this chapter, we explore the concept of group-living skills in extreme environments and offer recommendations for defining this team skill in context. We examine the broader psychology literature to see what findings may apply from the work–nonwork literature and other research examining social living situations. We consider implications for organizations looking to train this unique team skill, identify hypothesized predictors of healthy group-living skills, and establish avenues for research.

BACKGROUND Generally, there is a lack of research examining these informal living norms in work settings, as they have limited application to most organizations. However, the populations that may leverage and train skills related to group living operate in high-consequence environments where team disruptions may have severe and lasting safety repercussions, suggesting the benefit of quantifying this skill area to inform training would be significant. For spaceflight, group-living skills have become more salient as mission durations have increased. Short duration Space Shuttle missions were up to two weeks in length. Paraphrasing one Shuttle astronaut, ‘you can live with anyone for two weeks’ (Landon, Vessey, & Barrett, 2016). With the advent of space stations such as Skylab, Russia’s Mir, and the currently operational International Space Station (ISS), mission duration lengthened to months. Average ISS missions are currently six months in length, with the first one-year mission was completed in 2016 by astronaut Scott Kelly and cosmonaut Mikhail Korniyenko. In Kelly’s memoir of the experience, he discusses frictions with other crew members and the psychological stress of being away from loved ones and life on Earth (Kelly, 2017). Astronauts have expressed the sentiment that achieving a successful team composition with the right mix of people will become increasingly important for the future Mars mission of over two years, indicating the required ‘social integration and a level of interpersonal compatibility that helps mitigate conflicts among team members and that allows team members to rely on one another for support’ (Bell, Brown, Abben, & Outland, 2015, p. 550). The extreme duration of multiple years is further exacerbated by the small crew size of four to six people, the confined space that may be roughly the size of a typical recreational vehicle (RV), and the communication

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delay of up to 22 minutes between the Earth and Mars (Landon, Slack, & Barrett, 2018). In other words, the four person crew will only have each other for real-time social support and interaction, and conversely, there will be little opportunity for personal space and privacy, and no opportunity for crew rotations exists on the ISS. It is not unreasonable to assume that this small team may become analogous to a family unit in such a situation. The so-called ‘intimacy groups’ such as families and close friends are seen as more entitative than work groups or task groups. Groups seen with more entitativity, or the extent to which they’re perceived as a unit, correlate to higher personal importance and interaction (Fiske, 2009, p. 484). For those who work in astronaut teams, there is a heightened importance of nontask teamwork. Based on habitat design, these teams might need to spend their nonwork and nontask time together, including cohabitating to a greater or lesser extent. Therefore, group-living skills, or adaptable teamwork skills for nonwork time, are particularly valuable. In determining what aspects of group-living skills may hold the greatest promise to those seeking to leverage them for mission success, a broader search into the social and industrial/organizational psychology literatures, as well as the experiences of those living in such conditions, is warranted. Identifying predictors of individuals likely to perform well in extreme group-living situations would also enable selection teams to create a pool of well-qualified astronauts. For example, JAXA, the Japan Aerospace Exploration Agency, has successfully employed a selection hurdle in which the final 8–10 applicants live in isolation together in order to examine more closely group-living skills (among other skills) (Inoue & Tachibana, 2013). A review of team-oriented selection factors for long-duration missions has also identified a moderate to high level of team orientation (Landon, Rokholt, Slack, & Pecena, 2017). In addition, researchers may be able to develop specific group-living oriented countermeasures for those highly autonomous, isolated teams. Astronauts are trained on conflict management, stress management, and cross-cultural training (Barrett, 2016), which all have application to group-living skills. A careful review and definition of group-living skills may highlight gaps to be addressed by training developers and other researchers creating supportive countermeasures for the team.

WHAT WE KNOW FROM THE WORK–NONWORK LITERATURE The work–family literature, also called work–life or work–nonwork, captures people as they balance their roles as employees with their other nonwork roles. This line of research is particularly relevant for astronauts throughout the course of their missions, as they must balance their work life and their nonwork life while being confined to the same space vehicle and while living with the same crew. Mentioned earlier, it is not unreasonable to consider their crew as a pseudo-family unit; however, rather than use the term ‘work-family,’ we rely on the broader term ‘work-nonwork’ to more accurately discuss astronauts’ experiences in this section. This pseudo-family crew must provide both task-related support and socioemotional support to each other; astronaut crews may be further reliant on their fellow crew members when real-time communication to Earth is impossible (Landon et al., 2018). Here, we explore several work–life theories, discuss how they apply to astronaut teams, and provide propositions for use in future research on group-living skills.

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Proposition: Crew members with similar temporal and psychological integration preferences to each other will experience less harmful team conflict. In space, borders between work and nonwork time are highly unequal as to favor work at the potential expense of nonwork. In these cases, border theory tells us that astronauts will achieve better work/nonwork balance when they identify primarily with the heavier-weighted domain (Clark, 2000, p. 759). On the ISS, this translates to better balance for astronauts who consider themselves ‘workaholics.’ This is supported by interview data that demonstrates astronauts that are seen as ‘workhorses’ in their crew have higher capacity to work, not complain, and to accept tasks, are often favored compared with other members of the crew (paraphrased, personal communication with spaceflight operations expert). However, some have considered the potential for strain for these crew members due to high workloads, particularly as mission length increases. Others suggest that astronauts may face boredom as there may be lower workloads throughout a multi-year exploration mission, but the workload may be structured so that astronauts will use any ‘free’ time to train and practice skills (Landon et al., 2018). Besides training and practicing skills, contemporary astronauts on the ISS also enjoy spending their free time viewing the Earth from its orbit from the ISS, an activity from which astronauts derive pleasure and identity (Yaden et al., 2016). Therefore, both work and nonwork time in space form astronaut identities. However, future astronauts may achieve greater well-being if they identify more heavily with their tasks versus nontask time, as there will likely be heavy workloads and limited nontask ‘free’ time. Identification with work is related to its meaningfulness (Rosso, Dekas, & Wrzesniewski, 2010); work meaningfulness is predictive of self-reported benefits derived from the job (Britt, Adler, & Bartone, 2001). Additionally, engagement in meaningful work has been shown to shield deployed soldiers from workrelated stress (Britt & Adler, 1999); more broadly, meaningful work engagement can protect against performance errors, boredom, and well-being decreases (Britt, Jennings, Goguen, & Sytine, 2016).Therefore, the more astronauts finding meaning in their work, the more psychological benefits they may receive from the job. Proposition: For future spaceflight exploration, astronauts who identify with their tasks over their non-task time will experience better work/nonwork balance.

WorK recovery The effort-recovery model indicates that time spent working requires effort; the amount of effort a situation demands is determined by the work demands of the task (e.g., conditions, assignment), the work potential (e.g., specific abilities), and decision latitude, the extent to which a worker has autonomy in the task (Meijman & Mulder, 1998). Together work demands, work potential and decision latitude determine the work procedure including the amount of effort an employee needs to expend. The employee needs to recover from the effort expended or face a buildup of strain (Meijman & Mulder, 1998). Recovery is required as a preventative measure to protect both mood and performance over time. Suboptimal recovery threatens health and well-being. Specifically, recovery after a stressor (including work tasks) allows for an individual’s functioning to return to typical, pre-stressor levels (Meijman & Mulder, 1998). The greater a need for work recovery, the lower an employee’s

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well-being (Sonnentag, Mojza, Demerouti, & Bakker, 2012). Job characteristics like lower autonomy and high task demands create a need for work recovery, in line with the effort-recovery model. Astronauts in flight have high task demands (FlynnEvans, Gregory, Arsintescu, & Whitmire, 2016) and lower autonomy as their schedule is pre-determined by ground control, creating a situation where astronauts are ripe for experiencing strain. A potential organization-level solution may be to allow astronauts on exploration missions more autonomy over their work, which will necessarily be the case for a mission with significant communication delays. At present, astronauts and some mission simulation participants experience limited autonomy, particularly in their work duties; however, ongoing research is examining the effects of allowing self-scheduling and other means of autonomy. Additionally, there are also individual-level work recovery mechanisms. Studies have shown that the activities done during nonwork time can affect workplace engagement and proactive behavior at work for future performance (Sonnentag, 2003). Indeed, meta-analytic evidence also indicates that leisure activity satisfaction and leisure activity engagement are both positively correlated with subjective wellbeing (Kuykendall, Tay, & Ng, 2015). While the vast majority of the articles included in this meta-analysis are cross-sectional in nature, a few longitudinal studies indicate that the pattern of leisure remains positively correlated with well-being (Kuykendall, Tay, & Ng, 2015). Astronauts have demonstrated an affinity for many leisure activities, notably photography, ‘Here on ISS the time and the miles flow by at breathtaking speed. We mark our accomplishments by the week of successful tasks completed, and by the number of remarkable photographs we’ve been able to take – maybe because we want to try to permanently brand our brains with these sights’ (Stuster, 2016, p. 19). Clearly, astronauts use their leisure activities as a marker of time spent in space and as a way to gather meaning from their experiences. While more research should be conducted about the role of leisure activities over time, this kind of engagement in leisure activities may protect astronauts’ well-being. Providing time for individual and social leisure activities as well as the timely recognition of the need for both types among crew members will be important when living in confinement. Social activities such as talking to friends and physical activities such as working out are particularly useful for work recovery (Sonnentag & Zijlstra, 2006). Astronauts have a strict workout regimen designed to prevent muscular and skeletal degradation due to the lack of gravity (Ploutz-Snyder, Ryder, English, Haddad, & Baldwin, 2015). This may have an added psychosocial benefit as exercise has also been found to reduce the spillover of social undermining behavior from the work to home domains (Barber, Taylor, Burton, & Bailey, 2017). However, as physical fitness is considered a requirement of the job, these positive effects may be reduced. There may also be some variation among astronauts to the extent they engage in social activities. In the HI-SEAS analog, participants noted that having individual nonwork activities was useful, as individuals were less socially involved over the courses of the several-month-long mission. Likewise, some participants regarded group activities as another item on a to-do list rather than a means to achieving recovery, or even as a distraction from work (paraphrased, personal communication). This participant attitude reflects a need to study the moderating effects of extraversion and workload

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on the utility of socializing for work recovery. Mars 500 analog participants engaged in varying levels of social interaction as well, with two crew members individually withdrawing from the other four, which ultimately affected communication and cohesion (Basner et al., 2014). Relatedly, nonwork engagement can promote recovery, especially when the work and nonwork engagement require different resources (Sonnentag, 2003). However, astronauts in flight, especially astronauts on exploration missions, have a limited variety of individuals with whom to socialize in-person. Future research should investigate the potential moderating effects of fewer socialization partners on the relationship between social activities and work recovery. Proposition: Astronauts who are engaged in their nonwork activities will recover from work and be engaged in future work tasks.

conservAtion of resources tHeory According to the conservation of resources theory (Hobfoll, 2001), people tend to protect the social, emotional, physical, and temporal resources that they value. When these resources are threatened, lost, or not reaped after an investment, stress ensues. However, even at times when resources are not threatened or at risk of loss, individuals, families, and groups participate in proactive coping. This proactive coping includes stocking up on resource reserves, early action when threats are detected, and putting their groups at an advantage. One manifestation of proactive coping is contingency planning (Hobfoll, 2001). However, we assert that proactive coping can also manifest as building and maintaining social support. As proactive coping is related to reduced stress and building team efficacy, social support seems to fit as a tenet of resource conservation. Indeed, a military study of elite teams found that social support and team cohesion acted as a buffer against job-related stress. These cohesive, supportive teams reported better physical and psychological well-being compared with other units (Manning & Fullerton, 1988). In non-military settings, social support, specifically emotional support, has also been found to correlate negatively with strain and stressors. Importantly, one study also found that support offered by supervisors and coworkers were often comparable to support offered by family and friends (Fenlason & Beehr, 1994). This has also been observed in a spaceflight mission simulation, in which a six-person crew formed pairs with a go-to ‘buddy,’ which shifted over time or based on the issue at hand (paraphrased, personal conversation). Relatedly, in firefighter samples, more caring groups also tended to experience less member risk-taking outside of work (O’Neill & Rothbard, 2015). Indeed, as long as the support-provider has adequate skills, resources, and motivation, they are capable of enabling their close-others to thrive (Feeney & Collins, 2015). Some military organizations formally pair new soldiers in an attempt to leverage these benefits, dictating that these pairs never separate during work and nonwork duties except when ordered or during some hygiene tasks (personal communication, Adler). Taken together, there is evidence that the field of extreme teamwork could benefit from a more comprehensive examination of the nonwork time employees spend together, as it may have implications for well-being, stress, and strain.

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Proposition: Astronaut crew members should be trained on providing psychological and social support to each other, as this will allow them to better serve as pseudo-family units.

WorK-nonWorK enricHment According to work-family enrichment theory, the multiple roles which an individual enacts, i.e. parent, spouse, and employee, can result in positive affect and high performance in the other roles. Additionally, holding multiple roles may alleviate strain from one role. Role accumulation, or participation in multiple roles, suggests that participating in roles from the work and nonwork domains increase one’s well-being beyond participation in roles from only one domain (Greenhaus & Powell, 2006). Role participation in the work domain entails skill development, perspective-taking, and resource accumulation (i.e., psychological, physical and social-capital resources) that produces positive affect and performance in both work and nonwork roles. This effect is bidirectional and may be especially true when the resources curated in one domain are readily translatable to the other domain (Greenhaus & Powell, 2006). Astronauts’ work roles include leader, follower, and helper, as the astronaut corps emphasizes a shared leadership model, where each individual acts as a leader and a follower (Mulhearn et al., 2016). Astronauts also share social roles with each other, as friends and socioemotional caregivers, which may increase as astronauts begin to rely on asynchronous communication to their families and friends on Earth (Driskell, Driskell, Burke, & Salas, 2017; Landon et al., 2018). Broadly, the nature of astronauts’ work also allows for psychological and social-capital resources to be transferred between roles, as they perform all their roles with the same social group and in the same physical space (i.e., the crew vehicle). Specifically, due to the more salient nature of astronauts’ work roles relative to their nonwork roles, astronauts may be more likely to experience nonwork-to-work enrichment than work-to-nonwork enrichment. Thus, the social capital, psychological and physical resources, and affect acquired in the nonwork roles (e.g., caregiving, friendship, crew camaraderie) may enhance astronauts’ performance at work. Research shows that success in caregiving can increase positive affect and confidence (Stephens, Franks, & Atienza, 1997), and a related effect may happen within astronaut crews. Proposition: Nonwork-to-work enrichment may be the most relevant directional effect for astronauts, as their work life tends to be more salient than their nonwork life.

summAry In summary, the work–nonwork literature informs the conceptualization and potential implications of group living with four key theories. We connect these theories to context-relevant propositions. Border and boundary theory sheds light on (1) the importance of contextual boundary preferences for selection, (2) the role of homogeneous integration preferences for team conflict, and (3) work identity in astronauts’ work/nonwork balance. Work recovery literature indicates the need for engaging nonwork activities in space, while conservation of resource theory illuminates the impact of social support in maintaining well-being in LDSE. Lastly, work–nonwork enrichment theory suggests that astronauts’ social roles may enhance task performance.

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DEFINING GROUP LIVING: NASA ASTRONAUT CREW OFFICE EXPEDITIONARY SKILLS NASA’s Astronaut Crew Office, in recognition of the skills needed to work and live together during long-duration missions, developed a set of team-oriented expeditionary skills. The five skill areas are communication, leadership/followership, self-care/selfmanagement, team care, and teamwork/group living (Barrett, 2016). These nontechnical skills enable enhanced team and individual performance in tactical and long-duration situations. Using 15 years of experience from long-duration (i.e., up to six months) mission on the ISS, these five skills were solidified by experienced astronauts with the support of industrial/organizational psychologists and behavioral health experts circa 2013. Astronauts have been trained previously on some of these skills, but this effort updated and formalized the skills and provided a foundation to update related training strategies for long-duration missions. Each skill area defines the skill in the context of longduration spaceflight missions, provides a general expectation of behavior, and offers several example behaviors to crystallize the concept to trainees and certified astronauts. Table 11.1 provides the definition and behaviors for group-living skills. Notably, the group-living competency area is coupled with the overarching concept of teamwork in the astronaut’s model. Many of the behaviors described include elements that might apply in both work and nonwork domains. Additionally, the skill area of team care has overlapped with group-living skills as it addresses general issues of providing emotional support to teammates, managing supplies, mitigating conflict, monitoring each other for psychological and physical ailments, and adapting living and working habits to improve team cohesion. See Figure 11.1 for a summary of our construction of group living as related to traditional work team skills. While the expeditionary skills model was based on the expertise of long-duration astronauts, there is a need for validation of the model to delineate unique skill areas and reduce overlap where possible. The model’s definitions, expectations, and lists of example behaviors for each skill area provide a concrete basis for developing shared expectations to be used during training and in-mission. However, validation of the model would provide further benefit to training, particularly in clarifying which behaviors are effective in improving performance and well-being among crew members. Evaluations of these behaviors during training using a standardized yet flexible metric tailored to the needs of the astronauts may support trainers and mentors in providing targeted feedback and individualized training flows, increasing training efficiency and effectiveness, and accurately documenting readiness for a mission. We recommend the following considerations when defining group-living skills, based on the literature and interviews with subject matter experts on group-living skills in operational environments. First, we recommend removing traditional teamwork concepts from this skill area that pertain almost entirely to task work. Work/nonwork enrichment theory suggests that there is naturally a bleed-over effect between these domains that may enhance both, so careful contextualization of team processes that cross over (e.g., monitoring behavior, supporting behaviors, conflict resolution) is needed. Furthermore, the expeditionary skills model, as well as other well-established frameworks of teamwork (e.g., Salas, Shuffler, Thayer, Bedwell, & Lazzara, 2015), already include team skill areas of communication and

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TABLE 11.1 Astronaut Group-Living Skills Group Living and Teamwork: How individuals cooperate with each other and integrate into a team to achieve a shared goal, including accepting accountability and responsibility for actions and mistakes. Ability to identify and manage differences in opinion, differences in cultural interpretations, perception, technical knowledge, personality, etc. to complete a task or mission. Able to engage in interpersonal conversations constructively and demonstrate resilience (not easily offended) in difficult situations. Expectation: You will appropriately debrief and constructively discuss strengths, issues, and mistakes in both technical and interpersonal situations, and you will adapt your interactions to promote an integrated team outcome. Example Behaviors • Act cooperatively rather than competitively • Make use of available team resources (i.e. food) • Put a common ‘space faring culture’ ahead of one’s own national organizational and professional cultures and individual needs • Respect team members’ roles, responsibilities, task assignments, and workload • Accept responsibility and accountability for your portion of the team’s performance, particularly mistakes • Actively work to ensure a positive team attitude • Compensate for a team member’s negative attitude or substandard performance when applicable • Address potential sources for conflict and seek resolution • Keep calm in interpersonal conflicts • In conflict resolution, focus on what is wrong rather than who is wrong • Assert position in a manner to avoid personal conflict • Demonstrate respect for team members’ culture(s) and viewpoints • Respect differences in gender role expectations, behaviors, and attitudes • Use understanding of cultural factors and circumstances to interpret team members’ behaviors • Demonstrate tolerance of cultural differences and ambiguities • Communicate respectfully with people from different cultural and linguistic backgrounds

leadership/followership, two key competencies in any team setting. The model may be well-served by formally identifying a separate skill area labeled ‘Coordination’, for example, and moving work-oriented teamwork elements from the ‘Group-Living Skills’ area to ‘Coordination’ to distinguish the unique elements that set ‘GroupLiving Skills’ apart from other skill areas. Second, we recommend investigation and validation into the construct of group-living skills to identify any gaps in the definition, expectation, and exemplar behaviors. While this is a very salient issue that may influence teams in an isolated, confined, long-duration exploration space mission, there are other operational populations that would hold rich data for this validation work. Thus, researchers should seek out samples such as crews of submarines, transocean shipping, and oil rigs; deployed military units; survival and mountaineer teams; remote research outposts such as in Antarctica; and deployed medical teams. As this concept has not been formally and comprehensively investigated, data collection is needed. Third, we posit that group-living skills may be best defined by concentrating on the aspects addressing respect, tolerance, adaptability, perspectivetaking, and proactive behaviors to avoid and/or constructively address frictions as

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n e tio era anc nt d nce e i m ns ige n em g l for g o o l or r g n i n c i e t e i a al um int an ina n g p ration olv -mak h l d s o m n r a i e o ss iv or on ers ict coo blem cision nit oti ine lus nfl llab erp sk Ta Tid Co De Em Pro Mo Int Co Inc WorkNonworkoriented oriented team skills team skills Note: Skills listed and placement along continuum is for example purposes only.

FIGURE 11.1

Example continuum of work- and nonwork-oriented team skills.

Note: Skills listed and placement along continuum are for example purposes only.

well as actively work to ensure a positive team climate. Again, these characteristics must be contextualized to apply to living together, separate from broader interpersonal issues, and recognize the common goal of the team to maintain cohesion and mutual well-being in pursuit of achieving mission success. Fourth, we encourage researchers to investigate these definitional aspects in both qualitative (e.g., interviews, journal and text analysis) and quantitative methods, employing best practices for metric development to tease apart this skill area from other team skill areas.

PREDICTORS AND OUTCOMES OF GROUP-LIVING SKILLS IN EXTREME ENVIRONMENTS Although group-living skills have received attention in the growing niche of extreme-teams literature and past spaceflight research, there are aspects of previous studies and anecdotal reports that may inform predictors of positive and negative group-living situations. Likewise, this past work may also offer some indication of likely outcomes when group-living skills are adequately developed and maintained for an extreme team. Group-living skills in the context of long mission durations will have a cyclical influence on team functioning, with each group-living interaction influencing the next. Proactive and team-oriented individuals that demonstrate adequate group-living skills are essential for maintaining positive team functioning and enhancing team performance as team dynamics shift over time.

grouP living As An AstronAut selection fActor With the change in focus from short-duration to long-duration mission profiles, team orientation, the degree to which individuals prefer to work in group settings (Driskell & Salas, 1992), received greater attention from astronaut selection teams. During the early 1990s Space Shuttle-era (short-duration) and just prior to the U.S. and Russian cooperative Shuttle-Mir program (long-duration), an exploratory study of astronaut peer ratings specifically examined psychological selection issues for long-duration missions (McFadden, Helmreich, Rose, & Fogg, 1994). The researchers created performance measures based on prior experience in analog environments and performed a cluster analysis. The two final clusters were labeled Group Living (composed of performance measures of leadership, teamwork, difficult, or tolerant

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personality,  working on a three-month mission again with that person, and group living) and Job Competence (composed of knowledge, job performance, performance under pressure). The group-living factor explained 52% of the total variance. The group-living subcomponent of the factor was defined as being a good listener, considerate of others, helpful, and tolerant of individual/cultural differences. While some of this variance may be due to such related constructs as cultural competence, conceptually, there is a need to consider group-living skill as a construct independently. A late-1990s NASA job analysis (just prior to the beginning of the ISS program) of short- and long-duration nontechnical factors identified group-living skills as the seventh of ten most critical factors for short-duration, but the third of ten most critical factors for long-duration (Galarza & Holland, 1999). As part of the 2009 astronaut selection process, team exercises were implemented, which included a group-living component (Slack, 2016). This practice was continued for subsequent selection cohorts. In the 2014 job analysis conducted by NASA subject matter experts, with long-duration experienced astronauts’ input and ratings, group-living skills were specifically rated as highly important for a Mars mission profile and third overall, and received high ratings for long-duration ISS missions, but received more moderate importance ratings for a shuttle mission profile (Barrett et al., 2015). For a 2.5-year Mars mission, team orientation, group living, and related personality characteristics (e.g., adaptability, awareness, and tolerance toward others) will be critical.

trAditionAl PersonAlity Predictors There are several key personality factors that are likely to lead to group-oriented living behaviors. Astronauts are currently selected to be adaptable, motivated, resilient, and to possess typical team skills of communication, coordination, and leadership (Barrett, Holland, & Vessey, 2015; Landon et al., 2017). Many of these competencies align with the Big Five Factors model of personality, for which a review of astronaut selection for future missions identified a desired profile of high emotional stability, moderate to high agreeableness, and a range of extraversion, openness, and conscientiousness (above some minimum) scores, with an avoidance of extreme scores (Landon et al., 2017). Indeed, a sample of Space Shuttle-era astronauts were significantly more emotionally stable than normative samples and were moderately high to high on other factors (Musson & Keeton, 2011). Recently, personality researchers have suggested that examining the facets of the Big Five Factors may explain some of the ranges in acceptable scores (Bartone, Krueger, & Bartone, 2018), which may enhance the predictive power of personality measures in selection and team composition. Many of these traits align with the behaviors outlined in astronaut group-living skills (see Table 11.1) and have evidence supporting their importance in team functioning and performance. For example, those who spend the winter (i.e., winter-overers) in Antarctica with higher emotional stability had higher peerreported team compatibility, cohesion, and a supportive leadership style (Palinkas et al., 2000). Emotional stability is positively associated with social cohesion, flexibility, communication, and workload sharing, and negatively correlated with team conflict in organizations and spaceflight (Barrick, Stewart, Neubert, & Mount, 1998; Kass, Kass, & Samaltedinov, 1995). Similar patterns are revealed for the other Big

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Five Factors. Agreeableness enhances flexibility, a positive attitude, cooperation, and constructive conflict (Barrick, Patton, & Haugland, 2000; Bell, 2007). Openness is an important component of tolerating interpersonal differences and was a predictor of being identified as an ideal Antarctic winter-overer, as nominated by peers (Palinkas, Cravalho, & Browner, 1995). For conscientiousness, confinement in a small vehicle will likely require crew members to be particularly aware and considerate of how all their work and nonwork actions (e.g., making lunch in the galley with an awareness of all associated smells, cleanliness, and physical volume considerations) may impact others. However, those extremely high in conscientiousness may be less frustrated by dynamically changing and unexpected conditions inherent in an extreme environment (Palinkas et al., 1995), which was shown in a study of analogous populations (Musson, 2003). Interactions between personality factors may reveal different trends. For example, a lab study found teams of low conscientious and high agreeable team members were the least selfish and most cooperative, yet most productive and most fair (Roma, Hursh, & Hienz, 2016). There are other common psychological factors of interest that may drive groupliving behaviors. In the pre-Shuttle-Mir psychological selection factor study (McFadden et al., 1994), researchers found that astronauts with personality profiles of high goal orientation and motivation, awareness and warmth toward others’ emotional needs, and low competitiveness and aggression, tended to outperform peers on both group living and job competence peer ratings. Emotional intelligence may allow crew members to recognize one’s own emotional reactions and those of others, offer timely social support, preemptively avoid conflict and defuse negative situations, and successfully navigate conflicts that do occur. Motivation in the context of cooperation, ‘pulling one’s weight’, and exceling, as well as a general team orientation are other personality factors that may lead to engaging in group-living behaviors for the betterment of the team climate and cohesion. Finally, individual resilience, defined in the astronaut’s job analysis with elements of stress tolerance and adaptability (Barrett et al., 2015), enhances an individual’s capability to remain cooperative, cohesive, and positive when conflict and everyday friction or differences between crew members arise. A team of individuals with these characteristics is likely to recognize and adapt to the needs of others, and work toward the good of the team, while also honestly addressing interpersonal stressors and constructively negotiating potential conflicts within the team. More research is needed to examine the interaction of these personality factors and how they may specifically influence group-living outcomes in extreme environments. Selection for these personality characteristics continued into the longduration missions of the ISS and into today’s selection, with an eye toward Mars and lunar missions. However, these intuitive personality predictors may not account for all group-living behaviors; rather, training is likely needed.

develoPing grouP-living sKills In addition to selecting team members that are highly capable and well-suited to the extreme environment, extreme teams require extensive training. For example, military special forces endure weeks and months of specialized training to learn combat,

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weapons and vehicles, language, medical, engineering, and other skills needed for use in the field (personal communication, U.S. military psychologist). Likewise, newly selected astronauts, known as astronaut candidates, endure a two-year training program to learn space vehicle systems, language and first aid skills, geology and survival skills, and other relevant skills such as high-performance jet certifications (Landon et al., 2018). During the astronauts’ training, they explicitly learn and practice expeditionary skills during operationally oriented field training events. Other military groups, however, do not specifically identify nor separate out such a skill area. Instead, they train general team skills with the expectation that these skills are applied in the work and nonwork domains to maintain unit cohesion and combat readiness, and they teach relevant values that apply to nonwork situations such as showing respect and integrity (personal communication, U.S. military psychologist). One notable aspect incorporated into military training that is relevant to environments which blur the work/nonwork boundaries is humor. Humor is framed somewhat by the individual trainer of a unit, who adapts to the needs and norms of that particular unit, but training usually addresses this topic by examining how to use humor appropriately and in a supportive manner (personal communication, U.S. military psychologist). In other words, humor that is inclusive, not meant to exclude team members, and avoids targeting an individual may enhance team cohesion and act as a stress reliever. An astronaut’s journal entry highlights this effect as well: ‘Humor and joking around continue to be huge assets and quickly defuse any problems’ and ‘Joking and gentle harassment seem to be our technique for giving and receiving feedback as well as for defusing tension’ (Stuster, 2016, pp. 34–35). During training, group-living skills may be developed according to the best practices of team skills training (e.g., Salas, 2015). Following a training needs analysis, teams should learn definitions and expectations related to group-living skills, then engage in team tasks with follow-up feedback from peers and expert observers or coaches. Evaluations of the training should ensure that learning occurred and that group-living skills are transferred to operational settings and enacted during missions. For teams in long-duration exploration missions, practice of these skills with the intact team is important to develop group norms and adjust to each individual’s idiosyncrasies prior to the mission. Group-living skills are unlikely to be learned simply in a classroom setting or with short practice exercises. Trainers and trainees must commit to extensive and potentially time-consuming training activities that allow teams to immerse themselves in a nonwork setting with work colleagues. These training events may allow for the practice of conflict resolution and boundary setting related to atypical workplace topics such as personal tidiness and providing social and psychological support. Boundary setting, as trained for military personnel (personal communication, U.S. military psychologist), successfully negotiated between team members and at the team level, may offer a crew awareness of each other’s needs and personality differences, and create norms that mitigate potential conflicts. Importantly, these events with appropriate feedback allow individual trainees to understand his/her own needs related to work/nonwork balance, and finally translate that balance to the operational environment. Finally, team development may also take the form of team-building interventions, in which a team engages in processes such as goal setting, interpersonal relationship management, problem-solving,

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and role clarification, to enable them to solve their own problems. A meta-analysis of team building did not find a direct effect of team-building on performance; however, team-building was found to have a positive effect on cognitive (e.g., shared mental models), affective (e.g., trust), and process (e.g., coordination) outcomes (Klein et al., 2009), potentially improving performance indirectly. Each minute of astronaut time while they are in space is precious from a monetary perspective and from a scientific research perspective. Thus, establishing group-living skills, specific team norms, and team cohesion and trust to support stable team performance prior to launch is highly preferable to spending in-mission time navigating aspects of team forming, storming (i.e., experiencing conflict and recognizing differences), and norming.

suPPorting grouP-living sKills While the onus of achieving a psychologically healthy group-living environment seems to rest largely on the crew, the organization may also take steps to support the team on their mission. The physical environment may be one critical support mechanism. For space vehicles, every inch of the habitat is designed, creating the opportunity for building private and communal spaces that may naturally avoid or mitigate some team conflict. Astronauts recognize the importance of private crew quarters, particularly for long-duration missions (Kearney, 2016). Private crew quarters allow individuals to create personalized space by hanging pictures and mementos, engage with family and friends via personal emails and phone calls, and experience autonomy and control over the environment without coming into conflict with other crew members. All of these may act as a stress relief, and allow for recovery and preparation for later team interactions. Conversely, habitable volumes that allow for group meals, recreation, and other nonwork interactions, as well as team work tasks, are also critical. Human factors specialists and engineers should consider group living factors when designing astronauts’ living and work spaces. Crowding, which has been linked to stress, in these communal areas is a risk. A review of team habitability needs for future long-duration spaceflight noted the positive and negative effects of seeking privacy and withdrawal from others, noting that privacy is necessary for mental restoration and supports performance, but must be practiced in moderation to avoid potential temporal, spatial, or psychological fractures within the team (Kearney, 2016). This dichotomy has been observed repeatedly in spaceflight analogs and analogous environments. Countermeasures such as the cupola window on the ISS may allow astronauts to lessen the feeling of crowding. Virtual environments may artificially extend the environment, while providing private and individually controlled spaces that may mimic sensory experiences found back on Earth (e.g., jogging with a recorded friend, walking around one’s house or favorite outdoor space, receiving virtual items from those back home). These virtual environments may also serve as a training venue, in which the team skills may be refreshed and team tasks learned or practiced (personal communication with HI-SEAS participant). Group-living skill refresher training or reminders are other options. Providing nonwork activities to the crew, be they in virtual or physical form, recognizes the importance of supporting the whole person, not just the employee at work. Purposeful scheduling and honoring of nonwork time

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such as weekends, holidays, and celebrations reinforces the organization’s commitment to supporting crews during their nonwork time as well. In consideration of the entire multi-team system across the organization, active engagement with crew members during work and nonwork debriefs and with psychological support staff is another method. NASA’s Behavioral Health and Performance Operations group provides these critical services through direct psychological support with clinicians during training, with yearly check-ins, throughout an astronaut’s mission preparation and after launch, as well as additional crew and family support by a dedicated staff assigned to each astronaut for his/her mission (Sipes & Vander Ark, 2005). These  active, in-mission interventions from crew support personnel may include group-living discussions, constructive criticisms, and guided compromises, enabling the crew to maintain a healthy group-living environment.

CONCLUSION Group-living skill as a formally defined skill area is a recent addition to traditional team skill models. However, many organizations have been informally addressing this important issue for centuries, particularly as it applies for extreme teams in military settings. The heightened isolation and confinement inherent in spaceflight missions warrant greater attention to this skill area by researchers looking to define and validate the construct and by practitioners looking to select, develop, and support crew members. We presented four theoretical frameworks with related propositions, which provide researchers a starting point when examining group-living skills. Researchers may also take interest in specific components of group living, such as the interactions of Big Five personality constructs with related outcomes. Definition and validation of group-living skills as a construct also provide a foundation for creating selection criteria, training objectives and evaluations, and supportive countermeasures and psychological support practices. Perhaps the greatest driver for group-living skills in extreme teams stems from the context. Spaceflight, military deployments, and other situations in which teams are living and working together are often of high consequence and highly coupled; that is, any small disturbance in the team may cascade and snowball rapidly, resulting in threats to team safety and mission success. Expertise in group-living skills allows teams to maintain performance and well-being, even in the most unlikely of spaces.

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Supporting Spaceflight Multiteam Systems throughout Long-Duration Exploration Missions A Countermeasure Toolkit Jacob G. Pendergraft, Dorothy R. Carter, Hayley M. Trainer, Justin M. Jones, and Aaron Schecter The University of Georgia

Marissa L. Shuffler Clemson University

Leslie A. DeChurch and Noshir S. Contractor Northwestern University

CONTENTS Spaceflight Multiteam Systems.............................................................................. 239 Benefits of MTS Work Structures...................................................................... 241 Challenges of MTSs .......................................................................................... 242 SFMTSs in the Next Era of Spaceflight .................................................................244 Advancing a Countermeasure Toolkit for SFMTS Collaboration during LDEM..................................................................................................... 248 Phase I: Define the Task Context.................................................................. 248 Phase II: Anticipate the Likely Patterns of Interaction................................. 249 Phase III: Diagnose the Discrepancies between the Needed and Likely Patterns of Interactions...................................................................... 252 Phase IV: React to the Discrepancies by Deploying Countermeasures ....... 252 Conclusion ............................................................................................................. 254 References .............................................................................................................. 255

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Many of the most critical challenges facing today’s organizations require collaboration across ‘multiteam systems’ (i.e., MTSs) comprised of multiple distinct, yet interdependent, component teams (Mathieu, Marks, & Zaccaro, 2001; Zaccaro, Marks, & DeChurch, 2012). For example, EMTs and surgical teams often work together to address civilian emergency response (Mathieu, Marks, & Zaccaro, 2001); armor, infantry, and combat engineering teams often jointly tackle military missions (McChrystal, Collins, Silverman, & Fussell, 2015); response teams may attend to intrusions detected by other teams in cybersecurity contexts (Zaccaro, Dalal, Tetrick, & Steinke, 2016); and state and federal teams may coordinate their responses to natural disasters (DeChurch et al., 2011). NASA has long relied on spaceflight multiteam systems (i.e., SFMTSs) to accomplish important ‘superordinate’ goals (NASA, 1963; Vessey, 2014). From the early days of spaceflight, in which advanced aviation technology pushed the boundaries of the upper atmosphere and eventually succeeded in placing humans into orbit around the Earth (NASA, 2015a) and onto the moon (NASA, 1975), to the present expeditions aboard the ISS, NASA has marshalled the efforts of diverse and distributed teams to tackle the challenges of space exploration (Pendergraft et al., 2019). As we set our sights on Mars and other destinations beyond lower-Earth orbit, we must enable synchronized teamwork across SFMTSs with component teams that are separated by unprecedented degrees of space and time. If deployed successfully, the SFMTSs involved in long-duration exploration missions (LDEMs), such as a mission to Mars, will achieve some of the most ambitious goals in the history of humankind. However, LDEMs will also present new challenges for SFMTSs. Maintaining cooperation and collaboration between teams may be more difficult as the crew experiences increasing levels of isolation and distance from Earth. The substantial distances traveled during LDEMs and the resultant space-to-ground communication delays will present exceptional challenges. Indeed, the ‘team risk’ of LDEMs (i.e., ‘the risk of performance and behavioral health decrements due to inadequate cooperation, coordination, communication, and psychosocial adaptation within a team’); (Landon, Vessey, & Barrett, 2015, p. 5) has been widely acknowledged by NASA’s behavioral scientists as well as current astronauts as a critical threat to mission success during the upcoming period of LDEMs (Landon, Slack, & Barrett, 2018; Landon et al., 2015). Drastic increases in space-to-ground communication delays have prompted NASA to identify the team risk as an elevated priority when preparing for LDEMs. The team risk applies not only to the risk of teamwork failures among members of the crew, but additionally to the risk of teamwork failures between members of different component teams in SFMTSs. This chapter lays the foundation for the development and implementation of a ‘countermeasure toolkit’ that could help facilitate the teamwork processes across SFMTSs that will be needed to achieve the goals of LDEMs. We begin by leveraging the broader academic literature to clarify the key characteristics of SFMTSs and delineate the critical teamwork challenges SFMTSs are likely to encounter during a LDEM. Then, we advance a framework for a countermeasure toolkit that could allow NASA to systematically understand, anticipate, diagnose, and facilitate SFMTS functioning throughout the duration of future missions.

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SPACEFLIGHT MULTITEAM SYSTEMS MTSs are collective entities comprised of two or more ‘component’ teams that pursue proximal ‘team-level’ goals, and additionally, are bound together based on the presence of one or more shared ‘superordinate’ goals requiring that component teams ‘interface directly and interdependently in response to environmental contingencies’ (Mathieu et al., 2001, p. 289). The interdependencies between component teams may take different forms, including arrangements wherein the outputs of one team become the inputs of another in the system, or situations where two or more teams need to iteratively and reciprocally interact to solve problems (Mathieu et al., 2001). As Mathieu and colleagues note, MTSs are flexible ‘open systems whose particular configurations stem from the performance requirements of the environments that they confront and the technologies that they adopt’ (p. 291). MTSs often tackle important problems in operating environments that are ambiguous (i.e., challenges facing the system have no known correct answers), multifaceted (i.e., requiring the system to contend with multiple factors or challenges simultaneously), dynamic (i.e., changing continuously and/or dramatically), and/or time-sensitive or urgent in nature (Shuffler & Carter, 2018). Multiteam work structures play a critical role in spaceflight. For example, Figure 12.1 illustrates a simplified depiction of a SFMTS involved in an International Space Station (ISS) mission. In this figure, the smaller circles represent members of the SFMTS, the rounded squares around the people represent the boundaries of

FIGURE 12.1 Exemplar NASA spaceflight multiteam system and associated goal hierarchy.

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their component teams, and the larger rounded square represents the outer boundary of the SFMTS. Notably, the MCC is not an undifferentiated whole, but rather, is comprised of multiple distinct component teams. This SFMTS includes the crew on the ISS, the NASA MCC front room team headed by the flight director and the set of disciplinary back room teams that work in partnership with one or more flight controllers in the front room. Figure 12.1 also illustrates the hierarchical nature of the goals pursued by the members and teams of the SFMTS. The entirety of the system is dedicated to the superordinate goal of ensuring mission success and crew safety. Component teams contribute to the accomplishment of the highest-level goal through the accomplishment of team-level goals. In the SFMTS shown in Figure 12.1, the goal structure is largely oriented around the technical systems of the spacecraft, with each flight controller responsible for a given technical system. In turn, back room teams support each of these flight controllers, providing detailed and discipline specific information to their corresponding members on the front room team. To achieve the superordinate goal, different members and disciplinary teams must collaborate to accomplish the superordinate goal of achieving mission success and maintaining crew safety. The points where collaborative teamwork interactions are most critical are indicated by the arrows between members and teams. As in other MTS contexts, the intensity and nature of the interdependencies among members and teams may shift over the duration of the mission. Table 12.1 summarizes additional examples of how key definitional features of MTSs appear during a spaceflight mission. As noted in this table, spaceflight missions often involve numerous teams with hierarchical goal structures in which team-level goals must be balanced with the superordinate goal of the system and with the goals of other component teams. Representatives of different teams must coordinate their actions to balance time and resources across their various goals. Further, Table 12.1

TABLE 12.1 Definitional and Contextual Features of Spaceflight Multiteam Systems (SFMTSs) MTS Definitional Features Two or more distinct ‘component’ teams Hierarchical goal structure with proximal ‘team-level’ goals and more distal ‘superordinate’ goals

Need for teamwork processes between interdependent component teams

SFTMS Examples In addition to the crew, spaceflight missions involve numerous disciplinary back room teams that interface with one of the flight controllers on the front room team. Flight controllers and their respective back room teams are each responsible for different technical systems aboard the spacecraft (team goals), the demands of these technical systems must be balanced with those of the other technical systems to ensure crew safety and mission success (superordinate goals). Over the course of a mission, back room teams must communicate and collaborate with their flight controller representatives on MCC’s front room team, who in turn must relay relevant information to the crew. (Continued)

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TABLE 12.1 (Continued) Definitional and Contextual Features of Spaceflight Multiteam Systems (SFMTSs) MTS Contextual Features Ambiguous

Multifaceted

Dynamic

Urgent

SFMTS Examples SFMTSs will often contend with unprecedented technological and interpersonal challenges with no easy answers (e.g., addressing an unexpected technological breakdown or balancing the prioritization of diverse objectives among specialized and highly differentiated teams). Many of the tasks involved in spaceflight involve numerous interrelated components. For example, controlling the attitude and orientation of the ISS requires monitoring telemetry data from the station information from Russian flight control (TSuP), and GPS data. Task demands shift continually and sometimes unexpectedly as the mission advances (e.g., from mission launch to on-board experimentation, to on-board trouble-shooting for unexpected problems). The uncertain nature of spaceflight frequently gives rise to urgent task demands (e.g., putting out an on-board fire).

provides examples of the complex features of the environments SFMTSs operate within. As exemplified by the Apollo 13 mission, task demands are often dynamic and SFMTSs might have to address an urgent, unexpected, and multifaceted issue with no easy answer and while lacking perfect knowledge of the situation.

benefits of mts WorK structures There are many benefits to tasking complex organizational goals to MTS work structures with greater resource capacity than single standalone teams. For example, distributing work into separate component teams allows for an effective division of labor and resources, while simultaneously maintaining some of the benefits of large, standalone teams. Because MTSs can divide complex superordinate goals into team-level tasks and goals, broad challenges can be made more manageable. Differentiation of component teams can also result in increased motivation, as team members are able to focus on more immediate, achievable aims than the overarching, superordinate goal of the system (which can result in greater depletion and lower motivation; Porck et al. 2019). MTS structures are also more flexible and adaptive than more traditional organizational structures. As the demands of the task or operational environment shift with time, a MTS may be able to dynamically change its composition (i.e., integrating or removing component teams) to address these emerging needs. Further, the diverse perspectives contained within the differentiated component teams can allow the system as a whole to better adapt to challenging environments and novel circumstances.

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Indeed, throughout its operation, NASA has leveraged the benefits of multiteam work structures to accomplish complex and dynamic goals previously thought to be beyond human capabilities. For example, NASA’s rapid entrance into the field of manned spaceflight with Project Mercury required that multiple teams coordinate their efforts to support the technical systems and functions of the spacecraft. Multiteam coordination and collaboration was a major driving force in the successful maintenance of the Hubble Telescope in 1993 – ‘one of [the] most challenging and complex manned missions ever attempted’ (Ryba, 2010) – which was accomplished with no significant disruptions. During this mission, multiple teams of astronauts completed five days of extravehicular activities (EVAs), performing maintenance on the telescope and installing new components to ensure the continuing functioning of the satellite. During release at the conclusion of maintenance, the crew coordinated with ground control teams to troubleshoot irregular telemetry data from the satellite, resolving the issue within several hours.

cHAllenges of mtss Unfortunately, despite the great potential of MTSs to tackle important superordinate goals, these systems often fail to achieve their objectives, in part, because of coordination and collaboration failures across the boundaries of differentiated component teams (Luciano, DeChurch, & Mathieu, 2018). To achieve shared goals, members of different component teams often need to trust and feel cohesive with one another, coordinate their actions, share information, accept one another’s influence, and share a common understanding of the task and multiteam environment. However, these affective, behavioral, and cognitive relationships and teamwork processes often fail to arise across team boundaries or breakdown over time in MTSs given that these structures present numerous coordination challenges and collaboration barriers. Most MTS work structures present additional coordination demands which are not present in ‘stand-alone’ teams. For instance multiteam contexts often involve a higher level of ambiguity than those facing standalone teams, which can contribute to an uncertainty of coordination demands among system members (Shuffler & Carter, 2018). This uncertainty can contribute to losses in efficiency and corresponding drops in performance (Davison, Hollenbeck, Barnes, Sleesman, & Ilgen, 2012). Further, the development of effective forms of interteam collaboration is key to system performance, in contrast to the operations of standalone teams (Shuffler, Kramer, Carter, Thayer, & Rosen, 2018). MTS contexts also place added relational challenges on constituent members. Within MTSs, system members maintain boundaries between component teams – boundaries that may strengthen the perceived divisions among system members under certain conditions (Luciano et al., 2018). Such boundaries can be both necessary to the operation of the MTS and challenging to navigate for constituent members. Particularly where component team boundaries serve as salient dividers among system members, boundaries may contribute to the development of ‘faultlines’ within the system, further complicating relationships within the system (Lau  & Murnighan, 1998).

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Members of MTSs can also experience greater cognitive demands than members of stand-alone teams. For instance, the greater complexity of the MTS context can impede the development of beneficial collective cognitive states, such as a transactive memory system (i.e., TMS; Wegner, 1987). TMSs represent the shared knowledge among members of a group, as well as the metamemory of which group members and teams have stored different content areas of information, allowing for all task-relevant information within the system to be accessed without each individual member needing to know all pieces of information. The development of a functional TMS is likely to be especially relevant in a multiteam system context where members of different component teams hold specialized knowledge relevant to the accomplishment of the system goal. Frustratingly, the very complexity that allows for the integration of such diverse information can impede the effective leveraging of that expertise by system members. MTSs also present numerous challenges in the domain of leadership. MTS leaders must manage both states and behaviors within and between component teams, a far more complex pattern of interactions than is present within a standalone team. In particular, MTS leaders must contend with environmental complexity and ambiguity by engaging in strategy development and coordination behaviors (DeChurch & Marks, 2006), with both behavior sets playing critical roles in system performance during various episodes of performance within the system (e.g., a task such as a spacewalk). When developing strategy, MTS leaders must ensure that they are balancing proximate, team-level goals with the superordinate goal of the system. Moreover, Luciano and colleagues argue that MTSs with component teams that are highly differentiated and/or systems that operate in environments that are highly dynamic are particularly susceptible to system performance failures due to breakdowns in relationships and teamwork processes between teams. Whereas component team differentiation refers to the degree of difference and/or separation between component teams, system dynamism refers to the variety and instability within the system over time. Component teams can be differentiated from one another with regard to features such as their goals (i.e., teams differ with respect to the goals they prioritize), competencies (i.e., teams differ on the functional capabilities their members possess), norms (i.e., teams differ with respect to their policies and expectations), work processes (i.e., teams differ or are separate in their work processes), and information (i.e., teams lack information about other teams’ activities). Component team differentiation is a necessary and beneficial element of MTS work. However, extreme levels of differentiation between teams can also pose serious challenges to MTS collaboration and performance. For example, when teams prioritize conflicting goals, this could intensify other interteam divisions (Carter, 2016). When teams have vast differences in functional capabilities, work processes, or norms, the teams may have greater difficulty coordinating tasks and synchronizing their actions (Zaccaro et al., 2012). When information opacity is high, members of different teams may struggle to understand one another. Moreover, many MTSs experience dynamic changes both with regard to their internal processes and structures as well as their external environments and task demands. For example, dynamism may exist within a system with respect to changes

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in goal hierarchy (i.e., the frequency and magnitude of modifications in goal hierarchy), uncertainty of task requirements (i.e., uncertainty of component team activities required to fulfill system goals), fluidity of system structural configuration (i.e., changes in the linkages among component teams), fluidity of system composition (i.e., ‘churn’ in the system, within both team membership and the component teams comprising the system), and diversion of attention (i.e., duration and degree to which component team members’ attention is focused on matters other than multiteam system-related tasks). System dynamism can also result in serious coordination and collaboration challenges for MTSs. Although often an unavoidable consequence of the contexts in which MTSs operate, dynamism can be a disruptive force, potentially leading to breakdowns in MTS performance. For example, significant and/or frequent changes in goals over the course of the system’s operation may lead system members to seek grounding in more dependable and understandable team-level goals, at the expense of the superordinate goal of the system. In situations where uncertainty of task requirements is greater, shared mental models and communication processes may become more important factors in predicting system performance (Marks, Zaccaro, & Mathieu, 2000). Fluidity of system structural configuration can lead to uncertainty around members’ roles within the system and disrupt work processes, resulting in performance decrements if not appropriately countered. The reconstitution of membership involved in contexts where fluidity of system composition is occurring can destabilize relationships and reduce familiarity within the system. Finally, diversion of attention can result in obvious impacts on system performance, as resources and time are diverted away from the pursuit of the superordinate goal toward other tasks.

SFMTSs IN THE NEXT ERA OF SPACEFLIGHT NASA’s priorities have now shifted from short-term missions using the space shuttle platform toward long-duration and long-distance space exploration (Trump, 2017). Investments are being made to support a range of endeavors, including plans for the construction of the Orion spacecraft and an orbital platform that may one day orbit the moon and serve as a critical staging point for future missions aimed at Mars and more distant destinations (NASA, 2015b). During this next era of spaceflight – characterized by long-duration and long-distance missions – NASA will again need to carefully consider the challenges of SFMTS operations. NASA has contended with challenging SFMTS circumstances in the past, sometimes encountering substantial setbacks before successfully adapting in ways that allowed them to accomplish their goals. For example, although eventually representing a huge success, the relationships between NASA and Roscosmos during the Shuttle Mir program were at times tense and halting, as higlighted in interviews with NASA personnel (Foale, 1998; Barratt, 1998). Interview subjects repeatedly referenced difficulties in communication extending beyond those expected from the language barrier that existed between the two organizations. For instance, Michael Barratt acknowledged the improbability of successful collaboration with

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an organization which shared few of NASA’s philosophical or methodological approaches to operation (Barratt, 1998). Indeed, concerns about effective SFMTS coordination and collaboration were present from the earliest days of the organization. For example, the fire and crew loss during the Apollo 1 launch test in 1967 represented a notable multiteam collaboration disaster. Although a technical cause for the fire was identified in a subsequent investigation (proximity between corrosive coolant and vulnerable electrical wiring), the report of the Apollo 204 Review Board also found that lack of accountability and ownership of decision-making across component teams in NASA’s SFMTS represented a contributing cause of the disaster (NASA, 1967). The board noted that the test was not recognized as a potentially hazardous environment by NASA planners; as such the readiness level of emergency response personnel was low, potentially fatally contributing to the delays in rescue efforts. Fire response and medical teams were critically absent during the test as a result of this low level of preparedness. In response to these and other instances where breakdowns in SFMTS processes were found to be a contributing cause of failures and near-misses, NASA has repeatedly adapted to establish more effective coordination practices (Pendergraft et al., 2019). Adaptations to internal coordination practices have included the establishment of independent technical authorities for the dedicated evaluation of launch readiness outside the remaining hierarchy of the organization. Additionally, the responsibilities of individuals to report any concerns pertaining to launch readiness or crew safety has been reaffirmed and clarified. With respect to external coordination practices, NASA has greatly improved its ability to effectively coordinate across organizational boundaries. This is exemplified by the intensive international collaboration on the ISS project, and the numerous behavioral adaptations required to facilitate it. NASA flight controllers and astronauts have engaged in language training in Russian and are further trained in the operation of Russian equipment, as well as numerous other adaptations to the communication structures within the larger SFMTS. These adaptations represent important ways in which NASA has shifted its coordination practices in the past to meet increasingly complex SFMTS demands. However, LDEMs will place new demands on NASA’s SFMTSs and pose new challenges requiring new adaptations. LDEMs will require extensive cooperation with international partners and, potentially, the new relationships with emerging commercial actors. This will involve teams from multiple organizations designing, maintaining, and managing various systems aboard the spacecraft in addition to a large number of task-based specialist teams (particularly as the mission moves into the planetary phase of operation in the case of a Mars mission). As with other SFMTS operational contexts, component teams comprising SFMTSs will need to accomplish proximal goals while also collaborating with one another to achieve the superordinate goals of maintaining crew safety and promoting mission success for the entire duration of the mission. However, LDEMs will require unprecedented technical and interpersonal skills and efforts from all involved parties. Individuals and teams with different educational backgrounds, experiences, and norms will need to work collaboratively, and success will demand intensive forms of coordination and collaboration – particularly between members and teams

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whose goals are tightly linked. SFMTSs will need to integrate a large number of specialized teams and individuals to contend with LDEMs’ complexity. These specialized teams will need to coordinate efforts, information, and goal prioritization as the crew encounters changes throughout the mission, as well as any unforeseen challenges that are encountered. The extreme distances traveled during LDEMs will require the crew to operate in a far more isolated manner than during previous spaceflight missions. Delays in radio and telemetry space-to-ground communication (up to 24 minutes each way in the case of a planned mission to Mars) will mean that the crew of a LDEM will be unable to rely on ground control teams for solutions to the extent that past and existing spaceflight crews have done, particularly when the challenges facing the crew are unexpected (Landon et al., 2018). Isolated, confined, and extreme (ICE) environments of the type to be encountered during future LDEMs will place the crew under great strain for extended periods of time (perhaps two to three years or more) (Landon et al., 2018). Moreover, as illustrated in Table 12.2, high levels of differentiation and dynamism are likely to be present in the SFMTSs involved in LDEMs. Over the course of a LDEM, the SFMTS is likely to experience goal discordancy among component teams given the complexity of the mission and the range of activities being undertaken.

TABLE 12.2 Elements of SFMTS Differentiation and Dynamism in LDEMs Differentiation Element Goal discordancy Dissimilarity and incompatibility of goals and priorities across teams. Competency separation Norm diversity

Work process dissonance Information opacity

Distribution and disparity of knowledge and functional capabilities across teams. Dissimilarity and incompatibility of policies and expectations across teams. Separation and incongruence of work processes across teams. Absence and ambiguity of information about different teams’ activities. Dynamism Element

Hypothetical Example from an LDEM On a mission day, the flight controllers are prioritizing assessments of crew safety whereas the crew is prioritizing completing the scientific experiments on-board. Crew and front room team have access to different knowledge domains. Front room flight controllers and crew members believe that they operate in fundamentally different ways. NASA and Roscosmos front room teams work largely independently of one another, only periodically conferring. Flight controllers are unsure of the knowledge possessed by members of back room teams. Hypothetical Example from an LDEM

Change in goal Frequency and magnitude of As the mission unfolds, the goals pursued by hierarchy modification in goal hierarchy. MCC teams and the crew change frequently. Task uncertainty Duration and degree of uncertainty The spaceflight crew is frequently unsure of how of team activities required to to satisfy the requirements placed upon them by fulfill system goals. changing mission demands. (Continued)

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TABLE 12.2 (Continued) Elements of SFMTS Differentiation and Dynamism in LDEMs Dynamism Element

Hypothetical Example from an LDEM

Fluidity of system Frequency and magnitude of The relationships between members of the crew, structure changes in the linkages among front room, and back room teams are frequently teams. changing. Fluidity of system Frequency and magnitude of churn In addition to the teams comprising the SFMTS changing over the course of the mission, there composition in the system with regard to individuals and teams. is turnover of personnel within ground teams. Diversion of Duration and degree to which team Members of the different disciplinary ground attention members’ attention is focused on control teams tackle multiple disciplinary goals matters other than SFMTS tasks. that are not entirely related to the core spaceflight mission.

Similarly, competency separation is likely to characterize the SFMTS, as the diversity of task required to accomplish the mission will require many functional disciplines to address. The functional teams comprising the SFMTS may also operate in fundamentally different ways that suit their independent functional disciplines or organizational practices, creating perceptions of norm diversity over the course of a LDEM. This may be particularly true where the SFMTS spans organizational boundaries (i.e., between teams from NASA and various International Partner (IP) organizations). Adjacent to differing work norms, work process dissonance may be present between component teams with various time points in the mission requiring component teams to operate largely independently from one another, only periodically reconvening to share information. Finally, information opacity may be present to some degree as well, again particularly where the SFMTS spans organizational boundaries. LDEMs also present highly dynamic contexts. Even in the absence of unexpected events (e.g., equipment failures), changes in goal hierarchies may characterize LDEMs to a greater degree than previous missions. Owing partially to the extended mission time frame, the crew and ground control teams involved in a LDEM will likely need to change their focus in response to different stages of the mission as it unfolds. Task uncertainty may also factor prominently in the execution of LDEMs as the novel context of an extended manned mission beyond the Earth’s orbit will place unforeseen demands on the crew, requiring them to adapt in unexpected ways. Fluidity of system structure may take place over the course of an LDEM, possibly in response to changes in mission phase (and therefore alongside changes in goal hierarchy). The element of fluidity of system composition may be present as well. Given the extended time frame of an LDEM (potentially multiple years in the case of a mission to Mars), personnel turnover among ground control teams is likely to be present to some degree. At a more granular and ongoing level, the continuous operations of MCC over the course of LDEMs will involve consistent handovers between shifts, requiring additional effort to coordinate efforts and information across multiple groups of individuals within each of the MCC teams. A far more catastrophic example could be the loss or incapacitation of a crew member during the course of the mission.

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As we enter this new era of spaceflight, it is imperative that NASA’s SFMTSs prepare for the coordination and collaboration challenges posed by LDEM by leveraging the extant MTS literature to identify and expand upon best practices for supporting multiteam collaboration. In the highly complex and geographically distributed SFMTSs that will be involved in LDEMs, there are countless opportunities for breakdowns in collaboration. Indeed, the ‘team risk’ could cause major problems during planned LDEMs, as intensive collaboration is likely to play an important role for the entirety of the extended mission time frame. Importantly, the team risk spans beyond the risks of collaboration failures within the spaceflight crew; risk of performance decrements from coordination breakdowns between component teams in the SFMTS is also likely to factor prominently.

AdvAncing A countermeAsure toolKit for sfmts collAborAtion during ldem To provide a foundation for effective SFMTS performance in future missions, the remainder of this chapter advances a systematic ‘countermeasure toolkit’ framework designed to facilitate SFMTSs performance. Our framework, summarized in Table 12.3 consists of four key phases of pre- and post-launch countermeasure activities: define, anticipate, diagnose, and react. The define phase focuses on developing a detailed understanding of the task demands and teams involved in LDEM and the patterns of teamwork processes and psychological relationships that are ‘needed’ to achieve mission objectives. The goal of the anticipate phase is to predict the patterns of teamwork processes and relationships that are ‘likely’ to arise based on what we know about the task demands and features of the SFMTS. The third phase, diagnose, involves comparing the patterns of teamwork processes and relationships that are ‘needed’ to those that are ‘likely’ to arise in order to identify points at which the naturally occurring dynamics are likely to diverge from those needed for mission success. Finally, the fourth phase focuses on reacting to any inconsistencies identified in Phase III by deploying countermeasures that encourage the development of teamwork processes and relationships needed for mission success. These four phases and associated countermeasures are discussed in the following sections. Phase I: Define the Task Context The first phase, defining the task context, is the foundation for all subsequent phases. The goal of this phase is to understand the task demands facing the SFMTS. Additionally, this phase involves identifying the patterns of interdependent interactions and psychological relationships within and across teams that are needed to support mission success. These behaviors and relationships are particularly relevant among individuals or teams with high task interdependence (Marks, DeChurch, Mathieu, Panzer, & Alonso, 2005). One promising approach to develop an understanding of task and teamwork demands in SFMTSs is to leverage a systematic ‘multiteam task-analysis’ approach. Broadly, a ‘task analysis’ describes a set of methodologies that are used to identify the tasks, knowledges, skills, and abilities that are necessary to perform a job effectively (Sanchez & Levine, 1989). At the individual level, task analysis is most commonly used to understand what a job entails, develop training programs, or even to

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establish ways of evaluating employee performance (Bowers, Baker, & Salas, 1994). For those who work as part of a team, however, performing tasks effectively also requires interpersonal (i.e., teamwork) skills as a means of coordinating behavior within the team. Thus, a team task analysis consists of both identifying the essential knowledge, skills, and abilities required for the tasks performed as well as the necessary interdependencies and team processes among team members (Arthur, Edwards, Bell, Villado, & Bennett, 2005; Bowers, Morgan, Salas, & Prince, 1993). In the context of MTSs, a multiteam task analysis widens the scope of a team task analysis beyond the interpersonal skills needed to achieve team goals to identify the interteam relational competencies needed for the success of the system as a whole. MTS members and component teams have additional job demands related to the interteam relationships and interactions needed to accomplish shared superordinate goals (which may be broadly referred to as multi-teamwork), thus, an MTS task analysis must address both team as well as multi-team relationships and interactions needed to support team and multiteam goals. In addition to identifying the appropriate interdependencies and interaction processes needed to support team and system goals, activities in Phase I should also identify the psychological relationships that will support these interaction processes. This is particularly true of cases in which reciprocal and intensive forms of interdependence are called for, as positive psychological relationships are especially important to fostering success in these relationships. Given the additional complexity of multiteam contexts, a variety of approaches may need to be leveraged in order to understand the teamwork and taskwork demands of SFMTSs. For example, researchers and/or NASA personnel may leverage archival analyses of documents describing the processes of previous SFMTSs, interviews with subject matter experts, and observations of ongoing SFMTS operations. As the duration of mission timelines continues to expand with the continuous operations needed to support ISS expeditions and planned LDEMs, careful preparatory work must be done to develop an understanding of the extent and form of interdependence required within the SFMTS throughout the mission time frame. At any given point over the course of the mission, the required patterns of interdependence may take the form of totally detached operations (i.e., no interdependence), reciprocal interdependence (i.e., two or more teams ‘handing off’ products to one another), or intensive interdependence (i.e., two or more teams working more or less continuously with one another) among any given teams within the system. Notably, these relationships are not likely to be fixed, but will more than likely remain fluid over the course of the mission. Of these interdependent interactions, definitional countermeasures will need to identify the most mission-relevant interactions for the various stages of planned missions. Phase II: Anticipate the Likely Patterns of Interaction The second phase, anticipating the patterns of interactions among system members, is a critical expansion of the initial definitional actions in Phase I. Whereas the purpose of Phase I is to identify the relationships, behaviors, and patterns of interdependence that are needed for mission success, Phase II focuses on identifying those relationships and behaviors that are likely to occur within the system naturalistically. The extant literature on MTS functioning provides some guidance with regard to how patterns of psycho-social relationships, such as trust, informal influence,

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TABLE 12.3 Guiding Conceptual Framework: The Phases, Objectives, and Activities Involved in a Comprehensive Countermeasure Toolkit for Facilitating SFMTS Coordination, Collaboration, and Performance Exemplar Countermeasure Activities Phase

Objective

Pre-Launch Activities

Define

Understand task Conduct SFMTS task analysis to demands and the define SFMTS characteristics and patterns of teamwork task demands. behaviors and psychological relationships that are needed within and across component teams to achieve mission objectives. Anticipate Make predictions about Leverage existing empirical the patterns of knowledge regarding the teamwork processes functioning of MTSs under and psychological analogous conditions to make states that are likely predictions about LDEM SFMTS to arise within and functioning. across teams involved in LDEMs. Diagnose

Identify critical points where the patterns of teamwork processes and states that are likely to arise are misaligned with the patterns that are needed to support mission performance.

Diagnose system preparation by identifying inconsistencies between optimal patterns of collaboration versus likely patterns of collaboration.

React

Deploy interventions to address misalignments with regard to the likely versus needed patterns of collaboration, coordination, and communication within and across teams.

Enhance KSAOs and multiteam collaboration capabilities through expanded teamwork training; provide targeted training to pairs of highly interdependent component teams; restructure formal processes (e.g., incentives, communication channels, etc.) to match multiteam task demands.

Post-Launch Activities Continue to leverage SFMTS task analysis procedures to understand new task demands, new SFMTS characteristics, and needed patterns of teamwork processes and states within and across teams. Continue to leverage existing knowledge on MTS functioning and additional tools (e.g., computational models) to anticipate the impact of new task demands and system characteristics. Diagnose ongoing system functioning by identifying inconsistencies between optimal patterns of collaboration in new task contexts versus actual patterns of collaboration (e.g., through surveys, unobtrusive measures, feedback, etc.). React to existing inconsistencies by deploying reactive countermeasures (e.g., guided multiteam debriefs) and/or restructuring formal processes as appropriate to support new patterns of collaboration to meet new task demands.

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and communication, are likely to form across the SFMTSs involved in LDEMs. For example, theoretical work on MTS functioning suggests that the high levels of component team differentiation and system dynamism that are likely to characterize LDEM are likely to create faultlines between teams (Li & Hambrick, 2005) that severely stifle the development of positive collaborative relationships across team boundaries (Luciano et al., 2018). In addition to relying on the extant literature about MTS functioning, NASA may choose to leverage additional predictive methods to anticipate the challenges facing SFMTSs during LDEM. For example, predictive models of SFMTS dynamics could be developed based on projected task demands and system characteristics could help uncover the patterns of teamwork processes and relationships that are likely to occur throughout the mission. In particular, methods such as agent-based models (Macy & Willer, 2002) of SFMTS functioning, developed in conjunction with information gleaned from qualitative work (e.g., interviews, observations) with subject matter experts and validated using data from laboratory and analog contexts, could provide NASA with a powerful tool for predicting the patterns of relationships that are likely to arise during a planned LDEM mission.  Agent-based models are computer simulations that afford insights into emergent behavior resulting from actions and interactions that occur within complex systems (Macy & Willer, 2002). In an agent-based model, a set of agents, for example, crew and MCC members, are seeded with a set of characteristics (e.g. demographics, personality, team memberships, training experience) which replicate the composition of actual SFMTS component teams, as well as a set of theoretically-derived rules guiding their actions and interactions with other agents. During the simulation, the agents interact with one another, in accordance with their rules, thus generating networks of relational states within and between teams.  A key benefit of developing agent-based models of SFMTS functioning is that these models support ‘virtual experiments’ that help researchers understand what is likely to happen under different circumstances. In a virtual experiment, researchers run a series of simulations that are seeded with different scenarios (i.e., different starting values for task/tool, contextual, individual, and relational characteristics). Virtual experiments offer a unique opportunity to examine all possible combinations of SFMTS factors allowing extrapolation and interpolation of all possible scenarios for all possible data. Using computational methods (ABMs combined with virtual experimentation) alongside more traditional statistical methods helps maximize the utility of costly and highly limited data from LDEM analogs by doing substantial “pre-validation” of the theoretical models using data from field interviews/observations and laboratory test beds.  Importantly, the results of virtual experiments with agent-based models of SFMTS functioning can highlight where and among whom breakdowns in teamwork processes and relationships are most likely to occur. Therefore, the results of virtual experiments can help NASA personnel involved in mission planning and mission support to leverage additional countermeasures strategically to support mission success. Following the outset of a mission, predictive models of SFMTS dynamics can continue to be leveraged in an ongoing manner to anticipate the impact of new task demands and system characteristics on likely collaboration patterns. Additionally, as

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with their application prior to the outset of a mission, computer-based models can allow NASA personnel to test the potential outcomes of in-flight countermeasures made in response to expected or unexpected changes in the task environment. Phase III: Diagnose the Discrepancies between the Needed and Likely Patterns of Interactions Phase III, diagnose, involves making comparisons between findings from the first and second phases. The objective of Phase III is to identify the most crucial inconsistencies between the patterns of teamwork processes and relationships that are ‘needed’ in order to achieve mission objectives (Phase I) and the patterns that are ‘likely’ to arise (Phase II) given the characteristics of the system and the operating environment (e.g., component team differentiation factors, system dynamism factors). For example, Phase I may identify key boundary spanners in the SFMTS from different component teams who need to share a common understanding of the task, communicate regularly, trust one another, and operate highly interdependently in order to support mission performance. Yet, findings from Phase II may reveal that members of these two different teams are unlikely to form strong positive working relationships with one another given the vast differences between teams with regard to their norms, priorities, work processes, and geographic locations. Phase III aims to identify these types of key inconsistencies that are likely to threaten mission success. Such comparisons will need to take place both before mission outset, and throughout the mission as the situation evolves. For example, changing levels of autonomy within the crew may lead to breakdowns in communication with ground-based personnel over the course of an LDEM, requiring continuous evaluation and intervention. Phase IV: React to the Discrepancies by Deploying Countermeasures In the final phase, SFMTSs react to mission-critical inconsistencies between the ‘needed’ versus the ‘likely’ (or observed) patterns of teamwork processes and relationships identified in Phase III by applying countermeasures that directly intervene to support collaboration within and across teams. Two countermeasure approaches that hold particular promise for allowing SFMTSs to react to potential multiteam collaboration breakdowns are: (1) multiteam training and (2) multiteam debriefing. Research on team functioning has consistently demonstrated that team training and team debriefing approaches can have positive effects for team functioning. However, in order to address the additional complexities that exist in SFMTSs above and beyond single stand-alone teams, team training and debriefing approaches may need to be expanded to address the unique challenges and coordination demands of multiteam contexts.  Team training is a broad term used to describe practices that are used to teach team members the knowledge, skills, abilities, and teamwork processes to perform effectively as a team (Salas et al., 2008). Team training interventions can focus on a number of different domains of knowledge, skills, abilities, or processes and can be delivered in a multitude of different mediums (Goldstein & Ford, 2002). Although the nature of the taskwork targeted by team training will necessarily vary based on the work environment, some teamwork processes targeted by training regimens are likely to remain relatively constant across work environments. Many team training

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programs focus on developing skills related to coordination of teamwork behavior. Team training programs often target elements of the teamwork environment itself which are more likely to be domain general rather than germane to the specific work environment. Teamwork processes targeted by team training may include communication norms, leadership processes, and coordination and support behaviors critical to successful team operations.  The literature on team training broadly suggests benefits associated with training both taskwork and teamwork processes (Salas et al., 2008). Indeed, meta analytic results indicate that team training is most effective when coordination processes are integrated into the content of the training (Salas, Nichols, & Driskell, 2007). Team training has been linked to positive distal outcomes in business objectives (George, Hannibal & Hirsch, 2004). Further, and of particular interest in the context of NASA, team training has had positive effects in the achievement of workplace safety objectives (Salas, Burke, Bowers, & Wightman, 2001). However, the impact of team training is impacted by a number of situational moderators (Salas et al., 2008), as well as the nature of the tools, delivery method, and content used in the training (Salas & Cannon-Bowers, 1997). Research on team training has emphasized the particular effectiveness of team training in preparing to tackle novel (as opposed to routine) environments, again noteworthy in the context of planned LDEMs (Marks, Zaccaro, & Mathieu, 2000). Indeed, NASA currently engages in extensive team training targeting both taskwork and teamwork processes through a well-validated process known as Space Flight Resource Management (SFRM; O’Keefe, 2008). SFRM training focuses on three core elements of teamwork that are necessary for mission success: (1) communication; (2) leadership & team coordination; and (3) situational awareness & risk assessment. The SFRM program emphasizes that these teamwork skills must be explicitly taught, debriefed, and evaluated.  Although SFRM and other team training programs develop important skills which are foundational to the success of both teams and multiteam systems, the multiteam context presents additional demands on communication, leadership/coordination, and situational awareness processes beyond those of standalone teams. Indeed, many team training programs miss opportunities to enhance understanding and development of the multi-teamwork behaviors and relationships needed to accomplish the goals of multiteam systems. Therefore, to maximize the benefits of training in a multiteam work context, traditional team training approaches should be expanded to target teamwork processes and relationships both within as well as across team boundaries. For example, new multiteam training interventions (e.g., simulations, guided lessons, etc.) could be incorporated into NASA’s existing training program to better prepare trainees for collaboration in a multiteam context. Key areas of multiteam processes that might be addressed under such an expanded multiteam training program include boundary spanning across component teams, understanding multiple goal hierarchies, and communicating with people from different backgrounds which are likely to be present within the same SFMTS. Team debriefing refers to a class of retrospective protocols designed to reflect on past performance episodes in order to support team learning and future performance. Often, team debriefing serves to reinforce team members’ understanding

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and development of the teamwork skills taught during team training and prepare teams for subsequent phases of team performance. Team debriefing may occur following team training experiences, or team performance episodes in a work setting. In a typical team debrief, the team reflects on recent performance, discusses the events that occurred, and attempts to identify areas for improvement (Eddy, Tannenbaum & Mathieu, 2013).  Although team debriefing protocols can take on a variety of forms, the two most common types are unguided team debriefs (i.e., unstructured) and guided debriefs (i.e., structured) wherein team members are given direction as to what topics need to be discussed and how the discussions should unfold (Smith-Jentsch, CannonBowers, Tannenbaum, & Salas, 2008). For example, SFRM materials provide substantial guidance for team leaders or debrief facilitators with regard to structuring debriefs to most effectively reinforce SFRM concepts and ensure that NASA teams learn to ‘self-correct.’ Generally, research has shown that structured debriefing protocols are much more effective than unstructured debriefs (Halamek, 2008; Lacerenza, Marlow, Tannenbaum, & Salas, 2018). Self-led debriefs may especially benefit from structure (Eddy, Tannenbaum, &  Mathieu, 2013). Specifically, team debriefings are most effective when they are designed to systematically review both good and bad aspects of specific situations and promote the creation or refinement of team mental models (Lacerenza et al., 2018; Schon, 1983). In short, team debriefs should be participatory reflections on performance episodes which allow the team to solidify learning opportunities in preparation for future performance episodes (Tannenbaum & Cerasoli, 2013).  Like team-focused training, debriefing protocols focused exclusively on team processes may fail to sufficiently address the greater complexities of the multiteam context. Thus, existing team-focused debriefing protocols may need to be expanded to encourage learning and self-correction across the SFMTS as a whole. For example, an expanded multiteam debriefing protocol could be structured to ensure that teams gain a better understanding of the different work processes and knowledge domains within other teams, are reminded of the importance of interteam communication and collaboration and are working to integrate knowledge and information across teams in order to jointly solve problems.

CONCLUSION NASA is well on the way to successfully adapting to the challenges posed by LDEMs. During this coming era of LDEM spaceflight, effective SFMTS functioning will continue to be critical to achieving NASA’s priorities. Although it may be extremely difficult to support teamwork processes and relationships within and across SFMTS component teams throughout the extended time-frames of LDEMs, doing so is essential to mission success. NASA has overcome similarly imposing obstacles with regularity over the course of its 60-year history and continues to advance apace into this next frontier of human exploration. Toward these ends, this chapter advances a framework for identifying and countering the most critical challenges likely to face SFMTSs, with the goal of supporting the overarching goal of LDEM success and safety.

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Human Interaction with Space-Based Systems Kritina Holden Leidos, NASA’s Lyndon B. Johnson Space Center

Jessica J. Marquez NASA’s Ames Research Center

Gordon Vos NASA’s Lyndon B. Johnson Space Center

E. Vincent Cross II TRACLabs

CONTENTS Physical Factors...................................................................................................... 260 Cognitive Factors ................................................................................................... 263 Human–Computer Interaction........................................................................... 264 HCI: Information Presentation ..................................................................... 266 HCI: Caution and Warning Alarm Design.................................................... 268 HCI: Information Integration with Procedure-Based Tasks in Space........... 270 Human-Automation Integration......................................................................... 274 HAI: Levels of Automation in Electronic Procedures .................................. 278 HAI: Supervisory Control of Lunar Landings ............................................. 280 Environmental Factors ........................................................................................... 282 Visual Performance under Vibration.................................................................. 282 Fine Motor Skills............................................................................................... 285 Conclusion ............................................................................................................. 288 Acknowledgments.................................................................................................. 288 References .............................................................................................................. 288 Human factors is the study, discovery, and application of information about human abilities, human limitations, and other human characteristics to the design of tools, devices, machines, systems, job tasks and environments for effective human performance (Chapanis, 1996). Human factors can play a critical role in addressing the unique challenges of spaceflight, where humans must work effectively, efficiently, and safely in bulky, constraining, life-sustaining spacesuits, may be cognitively 259

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impaired due to isolation, sleep deprivation, and radiation, or must use tools, systems, and computer-based devices in a hostile microgravity environment far from Earth. The sections that follow summarize selected research in the areas of physical, cognitive, and environmental factors related to human interaction with space-based systems.

PHYSICAL FACTORS Human spaceflight activities occur within environments that are designed considering many different factors, only some of which are related to the human element and tasks that need to be performed within the volume. Some of the largest drivers of spaceflight systems are cost, schedule, and launch mass, all of which have a tendency to reduce the amount of interior volume available and allotted for human activities. However, it is critical to the mission of the spacecraft or habitat that the tasks required to be performed by the human crew can be successfully executed within the given volume. Complicating matters is that the interior of a spacecraft or habitat may not be a simple open space, but rather a compartmentalized one, with cordoned off spaces for sleeping, waste management, exercise, science, piloting activities, and medical operations. The design of these individual spaces, their size and dimensions in particular, as well as the way in which crew ingress (enter), egress (exit), and translate (move) among these volumes are highly critical, and require consideration of not just adequate total volumes, but also sufficient dimensions and layout to support the required or desired posture and biomechanical needs of a crewmember within the space. For example, sleeping quarters will often be longest in one particular dimension, in order to provide for the overall stature of a person (i.e., their height) in an elongated posture (similar to lying down). In contrast, a waste management compartment, which houses the commode for the crew, will likely need to support a posture more similar to a sitting or squatting pose, along with restraints and mobility aids to support the crew in holding and maintaining that posture in microgravity. Likewise, other environments such as a cockpit or piloting area will have specific equipment such as displays and controls that are used for controlling the spacecraft. The operation of controls such as these require consideration of multiple factors, including sufficient volume, adequate restraint so that the crewmembers do not push themselves away from the controls in microgravity, and proper placement of the controls so that they can be reached when both suited and unsuited, and possibly under varying g-loading scenarios such as docking with a space station (low to no g-loading) versus during atmospheric re-entry and landing (which could induce significant g-loading). The design of each of these different spaces and for each of these different tasks requires consideration of anthropometry, which refers to physical measurements of the human body. It is necessary to consider various critical dimensions of interest (driven by the individual tasks to be performed) as well as the population from which the potential end-users (i.e., crew) will be selected. Take for example overall stature: not only does stature vary from person to person within a given population, there are

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differing probability densities of stature within the population. The peak of a normal distribution curve for stature would be at the median/mean/mode of the distribution (i.e., the 50th percentile). However, there are tails of this distribution with much lower and much higher values than the mean. Table 13.1 below provides a specific example to illustrate this point. Designs ideally accommodate for the largest range of persons possible, and general design guidelines for spaceflight systems strive to accommodate the majority of the population. Though the specific numbers used may vary from program to program, a good target has been considered as the accommodation of ranges such as the 5th to 95th percentile or the 1st to 99th percentile for identified critical dimensions (e.g., total stature, seated stature, reach, shoulder breadth, upper/lower leg length, etc.). The importance of these dimensions and their consideration cannot be overstated. This is especially true for spacesuit design. Spacesuits are a special case where not only the lengths of various body segments are important, but also the circumferences and 3-dimensional contours and shapes of each body part. Historically, spacesuits have also had limited adjustability features, requiring suits of differing sizes to be produced, and sometimes under special circumstances for very specific anthropometries (i.e., for a specific person). The challenge is that there is a very low probability of encountering a true ‘99th percentile person.’ Rather, there are people who are 99th percentile in one or more dimensions, but have lower percentile values for other dimensions, sometimes much lower. For example, a person may have a long torso but shorter legs and average arms, or someone may have a large shoulder breadth but be somewhat petite in overall stature. These complicated interactions of differing measures have challenged suit designers, and continue to drive needs for suit adjustability and accommodation. The use of anthropometry and biomechanics in solving design challenges and in developing solutions for human spaceflight that accommodate for the ranges of the population desired is not much different from the way similar challenges are faced terrestrially, such as in the design of an automobile, aircraft, or water vessel. The unique challenges that do exist involve different levels of gravity (i.e., 0 g in low Earth orbit, and partial-g on other terrestrial bodies such as the moon or Mars), the need to accommodate multiple tasks in a restricted space, and some particularly unique volume driving tasks such as suit donning and doffing (putting the suit on and taking it off). Differing levels of gravity have different effects on posture, dynamic movement (e.g., walking, running, etc.), and the design of human accommodations. An excellent resource on this topic has been published by NASA

TABLE 13.1 Stature (Standing Height). Units in Centimeters (cm) Percentiles of the Distribution Male Female

Min

1st

5th

25th

50th

75th

95th

99th

Max

149.1 140.9

160.0 148.0

164.8 152.5

171.0 158.6

175.5 162.6

180.2 167.2

187.0 174.0

192.7 178.1

199.3 182.9

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in the form of the Human Integration Design Processes (HIDP) document (NASA 2014), particularly Chapter 4.5 (Design for Crewmember Physical Characteristics and Capabilities). This document is publicly available on the NASA Technical Report Server (ntrs.nasa.gov). In addition to the considerations already mentioned, the human body also undergoes several changes in microgravity that must be accommodated for, the most notable of which is spinal elongation. The spine, composed of vertebral discs, as well as the bony vertebral bodies, is under the effect of the compressive force of gravity when a person is sitting or standing on Earth. Normally, this compressive force, particularly in the waking day time hours when people are standing and moving about, causes some of the fluid in the vertebral discs to be expressed into the surrounding tissue. When sleeping at night, some small amount of this fluid reabsorbs into the disc, to be expressed again the next day. However, in the prolonged absence of gravity, a significant quantity of this fluid is reabsorbed, which results in a roughly 3% increase in a person’s stature (height). This increase in height must be accommodated in designs for microgravity (Young & Rajulu, 2012; Young & Rajulu, 2020). Figure 13.1 below, from Young and Rajulu (2020), illustrates how spinal elongation results in changes to stature during spaceflight.

FIGURE 13.1 Stature (height) measurements for astronauts on ISS. Measurements taken in cm before flight, during flight, and after flight. Illustrates transient increase in spinal elongation during spaceflight.

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COGNITIVE FACTORS Successful space missions depend heavily on the maintenance of crewmember cognitive capabilities, which include: attention, comprehension, memory, judgment and evaluation, decision-making, problem-solving, logical reasoning and computation, and prediction. Spaceflight hazards such as radiation, isolation, altered gravity, distance from the Earth, and a hostile/closed environment threaten to negatively impact these capabilities. Thus, human performance must be continually measured throughout a space mission to detect and mitigate threats to cognitive capabilities. Astronauts must be able to problem-solve, make decisions, and maintain situation awareness of the vehicle and habitat, without being overloaded in their tasking. Cognitive overload impacts performance when the quantity of information an individual must process in the time available exceeds their cognitive or mental resources. Individuals have limited cognitive resources for sensing, perceiving, interpreting, and acting upon information in the world. The amount of information an individual is able to acquire and process may be affected by stress, fatigue, time constraints, and the modality of that information (visual, auditory, etc.). Confusion can occur when the individual is unable to maintain a cohesive and orderly awareness of events and required actions and experiences, a state characterized by bewilderment, lack of clear thinking, or disorientation. Lack of transparency or predictability of a given task can lead to situations where users do not have a true understanding of the state of the system, which can also contribute to task overhead and confusion (Dix et al., 2004). During periods of confusion, an individual’s performance on one or multiple tasks may be considerably reduced (Wickens, 1991). Cognitive overload and confusion have been cited as causal reasons for multiple aviation accidents. An example of cognitive overload causing an incident in spaceflight is the June 1997 collision between the Russian spacecraft Progress 234 and the Mir Space Station, which caused the pressure hull to rupture and nearly led to the Mir being abandoned. High workload and stress of the crew due to repeated system failures throughout the mission likely contributed to reduced vigilance (Ellis, 2000). Conversely, cognitive underload can result in inadequate allocation of attention— a lack of a state of alertness or readiness to process immediately available information due to a sense of security, boredom, or a perceived absence of threat from the environment. Inappropriate allocation of attention may occur when an individual focuses attention on a limited number of cues, such that additional cues of equal or higher importance are ignored or not used appropriately. This channelization of attention, called cognitive tunneling or attentional tunneling, can lead to an unsafe situation in which the individual is unable to develop comprehensive awareness of the situation, and thus respond appropriately to critical events. Research on heads-up displays, three-dimensional displays, and fault management, for example, demonstrated that users may focus attention on some aspects of the task (e.g., visually compelling display elements), to the detriment or complete obliviousness to other aspects or events (Wickens, 2005). In other situations, the nature of the task, or reliance on automation may result in a failure by the individual to redirect their attention, recognize an automation failure, or seek out additional information that could improve decision-making (Sarter, Woods, & Billings, 1997).

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Cognitive tunneling in spaceflight is a real possibility, particularly during situations such as a difficult fault management problem (McCann & McCandless, 2003). During a Space Shuttle ascent, for example, which lasted approximately 8.5 minutes, the crew performed checks of various time-critical parameters and flight instruments; however, during this time they also had to be able to act quickly to assess and react to a fault (Huemer, Hayashi, Renema, Elkins, McCandless, & McCann, 2005). The same will be true for future vehicles. In fact, errors due to inappropriate allocation of attention between electronic procedures and system displays have been observed (Ezer, 2011), but new design concepts are being considered that will encourage crewmembers to allocate their attention equally between procedures and associated system displays. One of the biggest challenges is anticipating what problems may occur due to improper allocation of attention on long-duration missions. International Space Station (ISS) crewmember time is generally overbooked, given that so much crew time is required to maintain the ISS and keep equipment functioning. The simpler volume of a long-duration spacecraft will create a very different operational tempo. There will be dynamic phases of flight that require focused attention, and then long periods of time during the transit where the needs for crew attention will be greatly decreased. We must be sure that attention can be focused when necessary, e.g., to notice a downward trend in vehicle performance, detect a cabin leak that could cause life-threatening depressurization, or respond to an emergency alert. Finally, it is important to note that cognitive factors and vehicle/habitat design can have important synergistic effects. An inadequately designed hardware or software interface coupled with a crewmember under high workload, can result in increased errors and increased risk to mission success. Likewise, an excellent design can compensate for cognitive decrements in a deconditioned or overly stressed crewmember ­ by providing intuitive information presentation and aids for decision-making. Given that future space crews will be heavily dependent on computer-based devices and automation, human–computer interaction, and human-automation integration are both important areas of research where cognitive factors play an important role. Human factors research in each of these areas is described below.

HumAn–comPuter interAction Human–computer interaction (HCI) encompasses all the methods by which humans and computer-based systems communicate, share information, and accomplish tasks. When HCI is poorly designed, crews have difficulty entering, navigating, accessing, and understanding information. The Space Shuttle had hundreds of hard switches and buttons (see Figure 13.2); whereas, Exploration vehicles will feature primarily glass-based interfaces, requiring crew to rely on an input device to interact with software displays and controls (Ezer, 2011). Due to mass restrictions, the real estate for displayed information will continue to be limited, but the amount of information available for display will be greatly increased, posing challenges for information design and navigation schemes. Future vehicles will also fly many new technologies that must be usable with pressurized gloves, in microgravity, and under vibration. All of the information needed by crew must be available in a form that is intuitive and promotes proper attention and cognitive load.

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FIGURE 13.2 STS-126 Commander Chris Ferguson and Pilot Eric Boe on the Flight Deck (FD) of the orbiter Endeavour.

While not studied directly, HCI onboard spacecraft to date appears to have been adequate to achieve mission success. However, the Flight Crew Integration ISS Life Sciences Crew Comments Database, populated with post-flight crew debrief comments, continues to document comments related to usability issues. Crewmembers report that displays lack the common overall infrastructure and layout needed to promote ease of use and understanding of intended operations. Valuable ISS crew time is lost when crewmembers struggle with inadequate or inconsistent hardware or software interfaces. These issues have led to incorrect data entry, navigational errors, or inaccurate interpretation of the data in the displays. These errors can increase stress and frustration, and compromise crew safety, especially in the event of an emergency. Fortunately, most of these issues are mitigated with the help of Mission Control on the ground, and have never led to an emergency on the ISS. Such was not the case on Apollo 10, the ‘dress rehearsal’ for the Apollo 11 moon landing, which tested all aspects of the Apollo 11 mission except for landing. Upon descent stage separation and ascent engine ignition, the Apollo 10 lunar module began to roll violently because the crew accidentally duplicated commands into the flight computer. This was due to mode confusion caused by poor information display and crew communication issues. If not corrected, the capsule would have impacted the moon, killing all onboard (Shayler, 2000). Some HCI issues on ISS may have been masked by the fact that crews have near constant access to Mission Controllers, who monitor for errors, correct mistakes, and provide additional information needed to complete tasks. We do not know what

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types of HCI issues might arise without this ‘safety net.’ Exploration missions such as Mars will test this concern, as crews may be operating more autonomously due to communication delays and blackouts. Crew survival will be heavily dependent on available electronic information for just-in-time training, procedure execution, and vehicle or system maintenance. Examples of HCI research relevant to information display are discussed below. HCI: Information Presentation Long-duration missions will involve greater crew autonomy and increased dependence on computer-provided information needed to perform routine and complex tasks, as well as time- and safety-critical tasks. The current approach of calling Mission Control for questions, workarounds, and forgotten procedural steps will no longer be feasible due to communication and data sent across millions of miles, and in certain circumstances, may not be available at all (e.g. blackout due to Mars being on the other side of the sun). Crewmembers will have to rely solely on available electronic information for tasks such as vehicle/habitat operations, maintenance, and scientific exploration. There is an increased risk of human errors, frustration, and inefficiency when information is not available, is difficult to find, or is presented in the wrong format. Thus, careful design of information displays is critical to maintain performance. In particular, the use of display standards promotes consistency, which yields ease of learning and ease of use. An area where ease of use is particularly important at NASA is the design of displays for Extravehicular Activities (EVAs). Currently, spacesuit data are displayed on specific EVA informational displays on the EVA Display and Control Module (Donald & Mark, 2008). The Display and Control Module contains all displays and controls necessary for nominal operation and monitoring of the suit. The alphanumeric display of the Display and Control Module is limited to presenting only 12 characters, greatly restricting the amount of information shown. This limited display causes dependence on Mission Control personnel to monitor activities and spacesuit systems, and inform crew of any anomalies. However, in the near future, EVAs will take place in distant locations, creating a longer communication delay and other challenges that will require greater autonomy for the astronauts during missions. Without the help of Mission Control personnel on deep space missions, astronauts will need to be able to monitor their own space suit status and health data, as well as those of their crewmembers. Navigation and communication information will also be vital for exploratory missions. Unlike current missions with continual and real-time Mission Control support, astronauts will need to have greater flexibility and independence. There is a need for a crew interface that provides information on all aspects of an EVA. Many factors need to be considered when exploring an informatics interface for use during an EVA. Crewmembers are dependent on the information available within the spacesuit for monitoring their health and suit resources (e.g., battery power, oxygen remaining, crew biomedical data), procedure and task information, communication, and navigational data (Sándor, Cross, Thompson, & Pace, 2014). Additionally, a harsh unknown environment can cause cognitive strain, while a spacesuit with limited maneuverability adds physical constraints. Furthermore, there are concerns with legibility, ability to distinguish colors, and the ability to physically interact with the

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interface (Sándor, Cross, Thompson, & Pace, 2014). Crewmembers will need displays that provide good situation awareness, with a method for easy and accurate interaction with the displayed information without leading to a high workload. NASA has performed multiple experiments exploring approaches for providing information to astronauts during EVAs with the goal of maintaining workload and situational awareness. An Exploration Informatics system (xINFO) is composed of all of the non-critical information, not vital to the survival of the crewmember, but which increase the ­crewmember’s autonomy and efficiency during EVAs. The base functions of the xINFO system include the display of procedures, timelines, images and video, recording video, audio, suit data, and exploration field notes, aiding in navigation, and interfacing with EVA tools (Sándor, Thompson, Cross, & Pace, 2014), Figure 13.3 shows a prototype of an informatics display. When considering crew needs for long-duration Exploration missions, suits pose special challenges in terms of information display and interaction, given the limited display real-estate on the suit. Furthermore, helmets compromise vision and hearing, while gloves reduce mobility and touch. If informational displays are poorly designed, or not easily available, crewmembers will not have access to critical data, putting their mission and personal safety at risk. To address the question of future needs for EVA, in November 2009, a team from Glenn Research Center’s EVA Power, Avionics, and Software (PAS) engineers met with groups of the Constellation Program’s EVA stakeholders at NASA Johnson Space Center (JSC). The purpose of the initiative was to conduct a series of group interviews to document stakeholder needs, goals, and objectives for EVA PAS engineers. Seven stakeholder groups were interviewed: crew, mission operations, Altair/Lunar surface systems, space medicine, Ground Operations and Portable Life Support System (PLSS) Processing, EVA Safety, and Photo/TV. The interview questions covered a broad range of topics such as communications, information systems, caution and warning, displays,

FIGURE 13.3 Prototype informatics display for extravehicular activity.

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suit controls, data processing, navigation, and motion imagery/helmet camera for various concepts of operations (i.e., task and mission designs). A report summarized the capabilities recommended by the stakeholders and provided information on the degree to which these capabilities were supported by the stakeholders. Over a number of years, NASA has explored multiple approaches for providing performance critical information to astronauts using a limited display. This work has focused on presenting the four primary consumables that must be monitored in order for the crewmember to survive: oxygen, battery, water, and carbon dioxide. Studies have explored visual and auditory presentation of EVA consumable information by examining: table formats with and without highlight, similar to the current Mission Control visualization, four icons with the limiting consumable highlighted, a single icon showing only the limiting consumable, and sounds. Results show that highlighting the limiting consumable in tables decreases identification time. Furthermore, tables with the limiting consumable highlighted, and the four icons with the highlight did not differ in identification time. The single icon presentation also led to fast response times; however, participants commented that this format lacked information about other consumables, therefore having the potential to mislead users. The table without highlight and the sound presentations led to longer identification times than the rest of the presentation modes, and thus are not recommended to be used (Sándor et al., 2013). Additionally, NASA has investigated the visual presentation of EVA consumables with tables and multidimensional icons. Chernoff faces and stick figure results show that any of the visualizations are appropriate for highlighting limited consumables, since this requires identifying a single specific piece of information. In contrast, the four icons require scanning multiple features. Thus, displays which provide easy comparison of multiple features – tables and stick figures – led to better performance (Sándor et al., 2014). Current work within NASA is exploring the use of Optical See through Displays (OST-D) for EVA and intravehicular activities (IVA) tasks during long-duration missions to the lunar surface and beyond. An OST-D can be designed to meet the performance needs of crewmembers, while adhering to the restrictions associated with the helmet environment of an Exploration spacesuit (i.e., exploration extravehicular mobility unit or xEMU). An OST-D that meets the needs of an xEMU is being called a Heads-in Display (HID). Similar to Head-mounted Displays (HMD) and Head-up Displays (HUD), the HID will be a system that combines computergenerated (virtual) imagery with a ‘through the glasses’ image of the real world, through a slanted semi-transparent combiner. This allows for augmenting the environment viewed by crewmembers with virtual images to support EVA activities, such as life support and comfort control, communications, mission and task planning, localization and situation awareness, navigation, consumable monitoring and task execution (Mitra, 2018). HCI: Caution and Warning Alarm Design HCI is not only applicable to visual display design, but also includes interaction with other sensory modalities, such as auditory. In the world of aviation and other safetycritical environments, auditory information plays a critical role; the same is true in spacecraft operations. Systems designed for space operations include caution and

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warning systems, with associated messages and auditory tones for alerting. The current NASA Agency Human System standard defines three major classes of alarms, and requires unique auditory tones for each (NASA, 2015): • Emergency: Time-critical event that requires immediate action and crew survival procedures. Two events are typically identified as emergencies (fire and depressurization), and each has a unique tone. • Warning: Notification of an event that requires immediate action. • Caution: Notification of an event that needs attention but not immediate action. NASA programs have used the same set of auditory tones for these alarms for many years. With newer annunciation technologies, there is an opportunity to revise or enhance the standard set of tones. Given that some research shows tone alarms can have an unwanted startle effect that hinders operator decisionmaking (Stanton & Edworthy, 1999), speech alarms could be a good alternative or enhancement. They have been used successfully in aviation and are worthy of consideration. Two studies (Begault, Godfroy, Holden, & Sándor, 2008; Begault, Godfroy, Sándor, & Holden, 2007) evaluated auditory alarms currently in use at NASA, along with proposed new candidate alarms for fire, depressurization, warning, and caution. In these studies that used non-crew and crew subjects, one of the alternate designs included a speech component appended after the tone, termed a ‘speech suffix,’ which gave additional information about the cause of the alarm. Subjects rated each alarm in terms of suitability relative to the current alarm, perceived urgency level, and overall satisfaction. Subjects were also asked about the perceived value of including a speech suffix. The results showed that the inclusion of a speech suffix was preferred by both crewmember and non-crewmember subjects. A later study (Sándor, Begault, & Holden, 2010) investigated how quickly various types of auditory alarms with and without a speech suffix could be identified. Four types of speech alarms composed of combinations of warning tones, spoken warnings, and speech suffixes (that provide brief additional information related to the alarms’ cause or location) were tested. It is worth noting that most of the subjects were already somewhat familiar with the meaning of the tones, having worked on NASA programs, and all subjects received training at the start of the study. Nevertheless, the results of the 2010 study indicated that alarms starting with a speech component were identified significantly faster than alarms starting with a tone. Words in speech alarms (e.g., “fire”) are recognized more quickly than tones, which human memory must map to each specific meaning. Overall, non-crew subjects preferred the speech alarms; whereas, crew subjects tended to prefer the tones and tone/speech combinations. A final laboratory study (Sándor, Moses, Sprufera, & Begault, 2016) was conducted to replicate the results of the prior studies using improved versions of alarm recordings, and to evaluate the efficacy of the alarms in an analog environment while subjects were involved in tasks that increased their workload. For the lab portion of the study, which used 24 subjects, each type of alarm was presented in either a tone or

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speech (e.g., “Fire Fire”) version. Alarms were played with either (1) no background noise, (2) ISS communication loop noise, or (3) ISS fan noise. As in the prior studies, results showed that tones took longer to detect and classify than speech. In addition, response times for tone alarms were more affected by background noise (communication loop or fan) than speech alarms. Subjects also had higher accuracy rates for identifying speech alarms than tone alarms. A follow-on evaluation was conducted in the Human Exploration Research Analog (HERA), a habitat facility at the NASA Johnson Space Center used for simulating missions for the purpose of research. Six subjects performed an assembly task and a computer-based task, as alarms were sounded at unexpected times during the session. Upon hearing an alarm, subjects were told to cease their task and indicate the type of alarm heard on a survey form. They were then given instructions to go to a particular location in the habitat to retrieve a code to also enter into the form (the latter activity was performed for the purpose of increasing realism of the task). Results showed that 5 out of 6 participants responded faster to speech than tone alarms, with a mean difference of 4.8 seconds. The sixth participant had almost the same response time to both types of alarms. Four out of six participants also preferred the speech alarms over the tone alarms. They commented that speech alarms seemed easier and faster to identify, increased the awareness for the situation, and conveyed more urgency than the tone alarms. In sum, NASA laboratory studies and an evaluation in a more operational setting all had similar results, indicating advantages of speech alarms for space vehicles and habitats. When identification of the details of a problem is as important as detection, speech alarms with detail information regarding the warning may provide additional advantages. It will be particularly important for long-duration astronauts to have well-designed, information-rich alarms, since Mission Control will have a very limited oversight role due to communication delays and blackouts. Future research is recommended to investigate the combination of speech and tone alarms in operational, safety-critical settings, especially as it relates to diagnosing alarms and recognizing the actions required to resolve the situation (Frank, et al., 2015). HCI: Information Integration with Procedure-Based Tasks in Space Written procedures, sometimes referred to as checklists, provide a structured method of presenting task steps to ensure that an operator will properly perform a prescribed task in a standardized fashion. Operational checklists have served as an important tool in error management in a variety of applications where humans must interact with complex systems, such as in aviation and spaceflight. Such procedures have significantly contributed to the operational safety and reduction in human error in aviation and other domains; however, they are not without usability issues. Degani and Wiener (1993) identified several issues with paper checklists that could lead to human error including: inaccurate or inefficient steps that cause operators to deviate from the procedure, and misleading, inconsistent, or ambiguous phraseology. The same research also found that pilots using paper procedures are prone to memory errors. Pilots reported getting ‘lost’ in the procedures, forgetting which step they were on, or which steps had been completed (Boorman, 2001). These memory problems are exacerbated if the procedure is interrupted, which may cause the pilots to fail to return to the task altogether (Dismukes, 2012).

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Inadequate procedural information can result in crews not having the information they need to perform a task. Captain ‘Sully’ Sullenberger, the U.S. Airways pilot that guided 155 passengers and crew to an emergency water landing in the Hudson River in 2009, highlighted the need for emergency procedures with just the right level of information in his keynote address at the 2010 Human Factors and Ergonomics Society meeting in San Francisco, CA. Captain Sullenberger said that in the critical moment, he wished he had procedures that could collapse to contain just the key four or five steps minimally required for emergency landing (Sullenberger, 2010). The concept of operations in today’s spaceflight vehicles is one in which virtually all tasks are driven by procedures. From the beginning of the space program through the Space Shuttle era, astronauts have relied on Flight Data Files or paper checklists to complete their tasks. These thick books contained all of the procedures that were anticipated to be required during the mission. Use of this paper checklist in the operational space environment brought with it a number of challenges. First, crewmembers had to find ways to hold or mount the procedures book so that it would not float away in the microgravity environment when their hands were busy with tasks. Next, when working with multiple procedures, crew often relied on their fingers as placeholders for flipping back and forth between procedures, preventing use of those fingers and hands during task execution. Finally, pens or pencils were the mechanism for real time changes and annotations of the procedural steps – a common occurrence, as better ways of doing business were found once in orbit. Over a long-duration mission, multiple hand-written changes sometimes became illegible. Aerospace procedures have evolved from memory tools to support tools that help to ensure adequate, if not improved, situation awareness (Pelegrin, 2013). Different types of procedures and checklists can be found throughout aerospace, often stratified between normal/nominal, abnormal/off-nominal, and emergency procedures. Starting in the late 1990s, what had been a largely paper-based procedural regime evolved into an electronic one that is not only much less physically cumbersome (e.g., carrying/managing one or more paper procedure manuals), but one that also helps to overcome several design weaknesses associated with paper, including: the lack of a pointer to the current item; an inability to mark skipped items; and ‘getting lost’ when switching between checklists (Palmer & Degani, 1991). Thus, electronic procedures and checklists have become increasingly integrated into aerospace, improving information processing for operators, and reducing their workload, errors, and response times (Myers, 2016). Effective design of electronic procedures supports the activities that help maintain and improve operator situation awareness: situation management, control, and understanding of the situation (de Brito & Boy, 1999). Typically, operators set and verify parameters by scrutinizing one or more graphical displays presenting information on system state (e.g., items off/on; valves open/closed; landing gear lowered/ raised). However, when there is limited display space on which to present graphical information about system state, or when additional displays are not available, options are to provide more information-dense displays (more cluttered), or to require the operator to search for information across multiple displays. As early as 1993, NASA began assessing the benefits of electronic procedures for use in spaceflight to reduce launch weight and valuable stowage space. On STS-57,

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SpaceHab-1, human factors researchers conducted a study comparing paper and electronic procedures for a simulated computer-based propulsion task, and a noncomputer-based soldering task (Mount, et al., 1993). The electronic procedures experiment sought to define human factors requirements for electronic procedures in space environments. The study had only two crewmembers as participants, but results successfully demonstrated the feasibility and potential benefits of electronic procedures for onboard tasks. The participants noted that the best benefit of the electronic procedures over paper was the automatic highlighting/tracking of the current step. Today, astronauts onboard the International Space Station (ISS) use an electronic procedures system called the International Procedure Viewer (IPV). This tool is jointly used by flight crew and Mission Control to facilitate onboard task performance. Engineers in the Mission Control Center (MCC) are able to monitor and assist crew in the performance of procedures, for example, providing additional information for step completion, or noting missed steps. Even more sophisticated procedures tools are in development for future space vehicles, such as the Orion Multi-Purpose Crew Vehicle (MPCV). These procedures systems offer a wide variety of capabilities, such as automation of procedure steps, dynamic integration and highlighting within related graphical displays, step markers, timers, and reminders. In future missions, NASA plans to venture beyond low Earth orbit (LEO), where communication between the spacecraft and Earth will be subject to longer delays, and in some cases, will be unavailable. Crewmembers will have to rely much more heavily on the automated, computer-based tools available within their vehicle or habitat. Given the increased autonomy from Mission Control, missions will likely be even more dependent on procedures for day-to-day operations and survival. Crewmembers will need to be able to independently operate procedures without missing important steps, keep track of who is working each procedure (i.e., their crewmate, or the system automation), and maintain a view into the vehicle or habitat system’s health and status, along with the effects of their actions on those systems. Radiation poses another challenge to enhancing procedures for long-duration Exploration missions. When particles of ionizing radiation hit computer components, circuits can be inadvertently energized, erasing data and causing issues that can affect a spacecraft’s ability to work properly. Components can also be physically damaged, and computers can repeatedly fail or be permanently disabled. This will be of increasing concern, as space vehicles venture beyond the Van Allen radiation belts. To meet these challenges, computers must use special, radiation-hardened components; many of the advanced display capabilities that work in present-day aviation systems are not feasible for space use without additional research and development. Consider also that spacecraft software must be very robust and well-tested, as there are few to no opportunities during a mission to fix software bugs or incorporate upgrades. Given all of these challenges, along with budget considerations, some key questions for NASA are: For astronauts to successfully complete procedure-driven tasks on a long-duration space mission, what capabilities are absolutely necessary? What are the minimum types and number of information displays required? How should this information be integrated with electronic procedures to best support human performance? Procedures and vehicle/habitat systems information are closely tied. To perform a procedure-based task, an astronaut must read and understand the procedural

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instruction, review the current state of the system, send a state-changing command, and confirm the state was successfully changed within the system. A procedure may consist of hundreds of lines of instructions. As the physical distance between the displayed system information and the procedure increases, the crew’s activities become less efficient and more error-prone. However, a non-integrated display solution is less technically complex and typically much less expensive to develop. A key principle of display design involves the interface between the display of more than one information source and the integration or understanding of that information (Barnett & Wickens, 1988). Garner (1970) reported on what later became the theoretical foundation for the compatibility of proximity principle: to the extent that multiple channels of information must be mentally integrated or proximate, the information should also be physically integrated or proximate. However, it is unclear whether operators working with largely textual information additionally require a graphical component to represent system state, at least in terms of maintaining sufficient awareness of actions previously made, actions currently being made, and/or actions yet to be made. Thus, several questions exist in terms of operator situation awareness in the context of electronic procedures. Can operators maintain sufficient awareness of and performance with electronic procedures without an additional information source providing a graphical depiction of system state? Put another way, do operators need to refer to a graphical component to maintain sufficient awareness and acceptable workload, or can they complete the procedures effectively using textual information alone? Procedures that are closely integrated with relevant system displays (e.g., shown adjacent to one another or intermixed) may provide necessary context and improve performance, or they may increase complexity of eye scans and draw attentional resources unnecessarily. Procedures that are loosely integrated with relevant system displays (or not accompanied by displays at all) may provide efficiencies by reducing eye scans and attention draws, or may hinder performance due to lack of context. Several studies at NASA were conducted to investigate the importance of the degree of procedure/display integration. McCann and Godfroy (2010) performed a pilot study with four participants to determine the performance impact of forcing operators to time-share an electronic procedures display and the associated system summary display (by toggling back and forth on a single display unit). On half of the trials in the study, participants worked procedures while being able to simultaneously view the electronic procedures and the associated system summary display (combined display). On the other half of the trials, the procedures and the system summary display shared the same display area, forcing participants to toggle between the two displays. Results indicated that early in the study when participants had little practice with the task, there was no difference between the conditions. Later in the task, once participants were more practiced, there appeared to be considerable benefit of the combined display over the separate displays. To further investigate this same general question, a larger study was completed that involved 20 astronaut-like participants learning and performing tasks with an electronic procedures system in a simulated flight-control scenario (Holden et al., 2018). Participants completed procedures that included configuring systems, responding to alarms, performing malfunction procedures, and monitoring spacecraft launches.

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A  modified Situation Presence Assessment Method (SPAM; Durso, Hackworth, Truitt, Crutchfield, & Manning, 1998) was employed in the study to measure situation awareness. Participants completed the task under one of the following configurations: (1) Procedures-only (no graphical display), (2) Serial procedures (procedure or related graphical display was shown in a serial/toggle fashion), and (3) Simultaneous procedures (procedure and related system display were shown on the same display device). Task performance did not require use of a graphical display because telemetry values needed to perform the task were shown within the procedures in all conditions. Participants who only had procedures with telemetry values reported a lack of awareness of what their commands were really doing. Participants who could see the procedures and graphical displays, but only one at a time in sequence, said they felt uncertain about system states. Participants who could see the procedure and graphical display at the same time felt they had more context, and improved situation awareness—they felt more engaged. While no significant differences in situation awareness were shown with the SPAM response time measure, a simple self-rating of situation awareness did indicate significantly greater situation awareness in the simultaneous procedures condition. In addition, 17 of the 20 participants preferred being able to see the procedure and a related graphical display at the same time. Eye tracker results also indicate that although subjects did not need to view the graphical displays to perform their task, they spent significant time looking at the graphical display, presumably because it provided context that they found valuable. This study highlights the importance of providing graphical information to support task execution with electronic procedures.

HumAn-AutomAtion integrAtion Like HCI, human-automation integration (HAI) in spaceflight focuses on the interaction between the operator and a computer system. However, HAI focuses on that system being complex and automated, controlling a spacecraft and/or a vehicle subsystem. HAI is also referred to as human-automation teaming. Inadequately designed human-automation systems may lead to flight crew and Mission Control errors and inefficiencies, failed mission and program objectives, and an increase in crew injuries. For long duration, long distance spaceflight, it is an unmitigated risk in particular because these missions will require an increased dependency on automation and automated systems. Automation is the use of machines or computers, generally for the purpose of increasing productivity and reducing human cognitive workload (see Sheridan, 2002 for an overview). In space systems, a variety of amounts and types of automation are required to support a highly diverse set of functions and operations. The required automation systems will span ground and flight systems, and will support functions from controlling the habitat to conducting science experiments. The integration of automated systems with their human users will necessitate a variety of role divisions: authority and autonomy can be differently allocated between human and automation, and that allocation may change dynamically depending on task or context. It is important to note that increasing automation within spacecraft does not equate to eliminating human error or that people will no longer be needed. Inherently, human

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performance changes as the introduction of automation shifts the nature of work people must do, a critical trade-off that is often overlooked. Exploration-class missions will emphasize more dependencies on automation as our spacecraft systems evolve to support the long, intermittent data transmission delays. Failure to address human-automation integration will lead to ineffective and inefficient systems. When investigating human performance with respect to automation, the risks associated with autonomous systems are often downplayed (Cummings, 2017; Woods, 2016; Woods & Dekker, 2000). Human-automation integration researcher D. Woods points to ‘Doyle’s Catch’ (Alderson & Doyle, 2010) and summarizes quite succinctly: it is ‘presumed that because [autonomous] capabilities could be demonstrated under some conditions, extending the prototypes to handle the full range of complexities that emerge and change over life cycles would be straightforward’ (Woods, 2016, p. 131). This presumption is part of the risk of human-automation integration. There is a gulf of uncertainty within the life cycle of one autonomous system, not to mention the assumed advances between fruitful implementations of automation in one area will be successful in spaceflight operations. It should be assumed that automation and artificial intelligence are only as good as the implementation. Under uncertainty, automation is still notoriously brittle, while humans are adaptive and flexible. This is most certainly the case with human spaceflight; the novel environment is not well understood or completely modeled, and our spacecraft systems and subsystems are one-of-a-kind. Woods (2016) points out that multiple organizations, both in government and industry, fail to properly consider automation brittleness as its contribution to human-system integration. Furthermore, artificial intelligence, or machine learning, which is the basis for much of the forthcoming autonomous systems, depend on large amounts of training data to develop their reasoning algorithms. Insufficient training data leads to less reliable results. Additionally, recently it has been recognized that the resulting algorithms are biased, since the training data they use is systematically biased, for instance, face-recognition software misidentifies people based on race (Knight, 2017a). In industry, there are safety concerns as AI spreads to critical areas like medicine (Knight, 2017b). Inevitably, when these autonomous systems encounter an anomalous condition or conditions and context of use change, it is the human operator that is expected to jump in, adapt to the circumstance, identify the fault, and recover quickly. An appropriate analogy to successfully designing human-automation integration is to consider the ‘H-Metaphor’ (Flemisch et al., 2003). The analogy is derived from considering a horse as an autonomous vehicle; a person may guide and direct the horse, yet the horse will provide feedback to the rider, preventing dangerous situations. In the H-Metaphor, designers ought to consider the operator and the intelligent system as cooperative agents that share the same goal. Similarly, complex automation and robotic systems should be designed so as to enable the operator to be part of the system, providing guidance and receiving feedback. Human automation integration requires appropriately allocating functions among agents, be they people, automation, or robots, to drive efficient systems design. Inappropriate distribution of functions among humans and automated systems may result in inefficient and unsafe operations, which threaten mission completion. Task allocation determines the role people must play in future complex,

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automated systems. Unsuitable allocations may have the repercussions of overwhelming or underutilizing humans under normal and/or emergency situations. The way operators rely on or trust an automated or robotic system has a strong influence on how effectively the human system functions. In this context, researchers have focused on the degree of trust and reliance that humans place on their automation and robotics. Operators using complex automation and/or robotics may find themselves confused about what the automated or robotic system is doing because of the complexity of the task or because the relevant information is not available to the operator. This lack of automation transparency is often attributed to a variety of negative human-system performance effects. This becomes an essential part of how operators react under off-nominal conditions and unexpected situations. The amount of automation used is often referred to as levels of automation (LOA) (Sheridan & Verplank, 1978; Parasuraman, Sheridan, & Wickens, 2000; Sheridan & Parasuraman, 2005). How and when operators use automation will define the required crew task and performance. While Hancock et al. (2013) envision adaptive automation that adapts based on the human state, adoption of adaptive automation in human spaceflight operations is currently decades away from implementation. A meta-analysis of levels of automation transitions by Onnasch, Wickens, Li, and Manzey (2014) showed statistically that the 18 published works making up the evidence collectively indicate that automation necessitates a trade between levels of automation and human performance. Specifically, they analyzed the overall effect of the ‘degree of automation,’ which is the combination of ‘level’ and ‘stages of automation’ first proposed by Parasuraman, Sheridan, and Wickens (2000). While increasing the degree of automation helps in routine system performance and workload, it also negatively affects situation awareness and operators’ recovery from system failures (Onnasch et al., 2014). The authors conclude that if return-to-manual performance issues are of serious concern, operators should be kept involved in decision and action selection processes. When considering adaptive automation, levels of automation transitions also need to be taken into account. Transitions can be sequential or discontinuous and go from manual to fully automatic or vice versa. Automation-level transitions exhibit performance costs, such as task inefficiencies, particularly during the engagement and disengagement processes. When an operator changes from one level of automation to another, s/he must reorient to the current system status and operating level, which can decrease situation awareness and precipitate out-of-the-loop performance costs. Di Nocera, Lorenz, and Parasuraman (2005) maintain that the higher the current levels of automation (prior to a transition), the worse the ‘return-to-manual’ performance. However, it is acknowledged that this may not be the case as long as the interface is designed to facilitate information sampling and thereby maintain operator situation awareness (Di Nocera et al., 2005). Lack of automation transparency has been attributed to operators’ inappropriate knowledge acquisition (Glover, Prawitt, & Spilker, 1997) and inability to maintain mode awareness (Sarter & Woods, 1994). Mode-related errors (i.e., misunderstanding the state of the system) are known contributors in aviation accidents and incidents. There are two types of problems: difficulty in telling what mode a system is in and difficulty telling what a mode will do. For example, pilot training may teach only a subset of possible modes, leaving out unusual modes or those not used in airline

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operations policy. Pilot performance has latent problems, revealed in non-normal situations (Sarter & Woods, 1994). Problems can result because the system is in an unexpected or unrecognized mode or because the pilot could not predict behavior in an unfamiliar mode, or both (Sarter, Woods, & Billings, 1997). Mental models are also essential for maintenance of situation awareness; situation awareness is not only perception of elements in the current environment, but also the integration and comprehension of these elements, and the projection of future status based on comprehension (Endsley & Kiris, 1995). Dependency on automation has been shown to lead to decreased situation awareness (e.g., Strauch, 1997). Endsley and Kiris (1995) have shown that higher levels of automation are associated with out-of-the-loop syndrome, the consequence of complacency and degradation in skill and situation awareness resulting from prolonged supervisory control of automation. Kaber and Endsley (1997) conducted a study to examine the effects of levels of automation on operator performance and situation awareness. Results from this study reveal that participants were better able to recover from automation failures when the level of automation during the task involved human interaction (i.e., lower levels of automation). In the space domain – where operators conduct tasks in an extreme environment with high inherent safety risk – situation awareness is essential. Human operators in charge of monitoring and possibly overriding such an automated system may make or fail to make appropriate commands because they do not understand the system’s actual intention. The kind of failure associated with misjudged intention is formally similar to that of a simple mode error because when it occurs there is a mismatch between the operator’s estimate of the system’s intention (mode) and its actual intention (mode). Likewise, unexpected transitions from different levels of automation lead to operators being out-of-the-loop, which remains one of the biggest challenges in human-automation research (Endsley, 2017). Since the seminal paper by Lee and Moray (1992), the human-automation interaction community has been studying the relationship between trust and complex automated systems as well as the effect it has on human performance. The operator must be cautious not to over- or under-rely on automation or robotics. Over-reliance will diminish the operator’s situation awareness, placing all decision-making control in the hands of automation; under-reliance will inundate the operator’s mental workload by relying solely on him/herself. For example, operators did not detect the failure of a global positioning system component of the auto-navigation system on the Royal Majesty cruise ship. Their over-reliance on the automation resulted in the Royal Majesty running aground (Degani, 2004; Lee & See, 2004). Similarly, new research is focusing on understanding how trust in robotic agents affects humansystem integration. Robots are expected to transition from tools to teammates. As such, the human–robot relationship will transition to a relationship that is similar to human–human teamwork where trust plays a critical role (Ososky, Schuster, Phillips, & Jentsch, 2013). When trust is high, operators are likely to rely on automation, i.e., to act as indicated by alarms or hazard indicators, but may also be too ‘compliant’ and fail to respond to unidentified hazards or hazards incorrectly identified by the system (T. B. Sheridan & Parasuraman, 2005). Over-reliance is also considered a symptom of automation bias (M. L. Cummings, 2004; Mosier, Skitka, Heers, & Burdick, 1997;

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Skitka, Mosier, & Burdick, 1999). Automation bias is defined as where the operator has the tendency to disregard or search for contradictory information against the automation, potentially resulting in errors of commission and omission (Mosier & Skitka, 1996; Skitka et al., 1999). On the other hand, under-reliance emerges when the user does not trust the automation because it has failed too often. As a result, under-reliance could lead to automation not being employed (de Vries, Midden, & Bouwhuis, 2003; J. Lee & Moray, 1994). Additionally, operator reliance on automation can be affected by workload (Kirlik, 1993; C. D. Wickens & Dixon, 2007), and by a sudden change in automation performance (Lee & Seppelt, 2009). Trust in automation is interwoven with functional task allocation. For example, issues may arise at the transition point between prior manual control of a function to new automated control, as this change typically requires a reorganization of the operator’s tasks (Sarter et al., 1997). Tasks that require automation, like those that are time-critical and with little time for an operator to respond (Parasuraman et al., 2000), must be designed and built to be highly reliable to gain operators’ trust. We present two key examples of how this integration must be considered for future long-duration Exploration missions: automation in electronic procedures and supervisory control of lunar landings. HAI: Levels of Automation in Electronic Procedures Electronic checklists are largely believed to resolve many of the memory-related issues associated with paper checklists: displaying completed, pending, and skipped steps. Once digitized, the evolution of electronic checklists follows the predictable path of being integrated with the avionics systems, which can retrieve system data and automatically complete large portions of the checklists without the need for human intervention. At first, this may seem like an opportunity to reduce the workload of the operator by automating as much of the procedure as possible; however, the overuse of automation may have negative consequences on human-system interaction. Another challenge of spaceflight is that astronauts are frequently working with multiple procedures simultaneously, and multiple team members (whether human or automation). They must maintain situation awareness of the procedures and systems on which they are working, as well as the procedures and systems on which their crewmates or automation are working. This knowledge is important for maintaining adequate or balanced workload among the team, for preventing concurrent execution of conflicting or incompatible tasks, and for avoiding redundant procedure execution. It will be critically important that crewmembers using electronic procedures do not become complacent system managers, unaware of the consequences of their actions or the system’s state as they work through a task. The existing body of research on human-automation integration indicates that loss of situation awareness could indeed be a consequence as procedures become more automated and the users become more disengaged from the task. The assistance provided by the automation may discourage or impede the development or maintenance of accurate or complete cognitive models of the procedures and the systems they control. In fact, although not specifically in the context of automated procedure execution, multiple researchers have found

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that situation awareness is better at low to medium levels of automation, and poorer with full automation. Results on the effects on perceived workload have been mixed (Endsley & Kiris, 1995; Kaber, Onal, & Endsley, 2000; Kaber & Endsley, 2003). NASA previously evaluated automated electronic checklists with two levels of automation, and found that pilots were less likely to notice subsystem failures when using fully automated checklists that eliminated the need for pilots to interact with the checklist (Palmer & Degani, 1991). These findings are consistent with other findings within human-automation research, in that overly automated systems often result in complacency (Parasuraman, Molloy, & Singh, 1993), overreliance (Mosier, Skitka, Heers, & Burdick, 1997), and loss of situation awareness (Endsley, 1999). Thus, a key challenge is determining the level of automation that provides the best human performance, without compromising safety or introducing unnecessary risk. Schreckenghost, Bonasso, Kortenkamp, Bell, Milam, and Thronesbery (2008) demonstrated that the automation of electronic procedures can be used to implement adjustable autonomy for mission tasks normally performed by humans. In their studies, participants could adjust whether a procedure step was executed manually or automatically, which permitted a gradual shift to more automated operations while maintaining performance and situation awareness. Oman and Liu (2018) investigated how the allocation of procedural step execution between the human operator and automation, or level of automation, would affect task performance, situation awareness, and mental workload in a robotics task. Holden et al. (2018) also investigated the level of procedure automation, but in the context of a habitat management simulation. Both studies were conducted with high fidelity electronic procedures systems that are very similar to spaceflight systems currently in use. Although these studies focused on different task domains, and used different electronic procedures systems, the results were highly consistent: the high automation conditions produced the lowest workload, and the manual procedure conditions produced the highest workload. Both studies also found that high automation conditions degraded the operator’s ability to comprehend system states. High automation freed up resources to detect Level 1 situation awareness, but the lack of engagement appeared to hinder Levels 2 and 3 situation awareness. Some participants in the Holden et al. (2018) study reported ‘zoning out’ due to lack of engagement in the high automation condition. The mixed or intermediate level of automation was highly preferred in both studies. Oman and Liu (2018) found that task completion time was nearly the same as the high automation condition, so it appeared there was virtually no performance cost for having a manual component. The positive effect was that it improved participants’ situation awareness and allowed them to regulate task progress. Participants in the Holden et al. (2018) study noted this condition was the best compromise or ‘sweet spot’ in terms of speeding up task completion due to automating menial tasks, while requiring enough engagement to maintain situation awareness. Fully manual procedures had the longest completion times, and were the least preferred, with some participants indicating they felt like ‘button-pushing monkeys.’ Participant comments from the Holden et al. (2018) and Oman and Liu (2018) studies also revealed several important design considerations for implementing

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electronic procedures. When implementing mixed levels of automation, the indication of manual vs. automated step should be highly salient. If it is not clear when users need to re-engage in executing procedural steps, procedure execution will be unnecessarily slowed. When implementing highly automated procedures, the rate of display of procedural steps is an important consideration. A comfortable reading/processing rate varies by individual, and when the rate is too high, some users lose significant situation awareness. This suggests that the display rate for automated steps be customizable, where possible. Finally, key execution information, such as sub-step parameter values, should be visually available to the user. This information is critical for verification and continued situation awareness during procedure execution. The collective results from these studies are consistent with previous findings, and highlight important considerations for the design of electronic procedures for future long-duration, limited-resource spaceflight missions. HAI: Supervisory Control of Lunar Landings The human-automation integration challenge is illustrated with the example of reinventing lunar landers for future Exploration missions. Future lunar landers will likely be able to land anywhere at any time on the moon (NASA, 2008). This means a dramatically different system from what was used for the Apollo program, which only landed near the equator, on the Earth-facing side, during particular times of a lunar ‘day.’ Novel automation will be used to more precisely land in specific locations (Brady et al, 2009; Major, Duda, & Hirsh, 2011), particularly if landing near surface habitats is a requirement. As such, the role of astronaut pilots will change to execute a supervisory control task. Over several years, Draper Laboratories, NASA, and MIT have been researching how best to redesign lunar lander cockpits to integrate new automation. Cummings et al. (2005) envisioned a lunar lander cockpit that enabled astronauts to land in remote locations previously inaccessible to Apollo (e.g., poles) while improving cognitive workload and presenting automated information through new visualizations. It assumed automated checklists as well. Design requirements were proposed for a new novel vertical display (Smith & Cummings, 2006; Smith, Cummings, & Sim, 2008). NASA Ames studied human performance and handling qualities for new lunar landers (Bilimoria, 2009; Mueller, Bilimoria, & Frost, 2011, 2012). They used a combination of Cooper-Harper ratings and Task Load Index (TLX) ratings to assess control and guidance inputs. They report that a new velocity increment command improved handling qualities in comparison with Apollo-like controls. Additionally, a new lunar range display has been evaluated for the task of lunar landings (Stimpson et al., 2011). However, there are HAI challenges that have yet to be evaluated (Sim, Cummings, & Smith, 2008) with respect to how function allocation determines the role of the human operator and its effect on human performance. As such, NASA funded research to further study crew performance for the task of supervisory control of lunar landers (Hirsh et al., 2011; Wen, Johnson, Duda, Oman, et al., 2012; Hainley et al., 2013; Kaderka, 2014; Kaderka et al., 2013; Marquez & Ramirez, 2014). Several human-in-the-loop experiments were conducted at the Draper Laboratory fixed-based simulator, and six degree-of-freedom motion-based simulators at NASA Ames (Vertical Motion Simulator, VMS). Additionally, a

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closed-loop human-system model for representing the human in a complex system was used to analyze dynamic task allocation between the human and the system in operations (Wen, Duda, Slesnick, & Oman, 2011). Hainley and colleagues (2013) first evaluated the operator’s ability to ‘gracefully transition’ across different levels of automation, i.e., varying the operator’s role for the task of landing on the moon. ‘Gracefully transition’ was termed as a way of characterizing the operator’s flight performance, meaning that there would not be unsafe decreases in performance or unacceptable changes in workload or situation awareness. Operators were asked to land on the moon using a fixed-based simulator. Their research found that as operators transitioned from high levels to lower levels of automation, there was an increase in workload and a decrease in situation awareness. This was also the first set of experiments that established human performance measure of situation awareness through a tertiary task imbedded with the flight task: verbal callouts of altitude, fuel, and terrain hazards. Subsequent experiments leveraged similar measures (Duda, Prasov, York, et al., 2015; Karsinski, Robinson, Duda, & Prasov, 2016). Subsequent experiments were conducted in a motion-based simulator. These VMS experiments (led by Kaderka, Duda, and Marquez) emphasized understanding the effect of automation brittleness on human performance for the task of lunar landing. Participants with flight experience were asked to detect and diagnose system-level failures with varying levels of automation, with and without motion cues. The levels of automation were from low to high: (1) pitch/roll attitude rate command by pilot, (2) pitch/roll attitude rate command by pilot with incremental rate-of-descent control), and (3) fully automatic flight. Failures examined were: (1) thruster failure, (2) radar failures, and (3) fuel leak. The effect of how often a failure appeared (75% of trials had a failure vs. 25% of trials) was evaluated in one of the experiments. Results indicated that the pilots had a high correct hit rate on failure trials and correct rejection rate of 90.2% on no-failure trials (Kaderka, 2014). There was no effect of motion cues on flight performance or failure detection. Failures were also more easily detected in the high-frequency condition than in the low-frequency condition (Marquez & Ramirez, 2014). In all cases, mental workload and situation awareness decreased following a planned mode transition from automatic flight to manual control. Finally, additional studies at Draper’s fixed-based simulator evaluated visual attention (Kaderka, 2014). There was an effect of mode transition on the average dwell duration and number of visual fixations. Average dwell duration prior to failure detection was found to be higher on the instruments that were used to detect the failures, as compared with the no-failure conditions. This suggests that failure detection is a two-step process: pilots first notice a conflict between actual and expected instrument indications, and then confirm the observation by closely monitoring those instruments before reporting a failure. These research results are consistent with the known risks of inadequate humanautomation integration. As such, future research should emphasize lunar lander training for both skill development and retention in the context of varying levels of automation as well as the intersection of automation training, a research area largely unexplored in this domain.

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ENVIRONMENTAL FACTORS The spaceflight environment presents unique challenges for astronauts living and working in space. In addition to microgravity, long-duration space travelers will be faced with periods of acceleration and vibration, as well as long-term exposure to radiation and CO2. Environmental factors, combined with the stressors of isolation and separation from the Earth, can have a profound impact on human performance. That impact begins at launch. Astronauts must endure extreme vibration and acceleration forces during launch, while monitoring their progress and the vehicle’s health and status as they ascend. Fortunately, most space vehicles are highly automated with respect to launch, so minimal human interaction with controls is required. However, to successfully monitor an ascent, astronauts must be able to accurately perceive and understand what is displayed to them under launch acceleration and vibration. Once in orbit, the human body begins to experience profound effects due to microgravity. Astronauts are subject to bone and muscle loss, and a headward fluid shift that can cause pressure in their head and upper torso. Their lower limbs are no longer useful for moving around the space vehicle, and they must adapt to new ways of moving about and performing tasks. Full adaptation to this new environment takes weeks to months. While astronauts have been generally successful in completing their computer-based tasks onboard, the type of fine motor performance that involves pointing, clicking, and dragging has not been systematically measured, and little is known about how fine motor performance may change over time in microgravity, or after a return to the Earth’s gravity. These are important considerations since an astronaut who travels on a long journey to Mars, for example, will need to operate computer-based controls with accuracy to reconfigure systems, safe the vehicle, initialize their spacesuit, teleoperate rovers, assemble passageways, and bring up habitat systems. When the space mission is over and astronauts return to the Earth’s gravity, astronauts can suffer sensorimotor issues that temporarily impact balance and sometimes even the ability to walk normally in the early post-landing days (Reschke et al., 2018). Human factors researchers must consider the various decrements in physical performance throughout the different mission phases, which may influence how astronauts complete tasks requiring gross body movement and fine motor manipulations. In the selected examples below, we describe a line of human factors research related to visual performance under vibration, followed by a summary of recent research on the ISS related to the effect of long-duration microgravity on fine motor skills.

visuAl PerformAnce under vibrAtion A new Multi-Purpose Crew Vehicle (MPCV), Orion, is expected to take astronauts beyond low Earth orbit, to the moon and Mars. This vehicle reflects a return to a Mercury-Gemini-Apollo-like ‘capsule’ design, but with a larger crew size, and modern, more sophisticated interfaces and operation concepts. It will be launched atop a rocket with very different vibration profiles than the Space Shuttle, or even those of the Gemini and Apollo programs (Grimwood, Hacker, & Vorzimmer, 1969). The increased vibration profile has raised questions about the ability of astronauts to adequately read their displays during high vibration periods experienced during launch and ascent.

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Multiple studies have investigated this issue in the past. Display readability for semisupine (i.e., recumbent) participants undergoing vibration in the body x-axis (sternum-to-spine) was examined for a variety of representative vehicle-seating configurations by Taub (1964), Faubert, Cooper, and Clarke (1963), Shoenberger (1968), and Clarke, Taub, Scherer, Temple, Vykukal, and Matter (1965). The crew–vehicle interfaces in Orion will differ dramatically from the ‘steam-gauge’ and incandescent lamp displays examined in these historic vibration studies. Orion crews will be reading and processing alphanumeric text mixed with graphical elements such as schematics rendered on display panels similar to those used in commercial transport aircraft or modern laptop computers; thus, new vibration research was warranted. In 2009, two studies conducted at the Vibration Test Facility at Ames Research Center (see Figure 13.4) examined the combined impact of a target set of vibration levels and sustained 3.8 G on participants’ ability to see and process alphanumeric symbology (Adelstein, Anderson, et al., 2009a; Adelstein, Beutter, et al., 2009b). Results showed that, even under sustained 3.8 G into the chest, the ability of both general population and astronaut office participants to process digit triplets was relatively unaffected by 12-Hz vibration in the chest-to-spine direction with an amplitude as high as 0.3 g zero-to-peak. However, considerable and statistically significant levels of performance disruption occurred at 0.5 g-peak for small (10-pt font) stimuli, and at 0.7 g-peak for both 10-pt and 14-pt font stimuli viewed at a distance of 18 in. (45 cm). Next, a higher fidelity lab study was performed to: (1) measure visual performance and situation awareness during vibration by processing realistic flight display symbology and (2) determine whether there were any aftereffects of exposure to vibration on manual flight control (McCann et al., 2009). Manual flight control is a critical task that the crew may have to perform immediately following a vibration event. This study involved 13 astronaut pilots, mission specialists, and non-astronaut pilots. The situation awareness task used in the study was designed to force participants

FIGURE 13.4

Vibration test facility at Ames Research Center.

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to process multiple flight indicators, integrate the information from the indicators, and make a rapid four-alternative forced-choice decision. Participants were strapped in a semi-supine position in a vibration chair, looking up at a representative Orion display. For the first 10 seconds, all flight indicators indicated that the vehicle attitude and flight profile were nominal. After this point, 25% of the trials showed three indicators behaving in a manner consistent with the Attitude Display Indicator ball, while 75% of the trials showed 1 of 3 indicators behaving in a manner inconsistent with the Attitude Display Indicator. Participants pressed a response button on a joystick and simultaneously made a single-word announcement of their judgment indicating whether all indicators were in agreement, or naming which indicator was not in agreement. Response times and verbal responses were recorded. Vibration peak magnitude was either zero (providing a baseline control condition), 0.3, 0.4, 0.5, or 0.7 g as measured in the body chest-to-spine direction. At the end of the vibration period, the screen blanked for two seconds, and then the flight display reappeared, revealing some errors in the attitude of the vehicle. The participant used the control stick to reduce the error as quickly and accurately as possible (manual flight control task). Objective performance measures were taken to indicate ability to perform the manual correction following cessation of vibration. Results from the situation awareness task showed that as the vibration level increased, the objective measurements of task response time significantly increased. The subjective rating of workload increased, while the subjective rating of display usability decreased. Error rate was not affected by increases in vibration. Prior 1-G and centrifuge text-reading studies (Adelstein, Anderson, et al., 2009a; Adelstein, Beutter, et al., 2009b) showed similar response time increases with vibration, but also showed an increase in errors with increased vibration. During the follow-on manual flight control task, participants’ error rate for initial joystick input grew with the preceding vibration magnitude, suggestive of a shortterm vibration-induced aftereffect. Although there have been no documented issues with a crew’s ability to read the displays during Space Shuttle launches, the vibration levels and readability of the displays had not been explicitly measured prior to 2009. Informal crew comments regarding readability during launch have been mixed, with some reporting no issue and others mentioning difficulty. It was known that vibration levels on the new capsule-like space vehicles would be higher than the Shuttle, but having the actual vibration levels and visual performance measures from the Shuttle could help validate results from the laboratory studies. Thus, a study on the Space Shuttle (Thompson, Holden, Ebert, Root, Adelstein, & Jones, 2009) was completed on missions STS-119 and STS-128 to examine the effect of vibration (combined with G-load) during launch on participants’ ability to perceive different size fonts and graphic information (see Figure 13.5). Since this study was conducted in situ during the highly safety-critical launch phase, there were limited options for controlling experimental parameters. Astronaut participants in this study were seated on the shuttle mid-deck during launch, with accelerometers mounted on the back, pan, and headrest of their seats to capture vibration levels. The crew read information from four representative spacecraft displays during launch, while providing responses on a card attached to their knee-board. Because it was considered too dangerous to mount electronic displays

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FIGURE 13.5 Visual performance study on the space shuttle (STS-119).

in the mid-deck during launch, the crew viewed high-quality printed placards attached to a wall of mid-deck lockers at a 20-in. corrected visual distance. Each placard consisted of four displays, each display quadrant using a different character height: very small (0.11 in.), small (0.14 in.), medium (0.17 in.), and large (0.20 in.). For each phase of flight, the crew was asked to identify/mark the smallest readable quadrant display. After solid rocket booster (SRB) separation, they completed a short questionnaire about the display components (e.g., text, numbers, icons, colors), and the vibration environment. Results indicate that the subjectively rated, smallest readable font size increased with higher vibration. At pre-launch, and after SRB separation (when vibration was near zero), crew selected 0.11 in. as the smallest readable size. During high vibration levels, crew indicated that fonts as large as or larger than 0.14 in. would be desirable, especially if a critical decision had to be made at that time. Results from this collection of studies were used to provide future spacecraft designers with recommended vibration and font-size limits to ensure adequate visual performance.

fine motor sKills One lessor studied area of spaceflight performance is fine motor skills. Fine motor skills involve the integration of visual information and coordination of muscles, bones, and nerves to produce small, precise movements of the small muscle groups of the hands and fingers. Fine motor skills will be critical for interacting with hardware- and software-based controls to perform a variety of tasks such as information access, just-in-time training, subsystem maintenance, and medical treatment, among others. Fine motor skills are also critical for tasks involving hand controllers – flying a space vehicle, or teleoperating a robotic arm. What does extended microgravity do to fine motor performance? What about the effects of transition from the 1-g of Earth to the microgravity of the spacecraft, and then to a planetary surface

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with 1/3-g? Answers to these questions will be important as we venture beyond low Earth orbit (LEO) to a planetary surface. As many as 14 spaceflight studies on motor control have been conducted on Mir, Shuttle, and ISS. The majority of studies have used a joystick or arm-reaching task, with mixed results. Two medical studies conducted during parabolic flights (Rafiq, Hummel, Lavrentyev, Derry, Williams, & Merrell, 2006; Panait, Merrell, Rafiq, Dudrick, & Broderick, 2006) found that laparoscopic skills in parabolic flight were impaired compared with performance on the ground. Participants had increased force production, decreased task performance, and completed fewer tasks in microgravity. A shuttle Neurolab mission investigated surgery performed on rats (Campbell, Williams, Buckey, & Kirkpatrick, 2005), and found no decrement in manual dexterity, but increased task time as compared with ground performance. More recently, Moore, Dilda, Morris, Yungher, MacDougall, and Wood (2018) investigated the effects of gravitational transitions (i.e., post-flight landing) on motor performance. Results showed ISS standard duration crew had significant decrements, compared with preflight, in the ability to operate simulated vehicles within 24-hours of landing. Piloting performance and a rover-docking maneuver were also compromised. Moore et al. (2018) also report a 10% decline in subject manual dexterity on landing day. In future long-duration spaceflight missions, fine motor skills will be required to interact accurately with computer-based devices, such as touchscreens and gesture input devices. They are also relevant for the operation of small buttons and switches that are and will likely be part of vehicle, habitat, and spacesuit hardware in the future. Touchscreen tablets are already in use on the ISS for recreational and supporting tasks, and at least one touchscreen interface is under development by a commercial spaceflight company to be used as the primary means of commanding in the cockpit. We know that crews have been successful in interacting with their computers and touchscreen tablets in space, but no detailed performance measures have been taken until recently. Holden, Greene, Cross, and Feiveson (2018) completed a research investigation on the ISS, measuring fine motor skills of seven astronauts preflight, inflight over the course of a sixmonth mission on ISS, and postflight out to 30 days after landing. This investigation had two primary aims: (1) determine the effects of long-duration microgravity on fine motor performance and (2) determine the effects of different gravitational transitions on fine motor performance. Here ‘long-duration’ microgravity was defined as a six-month mission, and gravitational transitions were defined as the periods of early flight adaptation (after launch), and very early/near immediate post-flight (after landing). Classic tests of fine motor performance typically involve measuring control and coordination of the hands and fingers using pegboard tasks, geometric figure tracing, and copying tasks. Since these tests would not be easily completed in a microgravity environment, a touchscreen tablet-based fine motor skills test battery was developed and tested along with a pegboard task (Thompson, Holden, & Sándor, 2015). The test battery consists of four fine motor tasks: pointing, dragging, shape tracing, and pinch-rotate (see Figure 13.6). In the reciprocal pointing task, squares are tapped in order, either clockwise or counter-clockwise, depending on the instructions. In the dragging task, the small square is dragged to the distant rectangle and dropped in the rectangle; then repeated in the opposite direction. Dragging is horizontal or vertical, depending on instructions. In the shape-tracing task, a circle or square is traced in continuous motion in either the

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FIGURE 13.6 Fine motor skills test battery tasks.

clockwise or counter-clockwise direction. In the pinch-rotate task, the outer shape is ‘grabbed’ with the thumb and pointer finger, and the shape is rotated and pinched until it aligns with (lies on top of) the inner shape. The pointing and dragging tasks were adapted from the ISO 9241-9 (2000) set of standard tasks for evaluating cursor control devices. The shape tracing task is a classic test of fine motor performance. The pinchrotate task was developed by the team to represent a typical multi-touch operation. All tasks were performed both with a stylus and finger (per onscreen instructions), with the exception of the pinch-rotate task, which was only performed with a fingers. In this ISS study, the crew performed the task four times preflight as a baseline, two times in the first week onboard ISS, every week for the first three months of the mission, and then every fourteen days for the remainder of the six-month mission. Post-flight data collection was accomplished twice on landing day, and then on postlanding days 1, 3, 5, 15, and 30. Ground subjects matched to each crewmember on the basis of age, education, vision, hearing, and fitness level completed test sessions  on the same schedule as their crewmember match – lagged by two weeks. Fifteen-minute test sessions were completed in a consistent location throughout the mission. Test sessions near landing day were completed with a short (five minute) version of the test battery due to constraints on crew time. Holden et al. (2018) found no significant decrements in performance during the mission, for the seven crew participants, and small but significant decrements at the gravitational transition points. When astronauts enter the microgravity environment of the ISS, the body undergoes many changes including an upward shift of bodily fluids and a disruption of the sensorimotor system. Adaptation in microgravity occurs in stages over several weeks. Pointing, dragging, and shape-tracing task performance showed significant decrements during the first week on ISS, as compared with the ground controls. Performance then recovered, and gradually improved over the mission. Soon after landing, pointing and shape-tracing task performance again showed significant decrements compared with the ground controls. At 30 days post-landing, performance on the pointing task was still significantly worse than the ground controls. These results indicate that there is a fine motor performance decrement whenever astronauts go through a gravitational transition. The decrements are small, and whether this is due to space adaptation sickness (feeling nauseated), or to some other mechanism is unclear. From a human factors perspective, what matters is that these results indicate astronauts are unable to maintain stable performance after going through gravitational transitions. The potential impact of this is that after a long journey to another planet’s surface, astronauts may be unable to immediately interact with their computerbased devices with accuracy. Given that post-landing activities will include safety

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critical operations, this is a concern. Launching from a planet’s surface back into space may also cause problems. The performance decrements found were small and the tasks were simple, low-fidelity tasks on a touchscreen. However, given that the differences were reliable, and demonstrated with only seven participants, it is important to confirm these findings with additional participants and higher fidelity software interaction tasks. When crew land on Mars after a long journey, they must be able to accurately safe the vehicle, command the rover, and startup the habitat and their spacesuits without high error rates. If the results found in this study are confirmed, it may suggest the need for training countermeasures, or user interfaces that can adapt (e.g., larger or more tolerant controls for gravitational transition periods).

CONCLUSION The future of human spaceflight is one in which new, highly advanced spacecraft will take men and women beyond low Earth orbit, and eventually to Mars. The level of automation will be greater, and astronauts will be increasingly autonomous from Mission Control. While some research into information presentation, automation, and the effects of longduration microgravity have been completed, there is much more to learn. The majority of studies on ISS have been completed with crew who stay onboard only six months. Thus, crew performance data for missions beyond six months are extremely limited. We do not really know how long-term exposure to stressors such as isolation, microgravity, radiation, or distance from Earth will impact crew performance on a deep space mission, or their ability to be successful once they land on a planetary surface. After the long space journey, will the physical or cognitive abilities required to safe a vehicle, operate a rover, or power up a habitat be impaired, and for how long? Will crew trust the intelligent systems and automation that they will depend on? These are important questions that must be addressed with additional human factors research.

ACKNOWLEDGMENTS The research described in this chapter was funded by the NASA Human Research Program (HRP), NASA Extravehicular Activity (EVA) Program, and Orion Multi-Purpose Crew Vehicle (MPCV) Program. Except as cited otherwise, work was performed by civil servants and contractors as part of the Human Health and Performance Contract NNJ15HK11B (or former Bioastronautics Contract NAS9– 02078) through the National Aeronautics and Space Administration.

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Index abstraction 134–135, 143, 144 adaptation 2, 28, 34, 43, 50, 59, 74, 78–79, 90–91, 97–98, 100, 152–164, 176, 185–186, 196, 201, 204, 207, 238, 245, 282, 286–287 affect 51, 81, 94, 110, 153, 178–180, 184, 198, 201, 224; see also mood agent based model 60, 110–111, 113–116, 119, 121, 123–124, 126, 251 analogs 23–25, 28–39, 41, 43, 47–63, 68, 72–73, 79–81, 110, 114–115, 117, 119, 122, 152, 155–158, 163–164, 184, 220, 222–223, 227, 251, 269, 270 ANSMET (Antarctic Search for Meteorites) 30, 34, 79 Antarctica 15, 29, 30, 33, 38, 50, 52–53, 57, 74, 79, 164, 207, 226, 228–229; see also polar arousal 3, 4, 15, 89, 140 Asthenia 77 attention 3–8, 10, 12, 37, 206, 244, 247, 263–264, 273, 281 focus of 80, 140–141, 145 attitudes 113, 117, 173, 175, 178, 180, 181, 183, 196–198, 209, 222, 226 automation 91, 263–264, 272, 274–281, 288 autonomy 13, 28, 30–31, 51, 77, 109, 152, 157, 162, 221–222, 252, 266–267, 272, 275, 279 backup behaviors 153, 177, 185 Behavioral Health and Performance Operations 232 BHP Operations see Behavioral Health and Performance Operations Big 5 201; see also Five Factor Model biomarkers 42; see also physiological measures border theory 220–221 boredom 28, 76–77, 139, 221, 263 boundary theory 220, 224 carbon dioxide 12, 156, 268, 282 caution and warning alarm 268–269 charter 159, 161 check-in 161, 232 circadian rhythm 3–5, 37 CO2 see carbon dioxide coaching 177–178 cognitive or cognition systems 3, 15 team cognition 153, 173–174, 198, 207, 209

cognitive antidote 139–140, 145 cognitive load 80, 264 cohesion 14, 28, 31, 42, 60, 75, 81, 89, 92, 125, 154, 157–158, 162, 177–179, 197, 200, 204–205, 223, 227–231 collective orientation 198–199, 201, 210 collectivism 118, 175 conflict conflict management 99, 185, 199–203, 206, 210, 219 crew conflict (see interpersonal conflict) interpersonal conflict 93–94, 115, 200, 202, 226 task conflict 179, 202, 210 conservation of resources theory 223 contextual reinstatement 143–144, 145 coping styles 118 crew composition see team composition crew office expeditionary skills see expeditionary skills crew quarters 220, 231 crowding see social density culture cultural differences 77, 175–176, 178–180, 183–184, 226, 228 cultural diversity 172–180, 182–187 decision-making 6–7, 9, 50, 69, 162, 263–264, 277 team decision-making 88, 90, 126, 198–199, 206–209, 245 declarative memory 3, 6, 132–133, 137, 140, 142–145 Desert Research and Technology Studies see D-RATS disruption 8, 37, 92, 153, 155, 157, 162–163, 218, 242, 283, 287 distinctive responding 135–136, 144 domain 2–4, 6–7, 9, 15, 59–61, 90–91, 133, 137, 163, 174, 207, 220–222, 224–225, 230, 243, 246, 252–254, 270, 277, 279, 281 D-RATS (Desert Research and Technology Studies) 35–36 engagement 8, 141, 221–223, 232, 276, 279 disengagement 141, 276 envihab 52, 55 environmental stress or stressors 70, 77 ESA (European Space Agency) 50, 53–54, 79, 156 European Space Agency see ESA

295

296 EVA (extravehicular activity) 5, 59, 266–268, 288 expeditionary skills 225, 230 extravehicular activity see EVA fatigue 9–10, 28, 31, 37, 40, 70, 80, 99, 100, 140, 179, 197, 263 faultline 178, 184, 242, 251 field analog see also ICE mission simulation analog 222–223 (see also analog) fine motor skills 282, 285–288 Five Factor Model 118, 201 followership 89, 117, 120, 225–226 gateway see lunar gateway goal(s) goal attainment 209 goal orientation 208, 229 superordinate goal 197, 238–245, 249 team goal 12, 240, 249 gravity altered gravity 10, 263 microgravity 2, 10–11, 15, 24, 29, 48, 78, 260, 262, 264, 271, 282, 287–288 group living group living skills 217–219, 225–228, 230–232 Hawai’i Space Exploration Analog and Simulation see HI-SEAS HERA 31–32, 35–36, 38–39, 41, 43, 52, 59, 71, 79–81, 110, 117, 119–120, 122–123, 156, 159, 162, 164, 185, 197, 270 HI-SEAS 52, 54, 79, 122, 156, 220, 222, 231 Human Exploration Research Analog see HERA human-automation integration 264, 274–278, 279, 280–281 human-computer interaction 264 human-system integration 275, 276, 278 humor 230 IBMP (Institute of Biomedical Problems) 31–32, 39, 50, 52–53, 110, 157, 230 IMO see input-mediator-output framework information exchange 175, 178, 204 information integration 270 information presentation 264, 266 Input-mediator-output (IMO) framework 76, 153 Institute of Biomedical Problems see IBMP intravehicular activities 268 isolated, confined, controlled, or ICC 29, 31, 33–39, 41–43 isolated, confined, extreme, or ICE 29–31, 33–34, 39, 43, 69, 73, 75, 88, 90, 92, 95–96, 99, 110, 152–153, 155–160, 162–164, 196, 246 IVA see intravehicular activities

Index Japanese Space Agency see JAXA JAXA (Japan Aerospace Exploration Agency) 31, 39, 219 job analysis 197, 228–229 journals astronaut 76 knowledge declarative 137, 142, 181 language 3, 77, 80, 89, 111, 135, 144, 174, 178, 181, 230, 244, 245 leadership 11, 77, 89, 110, 117–118, 120, 177, 179–180, 224, 225–228, 243, 254 learning 3, 5–6, 8–9, 11–12, 97–98, 100, 112, 126, 131, 133–138, 140, 142–145, 151, 177, 186, 198, 199–200, 204, 207–208, 210, 230, 254, 266, 275 levels of automation 260, 277–281, 283 Lunar Gateway 113 Mars 500 57, 223 master 4, 43, 68, 133, 160 memory 3, 6–12, 132–139, 140–144, 201, 207–208, 210, 243, 264, 270, 271, 278 mir space station 12, 159 mission control ground control 197, 207, 222, 243, 247–248 mission control center (MCC) 35, 198, 205, 241, 247–248, 252, 273 modelling model application 115, 123, 126 model calibration 115, 119, 126 model construction 115–116, 126 model validation 115, 122, 126 monitoring monitoring behavior 177, 178, 205, 225 mood 6, 9, 14, 29, 40, 52, 60, 74, 78, 92–93, 159, 178, 179, 221; see also affect multiteam systems 8, 237, 241–242 NASA Extreme Environment Mission Operations see NEEMO NEEMO 10, 30, 79, 156 NEK (Nazemnyy eksperimental’nyy kompleks) 31, 32, 37, 50, 52, 110 neurobehavioral 1–2, 8, 11, 15, 37, 42 Neutral Buoyancy Simulator, or NBS 24–25 nomological network 153–155, 163 note-taking 137–138 novice 73, 133 personality see also five factor model personality fit 118–119 physiological measures 4, 89; see also biomarkers polar 68, 71–75, 79, 108, 110; see also antarctica power distance 89, 175–177

297

Index practice mental 138–139, 151 privacy 14, 47, 57, 63, 71, 78, 160, 219–220, 231 problem-solving 161, 178, 180, 199–201, 203, 206–209, 230, 264 problem-solving aids 160, 162 procedural 13, 132, 138, 141–145, 266, 271–273, 279–280 procedural memory 6, 131–132, 138, 142, 143, 145 procedural reinstatement 143 procedures 13, 24–25, 27, 30, 41, 55, 70, 96, 98, 100, 131, 141–143, 145, 161, 251, 260, 264, 266, 267, 270–274, 278–280 prototype 123, 135, 267 psychological safety 196, 199, 201–202, 206 psychological support 99, 230, 232 Psychomotor Vigilance Test see PVT PVT 37 radiation 1–2, 10–11, 15, 47, 88, 260, 263, 272, 282 RDoC (research domain criteria framework) 2, 3, 5 regulatory system 3, 5 Research Domain Criteria framework see RDoC resilience 7–8, 87–99, 151–164, 226, 229 retention 132, 133, 135–145, 281 reward 3, 6, 7 risk management 197 Russian Chamber see NEK selection 3, 15, 25, 29, 34, 39, 40, 42–43, 88, 98, 123, 156, 159, 179, 196, 199–200, 210, 219, 224, 227–229, 232, 276 self-monitoring 118, 120–121 sensor(s) 71, 81, 87, 90–91, 95, 100 sensorimotor 1, 3–5, 15, 28, 282, 287 shared see shared mental models shared mental models 125–126, 161, 176, 199, 203, 207–210, 231, 244 shuttle transport system see space shuttle situation(al) awareness 208, 253, 263, 267–268, 271, 273–274, 276–281, 283–284 skill acquisition 99, 140, 184 skill decay, or skill degradation 77 skill learning 7, 138 sleep 3, 5–6, 12–15, 24, 32, 38–41, 48, 70, 78, 95, 99, 110, 114–115, 119, 159, 204, 232, 260 social density 74, 231 social network 110, 113, 116–117, 119, 208 social processes 1, 4, 6, 7, 15, 112, 205 social support 75, 77, 157, 196, 223–224, 229 sociometric 119 space shuttle 10, 12, 26–27, 31, 179, 244, 284–285

speed-accuracy trade-off 140 stress exposure training 182–183, 187 stress management 97–99, 219 structure 2, 7, 27, 97, 109, 121, 134, 152, 161, 182, 240, 247, 254 STS see space shuttle subgroup 126 subgrouping 110, 126 system differentiation 243, 246, 251–252 system displays 264, 273 system dynamism 243–244, 251–252 task allocation 275, 278, 281 task analysis 248–250 task load index 280 task procedures see procedures team care 199, 201, 205, 206, 210, 225 team composition 51, 88, 97, 98, 109, 110, 111, 113, 114, 115, 117, 124, 126, 156, 164, 180, 182, 187, 210, 218, 228 team debriefing 100, 160, 182, 185, 187, 210, 252, 253, 254 team dynamics 57, 81, 125, 164, 174, 177, 227 team emergent state 162–163 team learning 97, 100, 187, 198, 199, 204, 207, 208, 210, 253 team problem-solving 206–208 testing effect 136–137 threat 3, 6, 11, 238, 263 TLX see Task Load Index training 11–13, 15, 25–26, 29–31, 34–35, 43, 49, 56–57, 63, 73, 79, 96–100, 122, 125–126, 131–132, 137, 139, 141–145, 156, 159–164, 179–187, 196, 199–200, 202, 206, 208–210, 217–219, 221, 225, 229–232, 245, 248, 250–254, 266, 269, 275, 276, 281, 285, 288 transactive memory systems 243 trust 278 trust in automation 99, 178, 197–199, 201, 203, 206, 209–210, 231, 242, 249, 252, 276–278, 288 uncertainty avoidance 175–176, 179 unobtrusive measure or measurement 59, 71, 89, 98, 250 valence negative 1, 3–6, 15 positive 1, 3–6, 15 validity external validity 121–122 face validity 121, 182 internal validity 121–122 variability of 142–143 vehicle/habitat design 219, 264 viability 116–117

298 virtual experiments 60, 114–115, 123–124, 251 visual performance 282–285, 288 wearable devices, or wearables 88–89, 95 winter-over 229 work recovery 221–223 work/non-work balance see work-life balance

Index work-family enrichment see work-nonwork enrichment work-life balance 220–221, 224, 230 workload 38, 47, 52, 69, 78, 80, 94, 96, 115, 118, 120–121, 203–205, 221–222, 226, 228, 263–264, 267, 269, 271, 273–274, 276–281, 284 work-nonwork enrichment 225