Reconceptualising Information Processing for Education [1st ed.] 9789811570506, 9789811570513

Table of contents :
Front Matter ....Pages i-xiv
Front Matter ....Pages 1-2
Learning and Memory in Modern Cognitive Psychology and Integrative Biology (Geoff Woolcott)....Pages 3-7
Modern Cognitive Psychology and Learning and Memory Processes (Geoff Woolcott)....Pages 9-12
Modern Integrative Biology and Learning and Memory Processes (Geoff Woolcott)....Pages 13-26
Connections Between Studies of Human Learning and Memory Processes in Modern Cognitive Psychology and Integrative Biology (Geoff Woolcott)....Pages 27-42
Contributions of Modern Cognitive Psychology and Integrative Biology to Educational Theories and Practices (Geoff Woolcott)....Pages 43-56
Front Matter ....Pages 57-59
Placing Human Learning and Memory in a Broad Context (Geoff Woolcott)....Pages 61-77
A Broad View of Information Processing Systems (Geoff Woolcott)....Pages 79-116
Front Matter ....Pages 117-119
The Universal Information Processing System and Educational Theories and Practices (Geoff Woolcott)....Pages 121-134
Universal Information Processing Systems, Generalised Educational Principles and Generalised Cognitive Processes (Geoff Woolcott)....Pages 135-160
Back Matter ....Pages 161-168

Citation preview

Geoff Woolcott

Reconceptualising Information Processing for Education

Reconceptualising Information Processing for Education

Geoff Woolcott

Reconceptualising Information Processing for Education

123

Geoff Woolcott School of Education Southern Cross University Lismore, NSW, Australia

ISBN 978-981-15-7050-6 ISBN 978-981-15-7051-3 https://doi.org/10.1007/978-981-15-7051-3

(eBook)

© Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Education plays a major role in human cultural accumulation in modern industrialised societies, but, until recently, educational theories and practices were reliant on studies of learning and memory based in the social and behavioural sciences and historical practice. The theory and practice of education have been influenced increasingly by studies in modern cognitive psychology, in particular, those studies with a focus on learning and memory considered in terms of human–environmental interaction and information processing. However, studies based in the scientific empiricism of integrative biology—some of which have had a similar focus— appear to have had less influence. This book is designed to rebalance these influences, examining learning and memory in a context of both modern cognitive psychology and integrative biology, as well as in the social and behavioural sciences and historical practice. In taking this broad approach, the book examines learning and memory processes in terms of information processing in a range of organisms and non-organismal structures and their environmental interactions. Based on commonalities in interactions and processes related to environmental connectivity, the book outlines the development of a novel conceptualisation of information in terms of Universal Information, a fundamental concept based in matter and energy systems. This conceptualisation of Universal Information is used to reframe system-wide information processing as a reconceptualisation encapsulated within Universal Information Processing Systems (UIPSs). This reconceptualisation is used to construct an overarching framework that may be applied to the examination of learning and memory processes, in a broad sense, in all organisms and structures. Within this framework, memory is described in terms of the range of possibilities or potentialities of any matter and energy system in a given time interval, and learning as any environmental information input or output that results in a change in that memory. This broad framework accommodates conventional views of human learning and memory and the book outlines the framework’s potential application in education. In particular, the framework is used to examine how educational theories and practices may be integrated scientifically, with an examination of the educational v

vi

Preface

principles of cognitive load theory used as an illustrative example. Further, the book explores how both derived and novel generalised educational principles may be developed within this framework and applied in modern education. Finally, the book examines how the UIPS framework may be used in the development of generalised cognitive models for learning and memory processes, in particular, models that are based on the consideration of environmental interactions and information pathways through an examination of network theory and complexity theory. The book is intended primarily for researchers who are considering information theory in educational contexts, and this includes researchers in computing and machine learning. Researchers using this book will, at last, have a conceptualisation of information that is not probabilistic or computational, but which has a sound philosophical basis as well as a sound basis in the biological sciences. The book will be useful across a variety of academic disciplines and may be suitable as a teaching text in a diverse range of courses in education, psychology, computer science, information technology as well as biology. The book would be useful, for example, in machine learning courses that consider holistic learning environments and their impact on learning structures as redefined in the book. The book is also intended for practitioners (educators). The application of this novel view of information theory in considering human learning and memory processes provides potential new approaches for educators in considering, for example, learning readiness as a type of system readiness—how can we make our classrooms better for learning interactions, and how can we enable student readiness in a more holistic but non-esoteric way? Such new approaches may be valuable, therefore, for university and classroom educators who are involved in considering the best ways for their students to learn, placing the emphasis back on a student-centred focus that considers all aspects of a student’s circumstances, including their physical self and its interaction with learning environments. Lismore, Australia

Geoff Woolcott

About This Book

In modern industrialised societies, each individual between the age of about five and sixteen may spend as many as twenty hours a week in an educational institution, with some individuals continuing this type of education for many more years. Such institutionalised education has become an important way for individuals to learn complex cultural information that would otherwise not be learned from parents, society or the natural environment, with teaching playing a prominent role in such education in many modern societies. This style of education has become an important part more generally of the process of the transmission and accumulation across society of culture, here considered as inclusive of all knowledge and skills— a type of cultural ratcheting. Cultural transmission can be considered not just in the passive sense of a conduit metaphor but in a sense that includes concepts of information processing, and the culture accumulated may include any information that is stored within or external to one human memory (e.g. in books and electronic media). The sharing of this accumulated culture is arguably a major factor in human survival and in the maintenance of human civilisation (see Woolcott 2011, 2016). Institutional education plays a role in ensuring that the transmission of accumulated culture is transgenerational and, in societies with the means for its support, such education may take place in many different contexts, from one-on-one interactions to multiple interactions within large groups. In the modern world, however, education in an institutional setting commonly takes place with a single teacher interacting with a large number of students. In industrialised societies, teaching practices and the educational theories upon which they are based are embedded in concepts of learning and memory that were developed largely from the social and behavioural sciences or through historical practice, with such theories and practices relying on an educational culture embedded in beliefs and assumptions. Such theories and practices may be based on differing basic assumptions about the nature of learning and memory, resulting in difficulties in communication across theoretical perspectives as well as problems with incommensurability and irreducibility. These difficulties may have been exacerbated by the lack of attempts to verify empirically the efficacy of many educational theories (see Wooclott 2013, 2016).

vii

viii

About This Book

Some of the educational theories and practices used in modern educational institutions, however, have been informed by phenomenological investigations of learning and memory and information processing in disciplines such as cognitive psychology. These include teaching practices based on the influential educational theories, such as Cognitive Load Theory (CLT). In some modern educational theories and practices, there has been an emphasis on examining the function of learning and memory in processing novel environmental information, an interaction sometimes referred to as problem-solving. Problem-solving, in this sense, does not refer to a more conventional view of the intention to change a system’s state from the current to the desired state (referred to in Chap. 2 as “solving problems”) but is more akin to the notion of dealing with novel sensory information in a context related to functions of the central nervous system as attention and working memory. Many modern educational theories and the diversity of teaching practices derived from them, however, remain largely unreconciled or irreconcilable, despite the dialogue that appears to be developing amongst educational researchers; such theories and practices may benefit from having a consistent theoretical background described in terms of a single system. It is partly for this reason that some researchers have argued for the scientific integration of the educational theories that form the basis of modern institutional education. Recent research in the biology-related sciences—here referred to under the umbrella term of integrative biology, in particular, from genetics and neuroscience—appears to offer a path towards such scientific integration, partly through provision of a more detailed account of learning and memory that involves the investigation of pathways between individuals and environment. Learning and memory, and human cultural accumulation have been investigated in integrative biology on a number of levels, from the coarse-grained phenomenological level to a more fine-grained chemical level. Such studies indicate that a view of human learning and memory may be developed, in an evolutionary context, from the consideration of the information pathways that connect the individual with the environment through the nervous system. The evolution of such pathways has enabled the growth, survival and reproduction of individuals within human society, and education utilises this already operational connectivity in order to accumulate culture that can be transmitted between individuals, as well as between generations. Conceptualisations of learning and memory can, however, be viewed from within a broad context that involves a consideration of environmental interactions of organisms more generally, including the consideration of drives and emotions and their impact on motivation and social interaction. Within this broad context, studies of networks and complex systems indicate that there are commonalities in patterns of connectivity associated with learning and memory in all organisms and non-organismal structures and systems. Such commonalities are relevant to studies of education, indicating the potential for embracing scientific models of cognition across organismal and non-organismal boundaries. Despite a lack of progress in the

About This Book

ix

direction of consensus, systemic or generalised views of learning and memory taken from within broad scientific contexts offer the potential for developing a single overarching framework that may be used to investigate human learning and memory, and education and teaching. In Part I of this book, environmental interaction and information processing are used to develop an argument for the usefulness in educational research of a broad view of learning and memory in terms of informational connectivity. In Part II, this argument is developed as the main focus of this book, with commonalities examined in information pathways and information processing related to environmental interactions of organisms and non-organismal structures. In Part II, an outline is also given as to how the consideration of these commonalities may be accommodated within a single framework through the development of novel and fundamental reconceptualisations of information and information processing systems. These reconceptualisations are not related closely to the probabilistic and mathematical conceptualisations seen in information theory or computing, and which have been utilised in modern integrative biology, but rather to conceptualisations based in the observable world of matter and energy. In using such conceptualisations, the varying descriptions of learning and memory seen in organisms and non-organismal structures have been integrated within a single system that is based in scientific assumptions. Within the framework constructed within this system, a broad description is developed of learning and memory processes in terms of information processing and environmental interaction. In Part III of the book (the final part), the descriptions of learning and memory developed within this framework have been used to examine the potential for educational theories and practices to be integrated scientifically. Here, it is argued that educational theories that can be expressed in terms of connectivity of matter and energy pathways may be so integrated. As an illustrative example, the book outlines how the educational principles described in CLT may be expressed in terms of this framework and argues, therefore, that these principles may be examined through modern scientific methodologies, procedures and protocols. In Part III, the idea of generalised educational principles developed within the broad framework is also examined. Several such principles are outlined, including generalised versions of the educational principles of CLT and two novel educational principles. Such principles may be useful in examining learning and memory more generally, as well as having specific applications in human education. Finally, Part III examines the potential use of the framework in the development of generalised cognitive models that may be applied to educational theories and practices, in particular, those that are based in the consideration of network theory and complexity theory. The reconceptualisations of information and information processing systems described in this book may be useful in obtaining a clearer understanding of the concepts of learning and memory as pathways of connectivity in environmental contexts. Such an understanding may enhance the understanding of human learning and memory, as well as education and cultural accumulation. In addition, the application of the learning and memory framework developed from these novel

x

About This Book

conceptualisations may lead to improvements in education and teaching that may serve the transgenerational transmission of human culture and the continued existence of humanity in the modern world.

References Woolcott, G. (2011). A broad view of education and teaching based in educational neuroscience. International Journal for Cross-Disciplinary Subjects in Education, Special Issue, 1(1), 601–606. Woolcott, G. (2013). Giftedness as cultural accumulation: An information processing perspective. High Ability Studies, 24(2), 153–170. Woolcott, G. (2016). Technology and human cultural accumulation: The role of emotion. In S. Tettegah & R. E. Ferdig (Eds.), Emotions, technology, and learning, (pp. 243–263). London, UK: Academic Press.

Contents

Part I

Learning, Memory and Education: Patterns and Connections

1 Learning and Memory in Modern Cognitive Psychology and Integrative Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 5

2 Modern Cognitive Psychology and Learning and Memory Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 11

3 Modern Integrative Biology and Learning and Memory Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Environmental Connectivity and Learning and Memory . 3.2 Integrative Biology and Learning and Memory Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Intrinsic Pathways, Emotions and Drives . . . . . . . . . . . . 3.4 Patterns of Connectivity in the Central Nervous System . 3.5 Patterns of Environmental Connectivity and Information Processing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

....... .......

13 13

....... ....... .......

14 16 18

....... .......

20 22

4 Connections Between Studies of Human Learning and Memory Processes in Modern Cognitive Psychology and Integrative Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Long-Term Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Long-Term Memory Processes . . . . . . . . . . . . . . . . . . 4.1.2 Memory Elements, Schemas and Pathways . . . . . . . . . 4.1.3 Repetition, Automation and Neuronal Activation . . . . . 4.2 Short-Term Memory and Working Memory . . . . . . . . . . . . . .

. . . . . .

. . . . . .

. . . . . .

27 28 28 30 31 32

xi

xii

Contents

4.3 Attention and Working Memory . . . . . . . . . . . . . . . . . 4.3.1 Information Processing Through Attention and Working Memory . . . . . . . . . . . . . . . . . . . 4.3.2 Limitations on Attention and Working Memory . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

........

34

........ ........ ........

34 35 37

5 Contributions of Modern Cognitive Psychology and Integrative Biology to Educational Theories and Practices . . . . . . . . . . . . . . 5.1 Educational Theory, Modern Cognitive Psychology and Environmental Context . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Educational Theory, Integrative Biology and Environmental Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Environmental Information Transmission, Intrinsic Networks and Educational Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

...

43

...

43

...

45

... ...

47 50

Part II Towards a Broad-Based Research Framework for Education and Teaching 6 Placing Human Learning and Memory in a Broad Context . . . . . 6.1 Learning, Memory and Connectivity in Organisms with a Centralised Nervous System . . . . . . . . . . . . . . . . . . . . . 6.2 Learning and Memory in Organisms with a Non-centralised Nervous System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Learning, Memory and Connectivity in Multicellular Organisms with No Nervous System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Commonalities in Learning and Memory Pathways: Plasticity in Multicellular and Unicellular Organisms . . . . . . . . . . . . . . . 6.5 Learning, Memory and Environmental Connectivity . . . . . . . . . 6.6 Learning, Memory and Non-organismal Connectivity . . . . . . . . 6.7 Learning, Memory and Behaviour . . . . . . . . . . . . . . . . . . . . . . 6.8 Information Pathways and Information Processing Structures . . 6.9 Learning and Memory in a Broad Sense . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 A Broad View of Information Processing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Information and Information Processing Systems . . . . . . . . . . . 7.2 Information Processing Systems in a Universal Sense . . . . . . . . 7.2.1 Memory Potential in a Universal Information Processing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Memory Expression in a Universal Information Processing System . . . . . . . . . . . . . . . . . . . . . . . . . . . .

..

61

..

62

..

63

..

63

. . . . . . .

. . . . . . .

65 66 67 68 69 70 71

.. .. ..

79 79 83

..

85

..

86

Contents

xiii

7.2.3 Learning Potential in a Universal Information Processing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.4 Temporality and Information Loss in a Universal Information Processing System . . . . . . . . . . . . . . . . . . . 7.3 Organisms and Non-organismal Structures as Universal Information Processing Systems . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Non-organismal Structures and Universal Information Processing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Organisms, Phenotypic Plasticity and Universal Information Processing Systems . . . . . . . . . . . . . . . . . . 7.3.3 Organismal Learning and Memory and Universal Information Processing Systems . . . . . . . . . . . . . . . . . . 7.4 The Human Organism as a Universal Information Processing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 The Human Universal Information Processing System . . 7.4.2 The Human Universal Information Processing System and Temporality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 The Human Universal Information Processing System and Information Loss . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Universal Information Processing Systems and Human Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Linked Information Systems Within the Human Organism . . . . 7.7 Universal Information Processing Systems and System-Wide Learning and Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.1 System-Wide Learning and Memory: Predictions and Concurrences . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.2 System-Wide Learning and Memory in Organisms . . . . 7.7.3 System-Wide Learning and Memory in Non-organismal Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part III

..

87

..

89

..

89

..

90

..

90

..

91

.. ..

92 92

..

94

..

96

.. ..

98 99

. . 101 . . 101 . . 103 . . 105 . . 106

Utilising the Broad Framework to Examine Educational Theories and Practices

8 The Universal Information Processing System and Educational Theories and Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Educational Theories and the Assumptions of Science . . . . . 8.2 Cognitive Load Theory and Scientific Integration . . . . . . . . . 8.2.1 Universal Information Processing Systems and the Information Store Principle . . . . . . . . . . . . . . 8.2.2 Universal Information Processing Systems and the Borrowing and Reorganising Principle . . . . . 8.2.3 Universal Information Processing Systems and the Principle of Random Genesis of Information .

. . . . 121 . . . . 121 . . . . 123 . . . . 124 . . . . 125 . . . . 126

xiv

Contents

8.2.4 Universal Information Processing Systems and the Narrow Limits of Change Principle . . . . . . . . . . . . 128 8.2.5 Universal Information Processing Systems and the Environmental Organising and Linking Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 9 Universal Information Processing Systems, Generalised Educational Principles and Generalised Cognitive Processes . . . 9.1 Universal Information Processing Systems and Generalised Educational Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.1 Generalising Educational Principles from Cognitive Load Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.2 Generating Novel Educational Principles from Consideration of Universal Information Processing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.3 Generalised Principles and Education . . . . . . . . . . . . . 9.2 Universal Information Processing Systems and Generalised Cognitive Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Universal Information Processing Systems, Generalised Cognitive Processes and Studies of Connectivity . . . . . 9.2.2 Universal Information Processing Systems, Generalised Cognitive Processes and Performance . . . . . . . . . . . . . 9.2.3 Universal Information Processing Systems, Generalised Cognitive Processes and Analysis of Patterns . . . . . . . 9.2.4 Universal Information Processing Systems, Generalised Cognitive Processes and Determination of Subject Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . 135 . . . 136 . . . 136

. . . 138 . . . 141 . . . 142 . . . 143 . . . 144 . . . 147

. . . 148 . . . 153

Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Glossary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

Part I

Learning, Memory and Education: Patterns and Connections

Part I of this book examines learning and memory in a gradually broadening context, from the human organism to organisms in general, through to non-organismal structures, in order to investigate and compare learning and memory processes. In so doing, Part I shows how this overview of learning and memory, based on environmental interaction and information processing, can provide an overview of human education in terms of informational connectivity. Figure 1 provides a schematic outline of Part I, showing how the broad context is obtained from an initial examination of human learning and memory, moving outward to increasingly generalised contexts. Part I initially explores how the understanding of learning and memory that has developed from studies in modern cognitive psychology, including those studies that have incorporated models of human connectivity and environmental interaction, has influenced educational theories and practices. Part I considers, for example, the conceptualisations of long-term memory, short-term memory, working memory and attention that have arisen in studies of learning and memory processes within cognitive psychology and their relationship to problem-solving of novices versus experts via schema acquisition. Part I then outlines how insights into learning and memory, obtained from integrative biology, as well as the combination of such insights with those from cognitive psychology and other disciplines within the social and behavioural sciences, offer potentially valuable contributions to the development of educational theories and practices. The notion of information pathways is introduced, as well as the idea that the examination of connections of such pathways is dependent on physical processes when considered in the context of integrative biology. In Chap. 3, for example, such pathways are discussed in terms of the connections of the central nervous system at multiple levels of scale from neuronal populations to entire brain regions. Part I then investigates environmental interaction and information processing as key components of the understanding of learning and memory. Such studies can provide commonalities in the processing of environmental information within individuals and in the patterns of connectivity generated by such environmental interaction. Chapter 4, for example, examines the connections across human learning and

2

Part I: Learning, Memory and Education: Patterns and Connections

Learning and memory in organisms and non-organismal structures Learning and memory in unicellular and multicellular organisms Learning and memory in multicellular organisms Learning and memory in multicellular organisms with a central nervous system Human learning and memory

Fig. 1 Learning and memory in increasingly generalised contexts

memory processes that have emerged from commonalities in studies of cognitive psychology and integrative biology, as well as from studies that have undertaken combination studies of both disciplines. Part I then outlines some of the educational theories that have been developed from these and other studies, with a focus on educational theories that have incorporated concepts related to environmental connectivity and information processing. This includes theories, for example, that are based on the conceptualisation of changes to long-term memory that are limited by attention and working memory. As part of this focus, there is an outline of how studies of commonalities in patterns of environmental connectivity have been related to modern educational theories and practices more generally. In Part I, therefore, studies of human learning and memory processes are examined with regard to commonalities in the processing of environmental information within individuals and in the patterns of connectivity generated by such environmental interaction. As part of this examination, there is an outline of how studies of commonalities in patterns of environmental connectivity have been related to modern educational theories and practices.

Chapter 1

Learning and Memory in Modern Cognitive Psychology and Integrative Biology

Many models of human memory that are based in cognitive psychology describe the encoding or storage of environmental information in memory, including knowledge or skills, as information recalled and applied on demand (Calvin 2013; Edelman 2007; Goonatilake 1991). Such models generally support mid-twentieth-century studies indicating that information is encoded as memory for short periods of time, for example, as short-term memory (STM), or for longer, generally indefinite periods of time, as long-term memory (LTM) (Miller 2003). Human learning can be considered as the process of the transfer and integration of information—information sometimes referred to in a collective sense as culture (inclusive of knowledge, skills and experiences; see Woolcott 2016)—into LTM. Education in an institutional environment is one way that such information is transmitted from one individual to another (Woolcott 2013, 2016). For any individual, some of this information may be reorganised internally and then added as additional information to that individual’s own LTM (Edelman 2007). This model of memory as information storage in LTM has proved useful in education and is based partly on studies of expert–novice differences and problem-solving across a number of learning areas. The concept was developed of an LTM that is not simply a repository of isolated and unrelated packets of information but rather the central structure of human cognition essential to dealing with novel sets of circumstances that provide the essential basis of problem-solving (Sweller et al. 2011). Studies in cognitive psychology, and phenomenological studies more generally, indicate that the mechanisms of human learning involve input information being attended to and processed in working memory (WM) in order for it to be added as knowledge and skills to LTM (Lachman et al. 1979). Based on such studies, it is considered that only some of the constant stream of environmental information that enters a human through the sensory system is attended to by the brain. The information attended to is compared effectively with information already held as memories in the brain in order for each human to interact with their environment (Edelman 2007). In this conceptualisation of learning and memory, therefore, some of the new information attended to is used as a basis for © Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_1

3

4

1 Learning and Memory in Modern Cognitive Psychology …

behaviour and stored for a short period as WM or STM, or for a longer period as part of LTM. The concepts of learning and memory, therefore, are closely tied to both the idea of attention and of STM or WM, with STM and WM sometimes considered as synonymous (Cowan 2016; Postle 2015). The concept of WM has various interpretations, but Sweller et al. (2011) refer to it, in terms of function, as performing the intellectual tasks associated with consciousness, which follows closely the sense of the concept as originally proposed by Miller (2003). The understanding of learning and memory that has developed from studies in modern cognitive psychology, including those studies that have incorporated models of human connectivity and environmental interaction, has influenced educational theories and practices (Bruner 1964; Lachman et al. 1979; Sweller et al. 2011). This can be seen, for example, in teaching practices based in such influential educational theories as activity theory (Vygotsky 1986), distributed cognition theory (Heersmink and Knight 2018), critical theory (Davis et al. 2008), constructivism (Zajda 2018), constructionism (Papert and Harel 1991), connectivism (Siemens 2017) and cognitive load theory (Sweller et al. 2011). The emergence in integrative biology, in particular in such disciplines as neuroscience, genetics and biochemistry, has affected our understanding of the function of learning and memory as described in studies in cognitive psychology (Baars and Gage 2010; Dehaene 2009; Howard-Jones 2018). Studies of the brain and its connectivity across the entire nervous system, from the coarse-grained phenomenological level to a more fine-grained chemical level, provide a more detailed view of the internal workings of the brain during cognitive processes, including the anatomical and biological processes involved in both WM (or STM) and attention (Edelman 2007; Goswami 2008; Llinás 2001; Tomasello 2014; Tonegawa et al. 2003). Such studies appear to support the connectionist cognitive frameworks used as a basis for recently developed educational theories (e.g. Heersmink and Knight 2018; Lachman et al. 1979; Siemens 2017; Sweller et al. 2011). Research in integrative biology has also detailed potential links between patterns of connections in the real world and their analogous record as memories in the brain (Calvin 2013; Goswami 2008; Rowland et al. 2016) as well as the patterns of connections that allow for efficient storage of large amounts of information in LTM (Edelman 2007; Sporns 2012). Such research, combined with recent developments in studies of networks and complex systems, lends support more generally to an argument for common patterns in the structure and development of networks or information processing systems (Baars and Gage 2010; Newman 2018), and this includes human cognitive systems (Sporns 2012). Some of these combination studies have been applied in educational contexts through theoretical approaches based on network theory and complexity theory (e.g. Davis et al. 2008). Despite these advances in critical knowhow, some educational researchers have suggested that the understanding of learning and memory that has developed in recent years from studies in integrative biology, with its focus on empirical science, has had much less influence than that of cognitive psychology, or than the social sciences and humanities (Blakemore and Frith 2000; Howard-Jones 2018; Sigman et al. 2014). Insights into learning and memory obtained from integrative biology, therefore, as

1 Learning and Memory in Modern Cognitive Psychology …

5

well as the combination of such insights with those from cognitive psychology and other disciplines within the social and behavioural sciences, offer potentially valuable contributions to the development of educational theories and practices. Educational researchers have begun to comment on the potential improvements in education and cultural accumulation that such insights offer, but with an acknowledgement that progress has been problematic (Sigman et al. 2014; Tokuhama-Espinosa 2019). However, studies in cognitive psychology and integrative biology as well as combination studies have indicated that environmental interaction and information processing are key components of the understanding of learning and memory. Some research in integrative biology, for example, has offered conceptualisations of learning and memory viewed from within a broad context that considers environmental interactions of organisms more generally, including considering drives and emotions and their impact on motivation and social interaction (Damasio 2006; Panksepp and Biven 2012). Within this broad context, recent developments in studies of networks and complex systems indicate that there are common patterns of connectivity associated with learning and memory, not just in humans and the cultural accumulation gained through education but also in other organisms and non-organismal structures and systems (Barabási 2016; Chaitin 2012; Quiroga 2019; Sporns 2012; Wolfram 2002). Such commonalities in patterns of connectivity are relevant to studies of education and point to a valuable expansion of educational theories so that they are tied more closely to scientific models of cognition that have been related to both humans and other organisms (Dehaene 2009; Godfrey-Smith 2017; Goswami 2008; HowardJones 2018). Research in connectivity, however, instead of leading to the development of a consensus on the nature of learning and memory, or its links with information processing, has led to a range of differing and sometimes conflicting views (Tononi et al. 2016; Woolcott 2010, 2011, 2016). Systemic or generalised views of learning and memory taken from within broad scientific contexts, however, offer some resolution through the development of a single overarching framework for use in investigating human learning and memory, and education and teaching. Acknowledgements Parts of this chapter are adapted from Woolcott, G. 2010. Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496). Strasbourg, France: Analytrics.

References Baars, B. J., & Gage, N. M. (2010). Cognition, brain, and consciousness: Introduction to cognitive neuroscience. Cambridge, MA: Academic Press. Barabási, A. L. (2016). Network science. Cambridge, UK: Cambridge University Press. Blakemore, S. J., & Frith, U. (2000). The implications of recent developments in neuroscience for research on teaching and learning. London, UK: Institute of Cognitive Neuroscience. Bruner, J. S. (1964). Towards a theory of instruction. Cambridge, MA: Harvard University Press. Calvin, W. H. (2013). How brains think: Evolving intelligence, then and now. London, UK: Hachette.

6

1 Learning and Memory in Modern Cognitive Psychology …

Chaitin, G. J. (2012). Life as evolving software. In H. Zenil (Ed.), A computable universe: Understanding computation and exploring nature as computation (pp. 1–23). London, UK: World Scientific. Cowan, N. (2016). Working memory capacity (Classic ed.). New York, NY: Routledge. Damasio, A. R. (2006). Descartes’ error (Revised ed.). London, UK: Random House. Davis, B., Sumara, D., & Luce-Kapler, R. (2008). Engaging minds: Changing teaching in complex times. New York, NY: Routledge. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin Viking. Edelman, G. M. (2007). Learning in and from brain-based devices. Science, 318(5853), 1103–1105. Godfrey-Smith, P. (2017). Complexity revisited. Biology and Philosophy, 32(3), 467–479. Goonatilake, S. (1991). The evolution of information: Lineages in gene, culture and artefact. London, UK: Pinter. Goswami, U. (2008). Reading, complexity and the brain. Literacy, 42(2), 67–72. Heersmink, R., & Knight, S. (2018). Distributed learning: Educating and assessing extended cognitive systems. Philosophical Psychology, 31(6), 969–990. Howard-Jones, P. (2018). Evolution of the learning brain: Or how you got to be so smart. London, UK: Routledge. Lachman, R., Lachman, J. L., & Butterfield, E. C. (1979). Cognitive psychology and information processing: An introduction. Hillsdale, NJ: Lawrence Erlbaum. Llinás, R. (2001). I of the vortex: From neurons to self . Cambridge, MA: MIT Press. Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141–144. Newman, M. E. J. (2018). Networks. London, UK: Oxford University Press. Panksepp, J., & Biven, L. (2012). The archaeology of mind: Neuroevolutionary origins of human emotions. New York, NY: WW Norton & Company. Papert, S., & Harel, I. (1991). Constructionism. Norwood, NJ: Ablex Publishing Corporation. Postle, B. R. (2015). Neural bases of the short-term retention of visual information. In P. Jolicoeur, C. Lefebvre, & J. Martinez-Trujillo (Eds.), Mechanisms of sensory working memory: Attention and performance XXV (pp. 43–58). London, UK: Academic Press. Quiroga, R. Q. (2019). Neural representations across species. Science, 363(6434), 1388–1389. Rowland, D. C., Roudi, Y., Moser, M. B., & Moser, E. I. (2016). Ten years of grid cells. Annual Review of Neuroscience, 39, 19–40. Siemens, G. (2017). Connectivism. In R. West (Ed.), Foundations of learning and instructional design technology. Montreal, Canada: Pressbooks. Sigman, M., Peña, M., Goldin, A. P., & Ribeiro, S. (2014). Neuroscience and education: Prime time to build the bridge. Nature Neuroscience, 17(4), 497. Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT press. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Explorations in the learning sciences, instructional systems and performance technologies. The Netherlands: Springer. Tokuhama-Espinosa, T. (2019). Five pillars of the mind: Redesigning education to suit the brain. New York, NY: WW Norton & Company. Tomasello, M. (2014). A natural history of human thinking. Cambridge, MA: Harvard University Press. Tonegawa, S., Nakazawa, K., & Wilson, M. A. (2003). Genetic neuroscience of mammalian learning and memory. Philosophical Transactions of the Royal Society of London, B, 358, 787–795. Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461. Vygotsky, L. S. (1986). Thought and language. Cambridge, MA: Harvard University Press. Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media. Woolcott, G. (2010). Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496). Strasbourg, France: Analytrics.

References

7

Woolcott, G. (2011). A broad view of education and teaching based in educational neuroscience. International Journal for Cross-Disciplinary Subjects in Education, Special Issue, 1(1), 601–606. Woolcott, G. (2013). Giftedness as cultural accumulation: An information processing perspective. High Ability Studies, 24(2), 153–170. Woolcott, G. (2016). Technology and human cultural accumulation: The role of emotion. In S. Tettegah & R. E. Ferdig (Eds.), Emotions, technology, and learning (pp. 243–263). London, UK: Academic Press. Zajda, J. (2018). Effective constructivist pedagogy for quality learning in schools. Educational Practice and Theory, 40(1), 67–80.

Chapter 2

Modern Cognitive Psychology and Learning and Memory Processes

Each and every individual can retain information as memories, and some of these memories, stored as long-term memory (LTM), can be recalled during a human lifetime. The memories stored as LTM can be classified broadly in terms of declarative systems and non-declarative systems (Squire 1994). Declarative memory— the memory of everyday facts and events—can be divided into semantic memory (facts) and episodic memory (events). Non-declarative memory—expressed through performance rather than recollection (as is the case with declarative memory)—is an umbrella term for a range of memory abilities that include acquisition of skills and habits and conditioned emotional responses (see summary in Howard-Jones 2008). In storing memories through learning, attended input information, held as short-term memory (STM), is processed consciously as working memory (WM) and added as knowledge to LTM (Cowan 2016; Miller 2003). This LTM can be thought of as a large and almost unlimited data store (Sweller et al. 2011). During such learning, the information in LTM may be added to or modified, and attention and WM can be viewed as mechanisms that allow the comparison of novel sensory information with memories in LTM and which add information to LTM in a limited way (D’Esposito and Postle 2015; Postle 2006, 2015). Besides studies of the nature of the stored information in LTM, investigations of LTM in cognitive psychology have included studies of the relation of LTM to problem-solving and thought. One of the theories that developed from such research was that, in human cognition, some information is stored in LTM as associations of information called schemas. The modern origins of schema theory can be found in Piaget (1928) and Bartlett (1932), although the theory was largely ignored for several decades during the Behaviourist era (see LeDoux 1996). Schema theory proposes that information in LTM is stored as connected elements or schemas and that multiple elements of information connected as chunks can be treated as a single schema (Gobet 2005; Gobet et al. 2016). The relevance of schemas to problem-solving was emphasised by Larkin and others (e.g. Larkin et al. 1980), who provided theory and data indicating that the possession of domain-specific schemas differentiated novices from experts in a © Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_2

9

10

2 Modern Cognitive Psychology and Learning and Memory Processes

particular area. As a result of this reasoning, the extent to which one was skillful or knowledgeable in an area is considered to depend on the number and sophistication of the schemas stored in LTM (Gobet 2005); such considerations have been applied successfully to educational theory in the development of models of instructional design for use in teaching (Sweller et al. 2011). Although information can be processed rapidly by WM, the number of schemas that may be held in WM at any given time is considered to be small, typically four to seven in a normal human adult (Cowan 2016; Miller 2003). Connected packets of information from LTM may be processed as large single schemas or chunks, whereas input information may be in discrete elements or small relatively unconnected schemas (Cowan 2016). Some researchers have argued that coherent structures of schemas in LTM can be understood as concepts (Bahr et al. 2019; Thompson 1994). In this view of cognitive architecture, learning requires that WM be actively engaged in the comprehension and processing of instructional material in order for to-be-learned information to be encoded into LTM either as single discrete elements or as elements combined together as schemas (Sweller et al. 2011). Schema theory provides a vehicle that permits multiple elements of information to be treated as a single element according to the manner in which those elements are to be used. Thus, a schema for solving problems permits the classification of problems according to their solution mode. A chess master, for example, has schemas that allow chessboard configurations to be classified according to the moves required. The term “solving problems” is used here in the sense, say, of solving a written mathematical problem and is not to be confused with the term “problem-solving”, which is reserved here for use in a context related to such functions of the central nervous system as attention and WM (sensu Sweller 1988, Tonegawa et al. 2003). While there is no metric for measuring the amount of information held in LTM, it might be noted that Simon and Gilmartin (1973) estimated that some chess grand masters have acquired many tens of thousands of schemas as required for their level of competence. It is reasonable to assume that similar numbers of schemas are required for skilled performance in areas more relevant to everyday life, including areas of performance in educational contexts. If so, LTM holds sufficient numbers of schemas to permit adequate levels of performance in the various areas in which an individual is competent, including all aspects of our daily lives that involve movement, either as covert or overt motor responses. Studies in cognitive psychology indicate that, ideally, learning should facilitate the automation of schemas so that at some point knowledge is processed unconsciously rather than consciously in WM (Schneider and Shiffrin 1977; Shiffrin and Schneider 1977). Problem-solving using automated knowledge frees WM capacity for problem-solving searches as compared to a situation where basic processes must be considered consciously (Kotovsky et al. 1985). Automation is required in order for information elements to be stored and used effectively as schemas, and this may require the reinforcement of connections between knowledge elements. The automation of simple (lower-level) schemas may be essential for constructing the more complex (higher-level) schemas needed to establish mental structures with which to evaluate information efficiently from the real world (Vandervert 1997, 2003).

2 Modern Cognitive Psychology and Learning and Memory Processes

11

Without the automatic processing of the letters of the alphabet, for example, due to the automation of schemas associated with recognising those letters, it would be difficult to combine those letters into the words and sentences required for reading and, without such automation, reading is likely to be slow and clumsy (Pinker 1997; Sweller et al. 1998, 2011). Similarly, it would be difficult to drive a car safely if a large portion of WM was needed to operate consciously the mechanisms used for driving, rather than a portion of that WM being available for monitoring the driving environment.

References Bahr, G. S., Allen, W. H., Bernhard, P. J., & Wood, S. (2019). The artificial memory of Mr. Polly: Memory simulation in databases and the emergence of knowledge. Leonardo, 52(3), 300–304. Bartlett, F. (1932). Remembering: A study in experimental and social psychology. London, UK: Cambridge University Press. Cowan, N. (2016). Working memory capacity (Classic ed.). New York, NY: Routledge. D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory. Annual Review of Psychology, 66, 115–142. Gobet, F. (2005). Chunking models of expertise: Implications for education. Applied Cognitive Psychology, 19, 183–204. Gobet, F., Lloyd-Kelly, M., & Lane, P. C. (2016). What’s in a name? The multiple meanings of “chunk” and “chunking”. Frontiers in Psychology, 7, 102. Howard-Jones, P. A. (2008). Philosophical challenges for researchers at the interface between neuroscience and education. Journal of the Philosophy of Education, 42(3–4), 361–380. Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problems hard? Evidence from Tower of Hanoi. Cognitive Psychology, 17, 248–294. Larkin, H., McDermott, J., Simon, D., & Simon, H. (1980). Models of competence in solving physics problems. Cognitive Science, 11, 65–99. LeDoux, J. E. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York, NY: Touchstone. Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141–144. Piaget, J. (1928). The child’s conception of the world. London, UK: Routledge. Pinker, S. (1997). How the mind works. New York, NY: W. W. Norton. Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, 23–38. Postle, B. R. (2015). Neural bases of the short-term retention of visual information. In P. Jolicoeur, C. Lefebvre, & J. Martinez-Trujillo (Eds.), Mechanisms of sensory working memory: Attention and performance XXV (pp. 43–58). London, UK: Academic Press. Schneider, W., & Shiffrin, R. (1977). Controlled and automatic human information processing: I. Detection, search and attention. Psychological Review, 84, 1–66. Shiffrin, R., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190. Simon, H., & Gilmartin, K. (1973). A simulation of memory for chess positions. Cognitive Psychology, 5, 29–46. Squire, L. R. (1994). Declarative and nondeclarative memory: Multiple brain systems supporting learning and memory. In D. L. Schacter & E. Tulving (Eds.), Memory systems (pp. 203–231). Cambridge, MA: MIT Press.

12

2 Modern Cognitive Psychology and Learning and Memory Processes

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285. Sweller, J., van Merriënboer, J., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Explorations in the learning sciences, instructional systems and performance technologies. Dordrecht, The Netherlands: Springer. Thompson, P. W. (1994). The development of the concept of speed and its relationship to the concept of rate. In G. Harel & J. Confrey (Eds.), The development of mulplicative reasoning in the learning of mathematics (pp. 181–236). Albany, NY: State University of New York Press. Tonegawa, S., Nakazawa, K., & Wilson, M. A. (2003). Genetic neuroscience of mammalian learning and memory. Philosophical Transactions of the Royal Society of London, B, 358, 787–795. Vandervert, L. R. (1997). The evolution of Mandler’s conceptual primitives (image schemas) as neural mechanisms for space-time simulation structures. New Ideas in Psychology, 15, 105–123. Vandervert, L. R. (2003). How working memory and cognitive modelling functions of the cerebellum contribute to discoveries in mathematics. New Ideas in Psychology, 21(1), 15–29.

Chapter 3

Modern Integrative Biology and Learning and Memory Processes

3.1 Environmental Connectivity and Learning and Memory Integrative biology, as the name suggests, involves the integration of various scientific disciplines within and related to biology, including rapidly expanding disciplines such as neuroscience. In terms of integrative biology, each human connects and interacts continually with the environment in both the inward and outward transmission of matter and energy. These connections are involved in such survival-oriented activities as growth, seeking warmth and avoidance of potential danger as well as reproduction. Learning and memory, in this sense, function as a part of the continual human connectivity with the environment, and education can be viewed as one of the ways of improving the effectiveness and efficiency of such functionality (Woolcott 2016). Matter and energy can take a number of different forms that contribute to learning and memory processes, as well as to other internal processes and functions. A major environmental input is matter in the form of food and this, as well as providing materials for growth, maintenance and reproduction, provides the major source of metabolic energy through the production of glucose used in cellular respiration. Matter in the form of water is also a major environmental input, either as a component of food or as free water. Adequate supplies of both glucose and water are essential for the effective functioning of the brain and the nervous system (see Riby 2004; Riby et al. 2004). The major source of environmental input as energy (referred to as sensory input or stimulus) enters the nervous system directly but involves energy to matter transfer, such as in the absorption by nerve cells of electromagnetic radiation (EMR) in the visible wavelengths, or involves the transfer of energy through matter-to-matter interactions, such as in the reception of sound as vibrations. Besides supplying material for growth, maintenance and production of cells and tissues, such matter and energy inputs are needed so that the body temperature is held constant and individuals can organise shelter, avoid ill health and injury, and have physical and social connections and interactions with other organisms (see discussion of primal © Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_3

13

14

3 Modern Integrative Biology and Learning and Memory Processes

affects, including homeostatic, sensory affects and emotional affects in Panksepp et al. 2017). The human nervous system allows for the accumulation, or remembering, of large amounts of information about the environment from a large variety of environmental connections (Calvin 2013; Edelman 1987, 1989, 1992), including connections with other humans, as well as with non-human organisms. Humans, like many other primates, do not generally live in isolation but rather in social groups where information about the environment (as skills, knowledge or experiences stored in long-term memory (LTM)) can be learned by copying what is seen or heard, as well as by problem-solving or as a discovery process (Goswami 2008; Sweller et al. 2011; Tomasello 1999, 2014). In modern education, the effectiveness of discovery approaches as compared to approaches that use problem-solving (sensu Sweller 1988) has been questioned, particularly in regard to instruction that requires learning to be completed in proscribed time intervals (Kirschner et al. 2006; Mayer 2004). Sweller (see, for example, Sweller et al. 2011) considers discovery and problemsolving in the same category of minimally guided instruction as contrasted to explicit instruction. In biological terms, ineffective education may not reinforce the accumulation of culture that is advantageous to the matter and energy interactions necessary for individual or group survival—interactions that remain necessary in modern society (Tomasello 1999, 2014; Woolcott 2016). In particular, teaching practices that lessen the sharing of accumulated culture, such as those that advocate the individual construction of knowledge by discovery, or, perhaps conversely, those that ignore the input from individual discovery, can in many ways inhibit the survival and reproductive ability of individuals. In the modern world, this is perhaps most apparent for many individuals in the education systems of some countries, and some individuals in every education system, who do not have access to the shared cultural information required for a reduction in mortality rates, injury, disease or disablement, or who do not have access to the shared cultural information required for improved social or economic circumstances and hence survival, growth and reproduction (see discussion in Galama et al. 2018; Hayward et al. 2015).

3.2 Integrative Biology and Learning and Memory Pathways In integrative biology, studies of human learning and memory, rather than being considered as based on a variety of analogies and assumptions as is the case in philosophy and social sciences, are predicated on the assumption that matter and energy interactions are the basis of all physical and biochemical processes, and that these are the only processes involved in determining learning and memory (Crick 1994; Lakoff and Johnson 1999). In integrative biology, therefore, the information stored as memory and the learning acquired through teaching can be considered in terms of the

3.2 Integrative Biology and Learning and Memory Pathways

15

matter and energy interactions that involve the human nervous system. Although there is no clear consensus as to the biological mechanisms of human learning, memory storage and brain function (Alberini et al. 2018; Arshavsky 2006; Churchland and Churchland 2002; Clark and Martin 2018; Marshall and Bredy 2016), there is some agreement that matter (chemical) and energy reactions and reaction pathways are key components, not only in humans but in all vertebrate and invertebrate animals (Baars and Gage 2010; Borges 2008; Honig and James 2016). In biological terms, information storage in the centralised nervous system of vertebrate and invertebrate animals, including storage as short-term memory (STM) and LTM, has a primary function of facilitating survival, including growth and reproduction, through problem-solving and the interaction of novel environmental information with information in LTM (Frankenhuis and Ellis 2017; Quiroga 2019; Sarathy 2018; Thornton and Lukas 2012; Tonegawa et al. 2003). The cognitive system that promotes this function has been conserved and improved upon in evolutionary mutations and genetic recombination (Cahalane and Finlay 2017; Calvin 2004; HowardJones 2018; MacLean 2016). In this problem-solving process, the environment, both internal and external to the organism, is thought to be represented in an organism as patterns of linked information, or pathways, in the nervous system. The interaction of such patterns as STM and LTM allows behavioural responses to develop through such fast-acting processes as predictive patterning as well as through other slower processes (Bullock 2002, 2003; Calvin 1996; Cotterill 2008; Woolcott 2016). In animals with a centralised nervous system, neurons and their supporting cells and structures appear to be the central feature of such pathways (Edelman 1987, 1989, 1992). This is partly due to the considerable structural flexibility that they achieve by the interaction, growth and decay of inter- and intra-neuronal dendritic connexions (Pascual-Leone et al. 2005) and more direct connections (as connexions; see Connors and Long 2004). This flexibility is referred to generally in terms of plasticity (Calvin, 2004; Citri and Malenka 2008; Lambert et al. 2019; Hartwigsen 2018; Kolb 2018). In humans, as in all other vertebrates and many invertebrates, most neurons are located in a number of dense cellular aggregations, or ganglia, regionalised in many invertebrate and vertebrate organisms in what is called a brain (Calvin 2004; Edelman 2007; Grillner 2003). All neurons, including those in the somatic nervous system rather than in the brain, are interconnected, often in a variety of differing pathways and with differing types of connectivity. As a result, the flow of information through the nervous system is regulated through what is essentially a series of delays, inhibitions and amplifications of matter and energy reactions— many of which are referred to under the banner of electrochemical reactions (Cotterill 2001, 2008; Grillner 2003; Kandel 2009; Squire and Kandel 2008). Within the nervous system, any neuronal connections, and groups of such connections, that function as pathways for the transfer of electrochemical signals are associated with memory formation in the brain, with some researchers arguing that some of these pathways, in fact, constitute LTM (Baars and Gage 2010; Borges 2008; Edelman 2007). The process that leads to the formation of such memory pathways is considered to be learning (Edelman 1987, 1989, 1992). During the regulated transfer of information through the nervous system, some pathways of neuronal connection

16

3 Modern Integrative Biology and Learning and Memory Processes

in LTM are reinforced or novel pathways of neuronal connections formed, and such novel pathways become part of LTM (Edelman 1987, 1989, 1992; Gobet et al. 2014; Snyder et al. 2004a). Furthermore, some researchers maintain that memory relies on patterns of activity in linked pathways (called neuronal assemblies) such that each of these patterns is analogous to a given experienced spatiotemporal context (Dehaene 2009; Dubnau et al. 2003; Greenfield 2000; Rowland et al. 2016). However, while electrochemical signals travel from neuron to neuron through such mechanisms as intracellular ion imbalances and intercellular neurotransmitter transmission, there are also electrical and magnetic influences that act from, across and on many cells at the same time. Regulation of neuronal activity by electromagnetic field oscillations or waves is a crucial aspect of coordinating cell activity and memory processes (Ba¸sar and Bullock 2012; Bullock 2002, 2003; Busch and Vanrullen 2010; Gregoriou et al. 2009; Karakas and Barry 2017). Such research has led to a view of memory essentially as pathways of connectivity that lie largely within the centralised part of the nervous system: the brain (Bullmore and Sporns 2009; Bullock et al. 2005; Goswami 2008; Howard-Jones 2018; Sporns 2010). Snyder and associates (Snyder and Mitchell 2001; Snyder et al. 2004a; Snyder et al. 2006) have examined evidence indicating that detailed input information is stored, particularly during early childhood, as discrete packets in the compacted sections of the brain, and that links within each packet are retained when the information is stored. There are also spatiotemporal links or associations developed or retained between information in one packet and the other. In circumstances where some of these links are inhibited, the remaining links form a network that is called a concept. This inhibition process is described as rather like creating a pattern with skyscraper lights (by turning off some of the lights and leaving on others), with the resultant pattern analogous to a concept, where concepts can be linked together in part or in full as meta-concepts (Snyder et al. 2004a, b). Pathways with strong connectivity, whether through repeated environmental input or through the effects of inefficient, inhibited or pruned connections that promote some pathways over others, function as dynamic spatiotemporal networks, and these automatically extracted dependencies are also sometimes referred to as concepts (Goswami 2008). In most cases, however, the automatic hierarchies that govern information processing ensure that some environmental information is limited or lost during concept formation (Mottron 2016).

3.3 Intrinsic Pathways, Emotions and Drives An organism with a centralised nervous system has an intrinsic body of knowledge that serves as a basis for memories accumulated during that organism’s lifetime after birth. Some researchers in integrative biology see this body of knowledge in terms of intrinsic pathways—for example, the fixed action patterns of Llinás (2001) or the reflexes of Cotterill (2001)—and these are considered common to many other organisms with a nervous system, besides humans. A similar system of built-in

3.3 Intrinsic Pathways, Emotions and Drives

17

pathways or patterns can be seen in simple organisms with only a limited selection of responses to stimuli. In some jellyfish, for example, connected neurons in a neural nerve net give an inbuilt all-or-nothing contractile response to touch (Prescott, 2007). This type of intrinsic pathway system leads to demonstrably more complex systems in even simple organisms with a centralised nervous system. In the invertebrate sea slug, Aplysia (Kandel 2009), for example, larger networks develop from simple intrinsic networks through processes such as feedback from motor neurons and sensors in muscles, and potentially from neuronal pathways with an activation bias due to their structure or their spatial location and are influenced by their stage of development (Calvin, 1996; Grillner, 2003; Turchin, 1977). In organisms with a nervous system centralised as a brain, intrinsic pathways are sometimes viewed as those arising from combinations of simple networks that are developed during growth (sometimes in utero and sometimes postpartum) through genetic interaction with the environment. Arguments for the developed and potentially developing nature of some intrinsic pathways have been supported by the modelling of reflexes considered to be inbuilt, such as face recognition (Butko et al. 2006). Some pathways considered as intrinsic are, in fact, considered in the overall context of memory as neuronal pathways constructed during the life of an organism. Learned fear has been shown to be an example of this (Damasio 1999, 2006). Some studies in integrative biology, generally referred to under the collective term “affective neuroscience”, indicate that most pathways of connectivity and, therefore, assemblies of cells involved in learning and memory include those intrinsic pathways that are tied closely to the somatic chemical and energetic pathways identified with emotions and drives (Davis and Panksepp 2018; Panksepp 2004; Panksepp and Biven 2012). Care must be taken in any generalisation from this, however, as the description of emotions in cognitive psychology and in everyday life can vary considerably from those used in modern integrative biology. Grandin and Johnson (2005), for example, working from a background in affective neuroscience provided by such researchers as Damasio (1999, 2006), Panksepp (2004) and LeDoux (1996), describe only four intrinsic primal emotions—fear, seeking (curiosity/interest/anticipation), rage and prey chase drive—referring to these as having a primary function as inbuilt brainbased motivators. Grandin and Johnson (2005) also describe four similarly intrinsic primary social emotions: sexual attraction and lust, separation distress (mother and baby), social attachment, and play and roughhousing. These emotions vary in intensity and probably frequency of expression between animal groups, and between animals within groups, including between human individuals (Davis and Panksepp 2018; Panksepp and Biven 2012). Other types of emotions (e.g. those described in cognitive psychology and everyday life) are considered as developing from such intrinsic pathways, but there is considerable variation as to what is considered as emotion, intrinsic or otherwise, even within affective neuroscience (Damasio 2006; LeDoux 1996, 2000). Although there are potentially many neuronal pathways that bias preferentially (either temporarily or permanently) the activation of neuronal assemblies, the development or activation of those pathways linked to emotions is involved in the formation and modification of all human memory. Such pathways contribute directly to

18

3 Modern Integrative Biology and Learning and Memory Processes

concept formation, for example, through reinforcement differential of any existing or newly formed connections in STM or LTM or provide additional connectivity through pathways to memories in LTM (Coricelli et al. 2007; Damasio 1999, 2006; LeDoux 1996, 2000; LeDoux and Brown 2017). Minsky (2006) argues that some emotions act to bias neuronal pathways so that particular intrinsic pathways, such as fixed action patterns (sensu Llinás 2001), are activated; this view appears to be supported by Cotterill (2001), who specified some of the links between emotional and neuronal pathways. Basic to the idea of intrinsic pathways, such as fixed-action patterns (Llinás 2001), reflexes (Cotterill 2001) or values (Edelman 1987), is the link between physical observation of somatic states, such as heart rate and skin temperature and other features regulated by muscle contraction, and the description by an observer of an emotional state that involves such somatic states. Some researchers consider that such observed emotional states can be differentiated as those that are due to intrinsic emotions and those that are learned. Rage, for example, is an intrinsic emotion, with learning required in order for that rage to be directed towards something specific. A number of researchers (Panksepp and Biven 2012; Davis and Panksepp 2018), in fact, differentiate intrinsic emotions from the behaviours that they call drives or instincts, such as hunger drive or sex drive, which are connected to monitoring bodily needs’ states, and which involve sometimes complex patterns of neuronal pathways in LTM that are developed through learning (see also Panksepp et al. 2017).

3.4 Patterns of Connectivity in the Central Nervous System Some biology-based research on information pathways in learning and memory has included studies of patterns of neuronal connectivity, including small-world and related networks (Sporns 2006; Sporns and Zwi 2004). Such studies have indicated that constraints on information processing in the brain are a consequence of its structure (Barabási 2002, Barabási 2016; Sporns 2010, 2012), including constraints due to its development as a directed system (Del Giudice and Crespi 2018; Evans 2019; Lü et al. 2016; van den Heuvel et al. 2016). Analysis of neuronal assemblies in some primates and other animals on multiple levels of scale, ranging from localised neuronal populations to entire brain regions, indicates that there are common characteristic patterns of connectivity (Barabási 2016; Byrne 2017; Sporns 2012). As is typical for small-world circuits, for example, some neuronal connectivity is such that there are not only small distances and large numbers of close connections between clusters of neighbouring neurons in an assembly (a small number of degrees of separation) but also a small number of connections between some neurons that are not in that neighbourhood (Barabási 2016; Kleinberg 2000; Rieke et al. 2007). There are similar types of connectivity between assemblies of neuronal clusters such that within the brain and the rest of the nervous system, there are many tiers of such connected networks linking even larger numbers of neurons (Sporns 2010, 2012). Models that indicate how neuronal

3.4 Patterns of Connectivity in the Central Nervous System

19

assemblies transmit information through mostly local communities (Casanova 2010; Opris and Casanova 2017) appear to support this type of connectivity. The information pathways through the brain appear to form a directional system, for example, where information is not transferred back to the environment through the sensory system, and there is a directional through-flow of information from sensory input to motor output, although with branching and feedback within information pathways and with feedback also from the muscles involved in any motor output (Cotterill 2001; Grillner 2003; Squire and Kandel 2008). This directional throughflow is modulated, as discussed above, by the nature of the types and numbers of neurons and connections as well as by the various types of connectivity that involve inhibition and bias of that flow. This through-flow includes some bidirectional flow options and parallel routing of information through separated biochemical pathways (Bullock 2002, 2003; Cotterill, 2001; Dubnau et al. 2003; Margulies et al. 2005). Despite the extensive growth of neuronal connections sometimes that precedes the creation of concept or meta-concept hierarchies (sensu Mottron et al. 2009; Snyder et al. 2004a), such concepts and hierarchies maintain a position as part of a directional flow. Both concepts and hierarchies, however, are sometimes activated indirectly through oscillatory mechanisms (Baars and Gage 2010; Llinás 2001), even though some directional flow appears to be maintained in all situations that are triggered by input information through problem-solving (sensu Tonegawa et al. 2003). The characteristic patterning of connectivity seen in the nervous system, such as the directed and small-world networks, and their typical effects, appears to be common in biology. The growth of structures and systems also depends on structures or systems that are already present—a type of structure building called “preferential attachment”. Such preferential attachment provides an inbuilt bias in connectivity that provides for functional parts that can be quickly connected because they reside only a few connections from each other (Barabási 2016). Such preferential attachments are limited, however, by the potential number of afferent connections that a neuron can support (Sporns and Zwi 2004). Concept formation (sensu Snyder et al. 2004a) is analogous to such preferential attachment since such concept formation involves the growth of connectivity and since this type of attachment is also involved in the way neuronal assemblies are linked as meta-concepts. On a molecular scale, simple biological processes, such as protein production and assembly from a deoxyribonucleic acid (DNA) template, also follow characteristic patterns, such as those delineated by small-world and scale-free modular networks (Barabási and Oltvai 2004); this is relevant to the overall structure and function of neural patterns, particularly since the connections grown between neurons that lead to the formation of LTM are the result, in large part, of such processes (Dubnau et al. 2003; Tonegawa et al. 2003). There is also inbuilt redundancy of some neuronal assemblies that provide for information transmission that is little affected by the loss of one or a small number of neurons within an assembly. This inbuilt redundancy provides for the re-routing of some information through a different region when the original transfer region is damaged (Bullmore and Sporns 2009; Sporns 2010). This does not mean that individual neurons or individual connections do not have a significant role as individuals,

20

3 Modern Integrative Biology and Learning and Memory Processes

as each neuron connected in a network, small-world or otherwise, contributes to a unified whole in a slightly different way (Quiroga 2012). Each neuron in an assembly that contributes to a synchronised average signal transfer, for example, can have (at the same time) a slightly different individual signal; this does not appear to be the case if neurons are connected at random or in an extremely ordered way (Lago-Fernández et al. 2000). Neuronal assemblies that grow through preferential attachment, like other networks that build in the same way, are not merely modular small-world networks but are also scale-free networks where only a small number of elements contain most of the connections, and hence stabilise in a power-law distribution (Barabási 2016; Bullmore and Sporns 2009; Sporns 2010). Such networks can be self-organising and flexible in structure, as well as tolerant of failure, and can build into substantial and complex hierarchies of modules connected through hubs (Easley and Kleinberg, 2010; Watts, 2004). There is evidence for such hubs in some cortical areas (Sporns 2010, 2012; Su et al. 2018; Vecchio et al. 2019) and in larger brain regions (Bullmore and Sporns 2009; Sporns 2010, 2012). Future research may further delineate the extent of such hierarchies in the human central nervous system.

3.5 Patterns of Environmental Connectivity and Information Processing Systems In an evolutionary sense, biological patterns of connectivity, such as the small-world and scale-free modular networks seen in neuronal and related connectivity patterns within the brain, are the result of Darwinian processes resulting from iteration with inbuilt error (Calvin 1996; Turchin 1977). Such patterns in neural connectivity can be considered, therefore, as a consequence of the survival of mutations that maximise the amount and efficiency of connectivity that uses a minimum of space and resources (Calvin 2004). Patterns such as small-world circuits may be common in the neural structure of all organismal forms that have a centralised nervous system, but the larger number of neurons in the human cerebral cortex have contributed to the human ability to form extensive and interlinked hierarchies of concepts and meta-concepts. Such neural structures are due in part to relatively simple alterations in regulating relative growth rates with development directionality (Cahalane and Finlay 2017; Dixon-Salazar and Gleeson 2010; Gibson 2002; Johnson et al. 2009), leading to such advantageous developments as hyperconnectivity (Casanova 2010; Lemprière 2019; Rinaldi et al. 2008; Whitfield-Gabrieli et al. 2009). Since there appear to be patterns of neural connectivity that we have inherited through evolutionary processes and since we are subverting these patterns for institutionalised education (e.g. Dehaene 2009; Goswami 2008), detailed knowledge of such patterns could potentially assist in the delineation of the educational theories and practices being applied in institutionalised education (Aranda and Tytler 2016; Horvath et al. 2016; Woolcott 2011).

References

21

What seems evident from studies in integrative biology and cognitive psychology is that, in order for organisms to process information that allows them to negotiate the world around them, there must be some correlation between the organisation of information in the outside world and that same information stored within the central nervous system in both the shorter and longer term (Turchin 1977; Vandervert 1997, 2003). Research in integrative biology has implied a link between patterns and connections in the real world and their record as memories in the brain, even though the record as memory involves a significantly different spatiotemporal arrangement of the information than that of the input to the nervous system (Calvin 1996, 2004, 2013; Rowland et al. 2016; Roy et al. 2018). There is evidence, however, that it is the interpretation of input information as patterns of connectivity that allows for efficient storage of large amounts of information in LTM (Edelman 1989; Sporns 2010, 2012). Such organisational patterning may be reflected, as some researchers have proposed, in the coordination of the muscle movements necessary for responses to patterns represented from the outside world (Cotterill 2001; Llinás 2001). This coordination includes even small movements, such as saccades in the visual system (Moore and Armstrong 2003). Studies of non-human systems—for example, Grillner’s (2003) studies of simple amphibious vertebrates and invertebrates (e.g. Kandel 2009)—have shown examples of such connections between the pattern of environmental input to an organism, the patterning of the information assimilated within the central nervous system, and both the internal and observed pattern of organismal muscle response and any subsequent movement. More generally, there has been recent recognition of the role that particular types of patterns of connectivity play in the relationships between an organism and its environment through interactions of the nervous and muscular systems. The recent developments in studies of networks and complex systems lend support to an argument for common patterns in the structure and development of information processing systems, such as the nervous system; this includes those referred to in educational theory as natural information processing systems (Sweller et al. 2011). Recent studies have shown that some of the generalised effects of network structures, such as those seen in information processing in the human nervous system (Sporns 2010, 2012), are independent of the nature or components of those structures, and these effects have been observed in a number of seemingly unrelated networks from economic models to transport systems to the World Wide Web (Barabási 2016; Newman 2018). Such commonalities in patterning have reinforced the view that there are common underlying components in systems and networks previously thought to be unrelated (Barabási 2016; Lü et al. 2016; Watts 2004). Development of such concepts, in particular, those concepts that relate to information pathways and information processing, in a context that is sensitive to the constraints of modern evolutionary biology, offer valuable insights into learning and memory and into educational theories and practices. The existence of such underlying components, for example, point to a valuable expansion of instructional principles such that they are tied more closely to biological models, such as those that describe the learning and memory processes that link an individual and their environment.

22

3 Modern Integrative Biology and Learning and Memory Processes

References Alberini, C. M., Cruz, E., Descalzi, G., Bessières, B., & Gao, V. (2018). Astrocyte glycogen and lactate: New insights into learning and memory mechanisms. Glia, 66(6), 1244–1262. Aranda, G., & Tytler, R. (2016). Aligning neuroscience findings with socio-cultural perspectives on learning in science. In J. C. Horvath, J. M. Lodge, & J. Hattie (Eds.), From the laboratory to the classroom: Translating science of learning for teachers (pp. 139–154). New York, NY: Routledge. Arshavsky, Y. I. (2006). The ‘Seven Sins’ of the Hebbian synapse: Can the hypothesis of synaptic plasticity explain LTM consolidation? Progress in Neurobiology, 80, 99–113. Baars, B. J., & Gage, N. M. (2010). Cognition, brain, and consciousness: Introduction to cognitive neuroscience. Cambridge, MA: Academic Press. Barabási, A. L. (2016). Network science. Cambridge, UK: Cambridge University Press. Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: Understanding the cell’s functional organization. Nature Reviews Genetics, 5, 101–114. Ba¸sar, E., & Bullock, T. H. (Eds.). (2012). Brain dynamics: Progress and perspectives (Vol. 2). Cham, Switzerland: Springer Science & Business Media. Borges, R. M. (2008). Plasticity comparisons between plants and animals: Concepts and mechanisms. Plant Signaling & Behavior, 3(6), 367–375. Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186–198. Bullock, T. H. (2002). Biology of brain waves: Natural history and evolution of an informationrich sign of activity. In K. Arikan & N. Moore (Eds.), Advances in electrophysiology in clinical practice and research (pp. 1–19). Wheaton, IL: Kjellberg. Bullock, T. H. (2003). Have brain dynamics evolved?—Should we look for unique dynamics in the sapient species? Neural Computation, 15, 2013–2027. Bullock, T. H., Bennett, M. V., Johnston, D., Josephson, R., Marder, E., & Fields, R. D. (2005). The neuron doctrine, redux. Science, 310(5749), 791–793. Busch, N. A., & Vanrullen, R. (2010). Spontaneous EEG oscillations reveal periodic sampling of visual attention. Proceedings of the National Academy of Sciences of the United States of America, 107(37), 16048–16053. Butko, N. J., Fasel, I. R., & Movellan, J. R. (2006). Learning about humans during the first 6 minutes of life. Proceedings of the Fifth International Conference on Development and Learning (ICDL06), Indiana, USA, June 2006. Byrne, J. H. (2017). Learning and memory: A comprehensive reference (2nd ed.). Cambridge, MA: Academic Press. Cahalane, D. J., & Finlay, B. L. (2017). Brain evolution and development: Allometry of the brain and a realization of the cortex. In S. V. Shepherd (Ed.), The Wiley handbook of evolutionary neuroscience (pp. 388–409). Chichester, UK: Wiley Blackwell. Calvin, W. H. (1996). The cerebral code: Thinking a thought in the mosaics of the mind. Cambridge, MA: MIT Press. Calvin, W. H. (2004). A brief history of the mind: From apes to intellect and beyond. Oxford, UK: Oxford University Press. Calvin, W. H. (2013). How brains think: Evolving intelligence, then and now. London, UK: Hachette. Casanova, M. F. (2010). Cortical organization: Anatomical findings based on systems theory. Translational Neuroscience, 1(1), 62–71. Churchland, P. S., & Churchland, P. M. (2002). Neural worlds and real worlds. Nature Reviews Neuroscience, 3(11), 903–907. Citri, A., & Malenka, R. C. (2008). Synaptic plasticity: Multiple forms, functions, and mechanisms. Neuropsychopharamcology, 33, 18–41. Clark, R. E., & Martin, S. J. (Eds.). (2018). Behavioral neuroscience of learning and memory (Vol. 37). Cham, Switzerland: Springer.

References

23

Connors, B. W., & Long, M. A. (2004). Electrical synapses in the mammalian brain. Annual Review of Neurosciences, 27, 393–418. Coricelli, G., Dolan, R. J., & Sirigu, A. (2007). Brain, emotion and decision making: The paradigmatic example of regret. Trends in Cognitive Sciences, 11(6), 258–265. Cotterill, R. M. J. (2001). Co-operation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: Possible implications for cognition, consciousness, intelligence and creativity. Progress in Neurobiology, 64, 1–33. Cotterill, R. M. J. (2008). The material world. New York, NY: Cambridge University Press. Crick, F. (1994). The astonishing hypothesis: The scientific search for the soul. New York, NY: Scribner’s. Damasio, A. R. (1999). The feeling of what happens: Body and emotion in the making of consciousness. London, UK: Heinemann. Damasio, A. R. (2006). Descartes’ error (Revised ed.). London, UK: Random House. Davis, K. L., & Panksepp, J. (2018). The emotional foundations of personality: A neurobiological and evolutionary approach. New York, NY: WW Norton & Company. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin Viking. Del Giudice, M., & Crespi, B. J. (2018). Basic functional trade-offs in cognition: An integrative framework. Cognition, 179, 56–70. Dixon-Salazar, T. J., & Gleeson, J. G. (2010). Genetic regulation of human brain development: Lessons from Mendelian diseases. Annals of the New York Academy of Sciences, 1214, 156–167. Dubnau, J., Chiang, A. S., & Tully, T. (2003). Neural substrates of memory: From synapse to system. Journal of Neurobiology, 54, 238–253. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. London, UK: Cambridge University Press. Edelman, G. M. (1987). Neural Darwinism: The theory of neuronal group selection. New York, NY: Basic Books. Edelman, G. M. (1989). The remembered present. New York, NY: Basic Books. Edelman, G. M. (1992). Bright air, brilliant fire. New York, NY: Basic Books. Edelman, G. M. (2007). Learning in and from brain-based devices. Science, 318(5853), 1103–1105. Evans, C. (2019). The neurobiology of reward: Understanding circuitry in the brain that shapes our behavior. In J. Kelso (Ed.), Learning to live together: Promoting social harmony (pp. 97–105). Cham, Switzerland: Springer. Frankenhuis, W. E., & Ellis, B. J. (2017). Toward a balanced view of stress-adapted cognition. The Behavioral and Brain Sciences, 40, e325. Galama, T. J., Lleras-Muney, A., & van Kippersluis, H. (2018). The effect of education on health and mortality: A review of experimental and quasi-experimental evidence (No. w24225). Cambridge, MA: National Bureau of Economic Research. Gibson, K. R. (2002). Evolution of human intelligence: The roles of brain size and mental construction. Brain, Behaviour, and Evolution, 59, 10–20. Gobet, F., Snyder, A., Bossomaier, T., & Harré, M. (2014). Designing a “better” brain: Insights from experts and savants. Frontiers in Psychology, 5, 470. Goswami, U. (2008). Cognitive development: The learning brain. Philadelphia, PA: Psychology Press of Taylor and Francis. Grandin, T., & Johnson, C. (2005). Animals in translation. New York, NY: Harcourt Books. Greenfield, S. (2000). The private life of the brain: Emotions, consciousness and the secret of the self . New York, NY: John Wiley & Sons. Gregoriou, G. G., Gotts, S. J., Zhou, H., & Desimone, R. (2009). High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science, 324(5931), 1207–1210. Grillner, S. (2003). The motor infrastructure: From ion channels to neuronal networks. Nature Reviews Neuroscience, 4, 573–586. Hartwigsen, G. (2018). Flexible redistribution in cognitive networks. Trends in Cognitive Sciences, 22(8), 687–698.

24

3 Modern Integrative Biology and Learning and Memory Processes

Hayward, M. D., Hummer, R. A., & Sasson, I. (2015). Trends and group differences in the association between educational attainment and US adult mortality: Implications for understanding education’s causal influence. Social Science and Medicine, 127, 8–18. Honig, W. K., & James, P. H. R. (Eds.). (2016). Animal memory. New York, NY: Academic Press. Horvath, J. C., Lodge, J. M., & Hattie, J. (Eds.). (2016). From the laboratory to the classroom: Translating science of learning for teachers. New York, NY: Routledge. Howard-Jones, P. (2018). Evolution of the learning brain: Or how you got to be so smart. London, UK: Routledge. Johnson, M. B., Kawasawa, Y. I., Mason, C. E., Krsnik, Z., Coppola, G., Bogdanovi, D., et al. (2009). Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron, 28(62), 494–509. Kandel, E. R. (2009). The biology of memory: A forty-year perspective. Journal of Neuroscience, 29(41), 12748–12756. Karaka¸s, S., & Barry, R. J. (2017). A brief historical perspective on the advent of brain oscillations in the biological and psychological disciplines. Neuroscience and Biobehavioral Reviews, 75, 335–347. Kirschner, P., Sweller, J., & Clark, R. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. Kleinberg, J. M. (2000). Navigation in a small world. Nature, 406, 845. Kolb, B. (2018). Brain plasticity and experience. In R. Gobb & B. Kolb (Eds.), The neurobiology of brain and behavioral development (pp. 341–389). London, UK: Academic Press. Lago-Fernández, L. F., Huerta, R., Corbacho, F., & Sigüenza, J. A. (2000). Fast response and temporal coherent oscillations in small-world networks. Physical Review Letters, 84(12), 2758. Lakoff, G., & Johnson, M. (1999). Metaphors we live by. New York, NY: Basic Books. Lambert, K., Eisch, A. J., Galea, L. A., Kempermann, G., & Merzenich, M. (2019). Optimizing brain performance: Identifying mechanisms of adaptive neurobiological plasticity. Neuroscience and Biobehavioral Reviews, 105, 60–71. LeDoux, J. E. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York, NY: Touchstone. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. LeDoux, J. E., & Brown, R. (2017). A higher-order theory of emotional consciousness. Proceedings of the National Academy of Sciences, 114(10), E2016–E2025. Lemprière, S. (2019). Autism mutation produces hyper-connected neurons. Nature Reviews Neurology, 15(6), 308–309. Llinás, R. (2001). I of the vortex: From neurons to self . Cambridge, MA: MIT Press. Lü, J., Yu, X., Chen, G., & Yu, W. (2016). Complex systems and networks. Berlin, Germany: Springer. MacLean, E. L. (2016). Unraveling the evolution of uniquely human cognition. Proceedings of the National Academy of Sciences, 113(23), 6348–6354. Margulies, C., Tully, T., & Dubnau, J. (2005). Deconstructing memory in Drosophila. Current Biology, 15, R700–R713. Marshall, P., & Bredy, T. W. (2016). Cognitive neuroepigenetics: The next evolution in our understanding of the molecular mechanisms underlying learning and memory? NPJ Science of Learning, 1, 16014. Mayer, R. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59, 14–19. Minsky, M. L. (2006). The emotion machine: Commonsense thinking, artificial intelligence, and the future of the human mind. New York, NY: Simon & Schuster. Moore, T., & Armstrong, K. M. (2003). Selective gating of visual signals by microstimulation of frontal cortex. Nature, 421, 370–373. Mottron, L. (2016). Is autism a different kind of intelligence? New insights from cognitive neurosciences. Bulletin de l’Academie nationale de medecine, 200(3), 423–434.

References

25

Mottron, L., Dawson, M., & Soulières, I. (2009). What aspects of autism predispose to talent. Philosophical Transactions of the Royal Society of London, B, 364, 1351–1357. Newman, M. E. J. (2018). Networks. London, UK: Oxford University Press. Opris, I., & Casanova, M. F. (2017). The physics of the mind and brain disorders. Cham, Switzerland: Springer International Publishing. Panksepp, J. (2004). Affective neuroscience: The foundations of human and animal emotions. Oxford, UK: Oxford University Press. Panksepp, J., & Biven, L. (2012). The archaeology of mind: Neuroevolutionary origins of human emotions. New York, NY: WW Norton & Company. Panksepp, J., Lane, R. D., Solms, M., & Smith, R. (2017). Reconciling cognitive and affective neuroscience perspectives on the brain basis of emotional experience. Neuroscience and Biobehavioral Reviews, 76, 187–215. Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401. Prescott, T. J. (2007). Forced moves or good tricks in design space? Landmarks in the evolution of neural mechanisms for action selection. Adaptive Behavior, 15(1), 9–31. Quiroga, R. Q. (2012). Concept cells: The building blocks of declarative memory functions. Nature Reviews Neuroscience, 13(8), 587–597. Quiroga, R. Q. (2019). Neural representations across species. Science, 363(6434), 1388–1389. Riby, L. M. (2004). The impact of age and task domain on cognitive performance: A meta-analytic review of the glucose facilitation effect. Brain Impairment, 5(2), 145–165. Riby, L. M., Meikle, A., & Glover, C. (2004). The effects of age, glucose ingestion and glucoregulatory control on episodic memory. Age and Ageing, 33, 483–487. Rieke, H., Roxin, A., Madruga, S., & Solla, S. A. (2007). Multiple attractors, long chaotic transients, and failure in small-world networks of excitable neurons. Chaos, 17, 026110. Rinaldi, T., Perrodin, C., & Markram, H. (2008). Hyper-connectivity and hyper-plasticity in the medial prefrontal cortex in the valproic acid animal model of autism. Frontiers in Neural Circuits, 2(4), 1–7. Rowland, D. C., Roudi, Y., Moser, M. B., & Moser, E. I. (2016). Ten years of grid cells. Annual Review of Neuroscience, 39, 19–40. Roy, A., Perlovsky, L., Besold, T. R., Weng, J., & Edwards, J. C. (2018). Representation in the brain. Frontiers in Psychology, 9, 1410. Sarathy, V. (2018). Real world problem-solving. Frontiers in Human Neuroscience, 12, 261. https:// doi.org/10.3389/fnhum.2018.00261. Snyder, A. W., & Mitchell, D. J. (2001). Paradox of the savant mind. Nature, 413, 251–252. Snyder, A. W., Bossomaier, T., & Mitchell, D. J. (2004a). Concept formation: ‘Object’ attributes dynamically inhibited from conscious awareness. Journal of Integrative Neuroscience, 3(1), 31– 46. Snyder, A. W., Mitchell, D. J., Ellwood, S., & Yates, A. (2004b). Nonconscious idea generation. Psychological Reports, 94, 1320–1325. Snyder, A. W., Bahramali, H., Hawker, T., & Mitchell, D. J. (2006). Savant-like numerosity skills revealed in normal people by magnetic pulses. Perception, 35, 837–845. Sporns, O. (2010). Networks of the brain. Cambridge, MA: MIT Press. Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT press. Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2(2), 145–162. Squire, L. R., & Kandel, E. R. (2008). Memory: From mind to molecules (2nd ed.). Greenwood Village, CA: Roberts & Company. Su, T., Guo, Y., Chen, Z., Zhang, S., Huang, X., & Feng, T. (2018). The neural basis underlying procrastination: A large-scale study of brain networks. Scientia Sinica Vitae, 49(1), 77–88. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.

26

3 Modern Integrative Biology and Learning and Memory Processes

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Explorations in the learning sciences, instructional systems and performance technologies. Dordrecht, The Netherlands: Springer. Thornton, A., & Lukas, D. (2012). Individual variation in cognitive performance: Developmental and evolutionary perspectives. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1603), 2773–2783. Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harvard University Press. Tomasello, M. (2014). A natural history of human thinking. Cambridge, MA: Harvard University Press. Tonegawa, S., Nakazawa, K., & Wilson, M. A. (2003). Genetic neuroscience of mammalian learning and memory. Philosophical Transactions of the Royal Society of London, B, 358, 787–795. Turchin, V. F. (1977). The phenomenon of science. New York, NY: Columbia University Press. van den Heuvel, O. A., van Wingen, G., Soriano-Mas, C., Alonso, P., Chamberlain, S. R., Nakamae, T., et al. (2016). Brain circuitry of compulsivity. European Neuropsychopharmacology, 26(5), 810–827. Vandervert, L. R. (1997). The evolution of Mandler’s conceptual primitives (image schemas) as neural mechanisms for space-time simulation structures. New Ideas in Psychology, 15, 105–123. Vandervert, L. R. (2003). How working memory and cognitive modelling functions of the cerebellum contribute to discoveries in mathematics. New Ideas in Psychology, 21(1), 15–29. Vecchio, F., Miraglia, F., & Rossini, P. M. (2019). Tracking neuronal connectivity from electric brain signals to predict performance. The Neuroscientist, 25(1), 86–93. Watts, D. J. (2004). Six degrees: The science of a connected age. New York, NY: W. W. Norton. Whitfield-Gabrieli, S., Thermenos, H. W., Milanovic, S., Tsuang, M. T., Faraone, S. V., McCarley, R. W., et al. (2009). Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 106(4), 1279–1284. Woolcott, G. (2011). A broad view of education and teaching based in educational neuroscience. International Journal for Cross-Disciplinary Subjects in Education, Special Issue, 1(1), 601–606. Woolcott, G. (2016). Technology and human cultural accumulation: The role of emotion. In S. Tettegah & R. E. Ferdig (Eds.), Emotions, technology, and learning (pp. 243–263). London, UK: Academic Press.

Chapter 4

Connections Between Studies of Human Learning and Memory Processes in Modern Cognitive Psychology and Integrative Biology

It appears that some of the educational theories and practices used in institutional education rest on a solid foundation derived from phenomenological studies in cognitive psychology and that this foundation is supported by the additional detail and clarification of concepts of learning and memory provided by studies in integrative biology. Modern integrative biology, for example, has detailed the concept, developed from studies in cognitive psychology, of information stored as both short-term memory (STM) and long-term memory (LTM) (Sweller et al. 2011; Postle 2006, 2015), suggesting that this information is stored by adding to or altering neuronal and related structures that develop as patterns of connected information through the processing of environmental input (Edelman 2007; Sporns 2010, 2012). In addition, integrative biology supports the limitations on attention and working memory (WM) reported from cognitive psychology (Sweller 1988, 1994) through the delineation of the detailed function of neural pathways, where limits in activation processes have been documented, for example, in the effects of directional flow, neural bottlenecks and inhibition (Bouchacourt and Buschman 2019; Cotterill 2001; Marois 2005). Studies that combine integrative biology and network theory have indicated further that some such limitations are due to patterns of connectivity related to neural structures, such as those that occur within the modular small-world and scale-free circuits in the human nervous system (Sporns 2010, 2012). In light of such support, the aim of successful educational theories and practices is to provide for environmental input and behavioural feedback that assist in the modulation, formation and, sometimes, retention of patterns of neuronal connections in human memory, with some focus on the limitations inherent in the function of the human information processing system in interacting with novel environmental input (Woolcott 2010, 2013). This chapter outlines how studies in integrative biology have elaborated the cognitive architecture (developed within cognitive psychology) that is involved in environmental interaction through problem-solving, and the support for an information systems approach to the examination of human learning and memory processes.

© Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_4

27

28

4 Connections Between Studies of Human Learning …

4.1 Long-Term Memory 4.1.1 Long-Term Memory Processes At all stages of organismal development, any information input to the central nervous system, regardless of the sensory modality, is changed into electrochemical signals (Bach-y-Rita 2004; Mountcastle 1998). Such input information is involved in the branching cascades of electrochemical interactions that, through the nervous system, influence any part of that organism. Perhaps counter-intuitively, some of this influence can be through inhibition or prevention of information transmission, with transmission speed within different cascades known to range from milliseconds to days (Bullock 2002, 2003; Cotterill 2001). Within the central nervous system, these electrochemical interactions lead to information storage as chemical and energetic changes to cells, and some of these changes manifest as growth or alteration of neural structures (Edelman 2007). Light photons (energy packets)—for example, input to the retina in the eye—are known to change to electrochemical signals that result in a variety of changes throughout the body through a cascade of feedforward and feedback interactions based in the nervous system. These signals are converted, for example, into motor reactions in a short time interval and into changes of the structures, such as the pathways between neurons, in the brain over a longer time interval. The electrochemical changes to the cells that lead effectively, individually and together, to storage of information in the central nervous system are associated with neuronal synapses and other types of cell connectivity within the nervous system (Connors and Long 2004; Edelman 1987, 1989, 1992; Kandel 2009). These changes are generally thought to involve varying combinations of activation and inhibition of unidirectional and bidirectional electrochemical pathways, sometimes called signal pathways, through the influence of long-term transmission and long-term depression of cell signalling, with such changes influencing potentially any information transmission from cell to cell across neuronal synapses or through the more direct physical cell connexions (Alonso and Goldmann 2016; Connors and Long 2004; Grillner 2003; Pieuchot et al. 2016; Richard and Joseph 2016). Information is considered to be stored as LTM, through establishment and reinforcement of neuronal connections (both synaptic and non-synaptic), connected in networks which are constantly being constructed or reconstructed. This dynamic connectivity is considered to be the key element of memory storage (Calvin 2004; Edelman 2007). Cotterill (2001) has presented a model of the general flow of information within the brain, supporting an argument that after information is transformed from sensory input to electrochemical signals, it follows directed pathways through the central nervous system and the brain. Cotterill’s (2001) model and the knowledge of temporal difference in flow and activation and inhibition processes go some way towards explaining how a complex and multi-connectional network of neurons can direct and store information (Bays 2018; Constantinidis and Klingberg 2016; Jones 2018; Robin and Moscovitch 2017). This is supported for the learning of language

4.1 Long-Term Memory

29

where Goswami (2008a, b) argues that incremental environmental input leads to the development of complex cognitive structures without any pre-encoded innate knowledge of such structures. As such, there is considerable evidence from studies in integrative biology to support the view from cognitive psychology that information is stored as human LTM through the processing of environmental interactions. This may be the case in all organisms that have a centralised nervous system, with such storage requiring the direct interaction of genes in ganglia or brain cells (Fajardo et al. 2018; Grillner 2003; Kandel 2009; Marshall and Bredy 2016). The amount of information stored as LTM relates to the degree of integration of environmental information through DNA transcription and translation that is initiated in brain cells in response to the processing of that information. Therefore, the interaction of environmental information input with the DNA contained within cells in the nervous system informs the development of cognitive structures and continues to influence, from LTM, all cognitive structures and functions. In the processes that lead to the formation of LTM, however, there is also some restructuring of neuronal connectivity that does not involve direct interaction of DNA (Arshavsky 2006; Routtenberg and Rekart 2005; Tonegawa et al. 2003), as well as some potential neurogenesis (Colangelo et al. 2019; Snyder 2019; Tashiro et al. 2007). Some models of human memory derived from studies based in integrative biology (Edelman 1987, 1989, 1992) do not require that a particular memory be stored in a particular place or by a particular neuron, and several models support the view that there are multiple brain pathways that support even a single memory (Dehaene 2009; Quiroga 2012; Squire and Kandel 2008). Such models accommodate the view that some long-term memories are generated and modified by processing through internal changes in information pathway connectivity, associations within collections of neurons or neuronal assemblies of the nervous system (Calvin 1996, 2004; Casanova and Casanova 2019; Clark and Martin 2018; Edelman 1987, 1989, 1992; Opris and Casanova 2017; Pascual-Leone et al. 2005). Additionally, within the nervous system, there may be sets of connected neurons that are biased towards activation with certain types of input information, and some of these activations function in STM as attractors and influence the processing of information that is stored as LTM (Calvin 1996; Kesner and Rolls 2001; Vandervert 2003). Since the storage of input information is thought to be through linked neurons and related pathways in assemblies throughout the brain, recall is sometimes considered as reactivated or re-entered pathways (Edelman 1987, 1989, 1992; Kesner and Rolls 2001). Effectively, the capacity for such information storage appears to be unlimited (Sweller et al. 2011), but, for certain kinds of information, the real-time storage in human LTM is limited (Dehaene 2009), as predicted when considering the brain as a system with upper limits on matter and energy capacity.

30

4 Connections Between Studies of Human Learning …

4.1.2 Memory Elements, Schemas and Pathways The detailing within integrative biology of the processes of connectivity involved in the storage of information as LTM also indicates support for the concepts developed in cognitive psychology of chunks and schemas as functional connections of groups of information pathways within LTM. Although there are variations in the descriptions of their connectivity, there is considerable evidence for such functional groupings (Edelman 1987, 1989, 1992; Snyder et al. 2004; Sporns 2010, 2012). In addition, the delineation of neuronal pathways as concepts and meta-concepts (Goswami 2008a, b; Mottron et al. 2009; Quiroga 2012; Snyder et al. 2004) appears analogous to the idea of schemas and their association as concepts (Sweller 1988, 1994; Thompson 1994). Regardless of the mechanisms of grouping cells and connections, the idea of such groupings or packets of information stored in the central nervous system corresponds to the idea in cognitive psychology of the storage of memory elements, and the connections of linked neuronal groupings or assemblies as concepts and meta-concepts correspond well to the idea of elements connected as chunks and schemas. Formation and reinforcement of links and associations between neuronal pathways affect the strength or longevity of concepts and meta-concepts, as is the case for linkages in schemas and chunks. There is evidence from integrative biology that the degree of reinforcement within concepts is proportional to the amount of input information corresponding to the information stored as that concept (Edelman 1987, 1989). Some links between concepts are strengthened through usage and deliberate practice, such as the links and concepts employed for everyday language usage, and these form the larger part of the basis for further concept building (Dehaene 2009; Goswami 2008a, b). As has been indicated in cognitive psychology, in arguing for the connection of elements in schema acquisition, studies in integrative biology have indicated that the making of neuronal assemblies through linked associations or concept building involves the dynamic processing of information, with cell connectivity changing through interactions with both the internal and external environment (Baars and Gage 2010; Edelman 1987, 1989, 1992; Pulvermüller 2018; Snyder et al. 2004). Recent studies in brain activation have indicated that concept development may involve many anatomical parts or differing locations of the brain, with the same concept involving different parts of the brain in different people (Dehaene 2009; Geake 2004; Haier 2016). In order that comparisons between input from the outside world and any representation as a concept within the brain can be made rapidly, Llinás (2001) has proposed that some concepts are transferred to storage in the cerebellum from where they are recalled, either consciously (awake) or unconsciously, and used in developing scenarios that link input information to stored information. Although there are several ways of describing concept (and meta-concept) formation (e.g. Edelman 1987; Goswami 2008a, b; Pulvermüller 2018; Quiroga 2012; Snyder et al. 2004), all such concepts can be considered as spatiotemporal if there is temporal linking of discrete information events that are spatially distributed (Calvin 1996, 2004; Rowland et al. 2016). Any such spatiotemporal representations, however,

4.1 Long-Term Memory

31

are subject to modification by environmental interactions at various times in an individual’s life. The development of such representations is promoted or inhibited by simple chemical or energetic mechanisms, such as those seen in reward and inhibition (Evans 2019; Mason et al. 2017; Murayama et al. 2010; Parker et al. 2009; van den Heuvel et al. 2016). Further, Lakoff and others (Lakoff and Johnson 1999; Lamb 1999; Shapiro 2019) have argued that there are conceptual domains that are involved in understanding concepts and that these domains are embedded in environmental interactions as physical experiences.

4.1.3 Repetition, Automation and Neuronal Activation Schema construction, by postulating the way in which information is stored in LTM, provided cognitive psychologists with a mechanism for learning (Sweller et al. 2011). In addition, it has been theorised that learning includes the automation of some such schemas. Automation in this sense refers to knowledge being processed unconsciously (rather than consciously in WM), with the automation of lower-level schemas considered as essential for the construction of higher-level schemas (Schneider and Shiffrin 1977; Shiffrin and Schneider 1977). For any human individual, the information stored in LTM does not depend on subject teaching at school but on the type of stimulus to the central nervous system from the entire environment outside of, and sometimes within, that nervous system. From the viewpoint of integrative biology, for any information to be learned well enough to be recalled automatically, the requisite input must provide sufficient stimulus within electrochemical signalling processes such that there are changes in LTM through the chemical and energetic changes in synapses and other neuronal pathways (Del Giudice and Crespi 2018; Edelman 1987, 1989, 1992; Kandel 2009; Opris and Casanova 2017; Routtenberg and Rekart 2005). In general, novel and strong and/or persistent electrochemical signals promote the formation of new connections between neurons in the brain, resulting in the formation or strengthening of LTM (Edelman 1987) and less strong or less persistent signal use, or cause changes to existing connections, leading to STM (Margulies et al. 2005; Melcher 2001). In a conscious person, any shorter-term storage can be thought of as information that is retained in memory for manipulation of environmental input or remembered information, such as in planning or executing behaviour (Calvin 2004). None of these changes last as long as those connections that determine longer-term information storage (Opris and Casanova 2017; Routtenberg and Rekart 2005; Roy et al. 2018; Tonegawa et al. 2003). Repetition of stimulus through neuronal pathways is an important process in learning as it relates to education (Geake 2009). Edelman (1989), in fact, argues that learning and instruction (instruction here meaning a stimulus that leads to the modification of neuronal pathways) are aspects of a system of natural selection of the well-used pathways. Modification of and addition to memory, however, may also

32

4 Connections Between Studies of Human Learning …

be through the activation and interconnection of information held already in LTM (Cotterill 2001; Edelman 1992; Mottron et al. 2009; Pascual-Leone et al. 2005).

4.2 Short-Term Memory and Working Memory Information within the central nervous system can be stored for various lengths of time, but, in cognitive psychology, a distinction has been made between information stored for short periods as STM (up to say, 20 s; Peterson and Peterson 1959) and for longer periods as LTM (Miller 1956). STM is sometimes seen in terms of a system of storage buffers, held as WM, that serve to hold transient information during conscious tasks (Baddeley 1986, 1992). This view has been reinforced by observation in some neuroscience studies of analogous buffer systems (Goldman-Rakic 1990). The combination of studies in cognitive psychology and integrative biology has led to the exploration of the concept of STM, and it is seen in a general sense as being equivalent to WM (Bartsch and Butler 2013; Kesner and Rolls 2001; Miyashita 2004) or at least sharing functions and neural resources (Cowan 2016; Kristjánsson et al. 2013; Soto et al. 2012), representing the capability of a flexible nervous system in attending to different kinds of information (Ma et al. 2014; Postle 2006, 2015). This concept of WM involves the recall of concepts from LTM and their comparison with new input, and full consciousness is thought to be crucial for WM and any associated attentional processes (Alkire et al. 2008; Curtis and D’Esposito 2003; D’Esposito and Postle 2015). Some researchers in cognitive psychology (Sweller 1988, 1994, 2004; Sweller et al. 2011) have described problem-solving as the interaction of novel input information with stored information in LTM using WM and attentional processes; some studies in integrative biology have even argued that such problem-solving is the main function of learning and memory (Tonegawa et al. 2003). Largely because the neural systems that have evolved in animals have enabled fast motor responses to sensory input, some researchers have suggested that human learning and memory processes are involved in some way with both sensory input and generation of a motor response (Calvin 1996, 2004; Cotterill 2001; Lakoff and Núñez 2000). In addition, some researchers have suggested that in some organisms with a complex central nervous system, motor responses can be delayed or inhibited (in some cases indefinitely) and this appears to be the case with many vertebrate and invertebrate organisms (Cotterill 2001; Humphrey 1992; Llinás 2001). Such delayed or inhibited responses become stored as LTM and are sometimes referred to as pre-motor scenarios (Cotterill 2001). In many organisms with a centralised nervous system (including humans), input sensory information can interact potentially with motor scenarios stored previously. Together, these have value in planning any behavioural response, predicting a course of behaviour that anticipates incoming stimulus or enacting a behavioural response (Calvin 1996, 2004; Grillner 2003). Such predictive patterning may be essential for fast muscular responses to environmental input in organisms with a centralised nervous system, and the operation of such patterning appears to require conscious attention where novel information

4.2 Short-Term Memory and Working Memory

33

input is involved (Cotterill 2001; Postle 2006, 2015). Many of these responses operate through activation of one from several predictions or scenarios, and this offers an advantage over the slower behavioural response that results from a single direct response to a stimulus. If someone throws a spear at you, for example, you need to be able to predict the spear’s path and activate one of several responses in order to get out of the way, as the time taken to process the information without such a predictive capacity may be longer than the time the spear might take to travel to your body (Calvin 2004). Predictive patterning of pre-motor and motor scenarios appears to be a significant feature of larger mammals, such as humans, which are thought to have the most sophisticated cognitive architecture (Greenfield 2000). In humans, this architecture includes WM and attentional processes that provide the pattern of linkages for such scenarios. This is further facilitated by large, highly interconnected cortical structures, such as the expanded prefrontal cortex (Llinás 2001). Some researchers have argued that this cortical advantage is an artefact or mangal of differential rates of anatomical development (Calvin 2004; Dixon-Salazar and Gleeson 2010; Finlay and Darlington 1995; Gibson 2002; Johnson et al. 2009). Regardless of the reason for the evolution of this larger cortical region, the human ability to deal with novel information through attention and WM (problem-solving) is influenced by cortical advantage (Goldman-Rakic 1990; Postle 2006, 2015). The human ability to generalise well from a local problem and its solution to a more global problem and solution is also considered to be an attribute that utilises STM and the large cortical structures in the central nervous system, such as the expanded prefrontal cortex (Greenfield 2000). Some aspects of human cognitive function, however, are inferior in function to that of other organisms with a centralised nervous system, and a human survival advantage is sometimes effected by compensatory abilities—one of which is thought to be advantageous concept-building architectures, particularly in relation to flexibility and speed of problem-solving (Grandin and Johnson 2005). A honeybee, for example, has a much better cognitive function than humans for locating and remembering the location of flowers that have nectar (Real 1994), but a human can formulate, through STM, the concepts involved in solving the problem of locating a nectar-filled flower, and then know where to quickly find the information needed to resolve the problem. Recent technological developments within integrative biology, such as the breeding of animals with knockout genes and the in situ chemical analysis of neurological structure and function, have attempted to delineate the processes that lead from information input to memory, with such processes documented for a number of invertebrate and vertebrate organisms (Dubnau et al. 2003; Grillner 2003; Heidenreich and Zhang 2016; Rai-Bhogal et al. 2018; Tonegawa et al. 2003). As a result of such studies, some scientists see memory storage as a relatively continuous process, with some of the STM becoming medium-term and some of the medium-term eventually becoming LTM, largely through the involvement of gene transcription and translation (Grillner 2003; Martinez 2019; Ranganath and Blumenfeld 2005; Tonegawa et al. 2003) and through the routing of memory traces through differing neuronal pathways and anatomical loci (Cotterill 2001; Dubnau et al. 2003; Margulies et al. 2005). Some

34

4 Connections Between Studies of Human Learning …

studies in integrative biology recognise, therefore, a variable duration for shorter-term storage of information, for example, the short-term, medium-term and anaesthesiadependent memory seen in fruit flies (Dubnau et al. 2003) and the short-term and medium-term memory recognised in humans (Delorme et al. 2018; Melcher 2001). Some such research, however, maintains that LTM is distinguishable from all of the shorter-term memories in that it involves permanent changes in connectivity due to transcription and translation of the DNA that produces the proteins needed for synapse growth, with such changes in connectivity being triggered by specific amounts and types of input communication (Dubnau et al. 2003; Kandel 2009; Kelleher et al. 2004; Todd and Marois 2004; Tonegawa et al. 2003).

4.3 Attention and Working Memory 4.3.1 Information Processing Through Attention and Working Memory Some researchers in cognitive psychology argue that WM facilitates schema building through the combination of novel sensory input and information from LTM (Sweller et al. 2011; Zhou et al. 2018). Such arguments incorporate the idea that the process of learning requires WM to be actively engaged in the comprehension and processing of to-be-learned information in order for that material to be encoded into LTM. The process of active engagement is sometimes referred to as attention (Cowan 2016). Since WM is considered to be limited in capacity and may hold information for only short periods—whereas LTM is considered to have a much larger capacity and may hold information for periods up to the individual’s lifetime—the limitations of WM, acting with attentional processes, act to protect LTM from any information changes that might deleteriously affect the use of information in day-to-day survival (Klingberg 2009; Sweller and Sweller 2006). Attention and WM have been investigated from the viewpoint of integrative biology and are viewed generally as mechanisms that allow the comparison, within the central nervous system, of novel sensory and other information with information already held in memory (Cotterill 2001; Eriksson et al. 2015; Postle 2006, 2015; Roy and Tonegawa 2017; Tonegawa et al. 2018). Some groundbreaking studies have shown, in fact, that attention has two components: one that acts directly from input sensory information (bottom-up) and one that acts from stored knowledge in LTM (top-down) (Itti et al. 2005; Marois 2005; Rao 2006; Riddle et al. 2019; Todd and Marois 2004). Cotterill (2001) has proposed that attention within such a system relies on the nervous system being interfaced with the muscular system as a basis for brain activation in the conscious or awake state, with three separate neuronal pathways (efference copy routes) that keep the system apprised of the current state of the body’s musculature, and the rate of any change to its current state. In such an activated brain, Cotterill (2001, 2008) sees attention as rising from competition

4.3 Attention and Working Memory

35

between neuronal assemblies that have an adjudication function in the mediation of choice between permitting current movement, switching to another, or stopping altogether—a form of internal conflict resolution that requires activation of neuronal assemblies. In detailing the anatomical and biological processes of neuronal and neural information pathways, Cotterill (2001) linked both WM (or STM) and attentional processes to regulatory or modulatory effects in the passage of information through the brain. In this context, WM provides continuity of interaction of the individual with the environment in order that predictive patterns or scenarios in neuronal assemblies can compete and, therefore, determine the activation of a particular scenario for a potential movement. Some researchers have argued that such comparison of and competition between predictive patterns is tied to the activation of one or several attractor states based in a linkage of memories as neuronal or related pathways in LTM (Calvin 1996, 2004; Rao 2006; Riddle et al. 2019). Such activation is determined by the input stimulus in STM, but sometimes also through an anticipatory mechanism which can be viewed as a component of attention (Bouchacourt and Buschman 2019). Even if such competition is not based on movement-based assemblies as Cotterill (2001) proposes, but rather on such processes as the self-organising interaction of information in a more generalised scenario of multiple attractor states (Calvin 1996, 2004; Rao 2006; Riddle et al. 2019), the word attention describes the system for activation of such processes. WM, which is effectively the process of competition between interacting neuronal assemblies as a result of information input or recall of input to the central nervous system, does not operate in an awake person without any attentional activation, although the competitive, comparative and memory storage processes that constitute WM can be described as seemingly independent of attentional processes (Cowan 2016; Postle 2006). In this respect, WM can be described as an interacting function of STM and LTM, activated through attention.

4.3.2 Limitations on Attention and Working Memory Unlike LTM, WM is considered to be limited, with the limitations generally described in terms of limitations on the number of items of information, as elements, schemas or chunks, that can be dealt with in any time interval. In proposing the idea of chunking, Miller (1956) suggested that the number of such items was seven plus or minus two. Cowan (2016) has suggested that the figure realistically should be four but has acknowledged that there is no universal agreement as to the number of items of information, or whether, in fact, chunking occurs in any physiological sense (see also Brady and Alvarez 2015). Usher and others (Usher et al. in Cowan 2001), for example, argue that inhibition and representational overlap could result in the number of items of information in WM being as low as one. Some researchers suggest further that it is difficult to describe in whole rather than fractional numbers

36

4 Connections Between Studies of Human Learning …

the limitations on the number of items of information in WM (Taatgen in Cowan 2001). The issue of the limitations of WM and attention has been further examined through studies in integrative biology proposing that WM and attention, as well as STM and LTM, use the same neuronal structures, even if any such structures in LTM are more permanent (Bays 2018; Constantinidis and Klingberg 2016; Eriksson et al. 2015; Snyder et al. 2004; Tonegawa et al. 2003). In this framework, information in WM (STM) is similar to information activated in LTM, with attention, therefore, becoming an issue of modulation of neural assemblies, many of which are in competition (Bouchacourt and Buschman 2019; Cotterill 2001; Calvin 1996). Within and between assemblies of both WM and LTM, however, some connectivity is inhibited and some connections deleted (Edelman 1987, 1989, 1992; Goswami 2008a, b; Snyder et al. 2004); this, in part, creates neuronal assemblies that are dissociated or discrete to varying degrees, and therefore exist effectively as separate items that could be considered as schemas or chunks. Regardless of whether WM is considered as an activation of a multitude of different neuronal assemblies in STM (Postle 2006) or as interacting items of information, the limitations of WM are known to vary during development to adulthood and into old age (Peng et al. 2018; Swanson 2017), or indeed with internal physiological variations on a day-to-day basis, such as variations due to tiredness or hunger (Jones et al. 2018; Riby et al. 2004). Studies in integrative biology indicate that an activated WM can be thought of as the operation of different neuronal processes, some of which operate within different time intervals (Bays 2018; Bouchacourt and Buschman 2019). These types of differential and potentially competitive activations lead to regulatory or modulatory effects that determine the amount of information, including information described in terms of the number of elements, chunks or schemas, that can be processed and the time frame in which the processing occurs. There are known to be differing time intervals for any regulation or modulation of information within WM, and regions such as the amygdala and the basal ganglia, for example, may enable faster modulation than, say, the cerebellum or other modulatory regions. Because of the interactive nature of competitive assemblies, however, any such region can influence potentially any thinking or a movement-based function (Cotterill 2001, 2008). There are also other potential sources of variation of modulation and sensory input. An important feature of many of these is in the enabling of interacting oscillations in different cortical areas as waves of particular frequencies (Bullock 2002, 2003; Jones 2018). Differing times are required for the maintenance of those oscillations that support the input information and the dying down of those oscillations that do not. These various oscillations, believed to originate in the thalamocortical system, are thought to be essential to conscious brain activation processes, but the presence of a single strong oscillation does not necessarily correspond to that associated with the incoming information (Crick and Koch 1998a, b). Cotterill (2001) has suggested that such a system of interacting oscillations will not prevail unless the motor-planning areas have already been activated—even though such activation may be at a threshold lower than that required for actual movement.

4.3 Attention and Working Memory

37

Within such a system, WM becomes an activation of neuronal assemblies with varying strengths of interlinking that is subject to such factors as chemical availability and reaction rates, physical states and modulatory effects that limit interactions of activated assemblies. Temporal variation is explained by the observation that environmental information input is in discrete bursts (Kolb 2003, 2018) and the argument that attentional processes are also enacted in discrete bursts determined by the interaction of such things as neuronal pathway times (efference copy routing times) (Cotterill 2001). Cowan (2016) is perhaps referring to this kind of limitation in referring to WM limits (including time limits) as limits in the focus of attention, a feature that has been observed externally in phenomenological studies. Some scientists (Dukas 2019; Todt 2001) have suggested that, even where an organism’s brain structures are different (such as those of birds and mammals), evolutionary pressure has selected attentional and WM systems that operate within a time frame that has enabled the survival of particular organisms. It follows that the time and content limitations of WM may optimise environmental information processing and, hence, environmental interaction through strategic use and retention of sensory information in an attentional system. Acknowledgements Parts of this chapter are adapted from Woolcott, G. 2010. Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496), Strasbourg, France: Analytrics.

References Alkire, M. T., Hudetz, A. G., & Tononi, G. (2008). Consciousness and anesthesia. Science, 322, 876–880. Alonso, J. L., & Goldmann, W. H. (2016). Cellular mechanotransduction. AIMS Biophysics, 3(1), 50–62. Arshavsky, Y. I. (2006). The ‘Seven Sins’ of the Hebbian synapse: Can the hypothesis of synaptic plasticity explain LTM consolidation? Progress in Neurobiology, 80, 99–113. Baars, B. J., & Gage, N. M. (2010). Cognition, brain, and consciousness: Introduction to cognitive neuroscience. Cambridge, MA: Academic Press. Bach-y-Rita, P. (2004). Tactile sensory substitution studies. Annals of the New York Academy of Sciences, 1013, 83–91. Baddeley, A. D. (1986). Working memory. Oxford, UK: Oxford University Press. Baddeley, A. D. (1992). Working memory. Science, 255, 556–559. Bartsch, T., & Butler, C. (2013). Transient amnesic syndromes. Nature Reviews Neurology, 9(2), 86–97. Bays, P. M. (2018). Reassessing the evidence for capacity limits in neural signals related to working memory. Cerebral Cortex, 28(4), 1432–1438. Bouchacourt, F., & Buschman, T. J. (2019). A flexible model of working memory. Neuron, 103(1), 147–160. Brady, T. F., & Alvarez, G. A. (2015). No evidence for a fixed object limit in working memory: Spatial ensemble representations inflate estimates of working memory capacity for complex objects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(3), 921.

38

4 Connections Between Studies of Human Learning …

Bullock, T. H. (2002). Biology of brain waves: Natural history and evolution of an informationrich sign of activity. In K. Arikan & N. Moore (Eds.), Advances in electrophysiology in clinical practice and research (pp. 1–19). Wheaton, IL: Kjellberg. Bullock, T. H. (2003). Have brain dynamics evolved?—Should we look for unique dynamics in the sapient species? Neural Computation, 15, 2013–2027. Calvin, W. H. (1996). The cerebral code: Thinking a thought in the mosaics of the mind. Cambridge, MA: MIT Press. Calvin, W. H. (2004). A brief history of the mind: From apes to intellect and beyond. Oxford: Oxford University Press. Casanova, M. F., & Casanova, E. L. (2019). The modular organization of the cerebral cortex: Evolutionary significance and possible links to neurodevelopmental conditions. Journal of Comparative Neurology, 527(10), 1720–1730. Clark, R. E., & Martin, S. J. (Eds.). (2018). Behavioral neuroscience of learning and memory (Vol. 37). Cham: Springer. Colangelo, A. M., Cirillo, G., Alberghina, L., Papa, M., & Westerhoff, H. V. (2019). Neural plasticity and adult neurogenesis: The deep biology perspective. Neural Regeneration Research, 14(2), 201–205. Connors, B. W., & Long, M. A. (2004). Electrical synapses in the mammalian brain. Annual Review of Neurosciences, 27, 393–418. Constantinidis, C., & Klingberg, T. (2016). The neuroscience of working memory capacity and training. Nature Reviews Neuroscience, 17(7), 438–449. Cotterill, R. M. J. (2001). Co-operation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: Possible implications for cognition, consciousness, intelligence and creativity. Progress in Neurobiology, 64, 1–33. Cotterill, R. M. J. (2008). The material world. New York, NY: Cambridge University Press. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioural and Brain Sciences, 24, 87–185. Cowan, N. (2016). Working memory capacity (Classic ed.). New York, NY: Routledge. Crick, F., & Koch, C. (1998a). Consciousness and neuroscience. Cerebral Cortex, 8, 97–107. Crick, F., & Koch, C. (1998b). Constraints on cortical and thalamic projections: The no-strong-loop hypothesis. Nature, 391, 245–250. Curtis, C. E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7(9), 455–463. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin Viking. Del Giudice, M., & Crespi, B. J. (2018). Basic functional trade-offs in cognition: An integrative framework. Cognition, 179, 56–70. Delorme, A., Poncet, M., & Fabre-Thorpe, M. (2018). Briefly flashed scenes can be stored in long-term memory. Frontiers in Neuroscience, 12, 1. https://doi.org/10.3389/fnins.2018.00688. D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory. Annual Review of Psychology, 66, 115–142. Dixon-Salazar, T. J., & Gleeson, J. G. (2010). Genetic regulation of human brain development: Lessons from Mendelian diseases. Annals of the New York Academy of Sciences, 1214, 156–167. Dubnau, J., Chiang, A. S., & Tully, T. (2003). Neural substrates of memory: From synapse to system. Journal of Neurobiology, 54, 238–253. Dukas, R. (2019). Animal expertise: Mechanisms, ecology and evolution. Animal Behaviour, 147, 199–210. Edelman, G. M. (1987). Neural Darwinism: The theory of neuronal group selection. New York, NY: Basic Books. Edelman, G. M. (1989). The remembered present. New York, NY: Basic Books. Edelman, G. M. (1992). Bright air, brilliant fire. New York, NY: Basic Books. Edelman, G. M. (2007). Learning in and from brain-based devices. Science, 318(5853), 1103–1105.

References

39

Eriksson, J., Vogel, E. K., Lansner, A., Bergström, F., & Nyberg, L. (2015). Neurocognitive architecture of working memory. Neuron, 88(1), 33–46. Evans, C. (2019). The neurobiology of reward: Understanding circuitry in the brain that shapes our behavior. In J. Kelso (Ed.), Learning to live together: Promoting social harmony (pp. 97–105). Cham: Springer. Fajardo, D., Vinasco, K., Montoya, J. C., Satizabal, J. M., Sanchez, A., & GarcÃa-Vallejo, F. (2018). Complex networks of interaction of genes located in the critical region of down syndrome expressed in the normal human brain. Biomedical Research, 29(18), 3415–3428. Finlay, B. L., & Darlington, R. B. (1995). Linked regularities in the development and evolution of mammalian brains. Science, 268, 1578–1584. Geake, J. G. (2004). Cognitive neuroscience and education: Two-way traffic or one-way street. Westminster Studies in Education, 27(1), 87–98. Geake, J. G. (2009). The brain at school: Educational neuroscience in the classroom. Berkshire, UK: McGraw Hill-Open University Press. Gibson, K. R. (2002). Evolution of human intelligence: The roles of brain size and mental construction. Brain, Behaviour, and Evolution, 59, 10–20. Goldman-Rakic, P. S. (1990). Cellular and circuit basis of working memory in prefrontal cortex of nonhuman primates. In H. B. M. Uylings, C. G. V. Eden, J. P. C. DeBruin, M. A. Comer, & M. G. P. Feenstra (Eds.), Progress in brain research (pp. 325–336). Amsterdam, The Netherlands: Elsevier. Goswami, U. (2008a). Cognitive development: The learning brain. Philadelphia, PA: Psychology Press of Taylor and Francis. Goswami, U. (2008b). Reading, complexity and the brain. Literacy, 42(2), 67–72. Grandin, T., & Johnson, C. (2005). Animals in translation. New York, NY: Harcourt Books. Greenfield, S. (2000). The private life of the brain: Emotions, consciousness and the secret of the self . New York, NY: Wiley. Grillner, S. (2003). The motor infrastructure: From ion channels to neuronal networks. Nature Reviews Neuroscience, 4, 573–586. Haier, R. J. (2016). The neuroscience of intelligence. Cambridge, MA: Cambridge University Press. Heidenreich, M., & Zhang, F. (2016). Applications of CRISPR–Cas systems in neuroscience. Nature Reviews Neuroscience, 17(1), 36–44. Humphrey, N. (1992). A history of the mind: Evolution and the birth of consciousness. London: Chatto & Windus. Itti, L., Rees, G., & Tsotsos, J. K. (Eds.). (2005). Neurobiology of attention. Burlington, MA: Elsevier. Johnson, M. B., Kawasawa, Y. I., Mason, C. E., Krsnik, Z., Coppola, G., Bogdanovi, D., et al. (2009). Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron, 28(62), 494–509. Jones, M. R. (2018). Time will tell: A theory of dynamic attending. New York, NY: Oxford University Press. Jones, N., Riby, L. M., & Smith, M. A. (2018). Glucose regulation and face recognition deficits in older adults: The role of attention. Aging, Neuropsychology, and Cognition, 25(5), 673–694. Kandel, E. R. (2009). The biology of memory: A forty-year perspective. Journal of Neuroscience, 29(41), 12748–12756. Kelleher, R. J., Govindarajan, A., Jung, H.-Y., & Kang, H. (2004). Translational control by MARK signalling in long-term synaptic plasticity and memory. Cell, 116, 467–479. Kesner, R. P., & Rolls, E. T. (2001). Role of long-term synaptic modification in short-term memory. Hippocampus, 11, 240–250. Klingberg, T. (2009). The overflowing brain: Information overload and the limits of working memory. New York, NY: Oxford University Press. Kolb, B. (2003). The impact of the Hebbian learning rule on research in behavioural neuroscience. Canadian Psychology/Psychologie Canadienne, 44(1), 14–16.

40

4 Connections Between Studies of Human Learning …

Kolb, B. (2018). Brain plasticity and experience. In R. Gobb & B. Kolb (Eds.), The neurobiology of brain and behavioral development (pp. 341–389). London: Academic Press. Kristjánsson, Á., Saevarsson, S., & Driver, J. (2013). The boundary conditions of priming of visual search: From passive viewing through task-relevant working memory load. Psychonomic Bulletin & Review, 20(3), 514–521. Lakoff, G., & Johnson, M. (1999). Metaphors we live by. New York, NY: Basic Books. Lakoff, G., & Núñez, R. E. (2000). Where mathematics comes from: How the embodied mind brings mathematics into being. New York, NY: Basic Books. Lamb, S. M. (1999). Pathways of the brain: The neurocognitive basis of language. Philadelphia, PA: John Benjamins Publishing. Llinás, R. (2001). I of the vortex: From neurons to self . Cambridge, MA: MIT Press. Ma, W. J., Husain, M., & Bays, P. M. (2014). Changing concepts of working memory. Nature Neuroscience, 17, 347–356. Margulies, C., Tully, T., & Dubnau, J. (2005). Deconstructing memory in Drosophila. Current Biology, 15, R700–R713. Marois, R. (2005). Two-timing attention. Nature Neuroscience, 8(10), 1285–1286. Marshall, P., & Bredy, T. W. (2016). Cognitive neuroepigenetics: The next evolution in our understanding of the molecular mechanisms underlying learning and memory? NPJ Science of Learning, 1, 16014. Martinez, D. (2019). Immediate and long-term memory and their relation to crystallized and fluid intelligence. Intelligence, 76, 101382. Mason, A., Farrell, S., Howard-Jones, P., & Ludwig, C. J. (2017). The role of reward and reward uncertainty in episodic memory. Journal of Memory and Language, 96, 62–77. Melcher, D. (2001). Persistence of visual memory for scenes. Nature, 412, 401. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97. Miyashita, Y. (2004). Cognitive memory: Cellular and network machineries and their top-down control. Science, 306, 435–440. Mottron, L., Dawson, M., & Soulières, I. (2009). What aspects of autism predispose to talent. Philosophical Transactions of the Royal Society of London, B, 364, 1351–1357. Mountcastle, V. B. (1998). Perceptual neuroscience. The cerebral cortex. Cambridge, MA: MIT Press. Murayama, K., Matsumoto, M., Izuma, K., & Matsumoto, K. (2010). Neural basis of the undermining effect of monetary reward on intrinsic motivation. Proceedings of the National Academy of Sciences of the United States of America, 107(49), 20911–20916. Opris, I., & Casanova, M. F. (2017). The physics of the mind and brain disorders. Cham: Springer. Parker, J. D. A., Saklofske, D. H., Wood, L. M., & Collin, T. (2009). The role of emotional intelligence in education. In J. D. A. Parker, D. H. Saklofske, & C. Stough (Eds.), Assessing emotional intelligence: Theory, research and applications (pp. 239–255). Dordrecht, The Netherlands: Springer. Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401. Peng, P., Barnes, M., Wang, C., Wang, W., Li, S., Swanson, H. L., et al. (2018). A meta-analysis on the relation between reading and working memory. Psychological Bulletin, 144(1), 48–76. Peterson, L., & Peterson, M. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193–198. Pieuchot, L., Vassaux, M., Marteau, J., Cloatre, T., Petithory, T., Brigaud, I., Chauvy, P.-F., Ponche, A., Milan, J.-L., Rougerie, P., Bigerelle, M., & Anselme, K. (2016). How cells surf the waves? Curvotaxis directs migration trough cell-scale natural landscapes. In: C.1 Materials, surfaces and interfaces for medical applications and health. San Francisco, CA: American Society for Cell Biology. Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, 23–38.

References

41

Postle, B. R. (2015). Neural bases of the short-term retention of visual information. In P. Jolicoeur, C. Lefebvre, & J. Martinez-Trujillo (Eds.), Mechanisms of sensory working memory: Attention and performance XXV (pp. 43–58). London: Academic Press. Pulvermüller, F. (2018). The case of CAUSE: Neurobiological mechanisms for grounding an abstract concept. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1752), 20170129. Quiroga, R. Q. (2012). Concept cells: The building blocks of declarative memory functions. Nature Reviews Neuroscience, 13(8), 587–597. Rai-Bhogal, R., Ahmad, E., Li, H., & Crawford, D. A. (2018). Microarray analysis of gene expression in the cyclooxygenase knockout mice—A connection to autism spectrum disorder. European Journal of Neuroscience, 47(6), 750–766. Rao, R. P. (2006). Models of attention. Encyclopedia of Cognitive Science, 10(1002/0470018860), s00370. Real, L. A. (1994). Information processing and the evolutionary ecology of cognitive architecture. In L. A. Real (Ed.), Behavioral mechanisms in evolutionary ecology (pp. 99–153). Chicago, IL: University of Chicago Press. Riby, L. M., Meikle, A., & Glover, C. (2004). The effects of age, glucose ingestion and glucoregulatory control on episodic memory. Age and Ageing, 33, 483–487. Richard, G., & Joseph, S. (Eds.). (2016). Biocommunication: Sign-mediated interactions between cells and organisms (Vol. 1). London: World Scientific. Riddle, J., Hwang, K., Cellier, D., Dhanani, S., & D’Esposito, M. (2019). Causal evidence for the role of neuronal oscillations in top–down and bottom–up attention. Journal of Cognitive Neuroscience, 31(5), 768–779. Robin, J., & Moscovitch, M. (2017). Details, gist and schema: Hippocampal–neocortical interactions underlying recent and remote episodic and spatial memory. Current Opinion in Behavioral Sciences, 17, 114–123. Routtenberg, A., & Rekart, J. L. (2005). Post-translation modification as the substrate for longlasting memory. Trends in Neurosciences, 28(1), 12–19. Rowland, D. C., Roudi, Y., Moser, M. B., & Moser, E. I. (2016). Ten years of grid cells. Annual Review of Neuroscience, 39, 19–40. Roy, A., Perlovsky, L., Besold, T. R., Weng, J., & Edwards, J. C. (2018). Representation in the brain. Frontiers in Psychology, 9, 1410. Roy, D. S., & Tonegawa, S. (2017). Manipulating memory in space and time. Current Opinion in Behavioral Sciences, 17, 1–6. Schneider, W., & Shiffrin, R. (1977). Controlled and automatic human information processing: I. Detection, search and attention. Psychological Review, 84, 1–66. Shapiro, L. (2019). Embodied cognition. London: Routledge. Shiffrin, R., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190. Snyder, A. W., Bossomaier, T., & Mitchell, D. J. (2004). Concept formation: ‘Object’ attributes dynamically inhibited from conscious awareness. Journal of Integrative Neuroscience, 3(1), 31– 46. Snyder, J. S. (2019). Recalibrating the relevance of adult neurogenesis. Trends in Neurosciences, 42(3), 164–178. Soto, D., Llewelyn, D., & Silvanto, J. (2012). Distinct causal mechanisms of attentional guidance by working memory and repetition priming in early visual cortex. Journal of Neuroscience, 32(10), 3447–3452. Sporns, O. (2010). Networks of the brain. Cambridge, MA: MIT Press. Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT press. Squire, L. R., & Kandel, E. R. (2008). Memory: From mind to molecules (2nd ed.). Greenwood Village, CA: Roberts & Company.

42

4 Connections Between Studies of Human Learning …

Swanson, H. L. (2017). Verbal and visual-spatial working memory: What develops over a life span? Developmental Psychology, 53(5), 971–995. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285. Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4, 295–312. Sweller, J., & Sweller, S. (2006). Natural information processing systems. Evolutionary Psychology, 4, 434–458. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Explorations in the learning sciences, instructional systems and performance technologies. Dordrecht, The Netherlands: Springer. Taatgen, N. A. (2000). Dispelling the magic: Towards memory. Invited Commentary on Cowan, N. (2001). Tashiro, A., Makino, H., & Gage, F. H. (2007). Experience-specific functional modification of the dentate gyrus through adult neurogenesis: A critical period during an immature stage. The Journal of Neuroscience, 27(13), 3252–3259. Thompson, P. W. (1994). The development of the concept of speed and its relationship to the concept of rate. In G. Harel & J. Confrey (Eds.), The development of mulplicative reasoning in the learning of mathematics (pp. 181–236). Albany, NY: State University of New York Press. Todd, J. J., & Marois, R. (2004). Capacity limit of visual short-term memory in human posterior parietal cortex. Nature, 428(6984), 751–754. Todt, D. (2001). Studies of STM properties in animals may help us better understand the nature of our own storage limitations: The case of birdsong acquisition. Behavioral and Brain Sciences, 24(1), 149–150. Tonegawa, S., Morrissey, M. D., & Kitamura, T. (2018). The role of engram cells in the systems consolidation of memory. Nature Reviews Neuroscience, 19(8), 485–498. Tonegawa, S., Nakazawa, K., & Wilson, M. A. (2003). Genetic neuroscience of mammalian learning and memory. Philosophical Transactions of the Royal Society of London, B, 358, 787–795. van den Heuvel, O. A., van Wingen, G., Soriano-Mas, C., Alonso, P., Chamberlain, S. R., Nakamae, T., et al. (2016). Brain circuitry of compulsivity. European Neuropsychopharmacology, 26(5), 810–827. Vandervert, L. R. (2003). How working memory and cognitive modelling functions of the cerebellum contribute to discoveries in Mathematics. New Ideas in Psychology, 21(1), 15–29. Woolcott, G. (2010). Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496). Strasbourg: Analytrics. Woolcott, G. (2013). Giftedness as cultural accumulation: An information processing perspective. High Ability Studies, 24(2), 153–170. Zhou, J., Yu, K., Chen, F., & Wang, Y. (2018). Multimodal behavioural and physiological signals as indicators of cognitive load. In S. Oviatt, B. Schuller, P. Cohen, D. Sonntag, G. Potamianos, & A. Krüger (Eds.), The handbook of multimodal-multisensor interfaces (Vol. 2, pp. 289–330)., Signal processing, architectures, and detection of emotion and cognition London: Morgan & Claypool.

Chapter 5

Contributions of Modern Cognitive Psychology and Integrative Biology to Educational Theories and Practices

5.1 Educational Theory, Modern Cognitive Psychology and Environmental Context The educational theories and practices utilised in educational institutions in modern industrial societies are based, to some extent, on the non-empirical social and behavioural sciences and on teaching practices based on prevailing culture (HowardJones 2008, 2018; O’Loughlin 2017; Riley 2019). There is some doubt as to the effectiveness of information transmission and environmental interaction in many cases and, as well, some concern that ineffective teaching practices are being reinforced (Lyon 2005; Sweller et al. 2011; Sylwester 1995). This situation persists, despite the fact that some of the educational theories based on modern cognitive psychology have lent themselves to empirical testing, in particular, those theories which have focused on the conceptualisation of a human cognitive architecture in which long-term memory (LTM) is a large memory store constrained by the limited capacity and duration of attention and working memory (WM), and on the processing of environmental information considered within such a conceptualisation (Sweller et al. 2011). Some researchers have argued that teaching practices based on such theories should be amenable to effective assessment (Kalyuga 2006; Sweller et al. 1998; Sweller et al. 2011) and that some teaching practices should be abandoned in favour of those that are more effective (Ellis et al. 2008; Klahr and Nigam 2004; Mayer 2004; Sweller et al. 2011). Some of the modern educational theories applied to pedagogy, and some of the teaching practices developed subsequently, emphasise methods of communicating that are sensitive to processing of environmental information, and such theories and practices are, therefore, sensitive to environmental contexts. This can be seen in the development of pedagogical approaches based on the application of the theories of such influential figures as Vygotsky (see Schnotz and Kürschner 2007) and Hutchins (see Moreno 2010), where the teaching practices specifically include interaction with the environment (e.g. where such practices are couched in terms of action learning; Revans 2017). In addition, a number of influential educational theories developed © Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_5

43

44

5 Contributions of Modern Cognitive Psychology …

prior to the development of modern cognitive psychology, such as those developed from studies of developmental learning stages (Bloom 1984; Piaget 1928), have been re-examined in light of modern cognitive psychology (for Bloom, see Anderson et al. 2001, and for Piaget and neo-Piagetian theories, see Demetriou et al. 2016; Huitt and Hummel 2003) and these appear to emphasise the environmental context. The focus in such theories and their application, however, relates more to this environmental context than to any conceptualisation of human cognitive architecture. Of the educational theories based on modern cognitive psychology that have been applied to teaching practices, cognitive load theory (CLT) (Mavilidi and Zhong 2019; Sweller et al. 1998, 2011) perhaps best accommodates the view of learning and memory as the processing of environmental information while, at the same time, maintaining the conceptualisation of human cognitive architecture in terms of a large LTM constrained by attention and WM processes that contribute to cognitive load or mental effort. CLT has been expanded to encompass the origins and evolution of this architecture, spanning and integrating ideas about how this evolution leads to the structures constituting that architecture and proposing ideas about instructional consequences that flow from knowledge of those structures (Sweller et al. 2011). This expansion of CLT has been based on a broad consideration not only of the natural information processing system of human cognition but also that of human evolution. The educational principles developed from such consideration now form the intellectual basis of CLT (Leahy and Sweller 2019; Paas et al. 2016; Sweller 2010, 2016; Sweller and Sweller 2006). Unlike other educational theories based on such conceptualisations, CLT has accommodated an evolutionary classification of knowledge (in this case Geary’s classification of knowledge) into biologically primary knowledge that we have acquired through evolutionary processes and biologically secondary knowledge that we learn because it is culturally important (Geary 2008, 2010; Sweller 2008, 2016). The educational principles of CLT have been used to establish guidelines that help to optimise information storage as LTM through teaching that uses specific models of instructional design based on cognitive load effects. Sweller and others, for example, have established several cognitive load effects (Chen et al. 2017; Zheng 2017) as well as provided evidence-based and testable instructional principles based on those effects (Sweller et al. 1998, 2011). A number of instructional strategies developed from such principles have been demonstrated empirically to be superior to those used conventionally (Kalyuga 2015; Paas et al. 1994; Sweller et al. 1998, 2011). Other testable effects have been developed from research based on CLT—for example, the expertise reversal effect that utilises the differentiation of instructional design for novice and expert learners (Kalyuga 2006; Kalyuga and Singh 2016). The instructional design consequences of CLT have been applied widely in education, for example, to hypermedia instructional materials (Kalyuga 2006) and self-directed learning (van Merriënboer and Sluijsmans 2010) as well as to assess the transaction cost of individual mental effort during group interaction (Janssen et al. 2010) and to consider emotional states (Plass and Kalyuga 2019).

5.2 Educational Theory, Integrative Biology and Environmental Context

45

5.2 Educational Theory, Integrative Biology and Environmental Context Even though some educational theories have incorporated concepts from modern cognitive psychology, it would appear that a few of these theories have incorporated related concepts from studies in integrative biology, preferring instead to focus on phenomenology in terms of observations made largely from outside the human system. As a result, even well-known and established concepts in integrative biology, such as Hebb’s (1949) concepts of information storage in LTM as hierarchies of neurons or Mountcastle’s (1978) concepts related to the view that different regions of the brain with similar basic structures may function in a similar way, are not incorporated as part of such educational theories. Part of the problem may be that the acceptance of the simplicity of memory storage mechanisms has been obfuscated by discussions of the nature of the information stored (see Bates 2016; Bawden 2007; Janich 2018; Kennedy 2011; Lloyd 2010; Logan 2012; Sholle 1999; Sloman 2011). The problem appears to be exacerbated by the lack of educational theorists who come from a background in the empirical sciences (Lyon 2005; Sylwester 1995). In addition, it may take some time for educational theories to accommodate any modern studies in integrative biology in a form that can, in turn, be evaluated by modern scientists who are also experienced educators (Bruer 1997; Fischer et al. 2010). In detailing learning and memory concepts in terms of environmental interaction, modern integrative biology has given insights into some of the concepts developed from studies in cognitive psychology, including LTM, WM and attentional processes, although with an implication that there may not be clear-cut boundaries between some of the neuronal activations involved in such processes (Bouchacourt and Buschman 2019; Constantinidis and Klingberg 2016; Margulies et al. 2005). What is clear from such detailing is that memories may be thought of as input environmental information that is stored in the brain through alteration or growth of neuronal and related connections (Edelman 2007; Kandel 2009; Markham and Greenough 2004). This means that any instruction, whether it relates to such things as intellectual processes, facts or sequences of movements, can only be successful in affecting memory in a student if there is a corresponding modification of neuronal and related connections in the short term (Delorme et al. 2018; Dubnau et al. 2003; Lambert et al. 2019; Melcher 2001), alteration or growth of new connections in the longer term (Edelman 2007; Routtenberg and Rekart 2005) or, in a few brain regions, growth of new neurons (Colangelo et al. 2019; Snyder 2019; Tashiro et al. 2007) within the brain of the individual to whom the instruction is being presented. On this basis, educational theories and practices should perhaps be focused on methods for modulating an individual’s formation, reinforcement and retention of any neuronal or related connections or patterns of connectivity that correspond to that individual’s remembering the information being presented and the time period over which that information is to be remembered. The detailing of learning and memory concepts in integrative biology also suggests that successful teaching should attempt to modulate the formation of novel neuronal connections and patterns of connectivity

46

5 Contributions of Modern Cognitive Psychology …

within the brain by linking them to patterns of neuronal connectivity that are already present, such that some of these novel connections may be reinforced sufficiently to become part of LTM. This accommodates similar concepts about building on prior knowledge that are encapsulated in some of the theories and practices based on modern cognitive psychology (Fischer et al. 2010; Sweller et al. 2011). Some studies in integrative biology consider stored memories to be, at least in part, based on pre-motor or motor scenarios (Cotterill 2001; Llinás 2001), including scenarios that activate in the observer a set of neurons (called mirror neurons) as well as related systems, similar to those in the person being observed (Bonini 2017; Rizzolatti and Craighero 2004). Lakoff and others (Lakoff and Johnson 1999; Lakoff and Núñez 2000) refer to the connection of abstract memories to motor scenarios as embodiment, where conceptual knowledge is considered to develop through bodily experiences. Some educational theories have incorporated the idea or motor scenarios in advocating learning experiences that are based on processes of learning that involve motor responses related to sensory input from the environment (Howard-Jones 2007; Paas and Sweller 2012; Ziegler and Phillipson 2012). Some educational theories have also utilised the mirror neuron concept, for example, in the design of the instruction that involves animation based on human motion (van Gog et al. 2009; Xia et al. 2017). Some such approaches may conflict with the view that some thinking processes are abstract and separate from sensory input and motor responses (Humphrey 1992; Greenfield 2000), and some educational theories and practices may not consider that learning, particularly in preschool children, should involve necessarily a requirement for the experience or observation of motor responses (see Tomasello 2014, 2016). Studies in integrative biology have provided a more detailed view of the information processing that is involved in the internal workings of the brain that respond to the instruction, for example, in detailing the processes that may contribute to the constraints due to attention and WM processes as reported from phenomenological studies in cognitive psychology (Postle 2006, 2015). A focus on some of these studies is the delineation of the limits in activation processes (e.g. from the effects of directional flow, neural bottlenecks and inhibition in neural pathways; Cotterill 2001; Fougnie and Marois 2006; Marois 2005; Miller and Buschman 2007). Some studies indicate that both WM and attention function through particular types of brain activation related to environmental information input, with some studies viewing WM in an activated brain as inclusive of processes such as attending to information (Cowan 2017; Baars and Gage 2010; Jones 2018). Several studies have documented in some detail the interaction between stored information and input environmental information in an effort to elucidate the categorisation of information within LTM (Dehaene 2007, 2009; Mottron et al. 2009) and the methods by which coherence is obtained from diverse sources of information, referred to in Adaptive Resonance Theory as the stability-plasticity dilemma (Grossberg 2006, 2019). Some studies have implied that activation of neural assemblies functions in a similar way to the recall of schemas as proposed in cognitive psychology, where input information in WM stimulates such activation or recall as part of a linked system of neuronal assemblies or an interaction of separate assemblies (Cotterill 2001; Snyder et al. 2004; Robin and Moscovitch 2017).

5.2 Educational Theory, Integrative Biology and Environmental Context

47

Detailing of neural systems in integrative biology, including attention, WM and LTM, and their influence on memory formation, however, is yet to have a major impact on educational theories and practices (Geake 2009; Howard-Jones 2008; Sylwester 1995) and in the shorter term may not impact on their efficacy if the assessment of learning remains based in external observation of student behaviour. However, detailing the biological processes involved in learning and memory may have some impact in the longer term if it assists in showing that some educational theories and practices and some models of instructional design that are assumed to be efficient are not so. One of the insights offered from studies in integrative biology is that a modern teaching environment does not necessarily deal directly with the environmental interactions necessary for survival, growth and reproduction over the time intervals required for evolutionary change. The WM and attentional processes that have been selected through evolution over such time intervals, for example, have been subverted, as have memory processes generally, for education as well as for other agendas (Calvin 2002, 2004; Dehaene 2007, 2009; Geary 2005). However, our culture recognises the value of knowledge accumulated through teaching and institutionalised education, and educational researchers are beginning to accommodate the view that WM, attention and other memory processes are subverted from at least part of their evolutionary role.

5.3 Environmental Information Transmission, Intrinsic Networks and Educational Theory Education in modern industrial societies, through the agency of institutionalised teaching, offers guidance that assists individuals in developing the informational connectivity necessary for interaction with the environment (e.g. in growing connected neural pathways, such as motor scenarios), and this style of education may be superior to a student having to develop such connectivity by learning through pure discovery (Klahr and Nigam 2004; Mayer 2004). The environmental information that is input to the nervous system as part of cognitive functioning is generally considered as information in the form of sensory stimuli, such as light or sound, but environmental input may be best thought of as the input of energy or matter. There are, for example, well-documented physico-chemical influences on cognitive functions due to matter input such as glucose and oxygen (Chung et al. 2007; Jones et al. 2018; Riby et al. 2004). Such non-sensory matter and energy inputs are considered in some educational studies. An example is the brain-mind-behaviour model of Frith and others (Blakemore and Frith 2000 and see Howard-Jones 2011). However, in this model, environmental factors are compartmentalised into those that affect the brain (oxygen and nutrition), mind (teaching) and behaviour (teaching tools) in order to demonstrate the connectivity between biology, cognition and behaviour. Physico-chemical (matter

48

5 Contributions of Modern Cognitive Psychology …

and energy) aspects of environmental information input may need to be made empirically assessable components of educational theories so that their effect on learning processes can be quantified. As such, a broader educational theory may need to examine the physiological baseline of the limitations of environmental information input with regard to such factors as health, toxicity and age, and relate this baseline more generally to learning in organisms with a nervous system. With regard to environmental input to the human learning and memory system, however, a further issue is that some educational theories appear to assume that sensory input is continuous, regardless of the modality of that input. These theories, therefore, do not take into account the degree to which changes in rate and amount of flow of discrete inputs may affect some learners (Woolcott 2011). This issue has been investigated in studies in integrative biology indicating that information processing within the brain is based on discrete sensory inputs that are linked (associated) within LTM over varying time intervals (Ba¸sar and Bullock 2012). Educational intervention techniques are being developed to accommodate this view, particularly in reading (Dehaene 2009; Merzenich 2007), and such interventions aim to control the flow of discrete sensory information in order that groups of similar information components, such as the phonemes that form the basis of a spoken language, can be associated more readily with related written symbols. The variety of time periods required for elements of information within the nervous system to be connected or associated may be part of the process of building a body of knowledge in LTM. This issue is beginning to be considered in studies of the variation in the time needed for the restructuring or building of new neural pathways, such as during sleep periods (Cox et al. 2018; Spencer et al. 2017), after a specific amount of sensory input at a given flow rate. The concept of information that is connected or associated over time is used in a broad sense in studies in integrative biology to include information that may be linked across the entire LTM through the growth of connections between neurons within the central nervous system, including any intrinsic connection pathways over a lifetime. Such associations of information may occur even though some groups of neurons may be relatively isolated from others (e.g. through the position of such groups within the flow of a directed system of information transmission; Cotterill 2001; Mottron et al. 2009; Nolte et al. 2019). Such intrinsic connection pathways or networks are well-researched subjects of studies in integrative biology, although the term intrinsic may be interpreted in different ways in different disciplines, even within integrative biology (Dehaene 2007). In general, however, an intrinsic network is one that acts as a basis for further development. Such networks can be described in terms of neuronal and allied structures that are formed before or shortly after birth and which may influence the formation of memories throughout the course of an individual’s life. Some researchers describe subgroups of the larger association of information that is LTM as subsidiary networks and assert that these are constructed from the intrinsic networks of the individual organism. Some examples of such subsidiary networks are the learned reflexes constructed from reflexes of Cotterill (2001), the meta-concepts constructed from concepts of Snyder (Snyder et al. 2004) and also

5.3 Environmental Information Transmission, Intrinsic Networks …

49

the re-entrant pathways constructed from the intrinsic values of Edelman (2007). Geary (2005, 2008, 2010, 2011) has perhaps acknowledged this building process in his division of knowledge into biologically primary knowledge and biologically secondary knowledge, where biologically secondary knowledge is built from an essentially intrinsic knowledge base that is present well prior to an individual’s time in institutional education. Biologically primary knowledge, however, can be built upon during an individual’s cultural interactions, including those involved in learning in institutional education, to become part of biologically secondary knowledge. Geary (2010) has indicated that humans may, therefore, have a bias based on intrinsic knowledge towards what could be termed culturally useful knowledge rather than the knowledge that may be taught in institutional education. Some researchers have argued that all informational connectivity in LTM appears to have a basis in intrinsic neuronal activation networks (Cotterill 2001; Edelman, 2007; Snyder et al. 2004), including those networks related to emotions and drives (Damasio 1999, 2003, 2006; Davis and Panksepp 2018; Grandin and Johnson 2005; LeDoux 1996, 2000; LeDoux and Brown 2017; Panksepp 2004; Panksepp and Biven 2012). There has been some research that relates such neuronal activations to motivation (Mohanty et al. 2008; Murayama et al. 2010), and studies in education are beginning to accommodate such research, for example, in relating educational theories and practices to emotional intelligence, emotional literacy (Bellocchi 2019; Dolev and Leshem 2017; Goleman 2006; Immordino-Yang and Damasio 2007; Kemp et al. 2005; Shanwal and Kaur 2008; Steiner 2003) and motivation (Brooks and Shell 2006; Shell et al. 2010). The dynamics of the environmental interaction involved in developing the subsidiary and larger networks built from intrinsic networks appear to change with the individual’s growth and development, and some larger networks and cognitive structures may develop early in the life of any individual. Changes in some of the chemical pathways in the brain occur both during the birthing process and after (from about 6 weeks after a natural birth), and these lead to an arguably predominant environmental influence on the growth of subsidiary and larger networks from information taken in through the sensory cells in the eye’s retina (Calvin 2004). Human cognition, however, remains reliant on developing internal cognitive structures that are influenced by all of the individual’s interactions with the external world as well as with those of the internal environment (Cotterill 2001; Woolcott 2011, 2013). During an individual’s development, there may also be sensitive periods that occur when the neurons and synaptic junctions that form the subsidiary and larger networks are being generated in increasing quantity, for example, at ages up to 3 years (Calvin 2004; Edelman 2007; Tomasello 2014, 2016). Connections between neurons may be suppressed or inhibited (Snyder et al. 2004) or reinforced (Edelman 2007), with research on human connectivity unambiguously supporting periods of major synaptic growth and subsequent culling of connections at about 18 months (Baars and Gage 2010) and, perhaps, less unambiguously during adolescence (Giedd 2004; Sowell et al. 1999). However, the processes of growth and removal of such connections continue throughout human life as part of the association of information

50

5 Contributions of Modern Cognitive Psychology …

within LTM, generally referred to in terms of lifelong learning (Howard-Jones 2007; Swanson 2017; van Merriënboer and Sluijsmans 2010). Some studies in integrative biology have suggested that education should involve enriched environments in order that a minimum, or critical number, of neural associations are formed at such apparently important periods in human development (Calvin 2004; Howard-Jones 2007; Petersen 2017; Rutter and Azis-Clauson 2015). Edelman (2007) has presented a view of an idealised learning environment that enables subsidiary networks to be built up from intrinsic networks using the Darwinian principles of competition as the determining factor in retaining connections within what could be called memory elements or concepts. Developing educational theories along these lines may lead to teaching practices that relate specifically to the early years of education so that the growth of intrinsic or subsidiary networks is optimised, but such development has only recently started with the merging of biology-based concepts, such as those of Edelman (2007), with educational theory and teaching practices (Fischer et al. 2010; Goswami 2004, 2006; Smeyers 2016; Tokuhama-Espinosa 2018, 2019). On this basis, it can be argued that educational theories and practices should be based on the assumption that there is sufficient commonality in individuals’ intrinsic networks and the larger networks developed from them to allow the transmission of culture (sensu Tomasello 2014) from individual to individual, and that education serves to generate similar knowledge structures in LTM based on such commonality. The merging of integrative biology with institutionalised education, particularly where it is organised with a global congruence, should, therefore, serve the creation of commonalities of knowledge and the sharing of aspects of this knowledge within learning communities (including those in different generations) so that individuals within these communities can grow interconnected neuronal networks that can store this knowledge as part of the accumulation of culture across society.

References Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., et al. (Eds.). (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom’s taxonomy of educational objectives. New York, NY: Longman. Baars, B. J., & Gage, N. M. (2010). Cognition, brain, and consciousness: Introduction to cognitive neuroscience. Cambridge, MA: Academic Press. Ba¸sar, E., & Bullock, T. H. (Eds.). (2012). Brain dynamics: Progress and perspectives (Vol. 2). Cham: Springer. Bates, M. J. (2016). Information and the information professions: Selected works of Marcia J. Bates (Vol. 1). Berkeley, CA: Ketchikan Press. Bawden, D. (2007). Information as self-organized complexity: A unifying viewpoint. Information Research, 12(4), 12–4. Bellocchi, A. (2019). Early career science teacher experiences of social bonds and emotion management. Journal of Research in Science Teaching, 56(3), 322–347. Blakemore, S. J., & Frith, U. (2000). The implications of recent developments in neuroscience for research on teaching and learning. London: Institute of Cognitive Neuroscience.

References

51

Bloom, B. S. (1984). Taxonomy of educational objectives. Boston, MA: Allyn & Bacon. Bonini, L. (2017). The extended mirror neuron network: Anatomy, origin, and functions. The Neuroscientist, 23(1), 56–67. Bouchacourt, F., & Buschman, T. J. (2019). A flexible model of working memory. Neuron, 103(1), 147–160. Brooks, D. W., & Shell, D. F. (2006). Working memory, motivation, and teacher-initiated learning. Journal of Science Education and Technology, 15(1), 17–30. Bruer, J. T. (1997). Education and the brain: A bridge too far. Educational Researcher, 26(8), 4–16. Bruer, J. T. (2016). Where is educational neuroscience? Educational Neuroscience, 1, 2377616115618036. https://doi.org/10.1177/2377616115618036. Calvin, W. H. (2002). A brain for all seasons: Human evolution and abrupt climate change. Chicago, IL: University of Chicago Press. Calvin, W. H. (2004). A brief history of the mind: From apes to intellect and beyond. Oxford: Oxford University Press. Chen, O., Kalyuga, S., & Sweller, J. (2017). The expertise reversal effect is a variant of the more general element interactivity effect. Educational Psychology Review, 29, 393–405. Chung, S.-C., Kwon, J.-H., Lee, H.-W., Tack, G.-R., Lee, B., Yi, J.-H., et al. (2007). Effects of high concentration oxygen administration on n-back task performance and physiological signals. Physiological Measurement, 28, 389–396. Colangelo, A. M., Cirillo, G., Alberghina, L., Papa, M., & Westerhoff, H. V. (2019). Neural plasticity and adult neurogenesis: The deep biology perspective. Neural Regeneration Research, 14(2), 201–205. Constantinidis, C., & Klingberg, T. (2016). The neuroscience of working memory capacity and training. Nature Reviews Neuroscience, 17(7), 438–449. Cowan, N. (2017). The many faces of working memory and short-term storage. Psychonomic Bulletin & Review, 24(4), 1158–1170. Cotterill, R. M. J. (2001). Co-operation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: Possible implications for cognition, consciousness, intelligence and creativity. Progress in Neurobiology, 64, 1–33. Cox, R., Schapiro, A. C., & Stickgold, R. (2018). Variability and stability of large-scale cortical oscillation patterns. Network Neuroscience, 2(4), 481–512. Damasio, A. R. (1999). The feeling of what happens: Body and emotion in the making of consciousness. London: Heinemann. Damasio, A. R. (2003). Looking for Spinoza: Joy, sorrow, and the feeling brain. New York, NY: Harcourt. Damasio, A. R. (2006). Descartes’ error (Rev. ed.). London: Random House. Davis, K. L., & Panksepp, J. (2018). The emotional foundations of personality: A neurobiological and evolutionary approach. New York, NY: WW Norton & Company. Dehaene, S. (2007). A few steps towards a science of mental life. Mind, Brain, and Education, 1(1), 28–47. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin Viking. Delorme, A., Poncet, M., & Fabre-Thorpe, M. (2018). Briefly flashed scenes can be stored in long-term memory. Frontiers in Neuroscience, 12, 1. https://doi.org/10.3389/fnins.2018.00688. Demetriou, A., Shayer, M., & Efklides, A. (2016). Neo-Piagetian theories of cognitive development: Implications and applications for education. New York, NY: Routledge. Dolev, N., & Leshem, S. (2017). Developing emotional intelligence competence among teachers. Teacher Development, 21, 21–39. Dubnau, J., Chiang, A. S., & Tully, T. (2003). Neural substrates of memory: From synapse to system. Journal of Neurobiology, 54, 238–253. Edelman, G. M. (2007). Learning in and from brain-based devices. Science, 318(5853), 1103–1105. Ellis, J. B., Lamoureux, G., Awender, T., Wessel, D., & Donohoo, J. (2008). Of class, culture, and accountability. International Journal of Learning, 15(2), 25–34.

52

5 Contributions of Modern Cognitive Psychology …

Fischer, K. W., Goswami, U., Geake, J., & The Task force on the future of educational neuroscience. (2010). The future of educational neuroscience. Mind, Brain, and Education, 4(2), 68–80. Fougnie, D., & Marois, R. (2006). Distinct capacity limits for attention and working memory: Evidence from attentive tracking and visual working memory paradigms. Psychological Science, 17(6): 526–534. Geake, J. G. (2009). The Brain at school: Educational neuroscience in the classroom. Berkshire: McGraw Hill-Open University Press. Geary, D. C. (2005). Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology. In J. S. Carlson & J. R. Levin (Eds.), Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology. Psychological perspectives on contemporary educational issues (pp. 3–79). Greenwich, CT: Information Age Publishing. Geary, D. C. (2008). An evolutionarily informed education science. Educational Psychologist, 43, 279–295. Geary, D. C. (2010). Evolution and education. Psicothema, 22, 35–40. Geary, D. C. (2011). Application of evolutionary psychology to academic learning. In C. Roberts (Ed.), Applied evolutionary psychology (pp. 78–92). Oxford: Oxford University Press. Giedd, J. N. (2004). Structural magnetic resonance imaging of the adolescent brain. Annals of the New York Academy of Sciences, 1021, 77–85. Goleman, D. (2006). Emotional intelligence. New York, NY: Bantam Books. Goswami, U. (2004). Neuroscience and education. British Journal of Educational Psychology, 74, 1–14. Goswami, U. (2006). Neuroscience and education: From research to practice? Nature Reviews Neuroscience, 7(5), 406–411. Grandin, T., & Johnson, C. (2005). Animals in translation. New York, NY: Harcourt Books. Greenfield, S. (2000). The private life of the brain: Emotions, consciousness and the secret of the self . New York, NY: Wiley. Grossberg, S. (2006). Adaptive resonance theory. Encyclopedia of Cognitive Science, 10(1002/0470018860), s00067. Grossberg, S. (2019). A half century of progress toward a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders. In R. Kozma (Ed.), Artificial intelligence in the age of neural networks and brain computing (pp. 31–51). New York, NY: Academic Press. Hebb, D. O. (1949). The organization of behaviour. New York, NY: Wiley. Howard-Jones, P. A. (2007). Introduction to educational “neuromyths”. Transcript of keynote seminar of the all-party parliamentary group on scientific research in learning and education: ‘Brain-science in the classroom’. Conducted by the Institute for the Future of the Mind, England, UK. Howard-Jones, P. A. (2008). Philosophical challenges for researchers at the interface between neuroscience and education. Journal of the Philosophy of Education, 42(3–4), 361–380. Howard-Jones, P. A. (2011). A multiperspective approach to neuroeducational research. Educational Philosophy and Theory, 43(1), 24–30. Howard-Jones, P. (2018). Evolution of the learning brain: Or how you got to be so smart. London, UK: Routledge. Huitt, W., & Hummel, J. (2003). Piaget’s theory of cognitive development. Educational Psychology Interactive. Valdosta, GA: Valdosta State University. Retrieved June 2009, from http://www.edp sycinteractive.org/topics/cogsys/piaget.html. Humphrey, N. (1992). A history of the mind: Evolution and the birth of consciousness. London: Chatto & Windus. Immordino-Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1, 3–10. Janich, P. (2018). What is information? (E. Hayot & L. Pao, Trans.). Minneapolis, MN: University of Minnesota Press.

References

53

Janssen, J., Kirschner, F., Erkens, G., Kirschner, P. A., & Paas, F. (2010). Making the black box of collaborative learning transparent: Combining process-oriented and cognitive load approaches. Educational Psychology Review, 22(2), 139–154. Jones, M. R. (2018). Time will tell: A theory of dynamic attending. New York, NY: Oxford University Press. Jones, N., Riby, L. M., & Smith, M. A. (2018). Glucose regulation and face recognition deficits in older adults: The role of attention. Aging, Neuropsychology, and Cognition, 25(5), 673–694. Kalyuga, S. (2006). Instructing and testing advanced learners: A cognitive load approach. New York, NY: Nova Science. Kalyuga, S. (Ed.). (2015). Instructional guidance: A cognitive load perspective. Charlotte, NC: Information Age Publishing. Kalyuga, S., & Singh, A. M. (2016). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review, 28, 831–852. Kandel, E. R. (2009). The biology of memory: A forty-year perspective. Journal of Neuroscience, 29(41), 12748–12756. Kemp, A. H., Cooper, N. J., Hermens, G., Gordon, E., Bryant, R., & Williams, L. M. (2005). Toward an integrated profile of emotional intelligence: Introducing a brief measure. Journal of Integrative Neuroscience, 4(1), 41–61. Kennedy, J. E. (2011). Information in life, consciousness, quantum physics, and paranormal phenomena. Journal of Parapsychology, 75(1), 15. Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15, 661–667. Lakoff, G., & Johnson, M. (1999). Metaphors we live by. New York, NY: Basic Books. Lakoff, G., & Núñez, R. E. (2000). Where mathematics comes from: How the embodied mind brings mathematics into being. New York, NY: Basic Books. Lambert, K., Eisch, A. J., Galea, L. A., Kempermann, G., & Merzenich, M. (2019). Optimizing brain performance: Identifying mechanisms of adaptive neurobiological plasticity. Neuroscience and Biobehavioral Reviews, 105, 60–71. Leahy, W., & Sweller, J. (2019). The centrality of element interactivity to cognitive load theory. In S. Tindall-Ford, S. Agostinho, & J. Sweller (Eds.). (2019). Advances in cognitive load theory: Rethinking teaching (pp. 221–232). New York, NY: Routledge. LeDoux, J. E. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York, NY: Touchstone. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 23, 155–184. LeDoux, J. E., & Brown, R. (2017). A higher-order theory of emotional consciousness. Proceedings of the National Academy of Sciences, 114(10), E2016–E2025. Llinás, R. (2001). I of the vortex: From neurons to self . Cambridge, MA: MIT Press. Lloyd, A. (2010). Information literacy landscapes: Information literacy in education, workplace and everyday contexts. Cambridge, MA: Chandos. Logan, R. K. (2012). What is information? Why is it relativistic and what is its relationship to materiality, meaning and organization. Information, 3(1), 68–91. Lyon, R. (2005). The health report: 17 January 2005—Literacy. [Radio broadcast]. Australia: ABC. Retrieved April 2008, from http://www.abc.net.au/rn/talks/8.30/helthrpt/stories/s1266657.htm. Margulies, C., Tully, T., & Dubnau, J. (2005). Deconstructing memory in Drosophila. Current Biology, 15, R700–R713. Markham, J. A., & Greenough, W. T. (2004). Experience-driven plasticity: Beyond the synapse. Neuron Glia Biology, 1, 351–363. Marois, R. (2005). Two-timing attention. Nature Neuroscience, 8(10), 1285–1286. Mavilidi, M. F., & Zhong, L. (2019). Exploring the development and research focus of cognitive load theory, as described by its founders: Interviewing John Sweller, Fred Paas, and Jeroen van Merriënboer. Educational Psychology Review, 31, 499–508. Mayer, R. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59, 14–19.

54

5 Contributions of Modern Cognitive Psychology …

Melcher, D. (2001). Persistence of visual memory for scenes. Nature, 412, 401. Merzenich, M. (2007). Neuroscience via computer: Brain exercise for older adults. Interactions, 14(4), 42–45. Miller, E. K., & Buschman, T. J. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 315(5820), 1860–1862. Mohanty, A., Gitelman, D. R., Small, D. M., & Mesulam, M. M. (2008). The Spatial attention network interacts with limbic and monoaminergic systems to modulate motivation-induced attention shifts. Cerebral Cortex, 18(11), 2604–2613. Moreno, R. (2010). Cognitive load theory: More food for thought. Instructional Science, 38(2), 135–141. Mottron, L., Dawson, M., & Soulières, I. (2009). What aspects of autism predispose to talent. Philosophical Transactions of the Royal Society of London, B, 364, 1351–1357. Mountcastle, V. B. (1978). An organizing principle for cerebral function: The unit model and the distributed system. In G. M. Edelman & V. B. Mountcastle (Eds.), The mindful brain (pp. 7–50). Cambridge, CA: MIT Press. Murayama, K., Matsumoto, M., Izuma, K., & Matsumoto, K. (2010). Neural basis of the undermining effect of monetary reward on intrinsic motivation. Proceedings of the National Academy of Sciences of the United States of America, 107(49), 20911–20916. Nolte, M., Gal, E., Markram, H., & Reimann, M. W. (2019). Impact of higher-order network structure on emergent cortical activity. BioRxiv, 802074. https://doi.org/10.1101/802074. O’Loughlin, I. (2017). Learning without storing: Wittgenstein’s cognitive science of learning and memory. In M. A. Peters & J. Stickney (Eds.), A companion to Wittgenstein on education: Pedagogical investigations (pp. 601–614). Singapore: Springer. Paas, F. G. W. C., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24(1), 27–45. Paas, F. G. W. C., Renkl, A., & Sweller, J. (Eds.). (2016). Cognitive load theory: A special issue of educational psychologist. New York, NY: Routledge. Paas, F. G. W. C., van Merriënboer, J., & Adam, J. J. (1994). Measurement of cognitive load in educational research. Perceptual and Motor Skills, 79, 419–430. Panksepp, J. (2004). Affective neuroscience: The foundations of human and animal emotions. Oxford: Oxford University Press. Panksepp, J., & Biven, L. (2012). The archaeology of mind: Neuroevolutionary origins of human emotions. New York, NY: WW Norton & Company. Petersen, A. (2017). Brain maturation and cognitive development: Comparative and cross-cultural perspectives. New York, NY: Routledge. Piaget, J. (1928). The child’s conception of the world. London: Routledge. Plass, J. L., & Kalyuga, S. (2019). Four ways of considering emotion in cognitive load theory. Educational Psychology Review, 31(2), 339–359. Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, 23–38. Postle, B. R. (2015). Neural bases of the short-term retention of visual information. In P. Jolicoeur, C. Lefebvre, & J. Martinez-Trujillo (Eds.), Mechanisms of sensory working memory: Attention and performance XXV (pp. 43–58). London: Academic Press. Revans, R. (2017). ABC of action learning. New York, NY: Routledge. Riby, L. M., Meikle, A., & Glover, C. (2004). The effects of age, glucose ingestion and glucoregulatory control on episodic memory. Age and Ageing, 33, 483–487. Riley, S. (2019). Learning and memory. In S. Riley (Ed.), Mindful design: How and why to make design decisions for the good of those using your product (pp. 79–119). Berkeley, CA: Apress. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neurosciences, 27, 169–92.

References

55

Robin, J., & Moscovitch, M. (2017). Details, gist and schema: Hippocampal–neocortical interactions underlying recent and remote episodic and spatial memory. Current Opinion in Behavioral Sciences, 17, 114–123. Routtenberg, A., & Rekart, J. L. (2005). Post-translation modification as the substrate for longlasting memory. Trends in Neurosciences, 28(1), 12–19. Rutter, M., & Azis-Clauson, C. (2015). Biology of environmental effects. In A. Thapar, D. S. Pine, J. F. Leckman, S. Scott, M. J. Snowling, & E. Taylor (Eds.), Rutter’s child and adolescent psychiatry (6th ed., pp. 287–302). Chichester: Wiley. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19, 469–508. Shanwal, V. K., & Kaur, G. (2008). Emotional intelligence in education: Applications and implications. In R. J. Emmerling, V. K. Shanwal, & M. K. Mandal (Eds.), Emotional intelligence: Theoretical and cultural perspectives (pp. 153–170). New York, NY: Nova Science. Shell, D. F., Brooks, D. W., Trainin, G., Wilson, K. M., Kauffman, D. F., & Herr, L. M. (2010). The unified learning model: How motivational, cognitive, and neurobiological sciences inform best teaching practices. Dordrecht, The Netherlands: Springer. Sholle, D. (1999). What is information? The flow of bits and the control of chaos. MIT Communications Forum, paper posted 31 October, 1999. Retrieved April 2008 from http://web.mit.edu/ comm-forum/papers/sholle.html. Sloman, A. (2011). What’s information, for an organism or intelligent machine? How can a machine or organism mean? In G. Dodig-Crnkovic & M. Burgin (Eds.), Information and computation: Essays on scientific and philosophical understanding of foundations of information and computation (pp. 393–438). Singapore: World Scientific. Smeyers, P. (2016). Neuromyths for educational research and the educational field? In P. Smeyers & M. Depaepe (Eds.), Educational research: Discourses of change and changes of discourse (pp. 71–86). Cham: Springer. Snyder, J. S. (2019). Recalibrating the relevance of adult neurogenesis. Trends in Neurosciences, 42(3), 164–178. Snyder, A. W., Bossomaier, T., & Mitchell, D. J. (2004). Concept formation: ‘Object’ attributes dynamically inhibited from conscious awareness. Journal of Integrative Neuroscience, 3(1), 31– 46. Sowell, E. R., Thompson, P. M., Holmes, C. J., Jerniganz, T. L., & Toga, A. W. (1999). In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nature Neuroscience, 2, 859–861. Spencer, R. M., Walker, M. P., & Stickgold, R. (2017). Sleep and memory consolidation. In S. Chokroverty (Ed.), Sleep disorders medicine (pp. 205–223). New York, NY: Springer. Steiner, C. (2003). Emotional literacy. California, CA: Personhood Press. Swanson, H. L. (2017). Verbal and visual-spatial working memory: What develops over a life span? Developmental Psychology, 53(5), 971–995. Sweller, J. (2008). Instructional implications of David C. Geary’s evolutionary educational psychology. Educational Psychologist, 43, 214–216. Sweller, J. (2010). Cognitive load theory: Recent theoretical advances. In J. Plass, R. Moreno, & R. Breunken (Eds.), Cognitive load theory (pp. 29–47). New York, NY: Cambridge University Press. Sweller, J. (2016). Cognitive load theory, evolutionary educational psychology, and instructional design. In D. Geary & D. Berch (Eds.), Evolutionary perspectives on child development and education (pp. 291–306). Cham: Springer. Sweller, J., & Sweller, S. (2006). Natural information processing systems. Evolutionary Psychology, 4, 434–458. Sweller, J., van Merriënboer, J., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.

56

5 Contributions of Modern Cognitive Psychology …

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Explorations in the learning sciences, instructional systems and performance technologies. Dordrecht, The Netherlands: Springer. Sylwester, R. (1995). A celebration of neurons: An educator’s guide to the human brain. Alexandria, VA: Association for Supervision and Curriculum Development. Tashiro, A., Makino, H., & Gage, F. H. (2007). Experience-specific functional modification of the dentate gyrus through adult neurogenesis: A critical period during an immature stage. The Journal of Neuroscience, 27(13), 3252–3259. Tokuhama-Espinosa, T. (2018). Neuromyths: Debunking false ideas about the brain. New York, NY: WW Norton & Company. Tokuhama-Espinosa, T. (2019). Five pillars of the mind: Redesigning education to suit the brain. New York, NY: WW Norton & Company. Tomasello, M. (2014). A natural history of human thinking. Cambridge, MA: Harvard University Press. Tomasello, M. (2016). A natural history of human morality. Cambridge, MA: Harvard University Press. van Gog, T., Paas, F. G. W. C., Marcus, N., Ayres, P., & Sweller, J. (2009). The mirror-neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review, 21, 21–30. Van Merriënboer, J. J. G., & Sluijsmans, D. M. A. (2010). Toward a synthesis of cognitive load theory, four-component instructional design, and self-directed learning. Educational Psychology Review, 21(1), 55–66. Woolcott, G. (2011). A broad view of education and teaching based in educational neuroscience. International Journal for Cross-Disciplinary Subjects in Education, Special Issue, 1(1), 601–606. Woolcott, G. (2013). Giftedness as cultural accumulation: An information processing perspective. High Ability Studies, 24(2), 153–170. Xia, S., Gao, L., Lai, Y. K., Yuan, M. Z., & Chai, J. (2017). A survey on human performance capture and animation. Journal of Computer Science and Technology, 32(3), 536–554. Zheng, R. Z. (Ed.). (2017). Cognitive load measurement and application: A theoretical framework for meaningful research and practice. New York, NY: Routledge. Ziegler, A., & Phillipson, S. N. (2012). Towards a systemic theory of gifted education. High Ability Studies, 23(1), 3–30.

Part II

Towards a Broad-Based Research Framework for Education and Teaching

In Part II, the argument for a broad view of learning and memory in terms of informational connectivity is developed as the main focus of the book, with commonalities examined in information pathways and information processing related to environmental interactions of organisms and non-organismal structures. An outline is provided on how consideration of these commonalities may be accommodated within a single framework through the development of novel conceptualisations of information as Universal Information and information processing systems as Universal Information Processing Systems (UIPSs). These conceptualisations are not related closely to the probabilistic and mathematical conceptualisations seen in information theory or computing or in modern integrative biology but to those based in the observable world of matter and energy. In using such conceptualisations, the varying descriptions of learning and memory seen in organisms and non-organismal structures have been integrated within a single system that is based on scientific assumptions. Despite the abundance of studies on human learning and memory over the last century, there remains a divide between studies based on cognitive psychology— a discipline directed more towards philosophy and the social and behavioural sciences—and studies based on integrative biology, which emphasise empirically based studies of chemical structure and energetic relationships. This apparent divide is beginning to be bridged, to some extent, by the recent emergence of research on learning and memory that combines studies in cognitive psychology with those in integrative biology. Such research reflects a general level of interest in the combination of studies in philosophy and the social and behavioural sciences with studies in the natural sciences. There is also a general interest in finding applications of theory that are suitable for particular enquiries, as well as for preventing dead-end research paths where research in one discipline has already refuted the approach taken in another. Such combination and interdisciplinary studies, including those in new disciplines such as psychobiology and cognitive neuroscience, have begun to extend the scope of studies that explore human learning and memory, in particular, in the exploration of the detail of the matter (chemical) and energy processing that links the nervous

58

Part II: Towards a Broad-Based Research Framework for Education …

system with responses to environment. Researchers are beginning to comment on the implications for the future of education of such combination studies, including the potential of such studies to influence the development of educational theories and practices used in educational institutions. These studies should assist in resolving problems that relate to the incompatibility of the educational theories currently in use. Such educational theories are based in differing assumptions about the nature of learning and memory, with resultant difficulties in communication across theoretical perspectives as well as problems with incommensurability and the determination of effective teaching practices. The description of such educational theories in terms of a single system based in combination studies of human–environmental interactions should, therefore, be useful in developing teaching practices within a consistent theoretical background. Ideally, such a description should integrate scientifically, particularly with regard to an evolutionary context, the educational theories that form the basis of modern institutional education. Following this line of reasoning, a broad-based perspective on learning and memory processes based on combination and interdisciplinary studies is used in Part II to develop a single overarching framework within which to examine educational theories and practices. In particular, the framework examines those theories and practices that acknowledge the function of learning and memory in the processing of novel environmental information. Studies that combine cognitive psychology with integrative biology, inclusive of such disciplines as neuroscience, anatomy and physiology or molecular and cell biology, appear to offer a sound basis for such a framework. This is particularly the case for those studies that offer a view of the transmission of information between the environment and the nervous system in humans as well as other animals, where the underlying events and processes that occur at multiple levels of complexity are known in some detail, and where this view has been applied in some way to studies of learning and memory or to education. In order to develop such a framework, the chapters in Part II describe different types of learning and memory systems in organisms and non-organismal structures and outline the commonalities in information processing that serve their environmental interaction (e.g. in information transmission and storage). Concepts of learning and memory, therefore, are examined in a broad sense in relation to environmental interaction, inclusive of interactions that result in a change that is considered adaptive and inclusive of the wide variation in the application of the terms learning and memory, even within disciplines. Learning and memory processes are discussed in relation to the interactions involved in responding to environmental input or output, including perceptual changes and behavioural responses. The terms information and information processing are described in a broad sense that relates to environmental interaction, but, in these chapters, historical context will be used to assist in the clarification of the use of these two terms. The term information transmission will be used in a broad sense that involves information processing, accommodating descriptions related to the building or constructing of information, or the growing of information pathways, such as seen in neuronal connections. In the last chapter of Part II, commonalities in environmental information processing are used as the basis for a novel description of an information processing

Part II: Towards a Broad-Based Research Framework for Education …

59

system, based on the assumption that information as matter and energy is the basis of the environmental interactions observed in the universe. This broad system is used as a basis for an overarching framework that describes learning and memory processes in a very broad sense. Part III discusses the potential of this framework in examining educational theories and practices, with a focus on examining the educational principles of cognitive load theory developed from studies of natural information processing systems.

Chapter 6

Placing Human Learning and Memory in a Broad Context

Research that combines approaches from cognitive psychology and integrative biology has indicated that human learning and memory have been selected through evolutionary processes that enable growth, survival and reproduction through the interaction of the centralised nervous system with the environment (Calvin 1996, 2004; Dehaene 2007, 2009; Faye 2019; Edelman 1987, 1989, 1992). Learning and memory processes, however, are not considered to be confined necessarily to humans. Some studies in integrative biology have examined learning and memory processes at multiple levels of microscopic and molecular complexities and consider that not only do these processes occur in some form or other in all organisms with a nervous system (Dukas 2019; Squire and Kandel 2008), but they also occur in many other multicellular organisms (Borges 2005, 2008; Trewavas 2016) as well as unicellular organisms and viruses (Albrecht-Buehler 2005; Diaz-Munos et al. 2017; Martin and Gordon 2001; Richard and Joseph 2016; Tagkopoulos et al. 2008; Witzany 2018). Some researchers also consider that learning and memory processes exist in the non-organismal world. Consider, for example, the concepts of molecular memory as applied to data storage (Li et al. 2004) and the concept of machine learning in computer programs that are used to solve a given problem (Alpaydin 2004, 2016). In fact, learning and memory processes in inanimate objects have been described in terms similar to those used in describing learning and memory in organisms (Bentley 2007; Bentley et al. 2018; Dennett 1995; Edelman 2007; Honey et al. 2009; Sporns 2009). Some researchers argue, therefore, that not only is learning and memory an intrinsic feature of all organisms and their interaction with the environment, but learning and memory are also intrinsic features of non-organismal structures and their interaction with the environment (Woolcott 2011, 2013). Some researchers, in fact, assert that learning and memory can exist in any system that has structures, whether these systems are animate or inanimate, and that such memory may persist for a long time and affect the behaviour of the system (Ellis and Kopel 2019; Ghosh et al. 2018; Wolfram 2002). This assertion has implications for humans and the other systems with which they engage—for example, educated societies with a culture (inclusive of knowledge, skills and experiences; see Woolcott 2016) that enables © Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_6

61

62

6 Placing Human Learning and Memory in a Broad Context

the utilisation or management of large environmental systems containing organisms and non-organismal structures, many of which have learning and memory processes (Litfin 2018; Lovelock 2007; Margulis and Sagan 1995). It may be useful to re-examine the concepts of learning and memory across organismal and non-organismal structures, as well as across the systems of which they form a part, in order to place human learning and memory, and human cultural accumulation, in a broader framework. This re-examination should lead to a clearer understanding of the concepts of learning and memory in a context of environmental interaction—an understanding that is relevant to our continued existence in the modern world. Such an understanding should also enhance our culture by leading to developments in educational theories and practices, including changes in models of instructional design, that serve the transgenerational transfer of that culture. The following chapters in Part 2, examine various processes that are considered to be learning and memory in order to develop a broad context within which to examine human learning and memory (see outline in Woolcott 2010).

6.1 Learning, Memory and Connectivity in Organisms with a Centralised Nervous System The human nervous system is considered to be more complex than that of other primates and larger vertebrates, particularly as it relates to an increased apparent capability of a human forebrain (cerebral cortex) that is enlarged and well-developed relative to other parts of the brain and body (Cahalane and Finlay 2017; Calvin 2004; Gibson 2002). The learning and memory processes of organisms that, like humans, possess a centralised nervous system, however, have been found to have a number of commonalities (Baars and Gage 2010; Dukas 2019; Grandin and Johnson 2005; Humphrey 2002; Squire and Kandel 2008). In all organisms with a complex and centralised nervous system that includes a brain, ganglia or other aggregations of neuronal cell bodies, for example, learning and memory can be considered as a function of input environmental information and of the resultant reaction, if any, of an organism to the processing of such information within the nervous system (Baars and Gage 2010; Cotterill 2001; Grillner 2003; Kandel 2009; Marino 2017; Sigman and Dehaene 2005, 2006). Within many of these organisms, some, but not all, input information is retained in the short term, for example, as short-term memory (STM) or medium-term memory or, in the longer term, as long-term memory (LTM), through changes to the connections between neurons and changes within neurons and related structures (Calvin 1996; Dubnau et al. 2003; Edelmann 1987, 1989, 1992; Grillner 2003; Kandel 2009; Marino 2017; Routtenberg and Rekart 2005) (Fig. 6.1).

6.2 Learning and Memory in Organisms with a Non-centralised … Fig. 6.1 Learning, memory and connectivity in organisms with a centralised nervous system

63

Learning and memory in multicellular organisms with a central nervous system Human learning and memory

6.2 Learning and Memory in Organisms with a Non-centralised Nervous System Not only do animals with a centralised nervous system demonstrate learning and memory, but there is evidence that animals such as jellyfish (Cnidarians) that have non-centralised neurons or only rudimentary aggregations of neurons have learning and memory functions. This can be seen in the Cnidarian assessment of the environment and in some behaviours, such as swimming, which are under neuronal control (Martin 2002; Pallasdies et al. 2019; Satterlie 2017). Such neuronal control implies some kind of information storage or memory even though memory is not needed for all complex motor activities or environmental assessments (Satterlie 2017). Some Cnidarians have rhythmic behaviour that is internally generated, with coordinated patterns of motor response to complex sensory stimuli; this allows them to display the integrated global response to their environment that one would expect from having a memory (Pallasdies et al. 2019; Prescott et al. 2007). There is evidence also that Cnidarians have chemical signalling that uses endocrine messenger chemicals and synapses, similar in many ways to that seen in learning and memory mechanisms of organisms with a centralised nervous system (Martin 2002; Nilsson 2005; Satterlie 2017).

6.3 Learning, Memory and Connectivity in Multicellular Organisms with No Nervous System There are many other groups of multicellular organisms, such as plants and fungi, that have no neurons at all. Such organisms, however, have other systems that serve to receive and process any stimulus communicated from the environment, albeit with potential responses over generally longer time intervals (Baluska et al. 2018; Borges 2008; Burgos 2018; Trewavas 2016). Plants, for example, have three types of photoreceptors that allow them to forage for the optimal absorption of electromagnetic radiation. These receptors have a variety of functions, such as alerting a plant on the proximity of a neighbour or enabling the plant to modify its growth and development in the form of a shade-avoidance response, or allowing the plant to track gaps in a light-blocking canopy (Ballaré 1999; Borges 2008) (Fig. 6.2). Plants make genetically programmed modifications to changes of development in response to a stimulus or a situation, a modification achieved by an association

64

6 Placing Human Learning and Memory in a Broad Context

Fig. 6.2 Learning and memory in multicellular organisms

Learning and memory in multicellular organisms Learning and memory in multicellular organisms with a central nervous system Human learning and memory

between negative or positive reinforcement and a response or action; this is considered as learning (Gagliano 2017). Such positive reinforcement increases the intensity and/or the probability of the response or action, while negative reinforcement has the opposite effect and decreases the intensity and/or the probability of the response. Some support for this argument is seen in evidence of habituation in plants, demonstrated clearly for Mimosa and several other plant genera (Abramson et al. 2002), and in arguments that plants are able to discriminate between different types of stimulus—an associative learning function (Gagliano et al. 2018; Trewavas 2016). Learning in plants, and in sessile animals, is similar to that in motile animals, with such learning being seen as a reinforcement of an accelerated information flow rate (information flux) between a signal and a response (Borges 2008; Trewavas 2016). The strong similarities between the response and signalling systems in animals and plants indicate, therefore, that there is learning and memory in plants, and also in animals without neurons, just as there is with animals that have neurons. Additionally, in both plants and animals, such learning and memory can occur irrespective of whether or not there is a behavioural response (Borges 2008). There are similar learning and memory mechanisms in other multicellular organisms that have no nervous system, including the large group of organisms known as fungi, as well as within some multi-organismal associations, such as seen in the symbiotic relationships observed in lichens or in mycorrhizal (root fungi) associations. Learning in this sense can be associated with a number of observed phenomena, for example, with the changing of the rate of information transmission within and between fungi as well as between some plants and fungi in mycorrhizal associations (Simard 2018). Besides plants, fungi and sessile animals, many other multicellular organisms are considered capable of learning on the basis of such an accelerated information flux, although any resultant behavioural change may be slow when compared to that which results in the faster movement of motile animals as a result of their having neurons.

6.4 Commonalities in Learning and Memory Pathways …

65

6.4 Commonalities in Learning and Memory Pathways: Plasticity in Multicellular and Unicellular Organisms Even though learning and memory processes have been described in many different ways for different multicellular organisms, the commonalities of such processes can be considered under the umbrella of phenotypic plasticity, the ability of organisms with the same genotype to vary their developmental pattern, phenotype or behaviour (and this includes chemical and energetic reactions in the brain) in response to varying environmental conditions (Ancel and Fontana 2000; Borges 2008). Although the link between different types of phenotypic plasticity and learning requires clarification (Hoffmeyer 2003), some researchers argue that functional phenotypic plasticity, at least, is analogous to learning and memory in many organisms (Ghysen 2003; Dukas 2018) (Fig. 6.3). Functional plasticity also exists as intracellular plasticity in two large categories of unicellular organisms: prokaryotes (with no membrane-bound organelles) and eukaryotes (with membrane-bound organelles). There are a variety of mechanisms that both prokaryotic and eukaryotic organisms use to detect and respond to changes in the environment (e.g. those mechanisms seen in such phenomena as phototaxis and chemotaxis) (Casadesús and D’Ari 2002; Martin and Gordon 2001), and these show strong similarities to the response and signalling systems of multicellular organisms (Kilian and Müller 2002; Perbal 2003). Based on the concept of phenotypic plasticity, therefore, unicellular organisms can be said to learn because they respond (as do multicellular organisms) to variations in their environment using sensory mechanisms and information pathways within the cell (di Primo et al. 2000). Some researchers argue further that any unicellular organism, just like any multicellular organism, has

Learning and memory in unicellular and multicellular organisms Learning and memory in multicellular organisms Learning and memory in multicellular organisms with a central nervous system Human learning and memory

Fig. 6.3 Learning and memory in multicellular and unicellular organisms

66

6 Placing Human Learning and Memory in a Broad Context

a memory if its present state is determined partly by its past history (Casadesús and D’Ari 2002). The chemical pathways that constitute memory in unicellular organisms may exist in all cells, even those in multicellular organisms (Koshland 1977, 1980). This memory has been described as the quantity and quality of substances inside a cell at a given time (Jõers and Tenson 2016). On this basis, it can be argued that memory can be transgenerational, passed through cell lineages during the production of new generations by mitosis, a process described as phylogenetic learning (Kilian and Müller 2002; van Duijn 2017). Further, some unicellular organisms detect and respond collectively to environmental changes through intercellular information sharing using processes such as quorum sensing (Papenfort and Bassler 2016; Shapiro 1998; Whiteley et al. 2017), or share and interchange information using such processes as horizontal gene transmission (Cafini et al. 2017; Jain et al. 1999). Such information transmission can be considered as learning, using a similar argument to that of phylogenetic learning. Some researchers argue, perhaps controversially, that all eukaryotic animal cells, including those in multicellular organisms, demonstrate not only learning and memory but also intelligence since such cells can order and integrate a large amount of, at least, visual data (Albrecht-Buehler 2005; Levy 2017; Strevens 2017).

6.5 Learning, Memory and Environmental Connectivity Based on commonalities found from making such comparisons, learning in cellular organisms can be described in a general sense as the process of change in connectivity (e.g. in number, strength and type of connectivity) within the structures representing the pathway between an environmental signal and an organismal response (see Baquero 2017; Richard and Joseph 2016). On this basis, therefore, memory can be described in terms of the number, strength and type of connection. This connectivity may be as simple as a chain of chemical reactions that starts with a chemical in the environment interacting with the exterior of the cell but could include more complex chains and cascades of interactions that include a variety of energy and/or chemical (matter) components in a potentially large number of cells. Although it appears that such a series of interactions should lead to an organismal response to that environmental interaction, such a response may not be observable, and this would effectively constitute a null response. Such changes in connectivity can occur in all organisms and have also been observed in viruses, which are only sometimes considered as organisms even though they contain one of the two self-replicating molecules, deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) (Ligrone 2019; Margulis and Sagan 1995; Ohsaka 2019; Paul and Joyce 2004; Sadownik et al. 2016). Viruses as well as cellular organisms, therefore, have some kind of learning and memory mechanism. Environmental stimulus offers information to a parasitic lambda-phage virus, for example, which can either kill its host immediately by multiplying until the host’s cell walls burst (lysis)

6.5 Learning, Memory and Environmental Connectivity

67

or remain quiescent and confer immunity to infection upon its host (lysogeny). This activity by the virus is sometimes referred to as a bet-hedging strategy or invasion of a host bacterium, as it refers to decision-making based on an environmental stimulus (Deng et al. 2012; Obeng et al. 2016; Stumpf et al. 2002).

6.6 Learning, Memory and Non-organismal Connectivity The concept of learning and memory as resulting from the plasticity of connectivity is not limited in its application solely to organisms and has been applied more widely to non-organismal structures and systems (Alpaydin 2016; Bentley 2007; Bentley et al. 2018; Edelman 2007; Mitchell 2001; Sporns 2009). Such plasticity results from the potential for the connectivity between a structure and its environment to be changed (a type of information processing) so that there is sometimes a remembered relationship between elements of the environment and the structure, a relationship that may result in differing responses to different environmental elements (Woolcott 2011, 2013). A modern computer system is perhaps the most convincing example of a non-organismal structure that has a memory, and that can learn through processing information about its surroundings. Pressing the letters of the word “cat” (and, perhaps, the save function) on a computer keyboard, for example, can give rise to internal matter and energy responses—part of the plastic connectivity of the computer as a structure—that leads to the memory of that word in the computer. Some modern computers also incorporate self-generational and developmental considerations that allow them to arguably evolve and further develop memory through learning (Basanta et al. 2008; Bredeche et al. 2018; Ghosh and Tsutsui 2012; Iantovics et al. 2018; Siddique and Adeli 2013). Any assumption that a living structure is different to a non-living structure is not necessarily useful in considering learning and memory processes, and some researchers have explained learning in terms of an object (living or non-living) that is processing information about its surroundings (Dennett 1995). With reference to a photocell as an example, Dennett (1995) explained that any entity that possesses sensors, such as light-sensitive cells, may be described as sensitive to the environmental changes that its sensors detect, and such an entity may be capable, therefore, of learning. It can be argued on this basis that considering learning and memory as an interaction with the environment can be applied across all organisms and nonorganismal structures, and that such fundamental interactions can be viewed in terms of matter and energy pathways and information processing (Woolcott 2011, 2013).

68

6 Placing Human Learning and Memory in a Broad Context

6.7 Learning, Memory and Behaviour Even though the variation in the biological mechanisms of human learning and memory are not yet clearly understood (Alberini et al. 2018; Arshavsky 2006; Calvin 2004; Churchland and Churchland 2002; Hausser 2004; Marshall and Bredy 2016), there is some agreement that chemical and energetic reactions and reaction pathways are responsible and that such pathways have considerable structural and directional plasticity through growth (Citri and Malenka 2008; Grossberg 2006; PascualLeone et al. 2005). Information flux is a feature of any such plasticity, even when a behavioural outcome is not observed (Borges 2008; Zheng et al. 2011). Despite the fact that such connective plasticity without any resultant behaviour has been established in both organismal and non-organismal structures, learning and memory are sometimes described in terms of behaviour, generally as a progression of information from sensory input to motor output (Abramson 1994). Indeed, there are a number of different descriptions of learning couched in terms of behaviour. Ethological learning, for example, is described as involving a change in individual behaviour that in turn leads to an improved environmental adaptation, with the behavioural change influenced by amplification and experience, as well as epigenetic (evolutionary) learning (Panksepp 2004; Shepard et al. 2016). In contrast, behavioural learning can be described as a teleological process, where learning is considered to be based on the ability to form an interaction between stimulus input, information transmission, memory and behaviour in both an individual and reproducible way that benefits its possessor (Thompson 2004). However, some studies in integrative biology, rather than describing learning and memory processes solely in terms of behaviour, consider that a behavioural response (or the same behavioural response) does not always follow from learning or memory processes. These studies argue that there is, effectively, a three-way partition of input, processing and motor output. This can be seen, for example, in the process of sensitisation where the response to a repeated stimulus lessens and eventually ceases (Kandel 2009). Studies of simple multicellular organisms support the view that some learning takes place if there is an indication that a stimulus is remembered because of its effect on a chemical and/or energetic pathway, and that learning and memory, therefore, can be described more generally in terms of reproducible information pathways that involve both a stimulus and a potential, rather than an actual, response (Dubnau et al. 2003; Grillner 2003; Tonegawa et al. 2003). In these descriptions, a non-response and non-recognition scenario, or a scenario where an information pathway does not lead to a response, can be considered as part of the learning and memory pathway (Humphrey 2002). Some studies have outlined how such potentially discontinuous stimulus and response pathways have evolved in multicellular organisms (Calvin 2004; Edelman 1987, 1989; Graham 1934; Grillner 2003; Turchin 1977). Theories that consider learning and memory in terms of connectivity of information pathways include some contingency for the delay or inhibition of information within such pathways at several levels of organisation (Cotterill 2001; Snyder et al. 2004).

6.8 Information Pathways and Information Processing Structures

69

6.8 Information Pathways and Information Processing Structures In recent years, there has been some research that merges concepts related to learning and memory processes that are derived from evolutionary biology and cognitive psychology with concepts derived from education and the information sciences (Buss 1999; Kuhl et al. 2019; Mitchell 2001; Sweller et al. 2011). Some of these merged concepts relate specifically to learning and memory processes described in terms of energy and matter pathways in organisms and non-organismal structures (Bates 2005, 2006; Godfrey-Smith 2002, 2007). Research into human–computer interaction and artificial intelligence has led to a closer examination of connectivity of pathways involved in communication and information processing and the links of these pathways with those seen in evolution and cognitive function (Honey et al. 2009; Mitchell 2001; Sporns 2009). Some such studies, sometimes summarised by the term evolutionary computation (Fogel 1995; Ghosh and Tsutsui 2012), have included an examination of the connectivity of those pathways that involve evolutionary processes that result in an architecture of specialised cognitive subsystems or modules. This has included examining the role that the interaction of these subsystems of modules play in survival and reproduction (Bentley 2007; Bredeche et al. 2018; Kumar and Bentley 2003; Todd and Miller 1991; Siddique and Adeli 2013). In order to create the conditions that will lead to an evolution of increasing complexity among replicating machine-code programs, researchers are exploring these processes and pathways, and the processes of learning and memory more generally, in the medium of analog or digital computation and robotics (Almássy et al. 1998; Bentley 2007; Edelman 2007; Honey et al. 2009; Indiveri and Liu 2015; Knoll and Walter 2019; Kumar and Bentley 2003; Krichmar et al. 2005, 2019; Seth et al. 2005; Sporns 2009). With respect specifically to human cognition, some researchers are investigating how functional information processing structures—some of which are involved in learning and memory—can emerge in complex dynamical systems (Adamatzky 2014; Barabási and Oltvai 2004; Sporns 2009, 2012). Systems have been developed in which both dynamical and computational notions are necessary for a full account of a system considered in both functional and mechanistic terms, and in which there is no central executive needed to process the information encoded in particles as their collective dynamics are what affects information processing in the system (Bentley 2007; Edelman 2007; Honey et al. 2009; Krichmar et al. 2019; Rieke et al. 2007; Sporns 2009; Versluis et al. 2016). There is mounting evidence that similar systems have operated in human evolution and in the development, therefore, of human cognition, and that these are biological systems that interact and develop due to dynamic physico-chemical processes of the particles or units from which these systems are constructed. This can be illustrated by modelling the interaction and development of biological systems that are constructed from molecules in cellular environments (Chaves and Martins 2019; Chiricotto et al. 2016; Denton et al. 2003; Gierer 2004; Gierer and Meinhardt 1972; La Cerra and Bingham 2000; Li et al. 2017; Matthiessen 2017).

70

6 Placing Human Learning and Memory in a Broad Context

6.9 Learning and Memory in a Broad Sense On the basis of the commonalities derived from research that compares or combines studies of cognitive psychology and integrative biology, learning and memory processes may be considered broadly in terms of matter and energy pathways in both organisms and non-organismal structures (Woolcott 2011). This generalised view of learning and memory may be useful in comparing studies of human learning and memory with studies that accommodate concepts described as learning and memory in other organisms or in non-organismal structures, such as computers. In particular, this generalised view may be useful in establishing a relationship between energy and matter pathways and some of the information processing systems applied to studies of education, such as the natural information processing systems of cognitive load theory (CLT) (Sweller et al. 2011). Since such pathways are likely to be complex rather than linear, this generalised view could enhance the application of studies of networks and complex systems that are beginning to be seen in studies in education (Brown and Poortman 2018; Bruce et al. 2017; Carolan 2013; Daly 2010; Davis et al. 2008; Morrison 2012; Woolcott et al. 2017, 2018, 2019). In this broad view, learning and memory can be thought of as associated with the interaction of a structure’s matter and energy, including an organism or an organismal structure (a structure within and part of an organism), with matter and energy from the environment. This interaction can be described in terms of matter and energy pathways that exist as systems of informational connectivity. In any interaction of a structure with its environment, the matter or energy communicated into or out of the structure can be seen as information, and the connectivity or pathway between environmental information and the structure can be seen as spatiotemporal since the information is first in one place and then later in another. Some of the information communicated in any such interaction can lead to changes to the structure, including changes in matter and energy content or its connectivity within the structure. The information, as matter and energy, within a structure (including any internal structural or positional relationships, actual or potential) is the memory of that structure, regardless of the learning mechanism. For any structure, the communication of such information both into and out of a structure can be described in terms of learning if there is a resultant change in memory. Any change in information or informational connectivity within a structure is referred to here as information processing (Woolcott 2011, 2013, 2016). The state or activity of a structure, given such input or output of information, relies on the observation of change of the total information within the structure and, hence, incorporates any observed temporal change, such as growth or motor activity. This concept of information and information processing is discussed in more detail in the following chapters. In this broad sense then, learning and memory of a structure can be said to involve three temporally connected, but separable, stages in the transmission of information as matter and energy: (1) environmental information input to or output from an individual; (2) processing of resultant information changes within the individual (information processing); and (3) observable or reported changes in the individual resulting

6.9 Learning and Memory in a Broad Sense

71

from any such information processing. The broad and generalised view described here, in theory at least, may be used to describe all discrete matter and energy units within the universe as information processing systems and, hence, enables their comparison. The environmental communications described as learning, as well as the information content or connectivity described as memory, and any observed memory expression, are integral to all such systems, and the components of all such systems may operate in a similar way. This broad view, therefore, is useful in reconciling descriptions of information processing systems, in particular, those systems that are described as having a learning and memory function, provided that they are based effectively in descriptions of matter and energy pathways. Acknowledgements Parts of this chapter are adapted from Woolcott, G. (2010). Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496), Strasbourg, France: Analytrics.

References Abramson, C. I. (1994). A primer of invertebrate learning. Washington, DC: American Psychological Association. Abramson, C. I., Garrido, D. J., Lawson, A. L., Browne, B. L., & Thomas, D. G. (2002). Bioelectrical potentials of Philodendron cordatum: A new method for investigation of behavior in plants. Psychological Reports, 91, 173–185. Adamatzky, A. (2014). Unconventional computing. A volume in the encyclopedia of complexity and systems science (2nd edn). New York, NY: Springer. Alberini, C. M., Cruz, E., Descalzi, G., Bessières, B., & Gao, V. (2018). Astrocyte glycogen and lactate: New insights into learning and memory mechanisms. Glia, 66(6), 1244–1262. Albrecht-Buehler, G. (2005). A long-range attraction between aggregating 3T3 cells mediated by near-infrared light scattering. Proceedings of the National Academy of Sciences of the United States of America, 102(14), 5050–5055. Almássy, N., Edelman, G. M., & Sporns, O. (1998). Behavioral constraints in the development of neuronal properties: A cortical model embedded in a real-world device. Cerebral Cortex, 8, 346–361. Alpaydin, E. (2016). Machine learning: The new AI. Cambridge, MA: MIT Press. Ancel, L. W., & Fontana, W. (2000). Plasticity, evolvability, and modularity in RNA. Journal of Experimental Zoology, 288(3), 242–283. Arshavsky, Y. I. (2006). The ‘Seven Sins’ of the Hebbian synapse: Can the hypothesis of synaptic plasticity explain LTM consolidation? Progress in Neurobiology, 80, 99–113. Baars, B. J., & Gage, N. M. (2010). Cognition, brain, and consciousness: Introduction to cognitive neuroscience. Cambridge, MA: Academic Press. Ballaré, C. L. (1999). Keeping up with the neighbours: Phytochrome sensing and other signalling mechanisms. Trends in Plant Sciences, 4, 97–102. Baluska, F., Gagliano, M., & Witzany, G. (Eds.). (2018). Memory and learning in plants. Cham: Springer. Baquero, F. (2017). Transmission as a basic process in microbial biology. Lwoff Award Prize Lecture. FEMS Microbiology Reviews, 41(6), 816–827. Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: Understanding the cell’s functional organization. Nature Reviews Genetics, 5, 101–114.

72

6 Placing Human Learning and Memory in a Broad Context

Basanta, D., Miodownik, M. A., & Baum, B. (2008). The evolution of robust development and homeostasis in artificial organisms. Public Library of Science Computational Biology, 4(3), e1000030. Bates, M. J. (2005). Information and knowledge: An evolutionary framework for information science. Information Research, 10(4) paper 239. Bates, M. J. (2006). Fundamental forms of information. Journal of the American Society for Information Science and Technology, 57(8), 1033–1045. Bentley, P. J. (2007). Systemic computation: A model of interacting systems with natural characteristics. In A. Adamatzky, C. Tueuscher, & T. Asai (Eds.), International Journal of Parallel, Emergent and Distributed Systems (IJPEDS), Special issue on emergent computation (Vol. 22, no. 2, pp. 103-121). Oxford, UK: Taylor & Francis. Bentley, P. J., Brundage, M., Häggström, O., & Metzinger, T. (2018). Should we fear artificial intelligence? In-depth Analysis. European Union, Scientific Foresight Unit (STOA), March 2018 (PE 614.547), 1–40. Borges, R. M. (2005). Do plants and animals differ in phenotypic plasticity? Journal of Bioscience, 30, 41–50. Borges, R. M. (2008). Plasticity comparisons between plants and animals: Concepts and mechanisms. Plant Signaling & Behavior, 3(6), 367–375. Bredeche, N., Haasdijk, E., & Prieto, A. (2018). Embodied evolution in collective robotics: A review. Frontiers in Robotics and AI, 5, 12. https://doi.org/10.3389/frobt.2018.00012. Brown, C., & Poortman, C. L. (Eds.). (2018). Networks for learning: Effective collaboration for teacher, school and system improvement. New York, NY: Routledge. Bruce, C., Davis, B., Sinclair, N., McGarvey, L., Hallowell, D., Drefs, M., et al. (2017). Understanding gaps in research networks: Using spatial reasoning as a window into the importance of networked educational research. Educational Studies in Mathematics, 95(2), 143–161. Burgos, J. E. (2018). Is a nervous system necessary for learning? Perspectives on Behavior Science, 41(2), 343–368. Buss, D. M. (1999). Evolutionary psychology: The new science of the mind. Boston, MA: Allyn and Bacon. Cafini, F., Romero, V. M., & Morikawa, K. (2017). Mechanisms of horizontal gene transfer. In S. Enany & L. E. Crotty Alexander (Eds.), The rise of virulence and antibiotic resistance in Staphylococcus aureus (pp. 61–80). Rijeka, Croatia: InTech. Cahalane, D. J., & Finlay, B. L. (2017). Brain evolution and development: Allometry of the brain and a realization of the cortex. In S. V. Shepherd (Ed.), The Wiley handbook of evolutionary neuroscience (pp. 388–409). Chichester: Wiley Blackwell. Calvin, W. H. (1996). The cerebral code: Thinking a thought in the mosaics of the mind. Cambridge, MA: MIT Press. Calvin, W. H. (2004). A brief history of the mind: From apes to intellect and beyond. Oxford: Oxford University Press. Carolan, B. V. (2013). Social network analysis and education: Theory, methods and applications. New York, NY: Sage. Casadesús, J., & D’Ari, R. (2002). Memory in bacteria and phage. BioEssays, 24, 512–518. Chaves, M., & Martins, M. A. (2019). Molecular logic and computational synthetic biology. Cham: Springer. Chiricotto, M., Sterpone, F., Derreumaux, P., & Melchionna, S. (2016). Multiscale simulation of molecular processes in cellular environments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2080), 20160225. Churchland, P. S., & Churchland, P. M. (2002). Neural worlds and real worlds. Nature Reviews Neuroscience, 3(11), 903–907. Citri, A., & Malenka, R. C. (2008). Synaptic plasticity: Multiple forms, functions, and mechanisms. Neuropsychopharamcology, 33, 18–41.

References

73

Cotterill, R. M. J. (2001). Co-operation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: Possible implications for cognition, consciousness, intelligence and creativity. Progress in Neurobiology, 64, 1–33. Daly, A. J. (Ed.). (2010). Social network theory and educational change. Cambridge, MA: Harvard Education Press. Davis, B., Sumara, D., & Luce-Kapler, R. (2008). Engaging minds: Changing teaching in complex times. New York, NY: Routledge. Dehaene, S. (2007). A few steps towards a science of mental life. Mind, Brain, and Education, 1(1), 28–47. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin Viking. Deng, L., Gregory, A., Yilmaz, S., Poulos, B. T., Hugenholtz, P., & Sullivan, M. B. (2012). Contrasting life strategies of viruses that infect photo-and heterotrophic bacteria, as revealed by viral tagging. MBio, 3(6), e00373–12. Dennett, D. C. (1995). Darwin’s dangerous idea: Evolution and the meanings of life. New York, NY: Simon & Schuster. Denton, M. J., Dearden, P. K., & Sowerby, S. J. (2003). Physical law not natural selection as the major determinant of biological complexity in the subcellular realm: New support for the pre-darwinian conception of evolution by natural law. Biosystems, 71(3), 297–303. Diaz-Munos, S. L., Sanjuan, R., & West, S. (2017). Sociovirology: Conflict, cooperation, and communication among viruses. Cell Host & Microbe, 22, 437–441. di Primio, F., Müller, B. S., & Lengeler, J. W. (2000). Minimal cognition in unicellular organisms. In J.-A. Meyer, A. Berthoz, D. Floreano, H. L. Roitblat, & S. W. Wilson (Eds.), Simulation of adaptive behavior (SAB) 2000, Proceedings Supplement (pp. 3–12). Honolulu, HI: International Society for Adaptive Behavior. Dubnau, J., Chiang, A. S., & Tully, T. (2003). Neural substrates of memory: From synapse to system. Journal of Neurobiology, 54, 238–253. Dukas, R. (2018). Cognition and learning. In A. Córdoba-Aguilar, D. González-Tokman, & I. González-Santoyo (Eds.), Insect behaviour: From mechanisms to ecological and evolutionary consequences (pp. 257–272). London: Oxford University Press. Dukas, R. (2019). Animal expertise: mechanisms, ecology and evolution. Animal Behaviour, 147, 199–210. Edelman, G. M. (1987). Neural Darwinism: The theory of neuronal group selection. New York, NY: Basic Books. Edelman, G. M. (1989). The remembered present. New York, NY: Basic Books. Edelman, G. M. (1992). Bright air, brilliant fire. New York, NY: Basic Books. Edelman, G. M. (2007). Learning in and from brain-based devices. Science, 318(5853), 1103–1105. Ellis, G. F., & Kopel, J. (2019). The dynamical emergence of biology from physics: Branching causation via biomolecules. Frontiers in Physiology, 9, 1966. https://doi.org/10.3389/fphys.2018. 01966. Faye, J. (2019). How matter becomes conscious. Cham: Springer. Fogel, D. B. (1995). Evolutionary computation: Toward a new philosophy of machine intelligence. New York, NY: IEEE Press. Gagliano, M. (2017). The mind of plants: Thinking the unthinkable. Communicative & integrative biology, 10(2), 38427. Gagliano, M., Abramson, C. I., & Depczynski, M. (2018). Plants learn and remember: Lets get used to it. Oecologia, 186(1), 29–31. Ghosh, A., & Tsutsui, S. (Eds.). (2012). Advances in evolutionary computing: Theory and applications. New York, NY: Springer. Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of Things. CAAI Transactions on Intelligence Technology, 3(4), 208–218. Ghysen, A. (2003). The origin and evolution of the nervous system. International Journal of Developmental Biology, 47(7–8), 555–562.

74

6 Placing Human Learning and Memory in a Broad Context

Gibson, K. R. (2002). Evolution of human intelligence: The roles of brain size and mental construction. Brain, Behaviour, and Evolution, 59, 10–20. Gierer, A. (2004). Human brain evolution, theories of innovation, and lessons from the history of technology. Journal of Biosciences, 29(3), 235–244. Gierer, A., & Meinhardt, H. (1972). A theory of biological pattern formation. Kybernetik, 12, 30–39. Godfrey-Smith, P. (2002). Environmental complexity and the evolution of cognition. In R. Sternberg & J. Kaufman (Eds.), The evolution of intelligence (pp. 233–249). Mahwah, NJ: Lawrence Erlbaum. Godfrey-Smith, P. (2007). Information in biology. In D. Hull & M. Ruse (Eds.), The Cambridge companion to the philosophy of biology (pp. 103–119). New York, NY: Cambridge University Press. Graham, R. (1934). Pennsylvanian flora of Illinois as revealed in coal balls. I. Botanical Gazette, 95(3), 453–476. Grandin, T., & Johnson, C. (2005). Animals in translation. New York, NY: Harcourt Books. Grillner, S. (2003). The motor infrastructure: From ion channels to neuronal networks. Nature Reviews Neuroscience, 4, 573–586. Grossberg, S. (2006). Adaptive resonance theory. Encyclopedia of cognitive science. https://doi. org/10.1002/0470018860.s00067, s00067. Hausser, M. (2004). Storing memories in dendritic channels. Nature Neuroscience, 7(2), 98–100. Hoffmeyer, J. (2003). Baldwin and biosemiotics: What intelligence is for. In B. Weber & D. Depew (Eds.), Evolution and learning: The Baldwin effect reconsidered (pp. 253–272). Cambridge, MA: MIT Press. Honey, C. J., Sporns, O., Cammoun, L., Gogandet, X., Thiran, J. P., Meuli, R., et al. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 106, 2035–2040. Humphrey, N. (2002). The mind made flesh: Essays from the frontiers of evolution and psychology. London: Oxford University Press. Iantovics, L. B., Gligor, A., Niazi, M. A., Biro, A. I., Szilagyi, S. M., & Tokody, D. (2018). Review of recent trends in measuring the computing systems intelligence. BRAIN: Broad Research in Artificial Intelligence and Neuroscience, 9(2), 77–94. Indiveri, G., & Liu, S. C. (2015). Memory and information processing in neuromorphic systems. Proceedings of the IEEE, 103(8), 1379–1397. Jain, R., Rivera, M. C., & Lake, J. A. (1999). Horizontal gene transfer among genomes: The complexity hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 96(7), 3801–3806. Jõers, A., & Tenson, T. (2016). Growth resumption from stationary phase reveals memory in Escherichia coli cultures. Scientific Reports, 6, Article number 24055. Kandel, E. R. (2009). The biology of memory: A forty-year perspective. Journal of Neuroscience, 29(41), 12748–12756. Kilian, A. E., & Müller, B. S. (2002, November 18–22). Life-like learning in technical artefacts: Biochemical vs. neuronal mechanisms. In Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’02), Singapore (Vol. 1, pp. 296–300). Retrieved March 2006 from http://en.scientificcommons.org/20339282. Knoll, A., & Walter, F. (2019). Neurorobotics—A unique opportunity for ground breaking research. Munich: Chair of Robotics, Artificial Intelligence and Real-Time Systems. Technische Universität München Institut Für Informatik. Koshland, D. E., Jr. (1977). A response regulator model in a simple sensory system. Science, 196, 1055–1063. Koshland, D. E., Jr. (1980). Bacterial chemotaxis in relation to neurobiology. Annual Review of Neurosciences, 3, 43–75. Krichmar, J. L., Nitz, D. A., Gally, J. A., & Edelman, G. M. (2005). Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task. Proceedings of the National Academy of Sciences of the United States of America, 102(6), 2111–2116.

References

75

Krichmar, J. L., Severa, W., Khan, S. M., & Olds, J. L. (2019). Making BREAD: Biomimetic strategies for artificial intelligence now and in the future. Frontiers in Neuroscience, 13. https:// doi.org/10.3389/fnins.2019.00666. Kuhl, P. K., Liang, S. S., Guerriero, S., & van Damme, D. (2019). Developing minds in the digital age: Towards a science of learning for 21st century education. Educational Research and Innovation. Paris: OECD. Kumar, S., & Bentley, P. J. (2003). Biologically plausible evolutionary development. In A. Tyrrell, P. Haddow & J. Torresen (Eds.), Proceedings of the fifth international conference on evolvable systems: From biology to hardware (pp. 57–68). Berlin: Springer LNCS 2606. La Cerra, P., & Bingham, R. (2002). The origin of minds: Evolution, uniqueness and the new science of the self . New York, NY: Harmony Books. Levy, A. (2017). Causal order and kinds of robustness. In S. Gissis, E. Lamm, & A. Shavit (Eds.), Landscapes of collectivity in the life sciences (pp. 269–280). Cambridge, MA: MIT Press. Li, J., Green, A. A., Yan, H., & Fan, C. (2017). Engineering nucleic acid structures for programmable molecular circuitry and intracellular biocomputation. Nature Chemistry, 9(11), 1056–1067. Li, C., Fan, W., Lei, B., Zhang, D., Han, S., Tang, T., et al. (2004). Multilevel memory based on molecular devices. Applied Physics Letters, 84(11), 1949–1951. Ligrone, R. (2019). The birth of life. In R. Ligrone (Ed.), Biological Innovations that built the world (pp. 53–97). Cham: Springer. Litfin, K. J. (2018). Gaia. In N. Castree, M. Hulme, & J. D. Proctor (Eds.), Companion to environmental studies (pp. 55–59). New York, NY: Routledge in association with GSE Research. Lovelock, J. (2007). The revenge of Gaia: Why the earth is fighting back—And how we can save humanity. Santa Barbara, CA: Allen Lane. Margulis, L., & Sagan, D. (1995). What is life?. New York, NY: Simon & Schuster. Marino, L. (2017). Thinking chickens: A review of cognition, emotion, and behavior in the domestic chicken. Animal Cognition, 20(2), 127–147. Marshall, P., & Bredy, T. W. (2016). Cognitive neuroepigenetics: The next evolution in our understanding of the molecular mechanisms underlying learning and memory? NPJ Science of Learning, 1, 16014. Martin, V. J. (2002). Photoreceptors of cnidarians. Canadian Journal of Zoology/Revue Canadien de Zoologie, 80, 1703–1722. Martin, C. C., & Gordon, R. G. (2001). The evolution of perception. Cybernetics and Systems, 32, 393–409. Matthiessen, D. (2017). Mechanistic explanation in systems biology: Cellular networks. The British Journal for the Philosophy of Science, 68(1), 1–25. Mitchell, M. (2001). Life and evolution in computers. History and Philosophy of the Life Sciences, 23, 361–383. Morrison, K. (2012). School leadership and complexity theory. New York, NY: Routledge. Nilsson, D.-E. (2005). Photoreceptor evolution: Ancient siblings serve different tasks. Current Biology, 15(3), R94–R96. Obeng, N., Pratama, A. A., & van Elsas, J. D. (2016). The significance of mutualistic phages for bacterial ecology and evolution. Trends in Microbiology, 24(6), 440–449. Ohsaka, K. (2019). The origin of life: The first self-replicating molecules were nucleotides. PeerJ Preprints, 7, e27919v1. https://doi.org/10.7287/peerj.preprints.27919v1. Pallasdies, F., Goedeke, S., Braun, W., & Memmesheimer, R. M. (2019). From single neurons to behavior in the jellyfish. Aurelia aurita. arXiv preprint arXiv:1907.05060. Panksepp, J. (2004). Affective neuroscience: The foundations of human and animal emotions. Oxford: Oxford University Press. Papenfort, K., & Bassler, B. L. (2016). Quorum sensing signal–response systems in gram-negative bacteria. Nature Reviews Microbiology, 14(9), 576. Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401.

76

6 Placing Human Learning and Memory in a Broad Context

Paul, N., & Joyce, G. F. (2004). Minimal self-replicating systems. Current Opinion in Chemical Biology, 8(6), 634–639. Perbal, B. (2003). Communication is the key. Cell Communication and Signalling, 1, 1–4. Prescott, T. J., Bryson, J. J., & Seth, A. K. (2007). Introduction: Modelling and natural action selection. Philosophical Transactions of the Royal Society of London, B, 362(1485), 1521–1529. Richard, G., & Joseph, S. (Eds.). (2016). Biocommunication: Sign-mediated interactions between cells and organisms (Vol. 1). London: World Scientific. Rieke, H., Roxin, A., Madruga, S., & Solla, S. A. (2007). Multiple attractors, long chaotic transients, and failure in small-world networks of excitable neurons. Chaos, 17, 026110. Routtenberg, A., & Rekart, J. L. (2005). Post-translation modification as the substrate for longlasting memory. Trends in Neurosciences, 28(1), 12–19. Sadownik, J. W., Mattia, E., Nowak, P., & Otto, S. (2016). Diversification of self-replicating molecules. Nature Chemistry, 8(3), 264–269. Satterlie, R. (2017). Cnidarian neurobiology. In J. H. Byrne (Ed.), The Oxford handbook of invertebrate neurobiology (pp. 185–218). New York, NY: Oxford University Press. Seth, A. K., Sporns, O., & Krichmar, J. L. (2005). Neurobotic models in neuroscience and neuroinformatics. NeuroInformatics, 3(3), 167–170. Shapiro, J. A. (1998). Thinking about bacterial populations as multicellular organisms. Annual Review of Microbiology, 52, 81–104. Shepard, K. N., Chong, K. K., & Liu, R. C. (2016). Contrast enhancement without transient map expansion for species-specific vocalizations in core auditory cortex during learning. eNeuro, 3(6). Siddique, N., & Adeli, H. (2013). Computational intelligence: Synergies of fuzzy logic, neural networks and evolutionary computing. Chichester: Wiley. Sigman, M., & Dehaene, S. (2005). Parsing a cognitive task: A characterization of the mind’s bottleneck. Public Library of Science Computational Biology, 3(2), e37. Sigman, M., & Dehaene, S. (2006). Dynamics of the central bottleneck: Dual-task and task uncertainty. Public Library of Science Computational Biology, 4(7), e220. Simard, S. W. (2018). Mycorrhizal networks facilitate tree communication, learning, and memory. In F. Baluska, M. Gagliano, & G. Witzany (Eds.), Memory and learning in plants (pp. 191–213). Cham: Springer. Snyder, A. W., Bossomaier, T., & Mitchell, D. J. (2004). Concept formation: ‘Object’ attributes dynamically Sporns, O. (2006). Small-world connectivity, motif composition, and complexity of fractal neuronal connections. BioSystems, 85, 55–64. Sporns, O. (2009). From complex networks to intelligent systems. In B. Sendhoff, E. Körner, O. Sporns, H. Ritter, & K. Doya (Eds.), Creating brain-like intelligence: From basic principles to complex intelligent systems (pp. 15–30). Berlin: Springer. Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT Press. Squire, L. R., & Kandel, E. R. (2008). Memory: From mind to molecules (2nd ed.). Greenwood Village, CA: Roberts & Company. Strevens, M. (2017). Dappled science in a unified world. In H. K. Chao & J. Reiss (Eds.), Philosophy of science in practice (Vol. 379, pp. 69–85). Cham: Springer. Stumpf, M. P., Laidlaw, Z., & Jansen, V. A. (2002). Herpes viruses hedge their bets. Proceedings of the National Academy of Sciences of the United States of America, 99(23), 15234–15237. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Explorations in the learning sciences, instructional systems and performance technologies. Dordrecht, The Netherlands: Springer. Tagkopoulos, I., Liu, Y. C., & Tavazoie, S. (2008). Predictive behavior within microbial genetic networks. Science, 320(5881), 1313–1317. Thompson, E. (2004). Life and mind: From autopoieses to neurophenomenology. A tribute to Francis Varela. Phenomenology and the Cognitive Sciences, 3, 381–398. Todd, P. M., & Miller, G. F. (1991). Exploring adaptive agency II: Simulating the evolution of associative learning. In J.-A. Meyer & S. W. Wilson (Eds.), From animals to animats: Proceedings of

References

77

the first international conference on simulation of adaptive behaviour (pp. 306–315). Cambridge, MA: MIT Press. Tonegawa, S., Nakazawa, K., & Wilson, M. A. (2003). Genetic neuroscience of mammalian learning and memory. Philosophical Transactions of the Royal Society of London, B, 358, 787–795. Trewavas, A. (2016). Intelligence, cognition, and language of green plants. Frontiers in Psychology, 7, 588. Turchin, V. F. (1977). The phenomenon of science. New York, NY: Columbia University Press. van Duijn, M. (2017). Phylogenetic origins of biological cognition: Convergent patterns in the early evolution of learning. Interface Focus, 7(3), 20160158. Versluis, F., van Esch, J. H., & Eelkema, R. (2016). Synthetic self-assembled materials in biological environments. Advanced Materials, 28(23), 4576–4592. Whiteley, M., Diggle, S. P., & Greenberg, E. P. (2017). Progress in and promise of bacterial quorum sensing research. Nature, 551(7680), 313–320. Witzany, G. (2018). Memory and learning as key competences of living organisms. In F. Baluska, M. Gagliano, & G. Witzany (Eds.), Memory and learning in plants: Signaling and communication in plants (pp. 1–16). Cham: Springer. Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media. Woolcott, G. (2010). Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496). Strasbourg: Analytrics. Woolcott, G. (2011). A broad view of education and teaching based in educational neuroscience. International Journal for Cross-Disciplinary Subjects in Education, Special Issue, 1(1), 601–606. Woolcott, G. (2013). Giftedness as cultural accumulation: An information processing perspective. High Ability Studies, 24(2), 153–170. Woolcott, G. (2016). Technology and human cultural accumulation: The role of emotion. In S. Tettegah & R. E. Ferdig (Eds.), Emotions, technology, and learning (pp. 243–263). London: Academic Press. Woolcott, G., Chamberlain, D., Keast, R., & Farr-Wharton, B. (2017). Modelling success networks to improve the quality of undergraduate education. Quality in Higher Education, 23(2), 120–137. Woolcott, G., Chamberlain, D., Whannell, R., & Galligan, L. (2018). Examining undergraduate student retention in mathematics using network analysis and relative risk. International Journal of Mathematical Education in Science and Technology TMES, 50(3), 447–463. Woolcott, G., Keast, R., & Pickernell, D. (2019). Deep impact: Re-conceptualising university research impact using human cultural accumulation theory. Studies in Higher Education. https:// doi.org/10.1080/03075079.2019.1594179. Zheng, C., Quan, M., Yang, Z., & Zhang, T. (2011). Directionality index of neural information flow as a measure of synaptic plasticity in chronic unpredictable stress rats. Neuroscience Letters, 490(1), 52–56.

Chapter 7

A Broad View of Information Processing Systems

This chapter outlines a broad view of information processing systems based on the research findings presented in the previous chapters. This broad view develops the generalisations of learning and memory processes that were based in the commonalities of matter and energy pathways between organisms or structures and their environment. This view is described in terms of novel conceptualisations of information and information processing systems, and these conceptualisations serve as an infrastructure for the development of an overarching framework that may be applied universally to learning and memory processes. This framework is then applied specifically to descriptions of human interaction with the environment in a broad context that includes input and output of environmental information and its processing within the entire human organism. Human cognitive processes are accommodated within this framework through consideration of the human nervous system, inclusive of the brain, as a component information processing system of the human organism.

7.1 Information and Information Processing Systems There are many and varied uses of the term information (see discussion in Sholle 1999; Sloman 2011 and see Adami 2016; Barbieri 2016 and others in a special issue of Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences), but these uses are not confined to conveying the sense of informing as the putting into form or the imparting of learning or instruction (Machlup 1983). This is not to say that any one use of the term information is superior to another since there are numerous useful understandings of the term, in part due to variations in the sense of the term depending on its application in differing contexts (Bates 2005, 2006; Dodig-Crnkovic 2010; Janich 2018; Kennedy 2011; Lloyd 2010a, b; Luo and Pan 2016; Mingers and Standing 2014; Sloman 2011). Besides the various understandings of the term information, there are also various categorisations of those understandings, for example, in their placement into three, © Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_7

79

80

7 A Broad View of Information Processing Systems

albeit overlapping, categories as knowledge (Bates, 2005; Costa and Leite 2018; Dretske 1981; Mingers and Standing 2018), process (Koltay 2017; Pratt 1977) and thing (Schement 2017) (all three categorisations are discussed in Buckland 1991, and see discussion of Buckland 1991 in Bates 2005). The understandings of the term information are made additionally complex because they are sometimes seen as interwoven with the concepts of knowledge and meaning, as well as with the concepts of pattern and organisation (Bates 2016; Bawden 2007). Such interweaving can be seen in both cognitive psychology and integrative biology, in particular, in studies of human cognition involving investigations of information pathways and information processing couched in terms of information theory (Lachman et al. 1979; Tononi 2008; Tononi et al. 2016). Several influential information theories were developed during and after the 1940s, including the information theory of Shannon (Shannon 1948; Shannon and Weaver 1963), although these theories were not applied originally to either cognitive psychology or integrative biology but to communication technology and coding theory. Shannon’s influential theory, for example, has been extremely useful in viewing information in terms of a signal that carries information about a source and, therefore, allows a prediction of the state of the source (Chaitin 2012; GodfreySmith 2007a). Such theories, despite their origins in communication technology, have been applied, in conjunction with other information theories developed since the 1940s, in studies in integrative biology and cognitive psychology, albeit with varying degrees of success and controversy (Godfrey-Smith 2007a, b; Miller 2003; Slijepcevic 2019). Some of the successful applications in integrative biology have been in studies of genetics (Dretske 1981; Godfrey-Smith 2007a; Sherwin 2015) and in the examination of cognition, where probabilistic views of the source of information have been used in recent times to quantify facts about contingency and correlation in studies of human cognition and consciousness (Balduzzi and Tononi 2008; Tononi 2008; Tononi et al. 1998, 2016). Many cognitive events have been reliably correlated with environmental events (O’Reilly and Munakata 2000), even though there are difficulties in assigning probabilities to single events (Godfrey-Smith 2007a). The development of the concept of information processing in integrative biology, however, appears to have become dependent on the description of the information being used and the type of information processing being considered (Piccinini and Scarantino 2010). The concept of information transmission, or information flow, in some of these views, does not necessarily correspond to the concept of information processing since, as Godfrey-Smith (2007a) argues, all that is required for information flow is a certain kind of reliable association between what happens at the source and the state of a signal received by the receiver. In recent times, Chaitin (2011, 2012) has claimed that the methods of algorithmic information theory and its application in metabiology may be more successful in their application to integrative biology than other information theories, at least in descriptions of evolutionary processes (although Chaitin’s theory is arguably a computation theory rather than an information theory; see discussions of information vs. computation theories, Piccinini and Scarantino 2010). One of the reasons for Chaitin’s

7.1 Information and Information Processing Systems

81

view may be that other information theories when applied in biology—and this includes Shannon’s physical theory—fail to take into account some evolutionary features, such as shortcuts and truncations (Bates 2005; Godfrey-Smith 2007a, b). In any case, both Chaitin’s (2011, 2012) and Shannon’s (Shannon 1948; Shannon and Weaver 1963) theories are mostly concerned with mathematical descriptions of relationships between quantities of information and its transmission, storage and other manipulation, and there remain vastly differing views as to the use of such informational concepts in integrative biology, with some seeing it in an overall positive light as a crucial advance (Lean 2014, 2016; Williams 1992 in Godfrey-Smith 2007a) and others in a rather more negative one as distorting understanding and contributing to lingering genetic determinism (Francis 2003 in Godfrey-Smith 2007a). Investigations of information processing in cognitive psychology did not all persevere with the probabilistic and mathematical Shannon information description (Miller 2003), and some adopted a view of information as equivalent to knowledge represented in memory, with enquiry directed towards how such representations are formed through both conscious and unconscious internal processing of environmental information (Lachman et al. 1979; LeDoux 1996). This view is naturally linked with connectionist models of information processing, rather than the behaviourist models based in computation (Piccinini 2018; Piccinini and Scarantino 2010), and involves conceptualisations of information flow as differing from information processing (Faye 2019; Godfrey-Smith 2007a) or passive transmission (see discussion of the conduit metaphor in Fischer 2009). Connectionist models can be said to form the basis for a number of modern educational theories based in cognitive psychology, including cognitive load theory (CLT) (Sweller et al. 1998, 2011), that have built on concepts of information processing such as those of Miller (2003). However, there remains some ambiguity as to the nature of the information that is being transmitted and processed (Woolcott 2011, 2013). One useful way of resolving such ambiguity may be to develop a description of information that harmonises with concepts of information processing and information processing systems, as well as with concepts of learning and memory, that are used in both the natural sciences and the social and behavioural sciences (Woolcott 2010). This is problematic, however, largely because some descriptions of information in the social and behavioural sciences have arisen partly from philosophies that see information, and in particular, the cognitive processes that deal with information, as not having chemico-energetic involvement, that is, matter or energy involvement (see discussion in Bakhurst 2008; Faye 2019; Godfrey-Smith 2007a). This can be seen, for example, in the use of the terms emergent and mind as having a sense that is not related to matter and energy interactions (see discussion in Howard-Jones 2008). In scientific disciplines such as integrative biology, however, these interactions may be the only observable bases of human cognition (Crick 1994; Lakoff and Johnson 1999). Some modern studies have begun to resolve this apparent lack of harmonisation through the development of a view of information which, although based in matter and energy interactions, makes an attempt to embrace concepts such as knowledge

82

7 A Broad View of Information Processing Systems

and meaning as developed in the social and behavioural sciences. Taking a categorical view in many ways similar to that of Buckland (1991), Bates (2005, 2006), for example, has suggested that it may be useful to resurrect a 1970s’ view of information as the pattern of organisation of matter and energy (Parker 1974 in Bates 2005). This view appears compatible with some scientific descriptions—for example, those that consider information in terms of a hierarchy of the particular organised forms taken by matter or energy (Grobstein 1994)—and appears compatible with studies that consider that information is essentially an attribute of the form that matter and energy may take but not the matter and energy on their own (Faye 2019; Reading 2006; Stonier 1997). This view is compatible with some descriptions of informational concepts based in the social and behavioural sciences (Bates 2016), particularly where information is described in terms related to the information pathways of complex systems (Davis et al. 2008). The suggestion made by Bates (2005, 2006) appears to be supported in recent studies by researchers such as Pigliucci (2011), who describe information as any type of pattern of matter or energy that causes, or contributes to causing, the formation or transformation of other patterns. Bates (2005) characterises organised patterns as either a nonchaotic arrangement or a system—the latter characterisation being necessarily emergent since the sum of the elements constitutes a whole entity with its own distinct qualities. Pattern, in this description, is seen as not coherent necessarily (though not entropic) with any pattern given meaning only by a living being (but see a novel information-based view of entropy in Zurek 2018). This is perhaps similar to Jablonka’s (2002) view of environmental signals as having semantic information if an organism uses these signals in an appropriate way. However, in both views, information may include some part of the environment as well as some involvement of a living organism. The description of information that is developed in this book is, in fact, similar to that of Bates (2005, 2006). However, here information is viewed as all of the constituents of a matter and energy universe (sensu Gribbin 1994), where this information interacts (as in the case of a structure interacting with its environment) temporally in accord with the rules or dynamics of that observable universe, inclusive of the physico-chemical and energetic forces of that universe (Denton et al. 2003). This universe, naturally enough, is considered here to exist outside of human cognition and to be non-homogenous or complex (Bates 2005; Godfrey-Smith 2007a). A similar conceptualisation of information can be seen in the view that physical objects may share informational properties that are explicable in terms of the lower-level physical properties of the objects and the contexts in which the objects are embedded (Godfrey-Smith 2007a). This conceptualisation has some concurrence with the view expressed by Wolfram (2002) that the physical world, at its most fundamental level, consists of information—a view perhaps best expressed by Pigliucci (2011) in saying that information is not a third type of thing outside of matter and energy. Although this may contradict Pigliucci’s view that pattern is important in describing matter and energy interactions in terms of information (see above), it implies that he, like other researchers, considers matter and energy (and see “physicality” as described in Faye 2019) to be fundamental to those observed patterns.

7.1 Information and Information Processing Systems

83

A crucial difference between the novel conception of information here and other conceptualisations based in matter and energy as fundamental units of the physical and energetic universe is that a conformation or pattern of organisation (e.g. Bates 2005, 2006; Stonier 1997) is not considered to be necessary here for matter and energy to be considered as information. Instead, matter and energy here ARE information. Crucially, the conceptualisation considers information to include matter and energy in entropy (as a non-conformational state), whereas Bates’ (2005, 2006) view of information excludes matter and energy that is in total entropy—pattern for Bates is not bounded by conventional views of pattern, including chaotic patterns such as frost on a window. While it may be useful to consider objects in the universe to be information if they are organised in a pattern (even if such organisation is nondescribable or non-definable), in the conceptualisation presented here it is considered more useful that the universe can be arbitrarily divided into spatiotemporal components, whether or not it can be observed or proven that these components are in fact organised in what may be defined as a non-entropic conformation. In this conceptualisation, however, matter and energy organised in an observed pattern will be considered as a subcategory of information in a broad sense and such organisation in a pattern will be discussed later in this section in terms of a broad conceptualisation of human memory. Support for this conceptualisation can be seen not only in integrative biology or cognitive psychology but also in quantum physics, where some researchers consider that information is everything in the universe, where the universe can be considered as divided into two subsystems: the object under consideration and everything else (Feynman 1967 in Tegmark and Wheeler 2001). In such views, the object under consideration consists of matter and energy, with theoretical arguments that this matter and energy results from underlying quantum information and information processing (Lloyd 2006; Vedral 2010; and see discussion in Davies 2010).

7.2 Information Processing Systems in a Universal Sense Based on a broad description of information as any or all of the matter and energy in the universe (sensu Gribbin 1994), information processing can be described as any change in information at some point in time compared with that information at any other point in time. Within a matter and energy universe, any structure or system, including the universe or any spatiotemporal division of the universe, is described here in terms of information, as a Universal Information Processing System (UIPS). The universe as a whole, therefore, can be considered as a UIPS that contains an infinite number of spatial subdivisions that are also UIPSs, and each of these UIPSs can be considered over an infinite number of time intervals. Subdivisions may be described arbitrarily and may include large structures, such as the earth, or individual life forms, such as humans, or even portions of structures or life forms, such as the nervous system or other components of an organismal system (see similar arguments presented in Faye 2019, Sect. 7.1 or in Lovelock 2000, 2007). All such arbitrarily

84

7 A Broad View of Information Processing Systems

described subdivisions, where they consist of more than one component, may be considered as emergent; some of these emergent systems may be described as being organised in a pattern, depending on the definitions being utilised for the terms organised and the pattern. Within this description of information and information processing systems, any organismal or non-organismal structure can be considered as a UIPS that can undergo information processing in any specified time interval. Any UIPS may have environmental information entering (input) or leaving (output) during that time interval, where the environment can be considered as the information that is external to that UIPS. In the broad sense of a UIPS, if the information in the UIPS has not changed in a given time interval, no information processing is said to have occurred even though such a null change may be considered as one of the informational relationships that could have potentially occurred in that time interval. Recent studies of information processing systems have explored the unification of descriptions of information states and their transformational processes (e.g. in studies of evolutionary mechanics and theories of emergence; Crutchfield 1994; Rieke et al. 2007), with a debate as to whether the focus in studies related to such information processing systems, hierarchical, emergent or otherwise, should be on dynamics of change from state to state (learning) or on the computation of the state of an information structure (memory) (Faye 2019; Mitchell 1998). Describing the universe in terms of a UIPS, with component parts also being UIPSs, may seem to be taking a computation-state view, but dynamics are an integral part of an energy and matter universe, and the universal information within each UIPS has a dynamic reactivity inclusive of the physico-chemical and energetic forces of that universe (Denton et al. 2003). Dynamics of change and computation of state are integral, therefore, to a UIPS and relate to any input to and output from a UIPS over the time interval in which the UIPS is described. While there have been a number of approaches to studies of dynamic information processing systems in modern research (Bentley 2007; Edelman 2007; Lovelock 2007; Tononi 2008; Sweller et al. 2011), generalised approaches that may apply to both organisms and non-organismal structures, such as seen in the UIPS concept, have not been considered widely in studies in integrative biology or in cognitive psychology. A generalised approach that considers systemic and dynamic frameworks has been applied in a biological context, however, by Varela and associates (Maturana and Varela 1992; Rudrauf et al. 2003; Thompson 2007; Varela 1979; Varela et al. 1991). This approach shows how an integrated set of local biological processes within a given structure could emerge as an autonomous system, and how this entity could be classed as uniquely living and clearly separate from its environment. The approach taken in the description of a UIPS, however, differs in that it is directed more towards a generalised description of information transmission or flux between an arbitrarily described entity and its environment and any processing of information within that entity, whether or not that entity is living. As such, the UIPS conceptualisation accommodates the description of different types of information processing systems if such systems can be considered in terms of matter and

7.2 Information Processing Systems in a Universal Sense

85

energy and environmental interaction. This includes some of those systems described in terms of patterns of matter and energy, such as hierarchical systems (Grobstein 1994) or emergent systems (Bates 2005; Pigliucci 2011). Approaches such as those of Varela and others (e.g. Varela et al. 1991, Rudrauf et al. 2003), which differentiate biological and non-biological systems, may be accommodated within the UIPS framework.

7.2.1 Memory Potential in a Universal Information Processing System The overarching range of possibilities or potentialities in a given time interval of matter and energy within a UIPS, including the spatial position and connectivity of that matter and energy, is described here in a novel and broad sense using the term Universal System Memory Potential (Memory Potential) as the potential for certain matter and energy interactions to occur within a UIPS in a given time interval. The concept of Memory Potential takes into account the view that, for any UIPS in a given temporal domain, there is a limit to the number of potential matter and energy interactions (e.g. chemical reactions, energy transfers or inter-conversions of matter and energy) that can occur. Since the universe can be partitioned arbitrarily at various spatial levels (e.g. into a pond, ocean, planet, organism or other structure) and since each such partition is also a UIPS, then each such UIPS has Memory Potential. Additionally, each such spatial domain can be partitioned into a number of temporal domains (e.g. the ocean at time one and time two) and each of these also has Memory Potential. The use of a term involving the word memory may seem to conflict with the word memory as it is applied to human cognition, but, as will be discussed in more detail later, the concept of Memory Potential can be used in descriptions of the matter and energy reactions and connectivity with the environment that may occur both within and outside of any conventional description of human cognitive processes. The concept of Memory Potential of a UIPS, therefore, allows for the development of a framework with which to examine human memory in a broader context. The concept of Memory Potential accommodates the more conventional view that information is stored in a system as knowledge by virtue of the change that the information makes in the organisation of the system (the “system disposition” in Faye 2019, p. 236; Langlois 1983), although in a UIPS such a change may not necessarily be observable, and does not require a change in organisation or pattern (see also “nonmanifested disposition” in Faye 2019, p. 236). The concept of Memory Potential also accommodates the view that memory is the quality and quantity of substances in a cell at a given time (Kilian and Müller 2002) or that an organism has a memory if its present state is determined partly by its past history (Casadesús and D’Ari 2002; views outlined in previous chapters).

86

7 A Broad View of Information Processing Systems

As a non-organismal example, a molecule of water could be considered as a UIPS with Memory Potential. The water molecule is a dynamic system that due to its Memory Potential has the necessary information to undergo a limited number of changes of internal structure, due to electron movement, for example, within a given time interval. The water molecule, since it has that particular Memory Potential, does not have the information to become a different molecule, such as a molecule of glucose. With an addition of new information to the water molecule UIPS, its Memory Potential may alter such that it can undertake a new set of internal reactions, and this may include reactions that change its internal spatial relationships or even its separation into various combinations of component UIPS, such as oxygen and hydrogen and energy. In a broad sense, the water molecule UIPS has an internal memory of all of its potential reactions, given its current information and its history. The water molecule may be able to undergo more potential reactions than have been observed, but the known potential reactions of the water molecule UIPS can be referred to effectively as its Memory Potential.

7.2.2 Memory Expression in a Universal Information Processing System In a UIPS, while Memory Potential indicates potential informational relationships, there may be no observable change in the UIPS in many instances, even at the microscopic, atomic or subatomic level. In a particular time interval, therefore, any observed state of a UIPS is termed here as Universal System Memory Expression (Memory Expression). If a human nervous system is treated as a UIPS, for example, Memory Potential may give rise to changes in informational relationships over a given time period, with any observed change, such as growth of synapses, considered to be Memory Expression. Changes in informational relationships in a single time interval as Memory Potential may not lead necessarily to any observed change in the nervous system as Memory Expression in the same time interval but may lead to change in the Memory Expression in a different time interval. Any change in Memory Expression does not mean that there has been a change in Memory Potential of the UIPS and, hence, in its potential informational relationships, and the number of such relationships within the UIPS may be considerable without necessarily any change as Memory Expression. The generalised concept of memory in a UIPS effectively involves a concept of memory that has two parts: Memory Potential and Memory Expression (although Memory Expression is, in reality, an observation of a state and a dynamic of one of the potential informational relationships of Memory Potential). This separation, while maintaining a description of informational relationships or connectivity, parallels the view presented in earlier sections that the state of an organism or structure relates to the change in the information that constitutes that organism or structure and that informational transmission into and out of the environment does not necessarily mean

7.2 Information Processing Systems in a Universal Sense

87

that any change will be observed in a given time interval. Therefore, the description of the UIPS supports a consideration that memory processes, over a number of differing time intervals, may be separated effectively from both input from and output to the environment, or from any direct behavioural or other observable consequences.

7.2.3 Learning Potential in a Universal Information Processing System This section outlines the third part of the description of informational relationships or connectivity of a UIPS, which relates to a generalised view of learning based on changes that occur due to the input and output of environmental information. Within a UIPS, Universal System Learning Potential (Learning Potential) is said to occur if there is a change in Memory Potential due to a communication of environmental information into or out of that UIPS in a given time interval. As such, a change in Memory Potential is effectively a change in the stored information of a UIPS and Learning Potential is effectively the way in which this change occurs; this change parallels, in many ways, conventional views of learning and memory. Communication input of a water molecule into a nerve cell, for example, may potentially change the range of informational relationships (or Memory Potential) that may occur in that cell as a UIPS; as such, there has been Learning Potential for that cell UIPS. In a more conventional sense, the memory of the cell may be said to have changed through learning effected by a chain or cascade of chemico-energetic reactions due to information input, but there is a range of potential changes that may not be considered in such conventional views that are considered here as changes in Memory Potential due to Learning Potential. In human cognition, the input communication of information through the senses into the nervous system could be seen as a change in Memory Potential of that nervous system, considered as a UIPS. In this case, the input process that led to that change would be considered as Learning Potential. In this broad conceptualisation, however, Learning Potential relates to all changes to the Memory Potential due to information input or output, not just the observed changes as Memory Expression, over various time intervals. As such, conventional learning and memory processes, as observed in integrative biology and cognitive psychology, are included within but are not the only part of Learning Potential, Memory Potential and Memory Expression for a UIPS. Given also that conventional learning and memory processes usually relate to observations made in specific time intervals, there are other aspects of input to or output from the nervous system that may occur over a variety of differing time intervals and which are not considered in conventional views of learning and memory. Considering the informational transactions of the human cognitive system as interactions of component UIPSs of the human organism may offer a broader view of those transactions.

88

7 A Broad View of Information Processing Systems

For any UIPS, Learning Potential involves at least one environmental communication into or out of the UIPS, with a resultant change in Memory Potential of the UIPS. The concept of Learning Potential implies, therefore, that there is some change in the connectivity or communication with the environment as a result of any communication with a UIPS, as there would also have been an information change in that environment. For example, a cup of water considered as a UIPS that has received an input communication of energy from the environment, such as through heating, has had a change in its Memory Potential through Learning Potential. In addition, there is a loss of information in the form of energy from the surrounding environment. Within a UIPS, however, the concept of Learning Potential embraces any input or output that may result not only in an increase but also in a decrease in Memory Potential; there is no assumption here that Memory Potential would always increase as a result of any communication input or output. Using the cup of water example again, loss of energy from the water, for example, through cooling may result in a change in Memory Potential, but, in this case, it is because there has been a loss of information in the form of energy as Learning Potential. Of course, there has been a change in the environment as well. The construction of the UIPS framework of Memory Potential, Memory Expression and Learning Potential seems simple, even though it is based on studies that consider learning and memory processes in terms of signal transfer processes that consist of complex chains of interactions. This framework, however, may have wide applications in providing unambiguous descriptions of learning and memory that can be applied to any spatiotemporal division of the universe, as well as to the universe itself. There have been other recent broad conceptualisations of cognitive processes that have led to similarly wide potential applications to both organisms and non-organismal structures. In describing the concept of consciousness in terms of contingency and correlation, for example, Tononi and others (Tononi 2008; Tononi et al. 2016) have arguably separated the concept from a historical basis in human cognition through a foundation in studies of information flow and mathematics. As such, the concept of consciousness can now be applied potentially to non-organismal structures (Sanders 2012). Some researchers (Bates 2005; Godfrey-Smith 2002) have argued that it may be of benefit to broadly conceptualise other cognitive processes, such as knowledge, cognition, intelligence and mind, which do not have simple and agreed-upon meanings, such that they may have a wider application and, as well, be subject to the quantification seen in scientific empiricism. In the case of cognition, Godfrey-Smith (2002) has described it in a broad sense as a collection of capacities related to organismal processing of environmental information and negotiation of the environment and has outlined how this description may be applied to non-organismal structures via computer simulation (the genetic algorithm of Todd and Miller 1991). Broad conceptualisations, such as those of Tononi (2004, 2008) or Godfrey-Smith (2002), however, have not been applied to studies of education. The UIPS framework is used in Part III of this book to examine educational theories and practices.

7.2 Information Processing Systems in a Universal Sense

89

7.2.4 Temporality and Information Loss in a Universal Information Processing System The description of a UIPS here involves information processing over differing time intervals. This temporal aspect makes the UIPS concept more complicated since the time period of a UIPS under consideration needs to be implied or indicated. Viewing a UIPS in this context, however, formalises the temporal aspect of information processing as an integral part of a given structure or other matter and energy entity. As will be discussed later in this section, with regards to the human UIPS, the involvement of a temporal aspect in studies of conventional learning and memory processes has been informative in that it enables a partition of learning and memory concepts into stages that are time dependent. The description of a UIPS here may also involve changes in Memory Potential and Memory Expression through Learning Potential in the form of information loss. Therefore, any account of the storage of information in the human UIPS must consider information loss, as well as information gain, over specified time intervals. This is discussed in more detail in later sections.

7.3 Organisms and Non-organismal Structures as Universal Information Processing Systems The informational relationships that occur through the pathways of matter and energy interactions between an organism or non-organismal structure and its environment can be viewed within the UIPS concept in terms of the broadly described learning and memory processes of Learning Potential, Memory Potential and Memory Expression. These three processes form the basis of an overarching framework that is useful in describing all of the informational interactions of an organism or nonorganismal structure as well as the informational interactions of any component structures (Fig. 7.1).

The universal context for learning and memory in organisms and nonorganismal structures Fig. 7.1 The universal context for learning and memory in organisms and non-organismal structures

90

7 A Broad View of Information Processing Systems

The universal context for learning and memory in organisms and nonorganismal structures The universal context applied to non-organismal structures

Fig. 7.2 The universal context for learning and memory in nested organisms and non-organismal structures

7.3.1 Non-organismal Structures and Universal Information Processing Systems The concept of a UIPS can be used to describe all spatial structures, non-organismal as well as organismal. As such, the UIPS framework has the additional capacity to describe processes in non-organismal structures that, due to internal mechanisms for adaptive change (Godfrey-Smith 2010), are similar to conventional learning and memory processes in organisms. For example, the discrete spatial entity of the earth, which can be described as a UIPS, has been ascribed by Lovelock and others (Lovelock 1995, 2000, 2007; Lovelock and Margulis 1996) as a system of learning and memory that may involve such mechanisms for adaptive change. Lovelock’s Gaia concept, in fact, has been used to model an observable response of the earth to input originating outside the earth system (e.g. in the Daisyworld simulation where there is growth of light-absorbing plants on the earth’s surface in response to an increase in light input to the earth; Wood et al. 2008). There have also been studies that have examined learning and memory processes in non-organismal structures that are analogous to such processes in humans, such as in the studies of neurorobotics by Sporns and others (Krichmar and Reeke 2005; Krichmar 2018; Krichmar et al., 2005; Seth et al. 2005; Sporns 2009), where robot sensors interact with their environment to direct the robot to particular locations through a learning experience. This type of learning and memory described for the earth, robot or other non-organismal structure can be viewed within the UIPS framework, where there is Learning Potential from any information input to or output from the system that leads to changes in the chemical or energetic potential of the system’s Memory Potential. As such, this may have observable effects in particular time intervals as Memory Expression (Fig. 7.2).

7.3.2 Organisms, Phenotypic Plasticity and Universal Information Processing Systems As discussed earlier, phenotypic plasticity—the ability of organisms with the same genotype to vary their developmental pattern, phenotype or behaviour in response to varying environmental conditions—is one way of generalising a learning and memory function in both animals and plants (Baluska et al. 2018; Borges 2005;

7.3 Organisms and Non-organismal Structures as Universal …

91

Dukas 2018; Ghysen 2003; Trewavas 2016; Woolcott 2010, 2011). Since the concept of phenotypic plasticity, based as it is in scientific studies, involves matter and energy communication between an organism and its environment, this plasticity can be viewed within the UIPS framework in terms of Learning Potential, Memory Potential and Memory Expression. This can be illustrated by considering, in a particular time period, the environmental input to a plant that provides it with the potential to change its behaviour as a reaction to a stimulus. The environmental input can be seen as Learning Potential that leads to a change in Memory Potential, and any observed change in the behaviour of the plant in a given time period (as implied in the concept of phenotypic plasticity) is a change in Memory Expression. The signal pathway for such phenotypic plasticity in an organism, from environmental stimulus to any resulting behaviour, can be modelled at several levels of complexity, including macroscopic, microscopic and molecular. This signal pathway can also be modelled in a UIPS by considering an interacting series of component UIPSs and their connection to the external environment. Phenotypic plasticity may be considered as the response of an individual or system to a stimulus from the environment; however, in considering an organism as a UIPS, there may be no response (or differing responses) over differing time intervals, or there may be Memory Expression that is not considered conventionally as behaviour. In terms of a UIPS, therefore, any observed phenotypic plasticity may be considered as only part of the entire learning and memory process of an organism.

7.3.3 Organismal Learning and Memory and Universal Information Processing Systems Some conventional descriptions of learning and memory describe behaviour that occurs in response to the same stimulus, such as the classical and operant conditioning seen in habituation and sensitisation. These can be viewed within the framework provided by the UIPS concept. Habituation describes learning where there is a progressive diminution of a response to a specific stimulus, given the repetition of that stimulus, with habituation described for multicellular organisms such as plants (Abramson et al. 2002; Gagliano et al. 2018), fungi (Boisseau et al. 2016) and animals (Kandel 2009). Habituation viewed within the UIPS framework can be described as a specific type of change to Memory Potential in a specific UIPS, with the same repeated input communication as Learning Potential, such that there is a different Memory Expression with each repeated input over a given time period. Any difference in Memory Expression due to these repeated inputs would result from a different set of potential reactions available as Memory Potential. Some of the changes in Memory Expression, for example, would be the observed reactions involved as the repeated lessening extent of the response to a stimulus (Kandel 2009). Sensitisation has also been described for animals (Kandel 2009) and plants (Abramson 1994; Nakano et al. 2015), where the response to a repeated stimulus is amplified rather

92

7 A Broad View of Information Processing Systems

than decreased. In this case, sensitisation viewed within the UIPS framework is where Memory Expression can be said to change in similar ways as in habituation but resulting from different information content as Memory Potential. This would imply that both multicellular and unicellular organisms, treated as UIPSs, demonstrate both habituation and sensitisation. Some researchers have argued, in fact, that some unicellular organisms demonstrate, at the very least, habituation (Di Primio et al. 2000; Tang and Marshall 2018).

7.4 The Human Organism as a Universal Information Processing System 7.4.1 The Human Universal Information Processing System A human individual, like any spatial entity in the universe, can be considered to be a UIPS, with the matter and energy that constitutes that UIPS being the information processed in a given time period. Treating a human as a UIPS implies that Learning Potential occurs as an integral part of changes in human states and dynamics over various time periods and that Memory Potential is a function of the individual as a whole. This viewpoint is similar to that of studies that see human learning and memory as system-wide holistic processes, dependent on the entire organism and not just the nervous system (Squire and Kandel 2008). Additionally, the idea of a human UIPS as an entity that is linked with the environment through the communication of information is similar to the view of environmental interaction as integral to human learning and memory (Degenaar and O’Regan 2017; Järvilehto 1998a, b, 1999, 2000, 2009; Moore and Depue 2016; O’Regan et al. 2005). Studies where learning and memory are viewed in terms of system-wide interactions due to environmental inputs and outputs support the view of a human as a UIPS (Fig. 7.3).

The universal context for learning and memory in organisms and nonorganismal structures The universal context applied to non-organismal structures The universal context applied to organisms, e.g. the human organism

Fig. 7.3 The universal context for learning and memory in nested organisms and non-organismal structures, including the human organism

7.4 The Human Organism as a Universal Information Processing System

93

This does not mean, however, that the centralised human nervous system may not be treated as a component UIPS, even though, unlike many component UIPSs that constitute the human UIPS, the nervous system has input directly from the external environment through sensory neurons. The nervous system, as a component of the human UIPS, has its own Learning Potential and Memory Potential that may or may not give rise to changes in Memory Expression in a specified time period. Even though any inputs or outputs have observable consequences in particular time intervals, such as synapse growth or muscle stimulation via motor neurons, there may be other changes in Memory Expression that can be observed over longer or shorter time intervals, as well as changes to Memory Potential that are not observed. Such a treatment implies that the contribution of the nervous system to human learning and memory can be evaluated independently of the contribution of other component systems. Treating the human nervous system as a UIPS implies additionally that conventional views of learning and memory may need to consider all inputs to and outputs from that nervous system, including those from other component UIPSs within the human organism. There is, in fact, evidence that somatic inputs to the nervous system—for example, oxygen and glucose provided through the circulatory and respiratory systems—are essential for the operation of learning and memory (Chung et al. 2007; Jones et al. 2018; Riby et al. 2004). Such evidence, however, is not generally considered in models of the function of the nervous system, or in models of learning and memory processes that relate to education and teaching (Woolcott 2011, 2013). As discussed earlier, the view of an organism, such as a human, from within the UIPS framework may embrace conventional descriptions of learning and memory, including habituation and sensitisation. The consideration of UIPSs indicates that the pathways between the environment and the human organism that involve the nervous system, and that result in the observation of the conventional learning and memory responses (whether classified as conditioning or other similar behavioural responses) may be a portion or subgroup of the much larger number of information pathways that constitute human Learning Potential, Memory Potential and Memory Expression in the human UIPS over various time intervals. Memories considered as part of conventional long-term memory (LTM) can also be considered as part of this subgroup of pathways since they involve the formation of information pathways between the environment and organism that involve the nervous system, where a given environmental input gives rise to or strengthens pathways that result in the same behavioural response (or non-response), given the development of appropriate pathways that connect the muscular system. Memory Expression, in all such subgroups, however, can only be assessed by observing behaviour over particular time periods, and some information pathways may not give rise to an observable response. On a different level of complexity, interactions of the nervous system with its environment, as viewed within the UIPS framework, may be used to describe emergent functions of that nervous system, such as attention and working memory (WM). Such functions can be considered as involving subgroups of matter and energy pathways within components of the nervous system, such as neuronal assemblies, whose

94

7 A Broad View of Information Processing Systems

interactions over varying time periods are determined from observation of changes as Memory Expression. The temporal sequencing of environmental information transmission to the nervous system (effected by attention and WM) may contribute to the slow alteration, relative to the rate of information input, of LTM, and this can be encompassed within a description of a human as a UIPS, with neuronal assemblies and other interacting component UIPSs functioning effectively as the nervous system. Conventional learning mechanisms and memory processes for a human, however (as would be the case for any organism or non-organismal structure treated as a UIPS), can be separated from other processes embraced potentially in the broader view of the UIPS framework by describing the specific matter and energy pathways that apply to those conventional mechanisms and processes. This means that the discussion of conventional human learning and memory processes (e.g. in terms of emergent systems, such as attention and WM) and their role in the problemsolving that occurs with environmental interaction (sensu Tonegawa et al. 2003) may require that the description of these emergent systems be made more specific in relation to system-wide processes in a UIPS. As such, a more complete description of such emergent systems within the UIPS framework requires the application of more complex descriptions of the pathways involved in human connectivity with the environment.

7.4.2 The Human Universal Information Processing System and Temporality Examination within the UIPS framework of Learning Potential, Memory Potential and Memory Expression over differing time periods sheds some light on how component systems within the human organism contribute to conventionally viewed learning and memory processes of the entire organism through both long-term and short-term interactions. In the human UIPS, for example, while there may be changes to Memory Potential of a neuronal assembly in the short term due to sensory input, there may be other changes in Memory Potential due to information received from other sources, such as from muscle cells or from the circulatory system, as well as from information outputs. As such, it may be useful to assess the impact of all such changes on Memory Expression over differing time intervals (Woolcott 2011, 2013). Issues related to time intervals in conventional learning and memory processes have been considered in some studies in integrative biology and help to form at least part of an overall picture as viewed from within the UIPS framework since these processes are generally described in terms of information pathways and matter and energy interactions. Observations have been documented, for example, of delays in parallel processing of information within the nervous system, where aspects of the same information input are processed in differing time periods and via information transmission through branching patterns of interaction (Bouchacourt and Buschman

7.4 The Human Organism as a Universal Information Processing System

95

2019; Dehaene 2007; Cotterill 2001; Margulies et al. 2005; Nolte et al. 2019). Such processing in real time, a feature of the human nervous system, allows adaptation to environmental change without necessarily any genetic and generational changes (Calvin 2002, 2004; Stanley 1996). Even though changes in the synapses and neuronal connections in the brain may alter in the short term through non-DNA-related chemical or energetic interactions, changes in the long term may involve genetic interactions (e.g. Marshall and Bredy 2016; Routtenberg and Rekart 2005; Shi et al. 2013; Tonegawa et al. 2003), and even longer-term changes may occur through variation in time of transfer of information through the various pathways and modes used in the central nervous system (Ba¸sar and Bullock 2012; Cotterill 2001, 2008; Cox et al. 2018; Opris and Casanova 2017; Roy et al. 2018). Such longer time intervals have also been considered in studies that deal with issues such as the effect of periods of sleep on learning efficiency (Stickgold and Walker 2005; Spencer et al. 2017; Walker 2008). The idea of temporality as an issue in learning and memory has been considered in cognitive psychology; for example, it has been considered in the idea of schema or concept building, where sequential information transmission into the nervous system is known to be crucial, particularly in relation to critical or sensitive periods for effective learning in preschool and school-age children (Calvin 1996; Hodges and Gruhn 2018; Greenough 1975; Sinclaire-Harding et al. 2018). Several recent studies have also considered the amount of time that information may be retained as LTM after learning through the examination of the information transmission involved in such learning, and the number of repetitions required during learning for memory retention sufficient to allow automatic memory recall (Sweller et al. 1998; Grandin 2006; Ellenbogen et al. 2007; Inda et al. 2011). Perleth and others (Demetriou and Spanoudis 2018; Perleth and Wilde 2009; Zuo et al. 2017) have considered longer time intervals in their discussion of developmental learning trajectories for the achievement of high levels of expertise. Such levels of expertise may require long periods of practice within a specific domain, as Ericsson and others have suggested (Ericsson et al. 2009), but may also depend on having an active and aim-related focus, sometimes summarised under the umbrella term, motivation (Brooks and Shell 2006; Shell et al. 2010). The issue of longer time periods in learning and memory is also beginning to be explored in studies that embrace research showing that learning occurs prior to the entry of an individual to institutionalised education, or other schooling, and continues throughout an individual’s life (e.g. Dehaene 2007, 2009; Barth et al. 2005). Preschool concepts, however, may be effectively erased or at least inhibited by the concepts taught at school; De Lange (in OECD 2004) has argued that subjects taught in institutionalised education may not always build on the LTM available prior to such education but build a completely new and relatively unrelated LTM that may act to inhibit other memory components. This would indicate that teaching could be more effective if it builds on the concepts that exist prior to schooling (e.g. by incorporating the universal non-symbolic abilities of the human organism in order to enhance the symbolic learning in mathematics that cultural accumulation appears to require; Barth et al. 2005; Mulligan and Woolcott 2015; Mulligan et al.

96

7 A Broad View of Information Processing Systems

2018). Additionally, educational studies may not recognise the impact of temporal variations in learning and memory that result from such cognitive functions as WM or attention. This is partly because such functions can be simplified in terms of the temporal range of neuronal and other somatic dynamics operating within, and as a result of, those functions. This simplification has been useful in that changes due to information input to the nervous system from the environment (either external to or inside the human organism) may be observed in real time, but such simplification does not consider that there may be changes in Memory Expression over a range of time periods as the UIPS concept suggests.

7.4.3 The Human Universal Information Processing System and Information Loss In conventional studies, information transmission has been sometimes considered as originating with input flow from a sensory stimulus. These studies generally prioritise observations of the effects of the flow from sensory input to a behavioural output. In treating a human as a UIPS, as well as information input, loss of information as an output constitutes Learning Potential and, therefore, a change in Memory Potential; such loss of information leads to the observation of changes over varying time periods as Memory Expression. There is considerable research indicating that the loss of information on several levels of spatiotemporal complexity is important in conventionally viewed learning and memory. Loss of information through cell apoptosis, or genetically programmed cell death, for example (Labi and Erlacher 2015), as well as the pruning of synapses and inhibition of synapses or other connections between neurons (Edelman 1987, 1989, 1992; Hansel 2019; Lieberman et al. 2019; Snyder et al. 2004), are common processes in human development. At the cellular level, loss of information as neurotransmitters from the dendrite of a neuron is essential to the transfer of information across synapses, when energy and chemicals are transferred out of one neuron and into another (Kandel 2009). More broadly, electrochemical information is effectively lost when energy generated in the thalamocortical core is transferred through the brain as a low-voltage field activity—a well-documented part of the learning and memory processes in all animals with a centralised nervous system (Baars and Gage 2010). Such processes of loss can be considered as an integral part of the UIPS concept. The loss of neurotransmitters from a neuron as a UIPS, for example, can be considered as information output that is Learning Potential, which leads to a change in Memory Potential in that neuron. In addition, there may be at least one observable microscopic structural change (or Memory Expression) observed as growth of synapses. In this case, observable change as Memory Expression for that neuron can be seen over differing time intervals (e.g. over the interval from the time of formation of the neurotransmitter vesicle to the time of its release) (Connors and Long 2004).

7.4 The Human Organism as a Universal Information Processing System

97

Loss of information as Learning Potential, leading to changes in Memory Potential, can be argued to be a major driver of cognitive processes, as well as other internal body processes, such as respiration, where loss and gain of carbon dioxide and oxygen play a determining role in respiratory processes. Information loss within and between component UIPSs may be responsible for a significant amount of the change in Memory Expression of the human UIPS. In organisms with a centralised nervous system, for example, all motion requires muscular activity and any muscular activity driven by the nervous system is a result of loss of information from the nervous system and the gain of at least some of that information by muscle cells. Such information loss may result in a change in Memory Potential in those muscle cells that effect a change in their Memory Expression. Information is also lost from the muscular system, and some of this information may become input to the central nervous system (e.g. where there is feedback of spatiotemporal change from somatic positioning sensors in muscles) (Cotterill 2001, 2008; Crane 2015; Grillner 2003; Tassinary et al. 2017). In general, both input and output communications function at a number of levels, from macroscopic through to molecular, within and between component UIPSs within the human UIPS, and may work over a number of time intervals. In the nervous system, this communication includes the input and output to specific locations of such things as hormones, glucose and oxygen, in order that there is sufficient coordinated stimulus to relevant cells in relevant neuronal assemblies. There has been considerable research that indicates that, due to both information input and output, there are numerous internal changes in the human body (as Memory Potential) that do not manifest as easily observable changes (as Memory Expression), and changes within the central nervous system are no exception. The formation of concepts (sensu Snyder et al. 2004), for example, requires multiple complex changes to output information within neuronal assemblies before those concepts can stand out as pathways from the background pathways of the complete neuronal network and, even then, those complex changes may not result in Memory Expression. There are, at present, few cognitive or educational models that consider any estimate of the contribution that is made to learning effectiveness within the human organism by the total of any external information input or output, nor are there many models that incorporate all of the details of all of the pathways that are involved in communication with the non-human environment. Some models, however, such as those of Frith and others (Blakemore and Frith 2000; Morton and Frith 1995), are at least considering many of the inputs and outputs from several sources of information (including from component systems such as the circulatory and respiratory systems), even though these models have yet to incorporate an understanding of informational interactions over a variety of time intervals. The UIPS framework is useful as a descriptive tool through consideration of such informational interactions, including those of any component UIPS that may be considered as part of the totality of the conventional understanding of learning and memory.

98

7 A Broad View of Information Processing Systems

7.5 Universal Information Processing Systems and Human Connectivity In the conventional view of human learning and memory reported from cognitive psychology, as well as the processes that result in the input transmission of environmental information into storage as memories, there are also processes that serve to connect or associate that information as elements, chunks or schemas within LTM (Miller 2003; Postle 2015). Such information may, in turn, be reorganised through changes in connections or associations within LTM. The formation of such connections or associations within the brain is a feature of organisms with a centralised nervous system, and the elements, chunks or schemas may be analogous to the interlinked neuronal networks (such as those in neuronal assemblies) reported from studies in integrative biology. In some such studies, memories are considered to reside largely in the connections of the network of neurons and related structures that form the brain (Gibb and Kolb 2018; Edelman 1987, 1989, 1992; Opris and Casanova 2017). Humans appear to develop a much greater degree of such connectivity or association than other animals (Cahalane and Finlay 2017; Cotterill 2001, 2008; Dukas 2019; Grandin and Johnson 2005). This may account for rapid environmental assessment and predictive capability or planning seen in the human response to stimulus in problem-solving (Calvin 2002, 2004; Sarathy 2018). Such connectivity has been utilised in the learning that occurs through social interaction (Godfrey-Smith 2002; Siemens 2017; Utecht and Keller 2019), including the structured interactions seen in institutionalised education (Goswami 2008; Van Schaik 2006), and has had an impact, therefore, on cultural accumulation and cultural ratcheting in human societies (Tomasello 1999, 2014; Woolcott 2011, 2013, 2016) (Fig.7.4). The universal context for learning and memory in organisms and nonorganismal structures The universal context applied to non-organismal structures The universal context applied to organisms, e.g. the human organism The universal context applied to organismal structures, e.g. the human cognitive system

Fig. 7.4 The universal context for learning and memory in organisms and non-organismal structures, including the human organism and its cognitive system

7.5 Universal Information Processing Systems …

99

In treating the human organism as a UIPS, such internal connections and associations of information can be considered as resulting from interactions of component UIPSs within the nervous system, such as neurons and neuronal assemblies. If, for example, an assembly of several hundred neurons is treated as a UIPS, the assembly can be seen as having both input and output communication with its surrounding environment. This environment would include other neurons and assemblies as well as matter and energy as nutrients, such as oxygen and glucose. The Memory Potential of the assembly may change with any such communication or Learning Potential into or out of its environment; this may enable a range of Memory Expressions, observable in particular time periods, for example, as changes in connectivity within that neuronal assembly over a number of days. Viewing changes of a large number of such assemblies over a variety of differing time periods may lead to a variety of differing observations with regard to Memory Expression. This has been seen, in fact, in such things as structural changes to neuronal assemblies observed over time intervals from a few minutes to a few days or weeks (Ba¸sar and Bullock 2012). This is not to say, however, that such Memory Expression constitutes the entire learning and memory process as there may be many differing interactions over a number of differing time periods and, of course, some of these may be interactions of Memory Potential that are not observed. In a human nervous system as a UIPS, the concept of Memory Potential is compatible with some conventional views of memory storage because both views indicate that within each human individual there is a store of information gained from the external environment that can be treated as a store separate from other internal storage systems. Viewed from within the UIPS framework, however, human memory storage is system-wide as the Memory Potential of the entire human UIPS, and is not confined to a particular component UIPS (such as the brain or the nervous system). Treating the entire human as a UIPS that undergoes system-wide learning and memory processes embraces the view that conventional learning and memory are holistic (Järvilehto 1998a, b, 1999, 2000, 2009; Squire and Kandel 2008) and part of the human connection to the environment. Each communication into or out of the human UIPS may change Memory Potential of that human UIPS in relation to that environment. Changes in Memory Expression of the nervous system UIPS, therefore, can be considered as part of the range of observed changes of the human UIPS.

7.6 Linked Information Systems Within the Human Organism Treating a human as a UIPS implies that component UIPSs, such as the nervous system and muscular systems, are linked systems, but that each component UIPS can be considered to have its own dedicated Learning Potential, Memory Potential and Memory Expression, and their own environmental interactions. Each such

100

7 A Broad View of Information Processing Systems

component UIPS has environmental input from and output to many differing locations. As such, all such components are capable of communication with each other. In the nervous system, for example, sites for sensory information transmission from outside the human body are considered conventionally to be the sensory neurons located in organs such as the skin, nose, tongue, ears and the eyes. Changes in information in other somatic locations, however, are also detected by the nervous system, for example, by the monitors of a spatial position that are located within muscle fibres (Grillner 2003), or by monitors of information input, such as food and water, related to blood flow and content (Marty et al. 2007; McCormick and Bradshaw 2006). Changes in the nervous system can, in turn, be reflected in output to other such component UIPSs. There is also information input to the nervous system that arises in interactions of other component UIPSs of the human soma. Information such as oxygen and carbon dioxide input from the respiratory system, for example, is known to affect cognition (Jones et al. 2018; Riby et al. 2004) and chemical or energetic reactions related to cognitive function may be affected by such systemic factors as hormonal imbalance and developmental age (Hernandez and Gore 2017; McCormick and Bradshaw 2006; Topper et al. 2019; Walker 2008), with some such factors known to affect specific cognitive processes, such as WM (Chung et al. 2007; Espy and Bull 2005; Swanson 2017). The results of such studies, however, have rarely been integrated into a broad systemic perspective such as described in the UIPS concept here, where information input to the nervous system can be received from all of its environment, including other component UIPSs. Component interaction is a feature of conventional human learning and memory during problem-solving (sensu Sweller et al. 2011 or Tonegawa et al. 2003), where novel environmental input interacts with various components of the nervous system. During such problem-solving, for example, attentional processes activate WM that functions as a mechanism linking and organising that novel information (Postle 2015; Sweller et al. 2011). There is also a system-wide component interaction in the links that develop between the neuronal assemblies activated during such attention and WM processes, and other component systems, such as the muscular, circulatory and respiratory systems, as well as feedback links (e.g. where changes in the somatic position may give rise to further novel information input to the nervous system that requires further activation of attentional processes and WM) (Bouchacourt and Buschman 2019; Cotterill 2001; Miller and Buschman 2007). Some recent studies have used, albeit implicitly, the concept of such interacting component systems to generate novel methods for examining cognitive processing— for example, in the examination of brain activity in children (at about 6 months of age)—by observing muscular activity, such as staring and blinking (Calero et al. 2019; Lipton and Spelke 2003; Liu et al. 2019). In addition, the UIPS concept suggests that the observation of the interaction of different somatic systems as component UIPSs of the human UIPS, including the nervous system, may need to be considered over varying time intervals in order to obtain a complete picture of human learning and memory in a broad sense (Fig. 7.5).

7.6 Linked Information Systems Within the Human Organism

101

The universal context for learning and memory in organisms and nonorganismal structures The universal context applied to non-organismal structures The universal context applied to organisms, e.g. the human organism The universal context applied to organismal structures, e.g. the human cognitive system The universal context applied to learning and teaching

Fig. 7.5 The universal context for learning and memory in organisms and non-organismal structures, including the human organism and its cognitive system as applied to learning and teaching

Considering a broad view of the human as a UIPS, with a nervous system that is only one of many possible component UIPSs, implies that there may be a considerable exchange of information between other component UIPSs that may affect cognition; this exchange process is only beginning to be explored in current educational and cognitive models. For example, Lloyd (2010b, p. 2) discusses information in terms of an information literacy that “requires a person to engage with information in a landscape and to understand the paths, nodes and edges that shape a landscape”. However, some educational research is exploring environmental factors—at least in some time intervals—in order to demonstrate the connectivity between cognition, biology and behaviour (Blakemore and Frith 2000; discussed in more detail in Part III).

7.7 Universal Information Processing Systems and System-Wide Learning and Memory 7.7.1 System-Wide Learning and Memory: Predictions and Concurrences The conception of learning and memory in a UIPS as system-wide processes, which can be described in a framework of Learning Potential, Memory Potential and Memory Expression, was developed from consideration of commonalities found between the various descriptions of learning and memory processes described for

102

7 A Broad View of Information Processing Systems

organisms and non-organismal structures and accommodates all of the environmental and component interactions of such organisms and non-organismal structures as UIPSs. The UIPS conceptualisation of system-wide learning and memory processes as related to the total of all environmental and component interactions predicts that conventional views of learning and memory may be only part of such system-wide processing. The description of UIPS predicts, specifically, that the conventional view of human learning and memory—where learning is the process by which information is stored as memory in the brain, although related to interactions of component UIPSs of the nervous system—is only part of a system-wide interaction between the human individual and his or her environment. Similarly, the description of the UIPS predicts that learning and memory processes described for organisms and non-organismal structures more generally may also be only part of system-wide processing in such organisms and structures. With regard to human learning and memory processes, there are studies that concur with this prediction. For example, some studies in integrative biology provide evidence for a holistic view of human learning and memory that relates the memory of the entire organism to its environmental interactions (e.g. Degenaar and O’Regan 2017; Järvilehto 1998a, b, 1999, 2000, 2009; Squire and Kandel 2008; Moore and Depue 2016; O’Regan et al. 2005; Squire and Kandel 2008), and extend the conventional view of learning and memory from a basis in interactions of the nervous system. A concurring view is presented also in modern studies in the philosophy of education, where Bennett and others (Bakhurst 2008; Bennett and Hacker 2003) argue that some of the things that are generally considered as related to brain function, such as reasoning, deciding and remembering, are done by people—not just their brains—interacting with their environment (see also Baumeister et al. 2018; Hari et al. 2015; Immordino-Yang et al. 2018). In addition, there is research that concurs with a prediction that system-wide human learning and memory results from interactions of component UIPSs, for example, in examining the links between the immune and nervous systems. Descriptions of the human immune response have shown that the human immune system can be considered as a system separate from the nervous system, with antibodies produced in response to environmental stimuli external to that system (Edelman 1970; Edelman and Gally 1968; Marin and Kipnis 2013). The immune system, however, also has interactions with the nervous system that affects the organism as a whole (LeDoux 1996). Since the immune, nervous systems and other human systems, even though they may be studied as separate entities, can function as interacting component UIPSs, it can be argued that they must also function in system-wide learning and memory of the entire UIPS. There are also other studies, which, on a broad basis, concur with a prediction of system-wide learning and memory processes. The studies of Varela and colleagues (Rudrauf et al. 2003; Thompson 2004, 2007; Varela 1979; Varela et al. 1991), for example, consider that studies of brain functions must include phenomenological investigations of experience, even where these experiences are embodied. The implication is that environmental connections of the entire system—what Varela and others refer to as lived experiences—are a necessary part of the account of conventional

7.7 Universal Information Processing Systems and System-Wide …

103

human learning and memory processes, where brain dynamics are embedded in both a somatic and an environmental context (Cosmelli and Thompson 2009). Further support for this lies with Maturana and Varela’s (1992) conception of organismal life as autopoietic, with a boundary containing a molecular reaction network that produces and regenerates itself through system-wide effects or interactions. In this conception, albeit simplified here, an organism’s world is the sense it makes of its environment or its cognition (Thompson 2004); this implies a system-wide conception of learning and memory that may be embraced within the UIPS framework of Learning Potential, Memory Potential and Memory Expression.

7.7.2 System-Wide Learning and Memory in Organisms Treating any organism as a UIPS indicates that there is system-wide learning and memory through matter and energy interactions linked as pathways that enable that organism to change Memory Potential (sometimes Memory Expression) through Learning Potential. Organisms may have a number of differing pathways that connect with the environment, and all of these could be considered in a broad sense of a UIPS as learning and memory pathways. An example of this generalisation was considered earlier in this book, where unicellular organisms were considered as having a learning and memory function through interactions with the environment that changed the quantity and quality of substances inside a cell at a given time (Kilian and Müller 2002). This is also the case with the aggregations of cells that constitute multicellular organisms, as each cell within the organism has pathways that connect with other cells as well as the environment external to the cell (Albrecht-Buehler 2005)—the total of these interactions is the system-wide learning and memory of the UIPS. Within large multicellular organisms that have a well-described learning and memory system (Dukas 2019; Trewavas 2016; van Duijn 2017), there are components that receive information from their external environment and which can be described as having broadly described learning and memory functions within the UIPS framework. The immune system, discussed above, is an example of this, but any component system in an organism may act as a UIPS in a similar way. The UIPS concept predicts that the entire organism responds system-wide to the interactions of such component systems as well as to information from the environment. Research that concurs with this prediction has begun to document the developmental effects of environmental influences that have an effect on an entire organism through such interactions, for example, where environmental hormones may affect genes that regulate organismal development (Deb and Mandal 2017; Denver 2009; Gilbert 2005). Developmental changes due to environmental influences have been related specifically to cognition and may be relevant to education, for example, where such environmental influences are known to be involved in dyslexia and dyscalculia (Butterworth 2018; Butterworth and Laurillard 2016; Dehaene 2007, 2009; Goswami 2008) or autism spectrum disorder (Baron-Cohen 2008; Favre et al. 2019; Markram and Markram 2010; Opris and Casanova 2017).

104

7 A Broad View of Information Processing Systems

The prediction that learning and memory processes are system-wide in an organismal UIPS is also supported by research on the temporal interactions of organismal DNA (genetic interactions) with the surrounding environment that gives rise to system-wide effects. Plomin and others (Davis et al. 2007; Kovas and Tosto 2017; Plomin and Kovas 2005; Plomin et al. 2007), for example, in arguments for generalist genes rather than genes that are specific for intelligence, have shown that some genes contribute to many different functions within an organism through a variety of differing interactions. These interactions, as would be expected in a UIPS whose Memory Potential involves a number of differing pathways, result in networks consisting of a series of branching reactions and these may extend across the entire organism when examined over a variety of time periods. Such networks of spatiotemporal interactions may, in fact, be typical of system-wide interactions in a UIPS. The consideration of system-wide learning and memory processes within the human UIPS predicts that, over varying time periods, each individual produces chains of different networked environmental interactions, resulting in different ways of organising and structuring internal component UIPSs. This implies that there are a range of differing neurotypes, for example, within the human population that vary considerably from one person to another, resulting from differential development of the nervous system component UIPS within the human UIPS. In relation to neuronal structures, this prediction concurs with results from studies of human creativity by Haier and colleagues (Haier 2016; Haier and Jung 2008; Haier et al. 2005), who indicate that creativity and intelligence involve several different brain areas, resulting from a number of pathways that develop differentially in the brain as a result of human interaction with the environment. It is useful, therefore, to reassess studies of conventional learning and memory in cognitive psychology and integrative biology since such studies have assumed that individual subjects under study were neurotypical (sensu Happé and Vital 2009). There has been recent research, in fact, that indicates that differences between the cognition of individuals are being examined in relation to education (Biedro´n and Pawlak 2016; Farah 2010; Gubbels et al. 2018; Randler and Demirhan 2016; Thornton and Lukas 2012). This may prove to be a difficult task, however, since the range of different neuronal types needs to be determined and neuronal pathways would be necessarily complex rather than linear; however, there is some research that has begun to investigate such complex pathways (Appasani 2017; Sporns 2010, 2012; Tosches and Laurent 2019; Zuo et al. 2017). The task may be further complicated by the lack in integrative biology of normative data on any such neuronal variation. This is reflected in the lack of normative databases of cognitive development from studies in cognitive psychology (Freund 2005).

7.7 Universal Information Processing Systems and System-Wide …

105

7.7.3 System-Wide Learning and Memory in Non-organismal Structures One of the implications of considering all energy and matter structures as UIPSs is that the broad description of system-wide learning and memory processes, as well as applying to organisms, also applies to the description of non-organismal structures. In a computer viewed as a UIPS, for example, Learning Potential, Memory Potential and Memory Expression reside in the entire system rather than in any particular set of pathways that can be treated conventionally as a memory store. Machine learning and memory (Alpaydin 2016) are described generally for computers or other computational devices, such as cameras, in terms of a memory function that is attributed to a certain part of the machine, such as a memory board or a memory chip. These parts, however, do not function as a memory without the other parts of the machine, such as the keyboard or the power source, that permit the environmental interaction that is considered as Learning Potential. It is only through the system-wide interaction of all such parts that computer learning and memory can occur. Based on the UIPS concept, non-organismal structures have a number of differing learning and memory pathways. There are three well-researched, non-organismal structures where such pathways concur with the prediction of system-wide learning and memory. In the Gaia Hypothesis, for example, Lovelock (2000, 2007) proposed that the earth itself was a learning structure with a systemic memory. The successful Daisyworld simulation (Wood et al. 2008) indicates how the earth’s learning and memory processes operate through large-scale environmental interactions with organisms and structures on its surface, which, along with other components of this system, can be described in terms of matter and energy pathways. Another non-organismal structure or series of structures where there is a similar concurrence is the Darwin series of robots developed by Edelman, Sporns and colleagues (e.g. Edelman 2007; Krichmar and Reeke 2005; Krichmar 2018; Krichmar et al. 2005; Seth et al. 2005; Sporns 2009). These robots were developed to model human learning and memory processes, but learn and remember by analogous pathways that rely on the interaction of all parts of the robot. Another example can be seen in the computer programs developed by Bentley and others (Bentley et al. 2018; Bentley 2007; Kumar and Bentley 2003) and later developments (Alattas et al. 2019; Doncieux et al. 2015) based on studies of growth and change as seen in evolutionary processes, where system-wide learning and memory processes enable the program to change its internal structures through internal component interaction. All three of these non-organismal structures, or structural types, can be described in terms of the characteristics of a UIPS and can be described as demonstrating system-wide Learning Potential and Memory Potential, although through pathways that would be considered as different to the conventional learning and memory pathways in humans or machines, and with Memory Expression considered over differing time intervals. Some researchers (Balduzzi and Tononi 2008; Tononi 2008; Tononi et al. 2016) consider that mathematical descriptions of the flow of information (defined in terms of uncertainty similar to those of the Shannon description) can be used to explain

106

7 A Broad View of Information Processing Systems

system-wide effects, such as consciousness and lived experience, in a range of organisms and non-organismal structures. Tononi (2008) has indicated further that the concept of consciousness, on the basis of such mathematical description, relates to the capacity of any system to connect and use information, and can be measured in terms of integrated information—a view supported experimentally for human consciousness by Seth and others (Seth et al. 2011). Although this implies that descriptions of system-wide processes such as consciousness can be moulded to fit any system, including machines such as a computer or a camera (Sanders 2012), such systems may be differentiated on the basis of consideration of a conscious system as a single integrated entity that has a large repertoire of integrated states. In contrast, within the UIPS framework, all non-organismal structures and their component UIPSs can be described as having system-wide learning and memory processes, even though these are confined to descriptions of Learning Potential, Memory Potential and Memory Expression. This does not mean, however, that views such as those of Tononi (2008) are incompatible with the UIPS concept, but rather that these views would need to be examined from within the UIPS framework. The description of consciousness in terms of integrated states may lend itself to such an examination. Acknowledgements Parts of this chapter are adapted from Woolcott, G. (2010). Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496), Strasbourg, France: Analytrics.

References Abramson, C. I. (1994). A primer of invertebrate learning. Washington, DC: American Psychological Association. Abramson, C. I., Garrido, D. J., Lawson, A. L., Browne, B. L., & Thomas, D. G. (2002). Bioelectrical potentials of Philodendron cordatum: A new method for investigation of behavior in plants. Psychological Reports, 91, 173–185. Adami, C. (2016). What is information? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2063), 20150230. Alattas, R. J., Patel, S., & Sobh, T. M. (2019). Evolutionary modular robotics: Survey and analysis. Journal of Intelligent and Robotic Systems, 95(3–4), 815–828. Albrecht-Buehler, G. (2005). A long-range attraction between aggregating 3T3 cells mediated by near-infrared light scattering. Proceedings of the National Academy of Sciences of the United States of America, 102(14), 5050–5055. Alpaydin, E. (2016). Machine learning: The new AI. Cambridge, MA: MIT press. Appasani, K. (Ed.). (2017). Optogenetics: From neuronal function to mapping and disease biology. Cambridge, UK: Cambridge University Press. Baars, B. J., & Gage, N. M. (2010). Cognition, brain, and consciousness: Introduction to cognitive neuroscience. Cambridge, MA: Academic Press. Bakhurst, D. (2008). Minds, brains and education. Journal of Philosophy of Education, 42(3–4), 415–432. Balduzzi, D., & Tononi, G. (2008). Integrated information in discrete dynamical systems: Motivation and theoretical framework. Public Library of Science Computational Biology, 4, e1000091.

References

107

Baluska, F., Gagliano, M., & Witzany, G. (Eds.). (2018). Memory and learning in plants. Cham, Switzerland: Springer International Publishing. Barbieri, M. (2016). What is information? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2063), 20150060. Baron-Cohen, S. (2008). Autism and Asperger Syndrome: The facts. Oxford, UK: Oxford University Press. Barth, H., La Mont, K., Lipton, J., & Spelke, E. S. (2005). Abstract number and arithmetic in preschool children. Proceedings of the National Academy of Sciences of the USA, 102(39), 14116– 14121. Ba¸sar, E., & Bullock, T. H. (Eds.). (2012). Brain dynamics: Progress and perspectives (Vol. 2). Cham, Switzerland: Springer Science & Business Media. Bates, M. J. (2005). Information and knowledge: An evolutionary framework for information science. Information Research, 10(4) (paper 239). Bates, M. J. (2006). Fundamental forms of information. Journal of the American Society for Information Science and Technology, 57(8), 1033–1045. Bates, M. J. (2016). Information and the information professions: Selected works of Marcia J. Bates, Vol 1. Berkeley, CA: Ketchikan Press. Baumeister, R. F., Maranges, H. M., & Vohs, K. D. (2018). Human self as information agent: Functioning in a social environment based on shared meanings. Review of General Psychology, 22(1), 36–47. Bawden, D. (2007). Information as self-organized complexity: A unifying viewpoint. Information Research, 12(4), 12–14. Bennett, M., & Hacker, P. (2003). Philosophical foundations of neuroscience. Oxford, UK: Blackwell. Bentley, P. J. (2007). Systemic computation: A model of interacting systems with natural characteristics. In A. Adamatzky, C. Tueuscher, & T. Asai (Eds.), International Journal of Parallel, Emergent and Distributed Systems (IJPEDS), Special issue on emergent computation (Vol. 22, no. 2, pp. 103–121). Oxford, UK: Taylor & Francis. Bentley, P. J., Brundage, M., Häggström, O., & Metzinger, T. (2018). Should we fear artificial intelligence? In-depth Analysis. European Union, Scientific Foresight Unit (STOA), March 2018 (PE 614.547), 1–40. Biedro´n, A., & Pawlak, M. (2016). The interface between research on individual difference variables and teaching practice: The case of cognitive factors and personality. Studies in Second Language Learning and Teaching, 6(3), 395–422. Blakemore, S. J., & Frith, U. (2000). The implications of recent developments in neuroscience for research on teaching and learning. London, UK: Institute of Cognitive Neuroscience. Boisseau, R. P., Vogel, D., & Dussutour, A. (2016). Habituation in non-neural organisms: Evidence from slime moulds. Proceedings of the Royal Society B: Biological Sciences, 283(1829), 20160446. Borges, R. M. (2005). Do plants and animals differ in phenotypic plasticity? Journal of Bioscience, 30, 41–50. Bouchacourt, F., & Buschman, T. J. (2019). A flexible model of working memory. Neuron, 103(1), 147–160. Brooks, D. W., & Shell, D. F. (2006). Working memory, motivation, and teacher-initiated learning. Journal of Science Education and Technology, 15(1), 17–30. Buckland, M. K. (1991). Information as thing. Journal of the American Society for Information Science, 42(5), 351–360. Butterworth, B. (2018). Dyscalculia: From science to education. New York, NY: Routledge. Butterworth, B., & Laurillard, D. (2016). Investigating dyscalculia. In J. C. Horvath, J. M. Lodge, & J. Hattie (Eds.), From the laboratory to the classroom: Translating science of learning for teachers (pp. 172–190). New York, NY: Routledge.

108

7 A Broad View of Information Processing Systems

Cahalane, D. J., & Finlay, B. L. (2017). Brain evolution and development: Allometry of the brain and a realization of the cortex. In S. V. Shepherd (Ed.), The Wiley handbook of evolutionary neuroscience (pp. 388–409). Chichester, UK: Wiley Blackwell. Calero, C. I., Shalom, D. E., Spelke, E. S., & Sigman, M. (2019). Language, gesture, and judgment: Children’s paths to abstract geometry. Journal of Experimental Child Psychology, 177, 70–85. Calvin, W. H. (1996). The cerebral code: Thinking a thought in the mosaics of the mind. Cambridge, MA: MIT Press. Calvin, W. H. (2002). A brain for all seasons: Human evolution and abrupt climate change. Chicago, IL: University of Chicago Press. Calvin, W. H. (2004). A brief history of the mind: From apes to intellect and beyond. Oxford, UK: Oxford University Press. Casadesús, J., & D’Ari, R. (2002). Memory in bacteria and phage. BioEssays, 24, 512–518. Chaitin, G. J. (2011). Complexity, randomness and remarks on physics. In G. J. Chaitin, F. A. Doria, & N. C. A. da Costa (Eds.), Goedel’s way: Exploits into an undecidable world (pp. 31–53). London, UK: CRC Press. Chaitin, G. J. (2012). Life as evolving software. In H. Zenil (Ed.), A computable universe: Understanding computation and exploring nature as computation (pp. 1–23). London, UK: World Scientific. Chung, S.-C., Kwon, J.-H., Lee, H.-W., Tack, G.-R., Lee, B., Yi, J.-H., et al. (2007). Effects of high concentration oxygen administration on n-back task performance and physiological signals. Physiological Measurement, 28, 389–396. Connors, B. W., & Long, M. A. (2004). Electrical synapses in the mammalian brain. Annual Review of Neurosciences, 27, 393–418. Cosmelli, D., & Thompson, E. (2009). Embodiment or envatment? Reflections on the bodily basis for consciousness. In J. Stewart, O. Gapenne, & E. di Paolo (Eds.), Enaction: Towards a new paradigm for cognitive science (pp. 361–385). Cambridge, MA: MIT Press. Costa, S. M. D. S., & Leite, F. C. L. (2018). Theoretical overlaps between communication, information management and knowledge management in information science. Investigación Bibliotecológica: archivonomía, bibliotecología e información, 32(74). Cotterill, R. M. J. (2001). Co-operation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: Possible implications for cognition, consciousness, intelligence and creativity. Progress in Neurobiology, 64, 1–33. Cotterill, R. M. J. (2008). The material world. New York, NY: Cambridge University Press. Cox, R., Schapiro, A. C., & Stickgold, R. (2018). Variability and stability of large-scale cortical oscillation patterns. Network Neuroscience, 2(4), 481–512. Crane, B. T. (2015). Coordinates of human visual and inertial heading perception. PLoS ONE, 10(8), e0135539. Crick, F. (1994). The astonishing hypothesis: The scientific search for the soul. New York, NY: Scribner’s. Crutchfield, J. P. (1994). Is anything ever new? Considering emergence. In G. Cowan, D. Pines, & D. Melzner (Eds.), Santa Fe Institute studies in the sciences of complexity (Vol. 19, pp. 515–515). Reading, MA: Addison-Wesley. Davies, P. (2010). Amazon exclusive author one-on-one. In P. Davies & V. Vedral (Eds.). Retrieved March 2012 from http://www.amazon.com/Decoding-Reality-Universe-Quantum-Information/ dp/0199237697. Davis, O. S. P., Kovas, Y., Harlaar, N., Busfield, P., McMillan, A., Frances, J., et al. (2007). Generalist genes and the internet generation: Etiology of learning abilities by web testing at age 10. Genes, Brain and Behaviour, 7, 455–462. Davis, B., Sumara, D., & Luce-Kapler, R. (2008). Engaging minds: Changing teaching in complex times. New York, NY: Routledge. Deb, P., & Mandal, S. S. (2017). Endocrine disruptors: Mechanism of action and impacts on health and environment. In S. S. Mandal (Ed.), Gene regulation, epigenetics and hormone signaling (pp. 607–638). Weinheim, Germany: Wiley-VCH.

References

109

Degenaar, J., & O’Regan, J. K. (2017). Sensorimotor theory and enactivism. Topoi, 36(3), 393–407. Dehaene, S. (2007). A few steps towards a science of mental life. Mind, Brain, and Education, 1(1), 28–47. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin Viking. Demetriou, A., & Spanoudis, G. (2018). Growing minds: A developmental theory of intelligence, brain, and education. Abingdon, UK: Routledge. Denton, M. J., Dearden, P. K., & Sowerby, S. J. (2003). Physical law not natural selection as the major determinant of biological complexity in the subcellular realm: New support for the pre-darwinian conception of evolution by natural law. Biosystems, 71(3), 297–303. Denver, R. J. (2009). Stress hormones mediate environment-genotype interactions during amphibian development. General and Comparative Endocrinology, 164(1), 20–31. Di Primio, F., Müller, B. S., & Lengeler, J. W. (2000). Minimal cognition in unicellular organisms. In J.-A. Meyer, A. Berthoz, D. Floreano, H. L. Roitblat, & S. W. Wilson (Eds.), Simulation of Adaptive Behavior (SAB) 2000, Proceedings Supplement (pp. 3–12). Honolulu, HI: International Society for Adaptive Behavior. Dodig-Crnkovic, G. (2010). Biological information as natural computation. In J. Vallverdú (Ed.), Thinking machines and the philosophy of computer science: Concepts and principles (pp. 36–52). Hershey, PA: Information Science Reference (an imprint of IGI Global). Doncieux, S., Bredeche, N., Mouret, J. B., & Eiben, A. E. G. (2015). Evolutionary robotics: What, why, and where to. Frontiers in Robotics and AI, 2, 4. Dretske, F. (1981). Knowledge and the flow of information. Cambridge, MA: MIT Press. Dukas, R. (2018). Cognition and learning. In A. Córdoba-Aguilar, D. González-Tokman, & I. González-Santoyo (Eds.), Insect behaviour: From mechanisms to ecological and evolutionary consequences (pp. 257–272). London, UK: Oxford University Press. Dukas, R. (2019). Animal expertise: Mechanisms, ecology and evolution. Animal Behaviour, 147, 199–210. Edelman, G. M. (1970). The structure and function of antibodies. Scientific American, 223(2), 34–42. Edelman, G. M. (1987). Neural darwinism: The theory of neuronal group selection. New York, NY: Basic Books. Edelman, G. M. (1989). The remembered present. New York, NY: Basic Books. Edelman, G. M. (1992). Bright air, brilliant fire. New York, NY: Basic Books. Edelman, G. M. (2007). Learning in and from brain-based devices. Science, 318(5853), 1103–1105. Edelman, G. M., & Gally, J. A. (1968). Antibody structure, diversity, and specificity. Brookhaven Symposium in Biology, 21(2), 328–344. Ellenbogen, J. M., Hu, P. T., Payne, J. D., Titone, D., & Walker, M. P. (2007). Human relational memory requires time and sleep. Proceedings of the National Academy of Sciences of the United States of America, 104(18), 7723–7728. Ericsson, K. A., Nandagopal, K., & Roring, R. W. (2009). An expert-performance approach to the study of giftedness. In L. Shavinina (Ed.), International handbook on giftedness (pp. 129–153). Dordrecht, The Netherlands: Springer. Espy, K. A., & Bull, R. (2005). Inhibitory processes in young children and individual variation in short-term memory. Developmental Neuropsychology, 28(2), 669–688. Farah, M. J. (2010). Mind, brain and education in socioeconomic context. In M. Ferrari & L. Vuletic (Eds.), Developmental interplay of mind, brain, and education: Essays in honor of Robbie Vuletic (pp. 243–256). Dordrecht, The Netherlands: Springer. Favre, M. R., Markram, H., & Markram, K. (2019). Individual differences in sensory sensitivity: Further lessons from an autism model. Cognitive Neuroscience, 10(3), 171–173. Faye, J. (2019). How matter becomes conscious. Cham, Switzerland: Springer International Publishing. Feynman, R. P. (1967). The character of physical laws. Cambridge, MA: MIT Press.

110

7 A Broad View of Information Processing Systems

Fischer, K. W. (2009). Mind, brain, and education: Building a scientific groundwork for learning and teaching. Mind, Brain, and Education, 3(1), 3–16. Francis, R. (2003). Why men won’t ask for directions: The seductions of sociobiology. Princeton, NJ: University Press. Freund, L. (2005). The neurobiology of social interaction and its effect on early learning. In Transcript of a Keynote Address of the Brain, Neuroscience and Education SIG, AERA Conference, Montreal, April 2005. Gagliano, M., Abramson, C. I., & Depczynski, M. (2018). Plants learn and remember: Lets get used to it. Oecologia, 186(1), 29–31. Ghysen, A. (2003). The origin and evolution of the nervous system. International Journal of Developmental Biology, 47(7–8), 555–562. Gilbert, S. F. (2005). Mechanisms for the environmental regulation of gene expression: Ecological aspects of animal development. Journal of Biosciences, 30(1), 65–74. Gibb, R., & Kolb, B. (Eds.). (2018). The neurobiology of brain and behavioral development. London, UK: Academic Press. Godfrey-Smith, P. (2002). Environmental complexity and the evolution of cognition. In R. Sternberg & J. Kaufman (Eds.), The evolution of intelligence (pp. 233–249). Mahwah, NJ: Lawrence Erlbaum. Godfrey-Smith, P. (2007a). Information in biology. In D. Hull & M. Ruse (Eds.), The Cambridge companion to the philosophy of biology (pp. 103–119). New York, NY: Cambridge University Press. Godfrey-Smith, P. (2007b). Environmental complexity and the evolution of cognition. In R. Sternberg & J. Kaufman (Eds.), The evolution of intelligence (pp. 233–249). Mahwah, NJ: Lawrence Erlbaum. Godfrey-Smith, P. (2010). It got eaten. London Review of Books, 32(13), 29–30. Goswami, U. (2008). Cognitive development: The learning brain. Philadelphia, PA: Psychology Press of Taylor and Francis. Grandin, T. (2006). Thinking in pictures and other reports from my life with autism. New York, NY: Vintage, Random House. Grandin, T., & Johnson, C. (2005). Animals in translation. New York, NY: Harcourt Books. Greenough, W. T. (1975). Experiential modification of the developing brain. American Scientist, 63(1), 37–46. Gribbin, J. (1994). In the beginning: The birth of the living universe. London, UK: Penguin Books. Grillner, S. (2003). The motor infrastructure: From ion channels to neuronal networks. Nature Reviews Neuroscience, 4, 573–586. Grobstein, P. (1994). Variability in brain function and behavior. In V. S. Ramachandran (Ed.), The encyclopedia of human behavior (Vol. 4, pp. 447–458). San Diego, CA: Academic Press. Gubbels, J., Segers, E., & Verhoeven, L. (2018). How children’s intellectual profiles relate to their cognitive, socio-emotional, and academic functioning. High Ability Studies, 29(2), 149–168. Haier, R. J. (2016). The neuroscience of intelligence. Cambridge, MA: Cambridge University Press. Haier, R. J., & Jung, R. E. (2008). Brain imaging studies of intelligence and creativity: What is the picture for education? Roeper Review, 30(3), 171–180. Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2005). The neuroanatomy of general intelligence: Sex matters. NeuroImage, 25(1), 320–327. Hansel, C. (2019). Deregulation of synaptic plasticity in autism. Neuroscience Letters, 688, 58–61. Happé, F., & Vital, P. (2009). What aspects of autism predispose to talent. Philosophical Transactions of the Royal Society, B, 364, 1351–1357. Hari, R., Henriksson, L., Malinen, S., & Parkkonen, L. (2015). Centrality of social interaction in human brain function. Neuron, 88(1), 181–193. Hernandez, M. E., & Gore, A. C. (2017). Endocrine disruptors: Chemical contaminants—A toxic mixture for neurodevelopment. Nature Reviews Endocrinology, 13(6), 322. Hodges, D. A., & Gruhn, W. (2018). Implications of neurosciences and brain research for music teaching and learning. In G. E. McPherson & G. F. Welch (Eds.), Music and music education in

References

111

people’s lives: An Oxford handbook of music education (pp. 206–226). New York, NY: Oxford University Press. Howard-Jones, P. A. (2008). Philosophical challenges for researchers at the interface between neuroscience and education. Journal of the Philosophy of Education, 42(3–4), 361–380. Immordino-Yang, M. H., Darling-Hammond, L., & Krone, C. (2018). The brain basis for integrated social, emotional, and academic development. Washington, DC: National Commission on Social, Emotional, and Academic Development. Inda, M. C., Muravieva, E. V., & Alberini, C. M. (2011). Memory retrieval and the passage of time: From reconsolidation and strengthening to extinction. Journal of Neuroscience, 31(5), 1635–1643. Jablonka, E. (2002). Information: Its interpretation, its inheritance and its sharing. Philosophy of Science, 69, 578–605. Janich, P. (2018). What is information? (trans. Hayot, E. & Pao, L.). Minneapolis, MN: University of Minnesota Press. Järvilehto, T. (1998a). The theory of the organism-environment system: I. Description of the theory. Integrative Psychological and Behavioural Science, 33(4), 317–330. Järvilehto, T. (1998b). The theory of the organism-environment system: II. Significance of nervous activity in the organism-environment system. Integrative Psychological and Behavioural Science, 33(4), 331–338. Järvilehto, T. (1999). The theory of the organism-environment system: III. Role of efferent influences on receptors in the formation of knowledge. Integrative Psychological and Behavioural Science, 34(2), 90–100. Järvilehto, T. (2000). The theory of the organism-environment system: IV. The problem of mental activity and consciousness. Integrative Psychological and Behavioural Science, 35(10), 35–57. Järvilehto, T. (2009). The theory of the organism-environment system as a basis of experimental work in psychology. Ecological Psychology, 21(2), 112–120. Jones, N., Riby, L. M., & Smith, M. A. (2018). Glucose regulation and face recognition deficits in older adults: The role of attention. Aging, Neuropsychology, and Cognition, 25(5), 673–694. Kandel, E. R. (2009). The biology of memory: A forty-year perspective. Journal of Neuroscience, 29(41), 12748–12756. Kennedy, J. E. (2011). Information in life, consciousness, quantum physics, and paranormal phenomena. Journal of Parapsychology, 75(1), 15. Kilian, A. E., & Müller, B. S. (2002). Life-like learning in technical artefacts: Biochemical vs. neuronal mechanisms. In Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’02), November 18–22, Singapore (Vol. 1, pp. 296–300). Retrieved March 2006 from http://en.scientificcommons.org/20339282. Koltay, T. (2017). The bright side of information: Ways of mitigating information overload. Journal of Documentation, 73(4), 767–775. Kovas, Y., & Tosto, M. G. (2017). Generalist genes and developmental psychopathology. In L. Centifanti & D. M. Williams (Eds.), The Wiley handbook of developmental psychopathology (pp. 259–271). New York, NY: Wiley. Krichmar, J. L. (2018). Neurorobotics—A thriving community and a promising pathway toward intelligent cognitive robots. Frontiers in Neurorobotics, 12, 42. Krichmar, J. L., & Reeke, G. N., Jr. (2005). The Darwin brain-based automata: Synthetic neural models and real-world devices. In G. N. Reeke Jr., R. R. Poznanski, K. A. Lindsay, J. R. Rosenberg, & O. Sporns (Eds.), Modeling in the neurosciences: From biological systems to neuromimetic robotics (pp. 613–638). Boca Raton, FL: Taylor & Francis. Krichmar, J. L., Nitz, D. A., Gally, J. A., & Edelman, G. M. (2005). Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task. Proceedings of the National Academy of Sciences of the United States of America, 102(6), 2111–2116. Kumar, S., & Bentley, P.J. (2003). Biologically plausible evolutionary development. In A. Tyrrell, P. Haddow, & J. Torresen (Eds.), Proceedings of the fifth international conference on evolvable systems: From biology to hardware (pp. 57–68). Berlin, Germany: Springer, LNCS 2606.

112

7 A Broad View of Information Processing Systems

Labi, V., & Erlacher, M. (2015). How cell death shapes cancer. Cell Death and Disease, 6(3), e1675–e1675. Lachman, R., Lachman, J. L., & Butterfield, E. C. (1979). Cognitive psychology and information processing: An introduction. Hillsdale, NJ: Lawrence Erlbaum. Lakoff, G., & Johnson, M. (1999). Metaphors we live by. New York, NY: Basic Books. Langlois, R. (1983). Systems theory, knowledge and the social sciences. In F. Machlup & U. Mansfield (Eds.), The study of information: Interdisciplinary messages (pp. 581–600). New York, NY: Wiley. Lean, O. M. (2014). Getting the most out of Shannon information. Biology and Philosophy, 29(3), 395–413. Lean, O. M. (2016). Biological information. Doctoral dissertation, University of Bristol, Bristol, UK. LeDoux, J. E. (1996). The emotional brain: The mysterious underpinnings of emotional life. New York, NY: Touchstone. Lieberman, O. J., McGuirt, A. F., Tang, G., & Sulzer, D. (2019). Roles for neuronal and glial autophagy in synaptic pruning during development. Neurobiology of Disease, 122, 49–63. Lipton, J. S., & Spelke, E. S. (2003). Origins of number sense: Large number discrimination in human infants. Psychological Science, 14, 396–401. Liu, S., Brooks, N. B., & Spelke, E. S. (2019). Origins of the concepts cause, cost, and goal in prereaching infants. Proceedings of the National Academy of Sciences, 116(36), 17747–17752. Lloyd, A. (2010a). Framing information literacy as information practice: Site ontology and practice theory. Journal of Documentation, 66(2), 245–258. Lloyd, A. (2010b). Information literacy landscapes: Information literacy in education, workplace and everyday contexts. Oxford, UK: Chandos Publishing. Lloyd, S. (2006). Programming the universe: A quantum computer scientist takes on the cosmos. New York, NY: Alfred A. Knopf. Lovelock, J. (1995). The ages of Gaia: A biography of our living earth. Oxford, UK: Oxford University Press. Lovelock, J. (2000). Homage to Gaia: The life of an independent scientist. Oxford, UK: Oxford University Press. Lovelock, J. (2007). The revenge of Gaia: Why the earth is fighting back—And how we can save humanity. Santa Barbara, CA: Allen Lane. Lovelock, J., & Margulis, L. (1996). The Gaia hypothesis. Retrieved June 2000 from http://www. mountainman.com.au/gaia.html. Luo, T., & Pan, Y. (2016). Information as causality: An approach to a general theory of information. Journal of Information Science, 42(6), 821–832. Machlup, F. (1983). Semantic quirks in studies of information. In F. Machlup & U. Mansfield (Eds.), The study of information: Interdisciplinary messages (pp. 641–671). New York, NY: Wiley. Margulies, C., Tully, T., & Dubnau, J. (2005). Deconstructing memory in Drosophila. Current Biology, 15, R700–R713. Marin, I., & Kipnis, J. (2013). Learning and memory…and the immune system. Learning & Memory, 20(10), 601–606. Markram, K., & Markram, H. (2010). The intense world theory—A unifying theory of the neurobiology of autism. Frontiers in Human Neuroscience, 4, 224. Marshall, P., & Bredy, T. W. (2016). Cognitive neuroepigenetics: The next evolution in our understanding of the molecular mechanisms underlying learning and memory? NPJ Science of Learning, 1, 16014. Marty, N., Dallaporta, M., & Thorens, B. (2007). Brain glucose sensing, counterregulation, and energy homeostasis. Physiology, 22(4), 241–251. Maturana, H. R., & Varela, F. J. (1992). The tree of knowledge: The biological roots of human understanding. Revised edition. Boston, MA: Shambhala. McCormick, S. D., & Bradshaw, D. (2006). Hormonal control of salt and water balance in vertebrates. General and Comparative Endocrinology, 147(1), 3–8.

References

113

Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141–144. Miller, E. K., & Buschman, T. J. (2007). Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 315(5820), 1860–1862. Mingers, J., & Standing, C. (2014). What is information such that there can be information systems? Kent Business School Working Papers. Canterbury, UK: University of Kent. Mingers, J., & Standing, C. (2018). What is information? Toward a theory of information as objective and veridical. Journal of Information Technology, 33(2), 85–104. Mitchell, M. (1998). A complex-systems perspective on the “computation vs. dynamics” debate in cognitive science. In M. A. Gernsbacher & S. J. Derry (Eds.), Proceedings of the 20th Annual Conference of the Cognitive Science Society—Cogsci98 (pp. 710–715). Hillsdale, NJ: Lawrence Erlbaum Associates. Moore, S. R., & Depue, R. A. (2016). Neurobehavioral foundation of environmental reactivity. Psychological Bulletin, 142(2), 107. Morton, J., & Frith, U. (1995). Causal modelling: A structural approach to developmental psychopathology. In D. Cicchetti & D. Cohen (Eds.), Manual of developmental psychopathology (pp. 357–362). New York, NY: Wiley. Mulligan, J., & Woolcott, G. (2015). What lies beneath? The conceptual connectivity underpinning whole number arithmetic. In X. Sun, B. Kaur, & J. Novotná (Eds.), The twenty-third ICMI study: Primary mathematics study on whole numbers (pp. 220–228). Macao, China: University of Macau. Mulligan, J., Woolcott, G., Mitchelmore, M., & Davis, B. (2018). Connecting mathematics learning through spatial reasoning. Mathematics Education Research Journal, 30(1), 77–87. Nakano, M., Yoshioka, H., Ohnishi, K., Hikichi, Y., & Kiba, A. (2015). Cell death-inducing stresses are required for defense activation in DS1-phosphatidic acid phosphatase-silenced Nicotiana benthamiana. Journal of Plant Physiology, 184, 15–19. Nolte, M., Gal, E., Markram, H., & Reimann, M. W. (2019). Impact of higher-order network structure on emergent cortical activity. BioRxiv, 802074. https://doi.org/10.1101/802074. Opris, I., & Casanova, M. F. (2017). The physics of the mind and brain disorders. Cham, Switzerland: Springer International Publishing. O’Regan, J. K., Myin, E., & Noë, A. (2005). Sensory consciousness explained (better) in terms of ‘corporality’ and ‘alerting capacity’. Phenomenology and the Cognitive Sciences, 4(4), 369–387. O’Reilly, R. C., & Munakata, Y. (2000). Computational explorations in cognitive neuroscience: Understanding the mind by simulating the brain. Cambridge, MA: MIT Press. Organization for Economic Cooperation and Development (OECD). (2004). Learning sciences and brain research: 2nd literacy and numeracy networks meeting, 2004. Paris, France: OECD Publications. Parker, E. B. (1974). Information and society. In C. A. Cuadra & M. J. Bates (Eds.), Library and information service needs of the nation: Proceedings of a conference on the needs of occupational, ethnic and other groups in the United States (pp. 9–50). Washington, DC: U.S.G.P.O.. Perleth, C., & Wilde, A. (2009). Developmental trajectories of giftedness in children. In L. V. Shavinina (Ed.), International handbook on giftedness (pp. 319–335). Dordrecht, The Netherlands: Springer. Piccinini, G. (2018). Computation and representation in cognitive neuroscience. Minds and Machines, 28(1), 1–6. Piccinini, G., & Scarantino, A. (2010). Computation vs. information processing: Why their difference matters to cognitive science. Studies in History and Philosophy of Science, 41, 237–246. Pigliucci, M. (2011). What about information? EMBO reports, 12(92). https://doi.org/10.1038/ embor.2010.213. Plomin, R., & Kovas, Y. (2005). Generalist genes and learning disabilities. Psychological Bulletin, 131(4), 592–617. Plomin, R., Kovas, Y., & Haworth, C. M. (2007). Generalist genes: Genetic links between brain, mind, and education. Mind, Brain, and Education, 1(1), 11–19.

114

7 A Broad View of Information Processing Systems

Postle, B. R. (2015). Neural bases of the short-term retention of visual information. In P. Jolicoeur, C. Lefebvre, & J. Martinez-Trujillo (Eds.), Mechanisms of sensory working memory: Attention and performance XXV (pp. 43–58). London, UK: Academic Press. Pratt, A. D. (1977). The information of the image: A model of the communications process. Libri, 27(3), 204–220. Randler, C., & Demirhan, E. (2016). Special issue on achievement, chronotype and circadian patterns of cognition. International Online Journal of Educational Sciences, 8(5), 1–3. Reading, A. (2006). The biological nature of meaningful information. Biological Theory, 1(3), 243–249. Riby, L. M., Meikle, A., & Glover, C. (2004). The effects of age, glucose ingestion and glucoregulatory control on episodic memory. Age and Ageing, 33, 483–487. Rieke, H., Roxin, A., Madruga, S., & Solla, S. A. (2007). Multiple attractors, long chaotic transients, and failure in small-world networks of excitable neurons. Chaos, 17, 026110. Routtenberg, A., & Rekart, J. L. (2005). Post-translation modification as the substrate for longlasting memory. Trends in Neurosciences, 28(1), 12–19. Roy, A., Perlovsky, L., Besold, T. R., Weng, J., & Edwards, J. C. (2018). Representation in the brain. Frontiers in Psychology, 9, 1410. Rudrauf, D., Lutz, A., Cosmelli, D., Lachaux, J. P., & Le Van Quyen, M. (2003). From autopoiesis to neurophenomenology: Francisco Varela’s exploration of the biophysics of being. Biological Research, 36, 27–65. Sanders, L. (2012). Enriched with information: New theory doesn’t limit consciousness to the brain. Science News. Retrieved March 2012 from http://www.sciencenews.org/view/feature/id/338663/ title/Enriched_with_Information. Sarathy, V. (2018). Real world problem-solving. Frontiers in Human Neuroscience, 12, 261. https:// doi.org/10.3389/fnhum.2018.00261. Schement, J. R. (2017). Communication and information. In B. D. Ruben (Ed.), Between communication and information (pp. 3–33). New York, NY: Routledge. Seth, A. K., Sporns, O., & Krichmar, J. L. (2005). Neurobotic models in neuroscience and neuroinformatics. NeuroInformatics, 3(3), 167–170. Seth, A. K., Prescott, T. J., & Bryson, J. J. (Eds.) (2011). Modelling natural action selection. New York, NY: Cambridge University Press. Shannon, C. E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27(279–423), 623–656. Shannon, C. E., & Weaver, W. (1963). The mathematical theory of communication. Urbana, IL: University of Illinois Press. Shi, M., Kumar, S. R., Motajo, O., Kretschmer, F., Mu, X., & Badea, T. C. (2013). Genetic interactions between Brn3 transcription factors in retinal ganglion cell type specification. PLoS ONE, 8(10), e76347. Shell, D. F., Brooks, D. W., Trainin, G., Wilson, K. M., Kauffman, D. F., & Herr, L. M. (2010). The unified learning model: How motivational, cognitive, and neurobiological sciences inform best teaching practices. Dordrecht, The Netherlands: Springer. Sherwin, W. B. (2015). Genes are information, so information theory is coming to the aid of evolutionary biology. Molecular Ecology Resources, 15(6), 1259–1261. Sholle, D. (1999). What is information? The flow of bits and the control of chaos. MIT Communications Forum, paper posted 31 October, 1999. Retrieved in April 2008 from http://web.mit.edu/ comm-forum/papers/sholle.html. Siemens, G. (2017). Connectivism. In R. West (Ed.), Foundations of learning and instructional design technology. Montreal, Canada: Pressbooks. Sinclaire-Harding, L., Vuillier, L., & Whitebread, D. (2018). Neuroscience and early childhood education. In M. Fleer & B. van Oers (Eds.), International handbook of early childhood education (pp. 335–361). Dordrecht, The Netherlands: Springer.

References

115

Slijepcevic, P. (2019). Principles of information processing and natural learning in biological systems. Journal for General Philosophy of Science, 1–19. https://doi.org/10.1007/s10838-01909471-9. Sloman, A. (2011). What’s information, for an organism or intelligent machine? How can a machine or organism mean? In G. Dodig-Crnkovic & M. Burgin (Eds.), Information and computation: Essays on scientific and philosophical understanding of foundations of information and computation (pp. 393–438). Singapore: World Scientific. Snyder, A. W., Bossomaier, T., & Mitchell, D. J. (2004). Concept formation: ‘Object’ attributes dynamically inhibited from conscious awareness. Journal of Integrative Neuroscience, 3(1), 31– 46. Spencer, R. M., Walker, M. P., & Stickgold, R. (2017). Sleep and memory consolidation. In S. Chokroverty (Ed.), Sleep disorders medicine (pp. 205–223). New York, NY: Springer. Sporns, O. (2009). From complex networks to intelligent systems. In B. Sendhoff, E. Körner, O. Sporns, H. Ritter, & K. Doya (Eds.), Creating brain-like intelligence: From basic principles to complex intelligent systems (pp. 15–30). Berlin, Germany: Springer. Sporns, O. (2010). Networks of the brain. Cambridge, MA: MIT Press. Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT press. Squire, L. R., & Kandel, E. R. (2008). Memory: From mind to molecules (2nd ed.). Greenwood Village, CA: Roberts & Company. Stanley, S. M. (1996). Children of the ice age: How a global catastrophe allowed humans to evolve. New York, NY: Harmony Books. Stickgold, R., & Walker, M. P. (2005). Memory consolidation and reconsolidation: What is the role of sleep? Trends in Neuroscience, 28(8), 408–415. Stonier, T. (1997). Information and meaning: An evolutionary perspective. London, UK: Springer. Swanson, H. L. (2017). Verbal and visual-spatial working memory: What develops over a life span? Developmental Psychology, 53(5), 971–995. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Explorations in the learning sciences, instructional systems and performance technologies. Dordrecht, The Netherlands: Springer. Sweller, J., van Merriënboer, J., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296. Tang, S. K., & Marshall, W. F. (2018). Cell learning. Current Biology, 28(20), R1180–R1184. Tassinary, L. G., Cacioppo, J. T., & Vanman, E. J. (2017). The somatic system. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Cambridge handbooks in psychology. Handbook of psychophysiology (pp. 151–182). New York, NY: Cambridge University Press. Tegmark, M., & Wheeler, J. A. (2001). 100 years of quantum. Scientific American, February 2001, 68–75. Thompson, E. (2004). Life and mind: From autopoieses to neurophenomenology. A tribute to Francis Varela. Phenomenology and the Cognitive Sciences, 3, 381–398. Thompson, E. (2007). Mind in life: Biology, phenomenology, and the sciences of mind. Cambridge, MA: Harvard University Press. Thornton, A., & Lukas, D. (2012). Individual variation in cognitive performance: Developmental and evolutionary perspectives. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1603), 2773–2783. Todd, P. M., & Miller, G. F. (1991). Exploring adaptive agency II: Simulating the evolution of associative learning. In J.-A. Meyer & S. W. Wilson (Eds.), From animals to animats: Proceedings of the First International Conference on Simulation of Adaptive Behaviour (pp. 306–315). Cambridge, MA: MIT Press. Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harvard University Press. Tomasello, M. (2014). A natural history of human thinking. Cambridge, MA: Harvard University Press.

116

7 A Broad View of Information Processing Systems

Tonegawa, S., Nakazawa, K., & Wilson, M. A. (2003). Genetic neuroscience of mammalian learning and memory. Philosophical Transactions of the Royal Society of London, B Biological Sciences, 358, 787–795. Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(42). https://doi.org/10.1186/1471-2202-5-42. Tononi, G. (2008). Consciousness as integrated information: A provisional manifesto. The Biological Bulletin, 215, 216–242. Tononi, G., Edelman, G. M., & Sporns, O. (1998). Complexity and coherency: Integrating information in the brain. Trends in Cognitive Sciences, 2, 474–484. Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461. Topper, V. Y., Reilly, M. P., Wagner, L. M., Thompson, L. M., Gillette, R., Crews, D., et al. (2019). Social and neuromolecular phenotypes are programmed by prenatal exposures to endocrinedisrupting chemicals. Molecular and Cellular Endocrinology, 479, 133–146. Tosches, M. A., & Laurent, G. (2019). Evolution of neuronal identity in the cerebral cortex. Current Opinion in Neurobiology, 56, 199–208. Trewavas, A. (2016). Intelligence, cognition, and language of green plants. Frontiers in Psychology, 7, 588. Utecht, J., & Keller, D. (2019). Becoming relevant again: Applying connectivism learning theory to today’s classrooms. Critical Questions in Education, 10(2), 107–119. van Duijn, M. (2017). Phylogenetic origins of biological cognition: Convergent patterns in the early evolution of learning. Interface Focus, 7(3), 20160158. Van Schaik, C. P. (2006). Why are some animals so smart? Scientific American, 294(4), 48–55. Varela, F. J. (1979). Principles of biological autonomy. New York, NY: Elsevier. Varela, F., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human behaviour. Cambridge, MA: MIT Press. Vedral, V. (2010). Decoding reality: The universe as quantum information. Oxford, UK: Oxford University Press. Walker, M. P. (2008). Cognitive consequences of sleep and sleep loss. Sleep Medicine, 9, S29–S34. Williams, G. C. (1992). Natural selection: Levels, domains, and challenges. Oxford, UK: Oxford University Press. Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media. Wood, A. J., Ackland, G. J., Dyke, J. G., Williams, H. T. P., & Lenton, T. M. (2008). “Daisyworld”: A review. Reviews of Geophysics, 48, RG1001. Woolcott, G. (2010). Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496). Strasbourg, France: Analytics. Woolcott, G. (2011). A broad view of education and teaching based in educational neuroscience. International Journal for Cross-Disciplinary Subjects in Education, Special Issue, 1(1), 601–606. Woolcott, G. (2013). Giftedness as cultural accumulation: An information processing perspective. High Ability Studies, 24(2), 153–170. Woolcott, G. (2016). Technology and human cultural accumulation: The role of emotion. In S. Tettegah & R. E. Ferdig (Eds.), Emotions, technology, and learning (pp. 243–263). London, UK: Academic Press. Zuo, X. N., He, Y., Betzel, R. F., Colcombe, S., Sporns, O., & Milham, M. P. (2017). Human connectomics across the life span. Trends in Cognitive Sciences, 21(1), 32–45. Zurek, W. H. (Ed.). (2018). Complexity, entropy and the physics of information. New York, NY: CRC Press.

Part III

Utilising the Broad Framework to Examine Educational Theories and Practices

Commonalities in environmental information processing were used in Part II as the basis for a novel description of a Universal Information Processing System (UIPS), based on the assumption that Universal Information as matter and energy is the basis of the environmental interactions observed in the universe. This broad system was used as a basis for an overarching framework that describes learning and memory processes in a very broad sense. Part III discusses the potential of this framework in examining educational theories and practices, with a focus on the examination of the educational principles of Cognitive Load Theory (CLT) developed from studies of natural information processing systems. The descriptions of learning and memory developed within this framework are used to examine the potential for educational theories and practices to be integrated scientifically, and it is argued that educational theories that can be expressed in terms of connectivity of matter and energy pathways can also be integrated. As an illustrative example, Part III outlines how the educational principles described in CLT can be expressed in terms of the UIPS framework and argues that these principles may be examined through modern scientific methodologies, procedures and protocols. Part III also examines the idea of generalised educational principles developed within the broad framework, and the principles outlined include generalised versions of the educational principles of CLT and two novel educational principles. Such principles are useful in examining learning and memory more generally, as well as have specific applications in human education. Finally, Part III explores the potential use of the framework in developing generalised cognitive models that can be applied to educational theories and practices, in particular, those that are based on network theory and complexity theory. The UIPS concept is based on an assumption that all interactions in the universe are matter and energy interactions—an assumption that underwrites modern scientific studies, including studies in integrative biology that have examined environmental interactions and human learning and memory processes. Such conventionally described processes can be embraced within the UIPS framework of Learning Potential, Memory Potential and Memory Expression, but, as the previous section

118

Part III: Utilising the Broad Framework to Examine Educational …

has argued, the broader conceptualisation of UIPSs may cover more of the informational transactions of the human organism involved in these processes than do conventional descriptions. Consideration of the human as a UIPS also indicates that processes described broadly within the UIPS framework are system-wide and involve interactions of components within the entire UIPS as well as interactions with the environment. The description of the human organism as a UIPS, with its broader description of learning and memory interactions, may be useful, therefore, in examining educational theories and practices. The first chapter of Part III (Chap. 8) investigates whether the learning and memory interactions considered in educational theories can be expressed in terms of matter and energy pathways, and, hence, whether such theories can be integrated scientifically through consideration of such pathways within the UIPS framework. Since educational theories are based on differing assumptions about the nature of learning and memory, with resultant difficulties in communication across theoretical perspectives, the consideration of educational theories in terms of the UIPS framework is useful in regards to their application to teaching practices that require a consistent theoretical background. Scientific integration of educational theories through the single theoretical framework provided by the UIPS also offers the opportunity for quantitative measurement based on established scientific practices. Such integration within the UIPS framework may go some way, therefore, towards resolving some of the issues related to seemingly non-comparable and incommensurable educational theories, as well as resolving some of the widely differing and sometimes highly critical reactions to modern theoretical research perspectives. As an example of illustrating how the UIPS framework can be applied to the examination of educational theories and their potential scientific integration, this chapter focuses on CLT. This theory was developed initially from concepts related to the information pathways and information processing systems described in cognitive psychology (generally in relation to human knowledge), but the theory was re-evaluated and redeveloped using comparisons to, and analogies of, the natural information processing systems of human evolution and cognition. This chapter focuses specifically on an examination within the UIPS framework of the educational principles of CLT since the natural information processing systems in which these principles are based can be viewed in scientific terms, with the structure and function of the cognitive system arguably being a result of environmental interaction of an evolutionary system based on the interactions of DNA. This examination is used to determine whether these principles, which form the intellectual basis for CLT, can be integrated scientifically within the UIPS framework. The last chapter (Chap. 9) examines the potential applications of the UIPS framework in the development of generalised educational principles that may be useful in examining human learning and memory processes and in comparing those processes with those of other organisms and non-organismal structures. Several such principles are outlined, including generalised versions of the educational principles of CLT as well as two novel educational principles generated from considering learning and memory processes as system-wide within a UIPS. Such generalised principles are useful in extending empirical measurement to studies of learning and memory in

Part III: Utilising the Broad Framework to Examine Educational …

119

organismal as well as non-organismal UIPSs, such as computers and robots, while at the same time having applications specifically in human education and teaching. This last section examines the potential for the UIPS framework to be utilised in the analysis of non-linear and complex, rather than linear, information pathways as seen in the studies of network theory and complexity theory that have been applied in recent times to education. Since the UIPS concept is relational, linking structures and environment dynamically in a complex way (like the modern concepts of embodiment and enactivism), there may be potential in considering complexity theory through a UIPS lens.

Chapter 8

The Universal Information Processing System and Educational Theories and Practices

8.1 Educational Theories and the Assumptions of Science In a general sense, educational theories and concepts may be considered as based on descriptions of one or several components that can be viewed either singly or together as emergent systems, where the sum of the elements of that system constitutes a whole entity with its own distinct qualities. Consider, for example, the modern learning or education theories erected on large, sometimes theoretical and sometimes measurable components, such as communities, underlying psychological dynamics and behavioural relationships (Kop and Hill 2008) or memory capacity, task performance and zone of proximal development (Schnotz and Kürschner 2007). Each of these components is arguably a whole entity with its own distinct qualities and, although not all researchers would refer to each of them as emergent systems, recent research in complex systems in education indicates that many of these components may be treated effectively as at least elements of complex systems (e.g., Davis et al. 2008; Mowat and Davis 2010) which are necessarily emergent. Not all educational theories and concepts, however, can be compared using such components or systems, emergent or otherwise, since these components and systems may be based in differing basic assumptions as to what constitutes the basic components or system boundaries. In scientific studies, including studies in integrative biology, emergent systems, with their own distinct qualities, form the basis of many theories (e.g. theories related to cognition) (Casanova 2010; Faye 2019). The description of such emergent systems has developed in part because it is not usually considered useful to describe a spatiotemporal entity in terms of the qualities of its smallest constituents but rather in terms of the sometimes-expanded number of qualities that result from their interaction. An advantage in studying emergent systems in most science-based disciplines, as in integrative biology, is that many such systems are based in the same scientific assumptions and, therefore, may be comparable where emergent systems in educational theories may not be (Mitchell 2009). In the broad description of a Universal Information Processing System (UIPS) based in a matter and energy universe, any UIPS can be considered as emergent if © Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_8

121

122

8 The Universal Information Processing System and Educational …

it consists of more than one matter or energy component. While the concept of the sum of the elements of a system as constituting a whole entity is a useful part of the UIPS concept, there is no requirement for such emergent systems to be based in a subjective determination of what a distinct quality might be. Rather, the UIPS concept of emergence relies on observing separate entities of energy and matter as Memory Expression. Taking this approach does not mean, however, that some conventionally termed emergent systems cannot be accommodated as subgroupings of the emergent UIPS but rather that they can be accommodated in the same way that information as organised or patterned matter and energy can be accommodated as a subgrouping of information as matter and energy in a UIPS. As such, the UIPS concept may be useful in describing interactions of some of the components of the emergent systems in which educational theories are based, provided that such systems can be re-evaluated and reconstructed so that they can be described in scientific terms as the matter and energy pathways used as a basis for the UIPS concept. While this may seem like a complicated task, there have been some efforts made in recent times in re-evaluating and reconstructing in scientific terms some influential educational theories and concepts. This has been done, in fact, so that these theories may be considered as reconciled, or in a position to be reconciled, with modern science and its empirical methodologies. The developmental theories of Piaget (1928), for example, that grew from studies of the development of Piaget’s own children and that were later applied to education, were not based on scientific assumptions but have been re-evaluated and reconstructed recently as neo-Piagetian theory on the basis of modern studies in integrative biology and cognitive psychology (Demetriou et al. 2016; Epstein 1986; Huitt and Hummel 2003; Young 2019). In a similar way, there have been attempts to bring Bloom’s Taxonomy (Bloom 1956, 1984) into the fold of science (Anderson et al. 2001; Lau et al. 2018; Lee et al. 2016) since this educational concept forms the basis for teaching practices and curricula in many modern industrialised societies. Such re-evaluation and reconstruction may go some way towards enabling the expression of such educational theories and concepts in terms of the matter and energy interactions used as a basis for the UIPS concept. Matter and energy interactions may be more readily recognised in those educational theories that have explored biological conceptualisations of information pathways and information processing systems related to human interaction with the environment and the storage of knowledge as memories (Sweller 2004, 2007; Sylwester 1995)—even where the descriptions of information and information processing systems may vary. Scientific assumptions may be readily recognised in those educational theories that have some basis in modern scientific disciplines, such as integrative biology and, in particular, neuroscience (Calero et al. 2018; Fischer et al. 2010; Goswami 2008; Sigman et al. 2014; Tokuhama-Espinosa 2018, 2019). Potentially, therefore, it may be possible to describe the emergent systems that form the basis of some of these theories in terms of the matter and energy pathways of the UIPS framework.

8.2 Cognitive Load Theory and Scientific Integration

123

8.2 Cognitive Load Theory and Scientific Integration Sweller’s cognitive load theory (CLT; Sweller 1988, 1994; Sweller et al. 2011) is used here as a specific example to illustrate how educational theories may be integrated scientifically through examination within the UIPS framework. There were several reasons for selecting CLT for this examination, one being that this theory may potentially be adapted or expanded to accommodate concepts from other educational theories, such as the adaptations suggested for CLT by Schnotz and Kürschner (2007) to accommodate Vygotsky’s concept of zones of proximal development (Vygotsky 1978, 1986) or the concept of implicit learning and other types of unconscious learning (Beston et al. 2018; Kuldas et al. 2015). The selection of CLT was also partly because the theory is based on connectionist models of information processing that have been re-evaluated in relation to scientifically based natural information processing systems and partly because, as Sweller has indicated (Sweller 2010), there has been some recent reconsideration of CLT with regard to research from modern educational, psychological and scientific disciplines. Additionally, CLT has become one of the most cited learning theories in modern educational design (see Bruer 2016). It has been suggested, in fact, that the scientific integration of CLT and other educational theories may be sought in combinations of approaches from such modern research disciplines. In particular, this includes approaches from the social and behavioural sciences combined with those from the natural sciences (Moreno 2010), with arguably some basis in approaches from modern integrative biology (Gulson and Baker 2018; Haye et al. 2018; Sylwester 1995; Fischer et al. 2010). The focus in the examination here, therefore, is on the educational principles that underpin CLT (Sweller 2004, 2007), since these have been developed from studies that combine research from integrative biology and cognitive psychology in order to examine educational issues. There is potential, however, for the future examination within the UIPS framework of concepts related to the cognitive architecture assumed for CLT and other concepts developed from cognitive psychology and applied to education, such as cognitive load and mental effort. The educational principles developed and elaborated by Sweller and colleagues (Sweller 2004, 2007; Sweller et al. 2011) have allowed the expansion of CLT to incorporate critical human cognitive abilities, such as problem-solving and planning, and form an intellectual framework that underpins the development of instructional design within CLT. These educational principles were developed from the consideration of the two natural information processing systems: human cognition and human evolution (which Sweller refers to as natural because they are found in the natural world). Both of these systems can be described in scientific terms within modern integrative biology (Woolcott 2011). The two systems are linked since the structure and function of the human cognitive system develop partly through environmental interactions of the DNA that forms the physico-chemical basis of the evolutionary system. This does not mean, however, that these two systems provide scientific integration for the related educational principles since these principles are

124

8 The Universal Information Processing System and Educational …

not linked directly to these two systems but are linked by analogy and comparison to processing within these systems. Examining these principles within the UIPS framework, however, offers a way of describing the analogous and comparative processes in terms of UIPS processes that are based in scientific assumptions and offers a way of scientifically integrating those principles. Within these natural information processing systems, the analogous processes involve the role of DNA as a large information storage template for evolution; this is recognised as similar to that of long-term memory (LTM) as a large information store for cognition. Based in this recognition, Sweller (2004, 2007) has developed the conceptualisation of a large, sometimes centralised, store of information (through the information store principle) and the two learning mechanisms for altering information within the information store (the borrowing and reorganising principle) and the randomness as genesis principle. Sweller (2004, 2007) has also developed a principle that relates to the way that novel and effective information is derived, the narrow limits of change principle, as well as a principle that relates to the way that environmental information is organised and linked, the environmental organising and linking principle. The following section illustrates how these principles may be related to the UIPS concept by examining them within the UIPS framework.

8.2.1 Universal Information Processing Systems and the Information Store Principle In CLT, Sweller (2004, 2007) describes the information store principle, based on the processes seen in the natural information processing systems of cognition and evolution, as being the property of having a large information store. Within a UIPS and its components, the interacting chains and cascades of matter and energy interactions that give rise to changes in Memory Potential and Memory Expression through Learning Potential are similar to the types of interactions that lead to information stores in such natural information processing systems. Arguably, both evolution and cognition can be described in terms of such matter and energy interactions (Woolcott 2011, 2013). Since a UIPS is by definition an information store, there must be a number of UIPSs sufficiently large to parallel in dimension the large information store known to exist in human individuals as DNA, or as LTM, when such information stores are considered in terms of matter and energy. This large store of information may be seen in organisms with a DNA store similar in size to the human genome since these genomes would have similar structures, although there may be only a few larger organisms that have a cognitive store similar (in size at least) to that of human LTM (Finlay 2019; Finlay and Darlington 1995; Finlay et al. 2001). There may be many non-organismal UIPSs, however, that have such a large information store, considered generally in terms of component structures or systems (Alpaydin 2016; Wolfram 2002), if only because such UIPSs are large structures of matter and energy. The Earth, for example, has been treated effectively

8.2 Cognitive Load Theory and Scientific Integration

125

as such an information store in the Gaia Hypothesis and is, in fact, considered to have learning and memory functions (Lovelock 2007). As such, there are organisms and non-organismal structures that may, when considered as a UIPS, have an information store that is similar in size if not centrality to that seen in natural information processing systems. In a broad sense, therefore, the information store principle can be applied across many large UIPSs and would include considerations of spatial and temporal variation in Learning Potential and Memory Potential, and any observation of physico-chemical states as Memory Expression.

8.2.2 Universal Information Processing Systems and the Borrowing and Reorganising Principle In the borrowing and reorganising principle, Sweller (2004, 2007, 2010) describes the transfer of information between similar types of natural information processing systems. In the case of human evolution, information as DNA is passed between individuals through descendent lineages. In the case of human cognition, the information store is transmitted as LTM from one individual to another, with observation and imitation of other individuals important for such transmission (Sweller 1988, 1994; Van Schaik 2006; Tomasello 1999, 2014, 2016). There is a growing reliance on social interaction and institutionalised education for such information transmission (e.g. through teaching or mentoring) as well as a reliance on access to external information stores (Sweller 2004; Tomasello 1999, 2014, 2016; Woolcott 2016). Since information transmission and its potential reorganisation is an integral part of a UIPS, both human cognition and evolution can be considered as particular cases of UIPSs where information stored in an individual UIPS is, over a particular time interval, transferred to and reorganised in other similar UIPSs. This reorganisation could occur as variations in Memory Potential but may be observed only as Memory Expression. All organisms with a centralised nervous system capable of information storage as LTM would be examples of UIPSs with a functional borrowing and reorganising principle, as would all organisms with a DNA store. Some non-organismal UIPSs, such as computers, can transfer information to other similar UIPSs where it is reorganised and stored and, thus, may satisfy the borrowing and reorganising principle. The information theories of Shannon and others (Chaitin 2011, 2012; Shannon 1948; Shannon and Weaver 1963) were designed, at least in part, to describe such reorganisation and storage in information transmitted between non-organismal structures. In Sweller’s (2004, 2007) conceptualisation of educational principles, however, the large information store functions together with the borrowing and reorganising principle, and not all organisms may have a sufficiently large information store (either as LTM or DNA) that acts in combination with borrowing and reorganising processes that contribute to that information store. Single-celled organisms such as bacteria, for example, can transfer information, and this includes transferring information

126

8 The Universal Information Processing System and Educational …

transgenerationally as DNA or other cellular constituents. However, the DNA in a single-celled organism may not be as large a store of information as the DNA of a single cell of a multicellular organism, such as a primate, and the borrowing and reorganising principle and the memory store principle together may not apply to some single-celled organisms or even some small multicellular organisms. Some non-organismal UIPSs, such as computers, can be seen to operate as having a large information store and can transfer this information to other machines, stored with a similar organisation using the borrowing and reorganising principle. In UIPSs more generally, however, any transfer of universally common and small units of matter (such as molecules and ions) from one structure to another, if reorganised according to physico-chemical laws (Denton et al. 2003), may in effect be borrowing and reorganising. For a UIPS (considered in this sense to demonstrate both the information store principle and the borrowing and reorganising principle), there may be a need for an apparently regulated process of information transmission between any two UIPSs, and its subsequent integration, just as there would be between two similar natural information processing systems. In a UIPS, this regulated information transmission can be considered in terms of the units of information (matter and energy) being transferred, perhaps in some emergent form, from one UIPS to another, and then being reintegrated into the large information store of another UIPS, according to the range of interactions possible for such matter and energy within that UIPS. A solid structure, for example, that can be seen as a UIPS containing a large amount of information by way of small units of matter (e.g. ions or molecules), integrated into a whole, may have some of these small units transferred to and become integrated with another solid structure (also a UIPS) regulated by the same or similar matter and energy interactions. As such, there may be many UIPSs that satisfy both an information store and a borrowing and reorganising principle, with planet Earth as a prime example.

8.2.3 Universal Information Processing Systems and the Principle of Random Genesis of Information A second method for altering information through a learning mechanism was described in the principle of randomness genesis of information (Sweller 2004), which accounts for information not learned and remembered using the borrowing and reorganising principle. In human cognition, for example, information may be generated randomly during problem-solving (sensu Sweller 1988) in response to novel sensory input. This information can, as a result, add to LTM in various ways through a process of random generation and testing (such as trial and error) to ensure that any added information is appropriate to the problem at hand. In evolution, the internal changes to DNA may be through random mutation, a process that may provide only small numbers of changes to the large store of information after such changes are tested for effectiveness. Such changes in both

8.2 Cognitive Load Theory and Scientific Integration

127

human evolution and cognition can lead to a build-up of new information over long time periods if the tested changes are not detrimental to the organism’s survival and reproduction. Some of this information can be transferred across generations, through reproduction (evolution) and through education (cognition). Randomness as genesis, however, may not always be an effective tool for learning in educational institutions, as information can take longer to be stored in this way than through borrowing and reorganising (Kirschner et al. 2006; Mayer 2004; Sweller 2004); this could be a problem, for example, where educational objectives must be met in particular time frames. In the human nervous system, added sensory information can be stored in a variety of ways within LTM through the growth of new connections between neurons; this would be the case in random genesis and borrowing and reorganising, but information storage may also result from a recombination of elements of information (Baars and Gage 2010; Dehaene 2007, 2009; Edelman 1987, 1989; Mottron et al. 2009; Opris and Casanova 2017). The idea of random genesis, however, does not refer necessarily to these recombinations, which are not determined solely by sensory input but by internalised processes of integration of information stored already as LTM. Within a UIPS, some changes in information may be considered similar to those described for random genesis, where the term random is used in the general sense of a result that cannot be predicted, as appears to be the case in Sweller’s use of the term in random genesis (Sweller 2004). This would occur in a UIPS, for example, if novel information input or output, as Learning Potential, resulted in the possibility of particular informational connectivity in Memory Potential in one time interval but not in another (unpredicted and, therefore, effectively random) time interval, or resulted in informational connectivity that was not predicted or used previously. This change would need to be evident as Memory Expression in the UIPS, demonstrating the learning mechanism of borrowing and reorganising. In addition, the continued existence of that UIPS would effectively constitute a testing mechanism. Random genesis of information is certainly a possible scenario in some organismal UIPSs that have a large amount of DNA or a large centralised nervous system since the processes of random genesis in organismal evolution and cognition more generally are similar to those described by Sweller for the human organism (Sweller 2004). Random genesis, however, may not be as obvious in non-organismal UIPSs. A rock crystal considered as a UIPS, for example, may be formed from specific structural units, and the observed pattern of organisation of these units (as Memory Expression) may change through the interaction of the rock crystal UIPS with the environment (as Learning Potential and Memory Potential). A possible scenario for a type of random genesis would be when a rock crystal underground, which was borrowing and reorganising information in a general sense as particular molecules and ions were moved above ground such that the crystal was able to incorporate information as molecules and ions that was essentially novel. If the rock crystal is to form structures from this novel information, it could do so by developing patterns of connectivity that differ from those patterns developed formerly and, hence, demonstrate an effectively random method of integration of information. The continued existence of the rock

128

8 The Universal Information Processing System and Educational …

crystal would be effectively the testing mechanism. It can be argued, therefore, that the crystal can operate using the principle of random genesis of information. Arguably, if a UIPS is to be considered as comparable to a natural information processing system, all three of the principles discussed above need to act together. This, however, is not that difficult to incorporate into the description of many UIPSs, as randomness as genesis requires only that the large systems already discussed have more than one method of integration of information or differing time intervals for that integration. There may be many large organisms and large structures, including many computational devices, that satisfy such a criterion; examples include learning robots (Edelman 2007; Krichmar 2018) or the Earth as Gaia (Lovelock 2007; Wood et al. 2008). As such, a generalised learning mechanism similar to those seen in natural information processing systems may be an integral part of the description of many UIPSs, through whatever pathway is utilised for the input or output of information as Learning Potential and any subsequent Memory Potential or Memory Expression.

8.2.4 Universal Information Processing Systems and the Narrow Limits of Change Principle From the perspective of CLT, any additions to human memory storage made in response to consciously learned input must have narrow limits of change. In addition, the overall memory store as LTM should increase slowly in order for the positive benefits to be accrued in an individual through adequate testing of any change made through additions to the information store (Sweller 2004). Since the narrow limits of change observed in LTM during instruction are a function of attentional processes and limitations in working memory (WM), Sweller (2004) has presented arguments that instructional design must consider such narrow limits of change, particularly since these limits ensure that LTM does not become dysfunctional through the addition of inoperative complexity. Studies in integrative biology support the view of the limiting function of attention and WM during learning, though this may be considered in terms of differential activation of neuronal assemblies within the nervous system (Cowan 2017; Marois 2005; Postle 2006, 2015). There are also DNA selection processes that serve a similar function in evolution (Calvin 1996; Sherwin 2015). Consideration of the human UIPS indicates that there are component UIPSs, other than the nervous system, that may satisfy the narrow limits of change principle. This implies that the human UIPS also satisfies this principle. The immune system, for example, can be viewed as a Darwinian information processing system that operates in a similar way to evolution (Calvin 1996; Edelman 1970; Marin and Kipnis 2013). The human immune system produces antibodies in response to stimulation from the external environment, but, since only a small number of the numerous antibodies produced are utilised permanently as part of the immune system (Edelman 1970), it has narrow limits of change. It can be argued, therefore, that many organisms considered as UIPSs would demonstrate limits of change since at least two components of

8.2 Cognitive Load Theory and Scientific Integration

129

such organisms (the cognitive and the immune systems) could satisfy the relevant criteria. Machines or computer programs that mimic the learning processes of organisms with a centralised nervous system (Alattas et al. 2019; Bentley 2007; Doncieux et al. 2015; Edelman 2007; Krichmar 2018), considered as a UIPS, as well as other large non-organismal UIPSs, may also satisfy the narrow limits of change principle. “This is particularly the case” where they operate with small increments to Memory Potential that do not affect the continued existence of the structure and that are tested effectively through their appearance as small changes in Memory Expression. Some of these UIPSs may satisfy the criteria for Sweller’s randomness as genesis principle, as well as the information store and borrowing and reorganising principles (Sweller 2004). Earth as Gaia (Lovelock 1995), for example, satisfies all four criteria.

8.2.5 Universal Information Processing Systems and the Environmental Organising and Linking Principle Sweller refers to the environmental organising and linking principle as the ultimate reason for human cognition (Sweller 2004), largely because it facilitates a behavioural decision without having to first test behavioural options. This includes any decision to take no action, through linking recognised elements from the environmental stimulus and matching them with elements organised in schemas. In human cognition, large amounts of organised and linked information in LTM may be activated and used to determine responses to input environmental information (Ericsson and Delaney 1999; Loftus and Loftus 2019). Environmental organising and linking, as described in studies in integrative biology related to the function of LTM, can be seen as the storage of information from the environment as pathways of connectivity in the nervous system—pathways that can be recalled or re-entered in part or as a whole as patterns linked through hierarchies called generalisations or concepts (Edelman 1987; Mottron et al. 2009; Snyder et al. 2004). The recall of, or re-entry to, such stored patterns is considered an evolutionary advantage in that it allows for an organism to physically negotiate its environment through recognition, in various degrees of complexity, of patterns previously encountered and remembered (Cotterill 2001, 2008; Llinás 2001). A UIPS is, by definition, a system that interacts with its environment. There are many organismal UIPSs that could be considered as demonstrating the environmental organising and linking principle, where they connect input environmental information with stored information in order to respond to that environment. This is the case for any organism with a nervous system or other requisite system such as seen in plants, that is able to interact with its environment through the recall of information (Baluska et al. 2018; Borges 2005; Trewavas 2016). In a perhaps less obvious way, learning machines, such as learning robots and other related machines and

130

8 The Universal Information Processing System and Educational …

programs that interact with environmental information (Edelman 2007; Krichmar 2018; Sporns 2009; and see collected papers in Lungarella et al. 2007; Mareschal et al. 2007; Reeke et al. 2005), can be considered as UIPSs that demonstrate such organising and linking. Since natural information processing systems can be accommodated within the UIPS concept, and since UIPSs are linked at all times to the environment, delineating the environmental organising and linking principle in a UIPS may be simply a matter of recognising how organisation is defined for any particular UIPS, where that UIPS has an observable response to the environment as Memory Expression. Such organisation is a function of the physico-chemical laws that operate in matter and energy interactions, and two examples of such organised UIPSs operating within these laws are unicellular organisms, on a small scale, and planets, such as Earth, on a large scale. These UIPSs (or their component UIPSs) may process input environmental information with an end result of the organisation of information that allows an observable environmental response. Not all UIPSs, however, are able to demonstrate the environmental organising and linking principle in the same way as natural information processing systems because there is an implication that such a principle requires, in some cases at least, an observable response to the environment, as well as the operation of the principle in conjunction with the other four principles. Kalyuga (2011, p. 35) has argued that biologically evolved complex systems that rely on the five principles outlined above are both “flexible and adaptive to environmental change” and could be related to a class referred to as intelligent natural information processing systems. Kalyuga (2011), however, also supports the argument presented above that some, but not all, of the five principles could be applied to non-biological systems, such as the proto-intelligent systems of Stonier (1997)—these systems recall the discussion in Dennett (1995) of a light bulb that is processing information about its environment. Kalyuga (2011), in fact, invokes a parallel to the example of water molecules, related above, in describing the application of some of these principles to a general context of subatomic particles and their shell structures on the basis that subatomic particles could be “regarded as systems with stable patterns of organisation” (p. 35).

References Alattas, R. J., Patel, S., & Sobh, T. M. (2019). Evolutionary modular robotics: Survey and analysis. Journal of Intelligent and Robotic Systems, 95(3–4), 815–828. Alpaydin, E. (2016). Machine learning: The new AI. Cambridge, MA: MIT Press. Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., et al. (Eds.). (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom’s taxonomy of educational objectives. New York, NY: Longman. Baars, B. J., & Gage, N. M. (2010). Cognition, brain, and consciousness: Introduction to cognitive neuroscience. Cambridge, MA: Academic Press. Baluska, F., Gagliano, M., & Witzany, G. (Eds.). (2018). Memory and learning in plants. Cham, Switzerland: Springer International Publishing.

References

131

Bentley, P. J. (2007). Systemic computation: A model of interacting systems with natural characteristics. In A. Adamatzky, C. Tueuscher, & T. Asai (Eds.), International journal of parallel, emergent and distributed systems (IJPEDS), special issue on emergent computation (Vol. 22, no. 2, pp. 103–121). Oxford, UK: Taylor & Francis. Beston, P. J., Barbet, C., Heerey, E. A., & Thierry, G. (2018). Social feedback interferes with implicit rule learning: Evidence from event-related brain potentials. Cognitive, Affective, & Behavioral Neuroscience, 18(6), 1248–1258. Bloom, B. S. (1956). Taxonomy of educational objectives, Handbook I: The cognitive domain. New York, NY: David McKay. Bloom, B. S. (1984). Taxonomy of educational objectives. Boston, MA: Allyn & Bacon. Borges, R. M. (2005). Do plants and animals differ in phenotypic plasticity? Journal of Bioscience, 30, 41–50. Bruer, J. T. (2016). Where is educational neuroscience? Educational Neuroscience, 1, 2377616115618036. https://doi.org/10.1177/2377616115618036. Calero, C. I., Goldin, A. P., & Sigman, M. (2018). The teaching instinct. Review of Philosophy and Psychology, 9(4), 819–830. Calvin, W. H. (1996). The cerebral code: Thinking a thought in the mosaics of the mind. Cambridge, MA: MIT Press. Casanova, M. F. (2010). Cortical organization: Anatomical findings based on systems theory. Translational Neuroscience, 1(1), 62–71. Chaitin, G. J. (2011). Complexity, randomness and remarks on physics. In G. J. Chaitin, F. A. Doria, & N. C. A. da Costa (Eds.), Goedel’s way: Exploits into an undecidable world (pp. 31–53). London, UK: CRC Press. Chaitin, G. J. (2012). Life as evolving software. In H. Zenil (Ed.), A computable universe: Understanding computation and exploring nature as computation (pp. 1–23). London, UK: World Scientific. Cotterill, R. M. J. (2001). Co-operation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: Possible implications for cognition, consciousness, intelligence and creativity. Progress in Neurobiology, 64, 1–33. Cotterill, R. M. J. (2008). The material world. New York, NY: Cambridge University Press. Cowan, N. (2017). The many faces of working memory and short-term storage. Psychonomic Bulletin & Review, 24(4), 1158–1170. Davis, B., Sumara, D., & Luce-Kapler, R. (2008). Engaging minds: Changing teaching in complex times. New York, NY: Routledge. Dehaene, S. (2007). A few steps towards a science of mental life. Mind, Brain, and Education, 1(1), 28–47. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin Viking. Demetriou, A., Shayer, M., & Efklides, A. (2016). Neo-Piagetian theories of cognitive development: Implications and applications for education. New York, NY: Routledge. Dennett, D. C. (1995). Darwin’s dangerous idea: Evolution and the meanings of life. New York, NY: Simon and Schuster. Denton, M. J., Dearden, P. K., & Sowerby, S. J. (2003). Physical law not natural selection as the major determinant of biological complexity in the subcellular realm: New support for the pre-darwinian conception of evolution by natural law. Biosystems, 71(3), 297–303. Doncieux, S., Bredeche, N., Mouret, J. B., & Eiben, A. E. G. (2015). Evolutionary robotics: What, why, and where to. Frontiers in Robotics and AI, 2, 4. Edelman, G. M. (1970). The structure and function of antibodies. Scientific American, 223(2), 34–42. Edelman, G. M. (1987). Neural Darwinism: The theory of neuronal group selection. New York, NY: Basic Books. Edelman, G. M. (1989). The remembered present. New York, NY: Basic Books. Edelman, G. M. (2007). Learning in and from brain-based devices. Science, 318(5853), 1103–1105.

132

8 The Universal Information Processing System and Educational …

Epstein, H. T. (1986). Stages in human brain development. Developmental Brain Research, 30, 114–119. Ericsson, K. A., & Delaney, P. F. (1999). Long-term working memory as an alternative to capacity models of working memory in everyday skilled performance. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 257– 297). Cambridge, UK: Cambridge University Press. Faye, J. (2019). How matter becomes conscious. Cham, Switzerland: Springer International Publishing. Finlay, B. L. (2019). Human exceptionalism, our ordinary cortex and our research futures. Developmental Psychobiology, 61(3), 317–322. Finlay, B. L., & Darlington, R. B. (1995). Linked regularities in the development and evolution of mammalian brains. Science, 268, 1578–1584. Finlay, B. L., Darlington, R. B., & Nicastro, N. (2001). Developmental structure in brain evolution. Behavioural and Brain Sciences, 24(2), 263–278 (discussion 278–308). Fischer, K. W., Goswami, U., Geake, J., & The Task Force on the Future of Educational Neuroscience. (2010). The future of educational neuroscience. Mind, Brain, and Education, 4(2), 68–80. Goswami, U. (2008). Cognitive development: The learning brain. Philadelphia, PA: Psychology Press of Taylor and Francis. Gulson, K. N., & Baker, B. M. (2018). New biological rationalities in education. Discourse: Studies in the Cultural Politics of Education, 39(2), 159–168. Haye, A., Matus, C., Cottet, P., & Nino, S. (2018). Autonomy and the ambiguity of biological rationalities: Systems theory, ADHD and Kant. Discourse: Studies in the Cultural Politics of Education, 39(2), 184–195. Huitt, W., & Hummel, J. (2003). Piaget’s theory of cognitive development. In Educational psychology interactive. Valdosta, GA: Valdosta State University. Retrieved June 2009 from http:// www.edpsycinteractive.org/topics/cogsys/piaget.html. Kalyuga, S. (2011). Informing: A cognitive load perspective. Informing Science: The International Journal of an Emerging Transdiscipline, 14(1), 33–45. Kirschner, P., Sweller, J., & Clark, R. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. Kop, R., & Hill, A. (2008). Connectivism: Learning theory of the future or vestige of the past? International Review of Research in Open and Distance Learning, 9(3), 1–13. Krichmar, J. L. (2018). Neurorobotics—A thriving community and a promising pathway toward intelligent cognitive robots. Frontiers in Neurorobotics, 12, 42. Kuldas, S., Hashim, S., Ismail, H. N., & Abu Bakar, Z. (2015). Reviewing the role of cognitive load, expertise level, motivation, and unconscious processing in working memory performance. International Journal of Educational Psychology, 4(2), 142–169. Lau, K. H., Lam, T. K., Kam, B. H., Nkhoma, M., & Richardson, J. (2018). Benchmarking higher education programs through alignment analysis based on the revised Bloom’s taxonomy. Benchmarking: An International Journal, 25(8), 2828–2849. Lee, Y. J., Kim, M., Jin, Q., Yoon, H. G., & Matsubara, K. (2016). East-Asian primary science curricula: An overview using revised Bloom’s taxonomy. Singapore: Springer. Llinás, R. (2001). I of the vortex: From neurons to self . Cambridge, MA: MIT Press. Loftus, G. R., & Loftus, E. F. (2019). Human memory: The processing of information. New York, NY: Routledge. Lovelock, J. (1995). The ages of Gaia: A biography of our living earth. Oxford, UK: Oxford University Press. Lovelock, J. (2007). The revenge of Gaia: Why the earth is fighting back—And how we can save humanity. Santa Barbara, CA: Allen Lane. Lungarella, M., Iida, F., Bongard, J., & Pfeifer, R. (Eds.). (2007). 50 Years of AI, lecture notes in artificial intelligence 4850. Berlin, Germany: Springer.

References

133

Mareschal, D., Sirois, S., Westermann, G., & Johnson, M. H. (Eds.). (2007). Neuroconstructivism: Volume II. Perspectives and prospects. Oxford, UK: Oxford University Press. Marin, I., & Kipnis, J. (2013). Learning and memory…and the immune system. Learning & Memory, 20(10), 601–606. Marois, R. (2005). Two-timing attention. Nature Neuroscience, 8(10), 1285–1286. Mayer, R. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59, 14–19. Mitchell, M. (2009). Complexity: A guided tour. London, UK: Oxford University Press. Moreno, R. (2010). Cognitive load theory: More food for thought. Instructional Science, 38(2), 135–141. Mottron, L., Dawson, M., & Soulières, I. (2009). What aspects of autism predispose to talent. Philosophical Transactions of the Royal Society of London, Biological Sciences, 364, 1351–1357. Mowat, E., & Davis, B. (2010). Interpreting embodied mathematics using network theory: Implications for mathematics education. Complicity: An International Journal of Complexity and Education, 7(1), 1–31. Opris, I., & Casanova, M. F. (2017). The physics of the mind and brain disorders. Cham, Switzerland: Springer International Publishing. Piaget, J. (1928). The child’s conception of the world. London, UK: Routledge. Postle, B. R. (2006). Working memory as an emergent property of the mind and brain. Neuroscience, 139, 23–38. Postle, B. R. (2015). Neural bases of the short-term retention of visual information. In P. Jolicoeur, C. Lefebvre, & J. Martinez-Trujillo (Eds.), Mechanisms of sensory working memory: Attention and performance XXV (pp. 43–58). London, UK: Academic Press. Reeke, G. N., Jr., Poznanski, R. R., Lindsay, K. A., Rosenberg, J. R., & Sporns, O. (Eds.). (2005). Modeling in the neurosciences: From biological systems to neuromimetic robotics. Boca Raton, Fl: Taylor & Francis. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19, 469–508. Shannon, C. E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27(279–423), 623–656. Shannon, C. E., & Weaver, W. (1963). The mathematical theory of communication. Urbana, IL: University of Illinois Press. Sherwin, W. B. (2015). Genes are information, so information theory is coming to the aid of evolutionary biology. Molecular Ecology Resources, 15(6), 1259–1261. Sigman, M., Peña, M., Goldin, A. P., & Ribeiro, S. (2014). Neuroscience and education: Prime time to build the bridge. Nature Neuroscience, 17(4), 497. Snyder, A. W., Bossomaier, T., & Mitchell, D. J. (2004). Concept formation: ‘Object’ attributes dynamically inhibited from conscious awareness. Journal of Integrative Neuroscience, 3(1), 31– 46. Sporns, O. (2009). From complex networks to intelligent systems. In B. Sendhoff, E. Körner, O. Sporns, H. Ritter, & K. Doya (Eds.), Creating brain-like intelligence: From basic principles to complex intelligent systems (pp. 15–30). Berlin, Germany: Springer. Stonier, T. (1997). Information and meaning: An evolutionary perspective. London, UK: Springer. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285. Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4, 295–312. Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9–31. Sweller, J. (2007). Evolutionary biology and educational psychology. In J. S. Carlson & J. R. Levin (Eds.), Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology. Psychological perspectives on contemporary educational issues (Vol. 2, pp. 165– 175). Charlotte, VA: Information Age Publishing.

134

8 The Universal Information Processing System and Educational …

Sweller, J. (2010). Cognitive load theory: Recent theoretical advances. In J. Plass, R. Moreno, & R. Breunken (Eds.), Cognitive load theory (pp. 29–47). New York, NY: Cambridge University Press. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory: Explorations in the learning sciences, instructional systems and performance technologies. Dordrecht, The Netherlands: Springer. Sylwester, R. (1995). A celebration of neurons: An educator’s guide to the human brain. Alexandria, VA: Association for Supervision and Curriculum Development. Tokuhama-Espinosa, T. (2018). Neuromyths: Debunking false ideas about the brain. New York, NY: W. W. Norton & Company. Tokuhama-Espinosa, T. (2019). Five pillars of the mind: Redesigning education to suit the brain. New York, NY: W. W. Norton & Company. Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harvard University Press. Tomasello, M. (2014). A natural history of human thinking. Cambridge, MA: Harvard University Press. Tomasello, M. (2016). A natural history of human morality. Cambridge, MA: Harvard University Press. Trewavas, A. (2016). Intelligence, cognition, and language of green plants. Frontiers in Psychology, 7, 588. Van Schaik, C. P. (2006). Why are some animals so smart? Scientific American, 294(4), 48–55. Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press. Vygotsky, L. S. (1986). Thought and language. Cambridge, MA: Harvard University Press. Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media. Wood, A. J., Ackland, G. J., Dyke, J. G., Williams, H. T. P., & Lenton, T. M. (2008). “Daisyworld”: A review. Reviews of Geophysics, 48, RG1001. Woolcott, G. (2011). A broad view of education and teaching based in educational neuroscience. International Journal for Cross-Disciplinary Subjects in Education, Special Issue, 1(1), 601–606. Woolcott, G. (2013). Giftedness as cultural accumulation: An information processing perspective. High Ability Studies, 24(2), 153–170. Woolcott, G. (2016). Technology and human cultural accumulation: The role of emotion. In S. Tettegah & R. E. Ferdig (Eds.), Emotions, technology, and learning (pp. 243–263). London, UK: Academic Press. Young, G. (2019). Neo-models, neo-stages, networks. In G. Young (Ed.), Causality and development (pp. 241–269). Cham, Switzerland: Springer.

Chapter 9

Universal Information Processing Systems, Generalised Educational Principles and Generalised Cognitive Processes

By considering the principles of cognitive load theory (CLT) from within the universal information processing system (UIPS) framework, it can be argued that there is some potential for the scientific integration of those principles through their reinterpretation within that framework. This reinterpretation is possible largely because, although the principles were derived from analogies and comparisons related to the processing seen in human evolution and cognition, these principles can be reinterpreted in a very broad sense in terms of matter and energy pathways within the scientifically based UIPS framework (as indeed can the processes upon which they are based). The following sections develop these reinterpreted principles of CLT in the form of generalised principles that, as well as applying to human education, may apply to all UIPSs. Since the broader description of learning and memory processes within the UIPS framework may, in fact, cover more of the informational transactions of the human organism with its environment than seen in conventional descriptions of learning and memory, including those developed from studies of information processing systems in cognitive psychology (Lachman et al. 1979; Miller 2003), consideration of such generalised principles can give a broader view of the state and dynamics of human learning and memory processes over a variety of differing time intervals. Such consideration offers valuable insights into human learning and memory as it relates to educational theories and practices. The environmental interactions described as integral to UIPSs, and which form the basis of such generalised principles, are similar to those described in both simple and complex systems and networks (Barabási 2016; Cohen and Stewart 1995; Easley and Kleinberg 2010; Lü et al. 2016; Newman 2018; Newman et al. 2006). Theories related to the connectivity within complex systems have been related to education (Brown and Poortman 2018; Daly 2010; Davis et al. 2008; Morrison 2012), as have theories that describe connectivity in terms of networks (Kop and Hill 2008; Siemens 2017). The following sections, therefore, examine how the UIPS framework is useful in establishing a common theoretical interface between studies of learning and memory and the studies of networks and complex systems as used in education.

© Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3_9

135

136

9 Universal Information Processing Systems, Generalised Educational ...

9.1 Universal Information Processing Systems and Generalised Educational Principles 9.1.1 Generalising Educational Principles from Cognitive Load Theory Examination of natural information processing systems within the UIPS framework indicates that the interacting chains and cascades of matter and energy interactions of a UIPS may include the interactions seen in such natural systems. The conventional description of the learning and memory processes in human cognition—for example, those descriptions that form the basis of such educational theories as CLT (Sweller 2004)—may be described in terms of the interaction of component UIPSs within the human UIPS, such as the interactions of neuronal assemblies within the nervous system. The learning and memory processes for other multicellular organisms (Baluska et al. 2018; Borges 2005, 2008; Dukas 2019; Gagliano 2017; Trewavas 2016) can be described in a similar way since all such processes can be broken down into chains of matter and energy interactions. The processes upon which Sweller’s educational principles are based (Sweller 2004), as well as the principles themselves, can be described in terms of UIPSs, as seen in the previous sections and, in a very broad sense, these principles, like learning and memory processes, can be generalised so that they apply to all UIPSs. Any UIPS, for example, can be described as a matter and energy information store, over a particular time interval, and a generalised information store principle may, therefore, be applied across all UIPSs, provided that there is no requirement that such a store is large or centralised. Such an information store principle includes considerations of temporal as well as spatial variation in Memory Potential, and any relevant Memory Expression, in any UIPS. A generalised learning mechanism is also part of the description of a UIPS, through whatever pathway is utilised for the input or output of information, since Learning Potential is defined as integral to UIPSs and contributes to Memory Potential as part of the potential formation process of the cascades and chains of interactions within the UIPS, as well as any resultant Memory Expression, in a given time interval. Whether such input or output is considered as novel information depends on whether that input or output has occurred in a specified prior time period for the spatiotemporal UIPS under consideration. Information that is borrowed and reorganised and information that is randomly generated are both types of learning mechanisms that can occur in UIPSs, depending on the description of organised and random as discussed above. Within a UIPS, a generalised form of narrow limits of change could be described for the derivation of Memory Potential within a UIPS through input or output as Learning Potential, where there is a clearly stated proportion of change in Memory Expression, over a clearly stated time period, which would serve to determine any such generalised limit. The tested effectiveness of that information, and its effect on the possible interactions of Memory Potential, would depend necessarily on how

9.1 Universal Information Processing Systems …

137

that information alters the integrity of the UIPS so that it maintains a clearly defined Memory Expression. In heating a cold cup of water, for example, narrow limits of change might be determined through a description of how much heat would be absorbed so that the water does not change state and become a gas. Information can be considered effective if the input to the system heats the water but does not result in a change in state. Delineating the environmental organising and linking principle in a UIPS may be a matter of recognising how organisation is defined for any particular UIPS since a UIPS is by definition connected to the environment. In human cognition, organisation can be described in terms of patterns of connected neurons that form spatiotemporal concepts representing environmental interactions (Edelman 1987; Baars and Gage 2010). These patterns, when recalled, can be large in comparison with patterns formed from novel stimulus in working memory (WM; Postle 2015). In evolution, the organisation can be described as the patterns of connection observed in the links between the nucleic acid bases—an organisation that can be seen in the various combinations that encode environmental responses (Calvin 2004; van Duijn 2017). With regard to UIPSs in general, any matter and energy in the universe must be organised, since any spatial or temporal organisation must obey the physicochemical laws of that universe. Spatial organisation of information within any UIPS over a given time period is observable as Memory Expression, whether or not there is interaction of the UIPS with its environment through Learning Potential or resultant Memory Potential. Some structures, however, may have an organisation that differs from others in, say, the order in which small units of matter and energy (such as molecules and ions) are combined, and some UIPSs may have a more apparent organisation than others to particular observers. In some studies that consider information as patterns of matter and energy, for example, rather than information as just matter and energy as in the UIPS concept, the term organised is reserved for recognisable spatial or temporal patterns, depending on who or what is recognising those patterns (Bates 2005, 2006; Reading 2006). Since a UIPS, by definition, is a system that interacts with its environment, there are many organismal UIPSs that could be considered as demonstrating an environmental organising and linking principle if they link information (e.g. input environmental information) in a limited way with stored information in order to respond to that environment. There is, of course, an implication that this principle may require that any such UIPS has an observable environmental response (as Memory Expression). Despite such apparent constraints, there may be numerous organismal and non-organismal UIPSs that can be viewed as organised, and that can be seen to demonstrate such environmental linking. For example, a generalised linking principle can be seen in any organism with a nervous system or any other similarly constrained environmental response system (such as has been documented for plants and fungi, see Baluska et al. 2018) that is able to interact with its environment through the recall of information. In a perhaps less obvious way, but along similar lines, learning machines, such as those in the Darwin series of robots (Edelman 2007; Krichmar 2018), may be said to demonstrate such environmental organising and linking through their design as

138

9 Universal Information Processing Systems, Generalised Educational ...

artefacts that interact with their environment. At an even broader conceptual level, a unicellular organism (on a small scale) and planet Earth (on a large scale) may process input environmental information in either the entire UIPS or any component UIPS, with an end result of organisation of that information in the entire UIPS as an environmental response; this may be considered as environmental linking and organising if it is observed as Memory Expression. Comparison of UIPSs and natural information processing systems indicates that time intervals must be considered in any discussion of generalised educational principles, as Memory Potential and Memory Expression may operate in a number of mutually independent and differing time intervals (e.g. when Memory Potential alters but Memory Expression remains unchanged). The issue of time intervals has been considered in some studies of human cognition; for example, studies have demonstrated that processes such as WM may vary over the lifetime of an organism (Swanson 2017) and similar issues have been considered in studies of computer programs and robots that have a learning and memory function designed to imitate human learning and memory processes (Alpaydin 2016; Krichmar 2018). The UIPS concept suggests, however, that generalised principles may assist in the further consideration of such issues through their application over a broader range of organisms and their components, as well as over a range of differing environments and time intervals.

9.1.2 Generating Novel Educational Principles from Consideration of Universal Information Processing Systems In the section above, an examination of learning and memory processes within the UIPS framework indicated that not only may human learning and memory processes be considered in broader terms but generalised principles may also be developed from consideration within the UIPS framework of the educational principles of CLT with a view to applying these generalised principles to the learning and memory processes of all UIPSs. This section examines the potential for developing novel generalised principles from within the UIPS framework, outlining two novel educational principles developed from the consideration that the learning and memory processes of organisms and non-organismal structures are system-wide. Since the human organism can be considered in terms of a UIPS, such novel principles may have applications in studies of human learning and memory as well as in educational theories and practices (see outline in Woolcott 2010). The first novel principle developed within the UIPS framework, the Learning Preparedness Principle, can be described as a chemical (matter) and energetic learning preparedness where there is a minimum condition for internal information (or Memory Potential) of the entire UIPS in order for input and output communication of any information to occur and, therefore, to be considered as Learning Potential. In effect, this Principle can be seen in machines used in robotics where

9.1 Universal Information Processing Systems …

139

any environmental input or output, and subsequent internal interaction, depends on system preparedness. A minimum condition might be, for example, that the machine has sufficient power and without this power no input or output (as Learning Potential) would occur, and there could be no change in memory (either as Memory Potential or as Memory Expression) across the entire machine (see discussion in Woolcott 2011). Within a human considered as a UIPS, the Learning Preparedness Principle may seem to reflect an understanding about preparedness as a requirement for human learning—sometimes considered in terms of motivation and engagement (Goswami 2008; Shell et al. 2010). The Learning Preparedness Principle as described here, however, relates to system-wide preparedness for learning and memory in a UIPS. In particular, it indicates that certain matter and energy constituents must be available as minimal conditions in the appropriate time intervals, through the interactions of a number of differing component UIPSs, before there is Learning Potential or Memory Potential. As such, the Learning Preparedness Principle accommodates the view that human learning and memory are system-wide, and that there may be a number of reactants (e.g. simple chemicals such as water and glucose; Jones et al. 2018) that need to be available to the entire human organism, including the nervous system, which must be considered in any development of long-term memory (LTM) through learning. Set in terms of cognitive psychology, the Learning Preparedness Principle may have some equivalence to a type of Minimum Information Principle, with relevance to available knowledge structures for informing (such as schemas) and the capacity of information processing channels (see, for example, Kalyuga 2011). In establishing and testing such a Minimum Information Principle, it would need to be accepted that information processing capacity may be flexible and varies continuously between both “informer and client” (Kalyuga 2011). There needs to be an awareness, however, that a feature of the Learning Preparedness Principle is that it is based on the systemwide processes of a UIPS. For example, the filters of Gill (see, for example, Gill 2010 in Kalyuaga 2011) would all be engaged (not just those that refer to prior knowledge structures), and this would include “motivation” and “visceral” filters that change, inhibit or disproportionately amplify information in incoming messages. The UIPS reconceptualisation would indicate that there may also be other filters not yet considered in learning environments. The second novel principle, the Environmental Connectivity Principle, refers to the situation where information transmission between the environment and a UIPS may only occur if there is appropriate connectivity of that UIPS with that environment. A computerised learning system, for example, must have appropriate mechanisms (such as sensors or keys) for the input of information from its external environment. In the human UIPS, the Environmental Connectivity Principle reflects studies that see human learning and memory as holistic processes, where all parts of an organism are involved with the formation of every memory (Squire and Kandel 2008) and studies that see this involvement as extending into a connection with the environment (Järvilehto 2009).

140

9 Universal Information Processing Systems, Generalised Educational ...

Within the human system, this Principle indicates that for any memory storage through neuronal connectivity in the nervous system, and any resultant changes in the muscular system, there must be appropriate connectivity for information transmission between components within the human nervous system and the remainder of the human soma, as well as appropriate connectivity between the human soma and its environment. This can be illustrated clearly in some cases of autism, where such environmental connectivity may be compromised through differential connectivity between neurons, as well as differential neuronal construction, with the result that learning may be problematic (Casanova 2010; Opris and Casanova 2017) or, at least, exceptional (Mottron 2016). Set in terms of cognitive psychology, the Environmental Connectivity Principle may have some equivalence to a type of Information Filtering Principle, considering the work of Gill (2010) discussed above. This equivalence would indicate that there may be a need to qualify this Principle in terms of such filters or in terms of how the Principle would impact on methods recommended by CLT for reducing extraneous cognitive load. For example, a “working model” of instructional design, based on an Information Filtering Principle, would need to embrace methods that account for redundancy, transiency, advanced client knowledge and/or inadequate client prior knowledge (Kalyuga 2011, 2015). As was the case with the system-wide provisions of the Learning Preparedness Principle, an Environmental Connectivity Principle would need to consider information in terms of a UIPS. This may involve Learning Potential and Memory Potential, as well as Memory Expression, in both intelligent and proto-intelligent systems. The Environmental Connectivity Principle implies, among other things, that there needs to be an accurate account of information transmission and connectivity over various time periods in order to assess the effectiveness of learning and memory processes. Such accounting is being considered in studies of dynamic interactions in integrative biology, in particular in studies within neuroscience that focus on interactions of emergent neural systems and their relevance to education (Casanova and Trippe 2009; Haier 2016). This Principle also suggests that there may be emergent systems that are yet to be studied and that operate over different time periods or in different combinations of component systems—a suggestion supported by some studies of memory interactions over differing time intervals (Basar and Bullock 2012; Bullock et al. 2005). With regard to human learning and memory, this principle indicates that differences in human connectivity may need to be considered in educational theories, and this is supported by consideration of individual differences in cognition (Farah 2010; Friedman and Miyake 2017; Kanai and Rees 2011; Logie 2018). There may need to be, however, re-evaluation of some of the studies in cognitive psychology that underpin educational theory since such studies may not have taken into account differences in neuro-connectivity that may be present both within and between individuals (Fine 2014, 2017).

9.1 Universal Information Processing Systems …

141

9.1.3 Generalised Principles and Education The UIPS framework outlined here, and any generalised educational principles based within it, may provide an overall perspective from which to view a system-wide human interaction with the environment. This perspective may be useful in institutional education, where only a small fraction of the human UIPS may be targeted through teaching—the remainder being dedicated to such functions as maintenance of automatic systems, such as breathing and blood circulation, and the provision of the connectivity in the brain in the event of novel information input (Fox and Raichle 2008; Raichle 2010; Raichle et al. 2019). It may be relevant, for example, for educators to be aware of the constraints dictated by the type and extent of the interaction of component UIPSs within the human UIPS, particularly where many of these interactions may be contributing to conventional learning and memory, and where those contributions may vary over differing time periods. Effective teaching, therefore, may involve changing some of the component systems in a specified time interval, while keeping the remainder of the component systems at levels which support (or which at least do not interfere with) those changes. Some of these constraints are formally recognised here within the Learning Preparedness Principle and the Environmental Connectivity Principle. These and other generalised principles may be useful in considering the effect on component systems, including factors such as the amount of sleep, nutrient supply, emotions, concentration, body position and neuronal connectivity, and other factors that may affect the optimisation of learning. Some of these constraints are reflected in recent educational studies that have considered studies in integrative biology, an example being the brain/mind/behaviour model of Frith and others (Blakemore and Frith 2000; Morton and Frith 1995; Howard-Jones 2011) discussed briefly in earlier sections. In this model, input and output of environmental factors from several sources, including from component systems such as the circulatory and respiratory systems, were compartmentalised into those that affect the brain, the mind and behaviour in order to demonstrate the connectivity between biology, cognition and behaviour. This model has been cited, in fact, as an example of how studies of cognition should link brain and mind functions and learning behaviours as well as their constraints with relevant environmental levels (Howard-Jones 2008; Howard-Jones and Holmes 2017). For the brain, for example, the environmental level is characterised by biological factors such as oxygen and nutrition; for the mind, the environmental level includes educational as well as cultural and social influences; and for behaviour, the environmental level includes physical opportunities and restrictions (Morton and Frith 1995). It may be possible to accommodate models such as this within the UIPS framework through an examination of brain and behaviour interactions using generalised principles based on the UIPS concept. It would be necessary, however, for the environmental interactions of the model to be termed such that inputs, outputs and interactions are considered only in terms of matter and energy, or their emergent systems and structures within a human UIPS. As argued in previous sections, cognition and behaviour may be described within the UIPS framework in terms of Learning Potential and

142

9 Universal Information Processing Systems, Generalised Educational ...

Memory Potential, which describe the unseen workings referred to conventionally as cognition, as well as in terms of Memory Expression, which may describe observed changes in physical appearance or motion in space, and hence behaviour. Within the UIPS framework, however, while brain and behaviour are observable in a matter and energy universe, the mind is an abstraction that is difficult to reconcile in terms of Learning Potential, Memory Potential and Memory Expression. It may be possible, of course, to develop a conceptualisation of the mind based on using a combination of unseen elements in Memory Potential where they are known to correspond, over certain time periods, to particular forms of Memory Expression. Besides being used to consider the constraints on education and teaching, generalised principles developed within the UIPS framework may also be useful in reconciling some of the disparate approaches that have been taken in educational theory. This is partly because these principles may allow some comparison of differing educational approaches through consideration of commonalities that may exist in information processing, despite any apparent differences in analogies and assumptions. Such comparison may be limited, however, to educational approaches that can be considered in terms of matter and energy interactions (Woolcott 2011). The generalised principles may be useful, for example, in considering the commonalities of approaches to the concept of problem-solving as environmental interaction that involves novel information since such problem-solving may be central to any teaching practice and, therefore, an important aspect of educational theory (see Edelman in Sylwester 1995; Sweller 1988, 1994). In integrative biology, this concept of problem-solving is sometimes considered the main function of learning and memory (Grillner 2003; Tonegawa et al. 2003). The generalised principles may be useful in comparing this conceptualisation with that of problem-solving based in CLT (Sweller 1988, 1994) since these principles can be used to examine both conceptualisations scientifically.

9.2 Universal Information Processing Systems and Generalised Cognitive Processes It may be of considerable benefit for educators to be able to assess new empirical research and theoretical advances and relate them to the capabilities of students within a broad and integrated framework for cognition. Some researchers have argued for the need for such a broad framework, with Lucas (2005) commenting that the most striking feature apparent upon consideration of existing models of cognition is their lack of integration. The formulation of such a broad framework has been pursued in cognitive psychology, however, with approaches to cognition that consider information processing systems and connectionist models (Lachman et al. 1979; Sweller 1988, 1994, 2016). The formulation of a broad framework has also been pursued in integrative biology where some researchers have begun to formulate generalised cognitive models in a context that embraces the empirical constraints of

9.2 Universal Information Processing Systems …

143

modern biology while remaining sensitive to an evolutionary approach (Casanova and Casanova 2019; Edelman 1987, 1989, 1992; Squire and Kandel 2008). Some researchers have argued that such cognitive models may be useful in studies of education and teaching (Samuels 2009; Sylwester 1995; Woolcott 2011, 2013), although such models would require a framework that accommodates social interaction and other aspects of behaviour within such an evolutionary sensitivity (Cotterill 2001, 2008; Margoliash and Nusbaum 2009). The UIPS framework may be useful in this regard since it can be used to describe both cognition and behaviour (not only in humans but other organisms and non-organismal structures) and may be used, therefore, to formulate descriptions or models of cognitive processes that are both broad and integrated, based on system-wide learning and memory and environmental connectivity.

9.2.1 Universal Information Processing Systems, Generalised Cognitive Processes and Studies of Connectivity Along with the ongoing developments in education through broad or generalised approaches to cognition in cognitive psychology and integrative biology, there have also been developments through approaches that consider cognition in terms of the informational connectivity of complex systems and networks (Davis et al. 2008; Siemens 2017). Some such developments have combined studies of connectivity as network theory (sometimes referred to as graph theory; Easley and Kleinberg 2010; Watts 2004), with studies of the non-linear dynamics and self-organising components that can emerge or evolve as complex wholes as complexity theory (Davis et al. 2004). Both network theory and complexity theory have been applied to studies of human learning and memory through examining the complex structures and interactions of the nervous system (Sporns 2010, 2012). Complexity theory and network theory have also been applied to the examination of educational theory (Brown and Poortman 2018; Bruce et al. 2017; Carolan 2013; Daly 2010; Davis et al. 2008), as well as to teaching practices and educational leadership (Morrison 2012; Siemens 2017; Stamovlasis and Tasparlis 2005; Woolcott et al. 2017, 2018). Generalised approaches to cognition that are based on network theory or complexity theory have some similarity to the approaches developed within the UIPS framework because the broad description of information and information processing systems in the UIPS concept is based on a concept of environmental pathways that may be complex rather than linear and which may be connected as networks. In fact, network and complexity theories may be utilised as descriptive tools within the UIPS framework because such theories facilitate the description of the complex pathways and networks that are integral to UIPSs—whether or not these pathways and networks are describing internal and/or external connectivity. Describing the information pathways of UIPSs in terms of networks offers the additional advantage that such description may lead to a better understanding of the actual networks

144

9 Universal Information Processing Systems, Generalised Educational ...

of matter and energy pathways that may be used in processing and storage of that information as knowledge. In relation to educational theories and practices, the approach of Mowat and Davis (2010), based on complexity theory, is perhaps most significant in relation to UIPSs because it directly applies the work of Lakoff and others (Lakoff 1999, 2006; Lakoff and Johnson 1999; Lakoff and Núñez 2000), arguing for an embodied approach to educational theory—an approach associated with studies of the connectivity of the nervous system and environmental interaction, as is the UIPS concept. In the embodied approach, knowledge is considered to develop into conceptual domains through bodily experiences, and these conceptual domains are connected by conceptual metaphors that abstract patterns of inference from those experiences (Lakoff and Johnson 1999), an abstraction that may be automatic and unconscious (Lakoff and Johnson 1999; Mottron et al. 2009). Although such knowledge (connected through sensorimotor experiences and conceptual metaphors) is considered as emergent, the explanation of how this knowledge is obtained is grounded in modern integrative biology described in terms of complex systems of interaction (Khattar 2010). This explanation can be accommodated within the UIPS framework since obtaining human knowledge as a complex system of matter and energy connections can be linked to the consideration of organisms and their environmental interactions (Woolcott 2011, 2013).

9.2.2 Universal Information Processing Systems, Generalised Cognitive Processes and Performance Within the UIPS framework, human learning and memory processes can be considered as system-wide and related to the organism as a whole and its environmental interactions, including behaviour. A generalised concept of human cognitive processes can be considered in terms of system-wide processes resulting from interacting component UIPSs, some of which are linked to the external environment. Support for this system-wide approach can be seen in studies conducted within integrative biology and cognitive psychology that have related cognition and cognitive processes to characteristics of the whole organism rather than just cognitive subsystems (Godfrey-Smith 2002; Raichle et al. 2019; Squire and Kandel 2008), as well as in studies that relate cognition to the activity of the muscular system (Cotterill 2001, 2008; Llinás 2001). Support can also be seen in evolutionary perspectives on learning and memory that have been related to connectivity of processes and pathways in organismal and non-organismal structures and systems (Barabási 2016; Bentley et al. 2018; Edelman 2007; Sporns 2009, 2010, 2012) and in studies that have, in turn, applied such connectivity studies to education—for example, through the use concepts of embodiment (Lakoff and Johnson 1999; Lakoff and Núñez 2000; Mowat and Davis 2010) or enactivation (Degenaar and O’Regan 2017; Gallagher and Lindgren 2015; Knyazeva 2008; Varela et al. 1991).

9.2 Universal Information Processing Systems …

145

Besides being related to the broad processes of learning and memory as seen in the UIPS conceptualisation of Learning Potential and Memory Potential, a generalised view of human cognitive processes developed within the UIPS framework needs to consider Memory Expression in particular, since this relates specifically to any observed performance and its use in assessing cognitive processing. Studies in cognitive psychology have indicated that knowledge gained through conventionally termed learning and memory processes is assessed through observation of performances that vary from simple eye blinks to the sometimes complex sequences of movement seen in such activities as talking, reading and writing, and sports performances (Ericsson 2005; Lipton and Spelke 2003; Perone and Simmering 2017; Sweller, van Merriënboer and Paas 1998; Thelen and Smith 1998). Studies in integrative biology have demonstrated that such types of performance are based in muscular contractions that relate to environmental interactions and storage of information in memory (Cotterill 2001; Grillner 2003; Llinás 2001). Learning and memory processes and their relationship to performances (sometimes discussed in terms of motor activity) have been the subject of considerable recent research, some of which has been directed at examining individuals who demonstrate above-normal performances that are valued in particular societies (Kaufman and Sternberg 2008; Mottron et al. 2009). Some recent generalised models of cognition have been related to motor activity and performance in an educational context (Blakemore and Frith 2000), including in the investigation of high-level performance within such generalised conceptualisations (Ziegler and Phillipson 2012; Woolcott 2013). However, there remains no single overarching framework that is sufficiently broad and integrated to be used in an examination of the range of differing cognitive processes and their relationship to performance in an educational setting. The UIPS framework may go some way towards establishing such an examination, largely because the division of memory processes as Memory Potential and Memory Expression and the conceptualisation of system-wide learning and memory processes allow such a relationship to be examined in a dynamic and relational context of interacting component UIPSs of the human UIPS as well as environmental interaction. The UIPS concept suggests, for example, that interacting component UIPSs acting as an integrated whole, and not a single component UIPS, may be responsible for academic ability and for the means of its assessment. The concept of such interacting systems is supported by the research of Haier and associates (Colom et al. 2009; Haier 2016; Haier and Jung 2008), whose parieto-frontal integration theory (P-FIT) is based in research that shows that the amount of grey matter (neuronal cell bodies) activated across a number of different brain regions, which can be seen as interacting component UIPSs, can be correlated with test scores from several different assessments of creativity and intelligence. Like the UIPS concept, this theory may be useful because it is based on scientific approaches that may be applied empirically—in this case to the determination of general intelligence based on the brain’s measurable characteristics. More importantly, however, the P-FIT appears to support the implication from consideration of the UIPS concept that interacting systems, and not single systems, may be responsible for academic ability. Since the P-FIT is based in descriptions of

146

9 Universal Information Processing Systems, Generalised Educational ...

neuronal pathways, it may be possible to describe the P-FIT in terms of the UIPS framework. A view of generalised cognitive processes as developed with the UIPS framework suggests that there are many factors related to system-wide interacting component UIPSs, in addition to those mentioned above, that may play a role in both cognition and performance. This suggestion is supported by research in integrative biology. Processing time that is linked to white matter (neuronal connections), for example, is likely to play a key role in any assessment of intelligence (Haier 2016). Other factors that play a role in learning and memory, and their assessment through performance, may be related to such parameters as gender and age differences, and societal expectations (Geary in Butterworth 2006; Haier et al. 2005; Halpern et al. 2007; Shaw et al. 2006; Swanson 2017). All such factors may be described in terms of spatiotemporal interactions of component UIPSs and the environment if they can be described in terms of matter and energy pathways. However, it has been difficult to relate cognition and performance, in particular expert performance, to factors related to genetic attributes. Plomin and associates (Davis et al. 2007; Krapohl et al. 2014; Plomin and Kovas 2015; Plomin et al. 2007; Rimfeld et al. 2016; Selzam et al. 2017) have suggested that this is because the genes that contribute to learning and memory, and related performances, may be generalist genes that contribute to developing many parts of the human organism. This is supported by considering the system-wide processing within the UIPS framework since it would be expected that there are cascading chains of pathways (as Memory Potential) resulting from any interaction of DNA with its environment and that these pathways have Memory Expression in various parts of the UIPS in differing time intervals and may also be involved in interactions with the external environment. Such DNA interactions play a role in system-wide processing as part of a generalised view of cognition. The consideration of the human cognitive system as a separable component UIPS suggests that it is useful in education to consider areas of an individual’s assessed performance (particularly those areas in which a student has shown some capability) as resulting from interacting components of that individual as a UIPS. In this way, a student’s capability may be conceptualised as a nominated degree of capability that they have obtained in a culturally valued knowledge domain (Kaufman and Sternberg 2008), or the potential capability in such a domain for which they have an assessed performance, so long as it is recognised that various components of the student’s cognitive and related systems contribute differentially to that expressed capability (Woolcott 2011, 2013). Such consideration of the interaction of separable information processing components may be useful in examining aspects of cognition, such as motivation and emotion, as suggested in several studies of cognitive pathways (Davis and Panksepp 2018; LeDoux and Brown 2017; Panksepp 2004).

9.2 Universal Information Processing Systems …

147

9.2.3 Universal Information Processing Systems, Generalised Cognitive Processes and Analysis of Patterns The UIPS concept, although based in a system-wide view of cognition, supports the view that generalised cognitive processes are linked to specific cognitive subsystems, such as neuronal assemblies and their interactions, and that such subsystems are related through chains of connected pathways as interacting component UIPSs. This view can be applied in studies of the interactions involved in the pattern analysis that seems to be a feature of the human cognitive system since it continues to be useful to describe these interactions in terms of component UIPSs within the human nervous system. Although several capacities have been described for the brain in general terms (e.g. problem-solving, decision-making and action control), one of the strengths of the brain—and the entire nervous system—is in remembering and cross-analysing patterns observed from the real world (see Baars and Gage 2010). Studies in integrative biology have attempted, in fact, to describe broad and integrated cognitive processes based on examining the neuronal pattern analysis carried out during conventional learning and memory processes in the nervous system, with some researchers (Mottron et al. 2009) indicating that the detection, integration and completion of patterns in such analysis and the requisite grouping processes function primarily in the negotiation of the phenomenological world. One approach to such examination has been to compare pattern analysis in individuals who have savant syndrome, including individuals with autism spectrum disorder, with that in individuals described as neurotypical, where both groups were considered as gifted (Happé and Vital 2009). As has been discussed in previous sections, such a description of cognitive processes can be accommodated within the system-wide view of the UIPS concept. In association with this pattern analysis is the ability to produce new material within the constraints of the integrated structure, a broad cognitive process that is sometimes referred to as creativity (Beaty et al. 2016; Kenett et al. 2018; Mottron et al. 2009). This view of creativity aligns well with the view of Sweller and others (Plass and Kalyuga 2019; Redifer et al. 2019; Sweller 2009; Sweller and Mann 2011) in relating creativity to random genesis of information—a view that can be accommodated within the UIPS concept, as indicated in previous sections. Some researchers in integrative biology have related superior working memory and attention to high scores in assessments of the general factor of intelligence (g factor) or fluid intelligence (Colom et al. 2009; Haier 2016). Such neuronal processes appear to be related to creativity, adding support to a suggested integrated relationship between intelligence, giftedness and creativity through consideration of broad cognitive processes (Cotterill 2008; Geake 2009; Jung et al. 2009). Further, the UIPS concept suggests that these processes are, in turn, related to system-wide interactions of component UIPSs. The UIPS concept also suggests that limiting processes due to information loss as well as constraints due to component interaction are integral to a description of a

148

9 Universal Information Processing Systems, Generalised Educational ...

generalised view of cognition and that these limitations may impact on pattern analysis and the links seen between knowledge, intelligence, giftedness and creativity. Research in integrative biology has shown, in fact, that knowledge may be determined by automatic hierarchies that govern memory processing and concept generalisation through information loss and the limitation of the role of perception, although this may not be the case with savant syndrome (Happé and Vital 2009; Mottron et al. 2009). Grandin (2006), a noted researcher who has autism and savant syndrome, has argued that there may be an orientation towards pattern analysis that may be recognised as being related to one of three particular domains of learning and memory and, which, besides resulting from environmental interaction, may also be due to differences in connectivity within individuals. Grandin (2006) classifies these domains in terms of the memory that relates to visual or spatial information, including the memory of 3D images used in art and design or sport, the memory that relates to sounds and their representation in a language used from birth and the memory that relates to linking symbols and patterns, including patterns used in mathematics, music and a second language. Grandin’s learning and memory domains remain unexplored experimentally but are suitable for examination within the UIPS framework since there are three large interacting systems present in some individuals to varying degrees, with connectivity systems that are currently being described in scientific terms that can be reinterpreted in terms of matter and energy pathways (Casanova 2010).

9.2.4 Universal Information Processing Systems, Generalised Cognitive Processes and Determination of Subject Boundaries In modern educational institutions, one of the primary goals of teaching students is to optimise and facilitate the gain of expertise in a particular subject and align this with the requirements of a modern curriculum. In industrialised societies, such curricula are a major component of institutionalised education, with stand-alone subjects allied to the two main curriculum streams—the social and behavioural sciences and the natural sciences—that diverged during the industrial revolution. Mathematics and the recently developed computer sciences appear to maintain a separation from both streams but are strongly allied to science, technology and economics (Woolcott 2009). The development of subjects within the curricula used in modern institutionalised education has been essentially through teaching practices influenced by the requirements of trade-based economics in industrialised societies (Davis 2003; OECD 2003a). This, in turn, has contributed to the development of issues that relate to a lack of subject cohesiveness (Mowat and Davis 2010).

9.2 Universal Information Processing Systems …

149

Some of these issues are evident in the lack of accommodation of studies from modern research in the development of strategies that may target subject-based education, specifically with regard to research in integrative biology (Fischer et al. 2007, 2010; Lyon 2005; Schank 2015), as well as in the widely differing reactions to modern theoretical research perspectives which may be based in concepts and assumptions that appear to bear little relation to each other (Howard-Jones 2008; Schnotz and Kürschner 2007; Simon 2009). Similar issues have been explored in studies specific to high levels of expertise (Geake 2009; Woolcott 2012, 2013; Ziegler and Phillipson 2012), but there has been little in the way of intersecting such studies and studies of the broad range of human performance across student cohorts. Resolution of such issues could affect the direction that education generally, and curriculum construction specifically, takes in the future, both in terms of the content of subjects and the teaching practices used within subjects. The view that human learning and memory processes may be described in terms of generalised cognitive processes within the UIPS framework, and that such processes may be useful in examining education and teaching indicates that the framework may be useful in examining curriculum construction, both in general and within specific contexts, such as subject categories. As discussed in the previous section, the development of network theory and complexity theory in an educational context is supported by the consideration of the UIPS concept, given its basis in information pathways and environmental interactions that may be both networked and complex. It may be possible, therefore, to examine curriculum construction within the UIPS framework, by examining linear systems versus networks and complex systems (e.g. in the subject content of courses being taught in institutional education). The UIPS framework could be applied, for example, to several recent examinations of curriculum construction that have been conducted using network theory and complexity theory. Some such examinations have indicated that the failure of the connected hubs of networks, rather than any failure of linear links, is implicated in failures typical of the linear or centralised networks that form the basis of concepts taught in modern subject-based education (Khattar 2010; Mowat and Davis 2010). It follows that any teaching that assists in developing a large number of distributed and weakly linked networks may assist in developing concepts that are not linked through such hubs. Mowat and Davis (2010) have argued that the content of mathematics courses, for example, may be unified by considering the complex linkings of inferential metaphors upon which human thinking may be based (Lakoff and Johnson 1999; Lakoff and Núñez 2000) and have made suggestions as to how such unification can improve the teaching of mathematics. Such networks can be accommodated within the UIPS framework, with Memory Potential being analogous to the non-observable processes described in the links between nodes in such networks, and Memory Expression being used to describe the nodes that are the observed outcomes of subject teaching when the network approach is applied in practice. This is not to say that all learning and memory processes are complex or networked since there are instances where systems of rules that are applied within complex systems engender large-scale simplicity and linearity (Cohen and Stewart 1995). However, the consideration of learning and memory processes

150

9 Universal Information Processing Systems, Generalised Educational ...

as complex systems suggests what Kirshner and others (Kirshner et al. 2010) refer to as potential pedagogical departures from the linear and hierarchical approaches currently taken in institutional education. It may be useful, therefore, to examine these departures within the UIPS framework. While the suggestions offered through consideration of network theory and complexity theory offer a method for building on information processing theories (such as CLT), through a more detailed account of the information being taught and its basis in prior knowledge, the consideration of pathways within the UIPS framework offers a more complete, if more complex, account of the informational transactions relevant to curriculum construction. As such, considering network theory, complexity theory and networks through the lens of the UIPS concept may be extremely useful in reviewing the boundaries of subjects taught within the industrial model of education, such as mathematics and language, since such subjects appear to be artificial constructs (OECD 2003a; Dehaene 2007, 2009) that may be linked only through networks based on cognitive experiences of the physical environment, such as suggested by Lakoff and others (Lakoff and Núñez 2000). Such a UIPS lens may be useful in refining the historical partition of subjects into categories and these categories into learning stages, such as seen in the concepts of Piaget and Bloom (Anderson et al. 2000; Huitt and Hummel 2003). Such partitions have been useful in industrialised society in creating a literate and numerate workforce (OECD 2003a, b). However, a curriculum that is based on stage-related learning, whether based on age or on the concept of a linear (spiral) curriculum (Bruner 1964), may not be uniformly successful at certain educational stages (Khattar 2010; Mowat and Davis 2010). Curricula that move away from the hierarchies of learning that are an integral part of the partition of learning into developmental stages (Ernest 2010) may be useful in assisting individuals to achieve their educational potential. Consideration of the UIPS concept and its support for interpretations of information interactions based on network theory and complexity theory are useful in determining the direction of any such move. In particular, considering UIPSs provides support for treating individuals as learning systems that are adaptive and self-organising, as studies in complexity theory suggest (Coghlan and Rigg 2012; Davis et al. 2008, 2004, Dehaene 2007, 2009; Gattegno 2010; Hubka and Eder 2003). The UIPS concept may also support arguments for broadly contextualised rather than subject-oriented viewpoints (Araneda et al. 2019; Butterworth 2006, 2018; Dehaene 2007, 2009; Miller et al. 2019; Perleth and Wilde 2009). The generalised conceptualisations of human cognition couched in terms of the interaction of component UIPSs across the entire human organism, for example, support the view that learned concepts are not necessarily uniquely subject-dependent (Dehaene 2007, 2009) but, rather, that such concepts may integrate information across subjects. Some research in integrative biology has indicated, in fact, that conventional learning and memory processes are related to general attributes of a human cortical advantage, such as an ability to generalise, advantageous attention or WM processes, or an ability in problem-solving (Dehaene 2007, 2009; Goswami 2008). Although executive function, including WM (short-term memory) and related inhibitory processes, has been implicated specifically in subject performances (e.g. in mathematics performances;

9.2 Universal Information Processing Systems …

151

Bull 2008), this may be largely because such processes relate to generalised skills that are concerned with utilising strategies. It is well known, that, even though some regions of brain activation may correspond to concepts described as, say, mathematics or reading, many common brain regions may be activated during the processing of information in any subject (Dehaene 2007, 2009; Gelman and Gallistel 2004). In general, the processes involved in problem-solving (sensu Tonegawa et al. 2003) may activate and reinforce similar core neuronal networks (e.g. the networks needed for processing and organising complex information; Avena-Koenigsberger et al. 2018; Maess et al. 2001; Pessoa 2017). This is consistent with treating each individual as a UIPS since there may be differing component UIPSs that may process information in different ways and over different time intervals, but which may contribute to an assessable human performance across conventional subject boundaries (even if these UIPSs sometimes overlap). Consideration of the UIPS framework supports the view, therefore, that students can benefit from being taught the role of output as feedback to the learning system, as well as techniques for optimising the WM and attention associated with any problem-solving bias of the system in order that multistep or complicated problems can be addressed both within and across subject boundaries (Nelissen 1999; Sweller et al. 1998). The UIPS framework also suggests that students may benefit, as has been suggested in recent studies (Sriraman and Sondergaard 2009), from a greater emphasis on cross-subject concept formation, utilising such a problem-solving bias, rather than continuing the present emphasis on culturally determined subject categories (based on historical practice). One of the advantages of using a generalised view of cognition, such as seen in the UIPS framework, is not only that human learning can be compared across subjects but also that human learning and memory processes can be compared across nonhuman organisms and non-organismal structures. Such comparison has been made already within conventional subject boundaries, with studies of human learning and memory of mathematics, for example, compared with studies that embrace learning and memory concepts described in studies of other organisms that have demonstrated, arguably, mathematics learning (Devlin 2006). Such comparison has also been made in studies of what could be described as mathematics learning in nonorganismal structures, such as computers (Bentley 2007; Krichmar 2018; Sporns 2009). Broad-based studies have begun to detail generalised learning and memory concepts in organismal and non-organismal structures and systems (Alpaydin 2016; Borges 2008; Chaitin 2012; Dennett 1995, 1997). In addition, redescribing organismal and non-organismal learning and memory systems in terms of the broad information processing system as described in the UIPS framework may shed some light on any commonalities that those systems may have with regard to subjects taught in educational institutions. Some progress towards this has been made in studies of computing and robotics where machines have been programmed to perform learning and memory tasks that could be considered as subject-based, not through conventional computer logic but by using systems modelled on learning and memory processes in human and other organismal systems (Bentley 2007; Bentley et al. 2018; Krichmar 2018; Sporns 2009).

152

9 Universal Information Processing Systems, Generalised Educational ...

An important aspect of the consideration of subject-based education and teaching within a generalised view of cognition based in the UIPS framework is the issue of time periods in learning and memory. This issue is beginning to be explored in studies that embrace research showing that learning occurs prior to the entry of an individual to institutionalised education or schooling and continues throughout an individual’s life (Barth et al. 2005; Evers 2007; Howard-Jones 2007; Swanson 2017; Van Merriënboer and Sluijsmans 2010). Preschool concepts may be effectively erased or at least inhibited by the concepts taught at school. Indeed, De Lange (in OECD 2004) has argued that subjects taught in institutionalised education may not always build on the LTM available prior to such education but build a completely new, and relatively unrelated, LTM that may act to inhibit other memory components. Based on such argument, and the broad view from UIPS that time periods must be accommodated in any generalised description of cognition, teaching may be more effective if it builds on the concepts that exist prior to schooling (e.g. by incorporating the universal non-symbolic abilities of the human organism; Barth et al. 2005; Dehaene 2007, 2009) in order to enhance the subject learning that cultural accumulation appears to require (Woolcott 2011, 2013, 2016). It has been suggested in the novel framework outlined here that informational connectivity between each individual UIPS and its environment is central to learning and memory processes, and that the transmission of information into and out of the system must be considered as contributing to any potential change or processing of information over any given time interval. This is particularly the case in learning and assessment in subject-based education since both learning and assessment are related to the input, output and information processing that may result in Memory Expression. The UIPS framework appears to support learning theories that provide insights into the optimal conditions under which learned patterns and connections are formed, whether they be formed in the classroom (Schnotz and Kürschner 2007; Sweller et al. 1998) or other environments, such as those provided online (Kalyuga 2006, 2015; Woolcott et al. 2019). As such, the concept of a broad UIPS framework can provide a wider context for studies of learning and memory, of which subject learning may be only a part. There may be a need to reconfigure subjects in modern curricula, therefore, so that each subject can continue to serve the cultural accumulation upon which modern society is dependent. It may be useful to reconsider subjects within a broad conceptualisation of human learning and memory and human education that embraces modern science and modern educational theories, such as described within the UIPS framework. One of the main advantages of such a broad view is that subject education can be seen from a perspective of human learning and education considered more broadly, as well as from a perspective that embraces learning in other organisms and non-organismal structures. Additionally, the UIPS framework suggests that any development of subject-based education in the future may need to explore the links between input and output patterns of information and their processing within the human UIPS over varying time periods; this exploration includes the interaction of that UIPS and its component UIPSs with external organisms and non-organismal

9.2 Universal Information Processing Systems …

153

structures in the natural or man-made environment, as well as the interactions of such component UIPSs. Acknowledgements Parts of this chapter are adapted from Woolcott (2010). Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496), Analytrics.

References Alpaydin, E. (2016). Machine learning: The new AI. Cambridge, MA: MIT Press. Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., et al. (Eds.). (2000). A taxonomy for learning, teaching and assessing: A revision of Bloom’s taxonomy of educational objectives. New York, NY: Longman. Araneda, D., Guzmán, M. A., & Nussbaum, M. (2019). The national curriculum vs. the ideal curriculum: Acknowledging student learning interests. Oxford Review of Education, 45(3), 333– 349. Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018). Communication dynamics in complex brain networks. Nature Reviews Neuroscience, 19(1), 17–33. Baars, B. J., & Gage, N. M. (2010). Cognition, brain, and consciousness: Introduction to cognitive neuroscience. Cambridge, MA: Academic Press. Baluska, F., Gagliano, M., & Witzany, G. (Eds.). (2018). Memory and learning in plants. Cham, Switzerland: Springer International Publishing. Barabási, A. L. (2016). Network science. Cambridge, UK: Cambridge University Press. Barth, H., La Mont, K., Lipton, J., & Spelke, E. S. (2005). Abstract number and arithmetic in preschool children. Proceedings of the National Academy of Sciences of the USA, 102(39), 14116– 14121. Ba¸sar, E., & Bullock, T. H. (Eds.). (2012). Brain dynamics: Progress and perspectives (Vol. 2). Cham, Switzerland: Springer Science & Business Media. Bates, M. J. (2005). Information and knowledge: An evolutionary framework for information science. Information Research, 10(4) paper 239. Bates, M. J. (2006). Fundamental forms of information. Journal of the American Society for Information Science and Technology, 57(8), 1033–1045. Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 20(2), 87–95. Bentley, P. J. (2007). Systemic computation: A model of interacting systems with natural characteristics. In A. Adamatzky, C. Tueuscher, & T. Asai (Eds.), International Journal of Parallel, Emergent and Distributed Systems (IJPEDS), Special issue on emergent computation (Vol. 22:2, pp. 103–121). Oxford, UK: Taylor & Francis. Bentley, P. J., Brundage, M., Häggström, O., & Metzinger, T. (2018). Should we fear artificial intelligence? In-depth Analysis. European Union, Scientific Foresight Unit (STOA), March 2018 (PE 614.547), 1–40. Blakemore, S. J., & Frith, U. (2000). The implications of recent developments in neuroscience for research on teaching and learning. London, UK: Institute of Cognitive Neuroscience. Borges, R. M. (2005). Do plants and animals differ in phenotypic plasticity? Journal of Bioscience, 30, 41–50. Borges, R. M. (2008). Plasticity comparisons between plants and animals: Concepts and mechanisms. Plant Signaling & Behavior, 3(6), 367–375. Brown, C., & Poortman, C. L. (Eds.). (2018). Networks for learning: Effective collaboration for teacher, school and system improvement. New York, NY: Routledge.

154

9 Universal Information Processing Systems, Generalised Educational ...

Bruce, C., Davis, B., Sinclair, N., McGarvey, L., Hallowell, D., Drefs, M., et al. (2017). Understanding gaps in research networks: Using spatial reasoning as a window into the importance of networked educational research. Educational Studies in Mathematics, 95(2), 143–161. Bruner, J. S. (1964). Towards a theory of instruction. Cambridge, MA: Harvard University Press. Bull, R. (2008). Short-term memory, working memory, and executive functioning in preschoolers: Longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology, 33(3), 205–228. Bullock, T. H., Bennett, M. V., Johnston, D., Josephson, R., Marder, E., & Fields, R. D. (2005). The neuron doctrine, redux. Science, 310(5749), 791–793. Butterworth, B. (2006). Mathematical expertise. In K. A. Ericsson, N. Charness, P. J. Feltovitch, & R. Hoffman (Eds.), The Cambridge handbook on expertise and expert performance (pp. 553–568). Cambridge, UK: Cambridge University Press. Butterworth, B. (2018). Dyscalculia: From science to education. New York, NY: Routledge. Calvin, W. H. (2004). A brief history of the mind: From apes to intellect and beyond. Oxford, UK: Oxford University Press. Carolan, B. V. (2013). Social network analysis and education: Theory, methods and applications. New York, NY: Sage. Casanova, M. F. (2010). Cortical organization: Anatomical findings based on systems theory. Translational Neuroscience, 1(1), 62–71. Casanova, M. F., & Casanova, E. L. (2019). The modular organization of the cerebral cortex: Evolutionary significance and possible links to neurodevelopmental conditions. Journal of Comparative Neurology, 527(10), 1720–1730. Casanova, M. F., & Trippe, J. (2009). Radial cytoarchitecture and patterns of cortical connectivity in autism. Philosophical Transactions of the Royal Society of London, B, 364, 1433–1436. Chaitin, G. J. (2012). Life as evolving software. In H. Zenil (Ed.), A computable universe: Understanding computation and exploring nature as computation (pp. 1–23). London, UK: World Scientific. Coghlan, D., & Rigg, C. (2012). Action learning as praxis in learning and changing. In R. Woodman, W. Pasmore & A. (Rami) Shani (Eds.), Research in organizational change and development (pp. 59–89). Bingley, UK: Emerald. Cohen, J. S., & Stewart, I. (1995). Collapse of chaos: Discovering simplicity in a complex world. London, UK: Penguin. Colom, R., Haier, R. J., Head, K., Álvarez-Linera, J., Quiroga, M. A., Shih, P. C., et al. (2009). Grey matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence, 37(2), 124–135. Cotterill, R. M. J. (2001). Co-operation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: Possible implications for cognition, consciousness, intelligence and creativity. Progress in Neurobiology, 64, 1–33. Cotterill, R. M. J. (2008). The material world. New York, NY: Cambridge University Press. Daly, A. J. (Ed.). (2010). Social network theory and educational change. Cambridge, MA: Harvard Education Press. Davis, B., Phelps, R., & Wells, K. (2004). Complicity: An introduction and welcome. Complicity: An International Journal of Complexity and Education, 1(1), 1–7. Davis, B., Sumara, D., & Luce-Kapler, R. (2008). Engaging minds: Changing teaching in complex times. New York, NY: Routledge. Davis, K. L., & Panksepp, J. (2018). The emotional foundations of personality: A neurobiological and evolutionary approach. New York, NY: WW Norton & Company. Davis, P. J. (2003). Is mathematics a unified whole? SIAM News, 36(2), 1–3. Davis, O. S. P., Kovas, Y., Harlaar, N., Busfield, P., McMillan, A., Frances, J., et al. (2007). Generalist genes and the internet generation: Etiology of learning abilities by web testing at age 10. Genes, Brain and Behaviour, 7, 455–462. Degenaar, J., & O’Regan, J. K. (2017). Sensorimotor theory and enactivism. Topoi, 36(3), 393–407.

References

155

Dehaene, S. (2007). A few steps towards a science of mental life. Mind, Brain, and Education, 1(1), 28–47. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin Viking. Dennett, D. C. (1995). Darwin’s dangerous idea: Evolution and the meanings of life. New York, NY: Simon and Schuster. Dennett, D. (1997). Kinds of minds. London, UK: Phoenix Press. Devlin, K. (2006). The math instinct: Why you’re a mathematical genius (along with lobsters, birds, cats, and dogs). New York, NY: Thunder’s Mouth Press. Dukas, R. (2019). Animal expertise: mechanisms, ecology and evolution. Animal Behaviour, 147, 199–210. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. London, UK: Cambridge University Press. Edelman, G. M. (1987). Neural Darwinism: The theory of neuronal group selection. New York, NY: Basic Books. Edelman, G. M. (1989). The remembered present. New York, NY: Basic Books. Edelman, G. M. (1992). Bright air, brilliant fire. New York, NY: Basic Books. Edelman, G. M. (2007). Learning in and from brain-based devices. Science, 318(5853), 1103–1105. Ericsson, K. A. (2005). Recent advances in expertise research: A commentary on the contributions to the special issue. Applied Cognitive Psychology, 19, 233–241. Ernest, P. (2010). Mathematics and metaphor. Complicity: An International Journal of Complexity and Education, 7(1), 98–104. Evers, C. W. (2007). Lifelong learning and knowledge: Towards a general theory of professional inquiry. In D. N. Aspin (Ed.), Philosophical perspectives on lifelong learning (pp. 173–188). Dordrecht, The Netherlands: Springer. Farah, M. J. (2010). Mind, brain and education in socioeconomic context. In M. Ferrari & L. Vuletic (Eds.), Developmental interplay of mind, brain, and education: Essays in honor of Robbie Vuletic (pp. 243–256). Dordrecht, The Netherlands: Springer. Fine, C. (2014). His brain, her brain? Science, 346(6212), 915–916. Fine, C. (2017). Testosterone Rex: Unmaking the myths of our gendered minds. New York, NY: W. W. Norton. Fischer, K. W., Daniel, D., Immordino-Yang, M. H., Stern, E., Battro, A., & Koizumi, H. (2007). Why Mind, Brain, and Education? Why Now? Mind, Brain, and Education, 1(1), 1–2. Fischer, K. W., Goswami, U., Geake, J., & the Task force on the future of educational neuroscience (2010). The future of educational neuroscience. Mind, Brain, and Education, 4(2), 68–80. Fox, M. D., & Raichle, M. E. (2008). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience, 8, 700–711. Friedman, N. P., & Miyake, A. (2017). Unity and diversity of executive functions: Individual differences as a window on cognitive structure. Cortex, 86, 186–204. Gagliano, M. (2017). The mind of plants: Thinking the unthinkable. Communicative & integrative biology, 10(2), 38427. Gallagher, S., & Lindgren, R. (2015). Enactive metaphors: Learning through full-body engagement. Educational Psychology Review, 27(3), 391–404. Gattegno, C. (2010). In the beginning there were no words: The universe of babies (2nd ed.). New York, NY: Educational Solutions. Geake, J. G. (2009). The Brain at school: Educational neuroscience in the classroom. Berkshire, UK: McGraw Hill-Open University Press. Gelman, R., & Gallistel, C. R. (2004). Language and the origin of numerical concepts. Science, 306, 441–443. Gill, T. G. (2010). Informing business: Research and education on a rugged landscape. Santa Rosa, CA: Informing Science Press.

156

9 Universal Information Processing Systems, Generalised Educational ...

Godfrey-Smith, P. (2002). Environmental complexity and the evolution of cognition. In R. Sternberg & J. Kaufman (Eds.), The evolution of intelligence (pp. 233–249). Mahwah, NJ: Lawrence Erlbaum. Goswami, U. (2008). Cognitive development: The learning brain. Philadelphia, PA: Psychology Press of Taylor and Francis. Grandin, T. (2006). Thinking in pictures and other reports from my life with autism. New York, NY: Vintage, Random House. Grillner, S. (2003). The motor infrastructure: From ion channels to neuronal networks. Nature Reviews Neuroscience, 4, 573–586. Haier, R. J. (2016). The neuroscience of intelligence. Cambridge, MA: Cambridge University Press. Haier, R. J., & Jung, R. E. (2008). Brain imaging studies of intelligence and creativity: What is the picture for education? Roeper Review, 30(3), 171–180. Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2005). The neuroanatomy of general intelligence: Sex matters. NeuroImage, 25(1), 320–327. Halpern, D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Hyde, J. S., & Gernsbacher, M. A. (2007). Sex, math, and scientific achievement. Why do men dominate the fields of science, engineering and mathematics? Scientific American Mind, 18, 44–51. Happé, F., & Vital, P. (2009). What aspects of autism predispose to talent. Philosophical Transactions of the Royal Society, B, 364, 1351–1357. Howard-Jones, P. A., (2007). Introduction to educational “neuromyths”. Transcript of keynote seminar of the all-party parliamentary group on scientific research in learning and education: ‘Brain-science in the classroom’. Conducted by the Institute for the Future of the Mind, England, UK. Howard-Jones, P. A. (2008). Philosophical challenges for researchers at the interface between neuroscience and education. Journal of the Philosophy of Education, 42(3–4), 361–380. Howard-Jones, P. A. (2011). A multiperspective approach to neuroeducational research. Educational Philosophy and Theory, 43(1), 24–30. Howard-Jones, P., & Holmes, W. (2017). Neuroscience research and classroom practice. In J. C. Horvath, J. M. Lodge, & J. Hattie (Eds.), From the Laboratory to the Classroom: Translating Science of Learning for Teachers (pp. 139–154). New York, NY: Routledge. Hubka, V., & Eder, W. E. (2003). Pedagogics of design education. International Journal of Engineering Education, 19(6), 799–809. Huitt, W., & Hummel, J. (2003). Piaget’s theory of cognitive development. Educational Psychology Interactive. Valdosta, GA: Valdosta State University. Retrieved June 2009 from http://www.edp sycinteractive.org/topics/cogsys/piaget.html. Järvilehto, T. (2009). The theory of the organism-environment system as a basis of experimental work in psychology. Ecological Psychology, 21(2), 112–120. Jones, M. R. (2018). Time will tell: A theory of dynamic attending. New York, NY: Oxford University Press. Jung, R. E., Gasparovic, C., Chavez, R. S., Flores, R. A., Smith, S. M., Caprihan, A., et al. (2009). Biochemical support for the “Threshold” theory of creativity: A magnetic resonance spectroscopy study. The Journal of Neuroscience, 29(16), 5319–5325. Kalyuga, S. (2006). Instructing and testing advanced learners: A cognitive load approach. New York, NY: Nova Science. Kalyuga, S. (2011). Informing: A cognitive load perspective. Informing Science: The International Journal of an Emerging Transdiscipline, 14(1), 33–45. Kalyuga, S. (Ed.). (2015). Instructional guidance: A cognitive load perspective. Charlotte, NC: Information Age Publishing. Kanai, R., & Rees, G. (2011). The structural basis of inter-individual differences in human behaviour and cognition. Nature Reviews Neuroscience, 12(4), 231–242. Kaufman, S. B., & Sternberg, R. J. (2008). Conceptions of giftedness. In S. I. Pfeiffer (Ed.), Handbook of giftedness in children: Psycho-educational theory, research, and best practices (pp. 347–365). New York, NY: Springer.

References

157

Kenett, Y. N., Medaglia, J. D., Beaty, R. E., Chen, Q., Betzel, R. F., Thompson-Schill, S. L., et al. (2018). Driving the brain towards creativity and intelligence: A network control theory analysis. Neuropsychologia, 118, 79–90. Khattar, R. (2010). Brought-forth possibilities for attentiveness in the mathematics classroom. Complicity: An International Journal of Complexity and Education, 7(1), 57–62. Kirshner, D., Lerman, S, & Ricks, T. E. (2010). What does network theory contribute to theorization of mathematics teaching? Complicity: An International Journal of Complexity and Education, 7(1), 43–51. Knyazeva, H. (2008). Nonlinear cobweb of cognition. Foundations of Science, 14(3), 167–179. Kop, R., & Hill, A. (2008). Connectivism: Learning theory of the future or vestige of the past? International Review of Research in Open and Distance Learning, 9(3), 1–13. Krapohl, E., Rimfeld, K., Shakeshaft, N. G., Trzaskowski, M., McMillan, A., Pingault, J. B., et al. (2014). The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. Proceedings of the National Academy of Sciences, 111(42), 15273– 15278. Krichmar, J. L. (2018). Neurorobotics—A thriving community and a promising pathway toward intelligent cognitive robots. Frontiers in Neurorobotics, 12, 42. Lachman, R., Lachman, J. L., & Butterfield, E. C. (1979). Cognitive psychology and information processing: An introduction. Hillsdale, NJ: Lawrence Erlbaum. Lakoff, G. (1999). Philosophy in the flesh. A talk with George Lakoff. EDGE interview. Retrieved May 2008 from http://www.edge.org/3rd_culture/lakoff/lakoff_p1.html. Lakoff, G., & Johnson, M. (1999). Metaphors we live by. New York, NY: Basic Books. Lakoff, G., & Núñez, R. E. (2000). Where mathematics comes from: How the embodied mind brings mathematics into being. New York, NY: Basic Books. Lakoff, G. (2006). A response to Steven Pinker’s review of Whose Freedom? The battle over America’s most important ideas. Retrieved in June 2009 from http://scienceblogs.com/gnxp/ 2006/10/pinker vs lakff.php. LeDoux, J. E., & Brown, R. (2017). A higher-order theory of emotional consciousness. Proceedings of the National Academy of Sciences, 114(10), E2016–E2025. Lipton, J. S., & Spelke, E. S. (2003). Origins of number sense: Large number discrimination in human infants. Psychological Science, 14, 396–401. Llinás, R. (2001). I of the vortex: From neurons to self . Cambridge, MA: MIT Press. Logie, R. (2018). Human cognition: Common principles and individual variation. Journal of Applied Research in Memory and Cognition, 7(4), 471–486. Lü, J., Yu, X., Chen, G., & Yu, W. (2016). Complex systems and networks. Berlin, Germany: Springer. Lucas, C. (2005). Evolving an integral ecology of mind. Cortex, 41(5), 709–726. Lyon, R. (2005). The Health Report: 17 January 2005—Literacy. [Radio broadcast]. Australia: ABC. Retrieved in April 2008 from http://www.abc.net.au/rn/talks/8.30/helthrpt/stories/s1266657.htm. Maess, B., Koelsch, S., Gunter, T. C., & Friederici, A. D. (2001). Musical syntax is processed in Broca’s Area: An EMG study. Nature Neuroscience, 4, 540–545. Margoliash, D., & Nusbaum, H. C. (2009). Language: The perspective from organismal biology. Trends in Cognitive Science, 13(12), 505–510. Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141–144. Miller, P. W., Roofe, C., & García-Carmona, M. (2019). School leadership, curriculum diversity, social justice and critical perspectives in education. In P. Angelle & D. Torrance (Eds.), Cultures of social justice leadership (pp. 93–119). Cham, Switzerland: Palgrave Macmillan. Morrison, K. (2012). School leadership and complexity theory. New York, NY: Routledge. Morton, J., & Frith, U. (1995). Causal modelling: A structural approach to developmental psychopathology. In D. Cicchetti & D. Cohen (Eds.), Manual of developmental psychopathology (pp. 357–362). New York, NY: John Wiley & Sons.

158

9 Universal Information Processing Systems, Generalised Educational ...

Mottron, L. (2016). Is autism a different kind of intelligence? New insights from cognitive neurosciences. Bulletin de l’Academie nationale de medecine, 200(3), 423–434. Mottron, L., Dawson, M., & Soulières, I. (2009). What aspects of autism predispose to talent. Philosophical Transactions of the Royal Society of London, B, 364, 1351–1357. Mowat, E., & Davis, B. (2010). Interpreting embodied mathematics using network theory: Implications for mathematics education. Complicity: An International Journal of Complexity and Education, 7(1), 1–31. Nelissen, J. M. C. (1999). Thinking skills in realistic mathematics. In J. H. M. Hamers, J. E. H. Hamers, & B. Csapó (Eds.), Teaching and learning thinking skills (pp. 189–213). Lisse, The Netherlands: Swets & Zeitlinger. Newman, M. E. J. (2018). Networks. London, UK: Oxford University Press. Newman, M. E. J., Barabási, A. L. E., & Watts, D. J. (2006). The structure and dynamics of networks. Princeton, NJ: Princeton University Press. Opris, I., & Casanova, M. F. (2017). The physics of the mind and brain disorders. Cham, Switzerland: Springer International Publishing. Organisation for Economic Co-operation and Development (OECD). (2003a). Literacy network and numeracy network deliberations, January 2003. Paris, France: OECD Publications. Organisation for Economic Co-operation and Development (OECD). (2003b). Assessing scientific, reading and mathematical literacy: A framework for PISA 2006. Paris, France: OECD Publications. Organization for Economic Cooperation and Development (OECD). (2004). Learning Sciences and Brain Research: 2nd Literacy and Numeracy Networks Meeting, 2004. Paris, France: OECD Publications. Panksepp, J. (2004). Affective neuroscience: The foundations of human and animal emotions. Oxford, UK: Oxford University Press. Perleth, C., & Wilde, A. (2009). Developmental trajectories of giftedness in children. In L. V. Shavinina (Ed.), International handbook on giftedness (pp. 319–335). Dordrecht, The Netherlands: Springer. Perone, S., & Simmering, V. R. (2017). Applications of dynamic systems theory to cognition and development: New frontiers. In J. B. Benson (Ed.), Advances in child development and behaviour (Vol. 52, pp. 43–80). London, UK: Academic Press. Pessoa, L. (2017). A network model of the emotional brain. Trends in Cognitive Sciences, 21(5), 357–371. Plass, J. L., & Kalyuga, S. (2019). Four ways of considering emotion in cognitive load theory. Educational Psychology Review, 31(2), 339–359. Plomin, R., & Kovas, Y. (2005). Generalist genes and learning disabilities. Psychological Bulletin, 131(4), 592–617. Plomin, R., Kovas, Y., & Haworth, C. M. (2007). Generalist genes: Genetic links between brain, mind, and education. Mind, Brain, and Education, 1(1), 11–19. Postle, B. R. (2015). Neural bases of the short-term retention of visual information. In P. Jolicoeur, C. Lefebvre, & J. Martinez-Trujillo (Eds.), Mechanisms of sensory working memory: Attention and performance XXV (pp. 43–58). London, UK: Academic Press. Raichle, M. E. (2010). Two views of brain function. Trends in Cognitive Science, 14(4), 180–190. Raichle, M. E., Raut, R. V., & Mitra, A. (2019). How Many Types Are There? In W. Singer, T. J. Sejnowski, & P. Rakic (Eds.), The neocortex (pp. 97–108). Cambridge, MA: MIT Press. Reading, A. (2006). The biological nature of meaningful information. Biological Theory, 1(3), 243–249. Redifer, J. L., Bae, C. L., & DeBusk-Lane, M. (2019). Implicit theories, working memory, and cognitive load: Impacts on creative thinking. SAGE Open, 9(1), 2158244019835919. Rimfeld, K., Kovas, Y., Dale, P. S., & Plomin, R. (2016). True grit and genetics: Predicting academic achievement from personality. Journal of Personality and Social Psychology, 111(5), 780–789. Samuels, B. M. (2009). Can the differences between education and neuroscience be overcome by mind, brain, and education? Mind, Brain, and Education, 3(1), 45–55.

References

159

Schank, R. C. (2015). Teaching minds: How cognitive science can save our schools. New York, NY: Teachers College Press. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19, 469–508. Selzam, S., Krapohl, E., von Stumm, S., O’Reilly, P. F., Rimfeld, K., Kovas, Y., et al. (2017). Predicting educational achievement from DNA. Molecular psychiatry, 22(2), 267. Shaw, P., Greenstein, D., Lerch, J., Clarsen, L., Lenroot, R., Gogtay, N., et al. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440(7084), 676–679. Shell, D. F., Brooks, D. W., Trainin, G., Wilson, K. M., Kauffman, D. F., & Herr, L. M. (2010). The unified learning model: How motivational, cognitive, and neurobiological sciences inform best teaching practices. Dordrecht, The Netherlands: Springer. Siemens, G. (2017). Connectivism. In R. West (Ed.), Foundations of learning and instructional design technology. Montreal, Canada: Pressbooks. Simon, M. A. (2009). Amidst multiple theories of learning in mathematics education. Journal for Research in Mathematics Education, 40(5), 477–490. Sporns, O. (2009). From complex networks to intelligent systems. In B. Sendhoff, E. Körner, O. Sporns, H. Ritter, & K. Doya (Eds.), Creating brain-like intelligence: From basic principles to complex intelligent systems (pp. 15–30). Berlin, Germany: Springer. Sporns, O. (2010). Networks of the brain. Cambridge, MA: MIT Press. Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT press. Squire, L. R., & Kandel, E. R. (2008). Memory: From mind to molecules (2nd ed.). Greenwood Village, CA: Roberts & Company. Sriraman, B., & Sondergaard, B. D. (2009). On bringing interdisciplinary ideas to gifted education. In L. V. Shavinina (Ed.), International handbook on giftedness (pp. 1235–1256). Dordrecht, The Netherlands: Springer. Stamovlasis, D., & Tasparlis, G. (2005). Cognitive variables in problem solving: A nonlinear approach. International Journal of Science and Mathematics Education, 3, 7–32. Swanson, H. L. (2017). Verbal and visual-spatial working memory: What develops over a life span? Developmental Psychology, 53(5), 971–995. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285. Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4, 295–312. Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9–31. Sweller, J. (2009). Cognitive bases of human creativity. Educational Psychology Review, 21, 11–19. Sweller, J. (2016). Cognitive load theory, evolutionary educational psychology, and instructional design. In D. Geary & D. Berch (Eds.), Evolutionary perspectives on child development and education (pp. 291–306). Cham, Switzerland: Springer. Sweller, J., & Mann, L. (2011). The psychology of creativity and its educational consequences. In L. Mann & J. Chan (Eds.), Creativity and innovation in business and beyond: Social science perspectives and policy implications (pp. 223–238). New York, NY: Routledge. Sweller, J., van Merriënboer, J., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296. Sylwester, R. (1995). A celebration of neurons: An educator’s guide to the human brain. Alexandria, VA: Association for Supervision and Curriculum Development. Thelen, E., & Smith, L. B. (1998). Dynamic systems theories. In R. M. Lerner (Ed.), Handbook on child psychology: Vol. 1. Theoretical models of human development (5th ed., pp. 563–634). New York, NY: Wiley. Tonegawa, S., Nakazawa, K., & Wilson, M. A. (2003). Genetic neuroscience of mammalian learning and memory. Philosophical Transactions of the Royal Society of London, B, 358, 787–795. Trewavas, A. (2016). Intelligence, cognition, and language of green plants. Frontiers in Psychology, 7, 588.

160

9 Universal Information Processing Systems, Generalised Educational ...

van Duijn, M. (2017). Phylogenetic origins of biological cognition: Convergent patterns in the early evolution of learning. Interface Focus, 7(3), 20160158. Van Merriënboer, J. J. G., & Sluijsmans, D. M. A. (2010). Toward a synthesis of cognitive load theory, four-component instructional design, and self-directed learning. Educational Psychology Review, 21(1), 55–66. Varela, F., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human behaviour. Cambridge, MA: MIT Press. Watts, D. J. (2004). Six degrees: The science of a connected age. New York, NY: W.W. Norton. Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media. Woolcott, G. (2009). Mathematics education in modern industrialised society: Approaches from biology. Mathematics it’s mine: Proceedings of the 22nd Biennial Conference of the Australian Association of Mathematics Teachers, Fremantle, Western Australia (pp. 200–208). Adelaide, Australia: The Australian Association of Mathematics Teachers. Woolcott, G. (2010). Learning and memory: A biological viewpoint. In G. Tchibozo (Ed.), Proceedings of the 2nd Paris International Conference on Education, Economy & Society (pp. 487–496). Strasbourg, France: Analytrics. Woolcott, G. (2011). A broad view of education and teaching based in educational neuroscience. International Journal for Cross-Disciplinary Subjects in Education, Special Issue, 1(1), 601–606. Woolcott, G. (2012). Everything is connected: Giftedness within a broad framework for cognition. Invited commentary on Ziegler & Phillipson. High Ability Studies, 23(1), 115–117. Woolcott, G. (2013). Giftedness as cultural accumulation: An information processing perspective. High Ability Studies, 24(2), 153–170. Woolcott, G. (2016). Technology and human cultural accumulation: The role of emotion. In S. Tettegah & R. E. Ferdig (Eds.), Emotions, technology, and learning (pp. 243–263). London, UK: Academic Press. Woolcott, G., Chamberlain, D., Whannell, R., & Galligan, L. (2018). Examining undergraduate student retention in mathematics using network analysis and relative risk. International Journal of Mathematical Education in Science and Technology TMES, 50(3), 447–463. Woolcott, G., Chamberlain, D., Keast, R., & Farr-Wharton, B. (2017). Modelling success networks to improve the quality of undergraduate education. Quality in Higher Education, 23(2), 120–137. Woolcott, G., Seton, C., Mason, R., Chen, O., Lake, W., Markopoulos, C., et al. (2019). Developing a new generation MOOC (ngMOOC): A design-based implementation research project with cognitive architecture and student feedback in mind. European Journal of Open, Distance and E-learning, 22(1), 14–35. Ziegler, A., & Phillipson, S. N. (2012). Towards a systemic theory of gifted education. High Ability Studies, 23(1), 3–30.

Concluding Remarks

The novel conceptualisations of Universal Information and Universal Information Processing Systems (UIPSs) described in this book may be useful in obtaining a clearer understanding of the concepts of learning and memory as pathways of connectivity in environmental contexts. Such an understanding can enhance the understanding of human learning and memory, as well as education and cultural accumulation. In addition, the application of the learning and memory framework developed from these novel conceptualisations may lead to improvements in education and teaching that serve the transgenerational transmission of human culture and the continued existence of humanity in the modern world. There appears to be some reluctance in research generally, and in educational research specifically, to utilise comparisons of learning and memory processes, or cognitive processes, across different organisms, even when such processes are similar to those seen in humans. This book addresses this issue by comparing such processes in humans and other organisms as well as by comparing such processes as described for non-organismal structures, both natural and man-made. These comparisons are utilised to develop novel conceptualisations of information and information processing and the related conceptualisation of a UIPS. In these conceptualisations, information is considered as the common element of learning and memory pathways of all such organisms and structures, and this information is described in terms of the matter and energy that makes up the components of the organismal and non-organismal universe. Information processing is described as referring to changes in this information in particular time intervals, with each discrete entity in the universe described as a UIPS. The single theoretical system envisioned in the UIPS concept accommodates the description, within the UIPS framework, of the learning and memory pathways of different organisms and non-organismal structures, including those described in this book, where such pathways can be expressed in terms of matter and energy interactions. There appears to be a similar reluctance in research in accepting that human learning and memory processes depend not just on the nervous system but also on the numerous connections of each individual with the universe at large, through

© Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3

161

162

Concluding Remarks

even apparently simple connections to such environmental factors as heat and nutrients. It is only in recent times that more holistic views have begun to develop from systemic studies of human interaction with the physical environment, along with an acknowledgement that this environment includes other organisms, including other people. These holistic views are reflected in the UIPS framework, which describes the environmental interactions of all organisms and non-organismal structures in terms of broad learning and memory concepts integral to each UIPS. A single concept, Learning Potential, relates to this broad view of learning and refers to any change in information in a UIPS through environmental input or output. The broad view of memory, however, is divided conceptually into two parts: Memory Potential, which refers to the overarching range of possible interactions within the UIPS (e.g. chemical or energetic pathways); and Memory Expression, which refers to any observed changes in a given time interval. This bipartite concept of memory can be considered effectively as an unseen part, Memory Potential, and seen part, Memory Expression, although the interactions observed as Memory Expression are in actuality part of Memory Potential. The UIPS concept accommodates the consideration that environmental interactions of organisms and non-organismal structures can be described in terms of information chains and pathways, some of which may operate as complex systems or networks. This consideration is supported in research on the description of such networks in the human nervous system and elsewhere, and research indicating that, in general, the connectivity of such networks may be independent of the nature or components of those networks. The UIPS framework, therefore, encapsulates an infrastructure that has application to the description of connectivity in all organisms and non-organismal structures. In addition, besides acknowledging the relevance of component UIPSs and their interactions in the emergence and biological contexts, the UIPS framework does not require a separation of structure and system or a separation of biological and social interactions. The consideration here of the UIPS framework indicates that all aspects of internal and external environmental interactions, including the interaction of component UIPSs, may need to be considered in the study of human learning and memory processes. Conventional views of learning and memory in human individuals can be described in terms of connectivity with the environment, inclusive of connectivity within the nervous system and the brain—an holistic view with support from neuroscience. Considered within the UIPS framework, this can be viewed in terms of the interaction of differing component UIPSs operating within the larger dynamic human UIPS that acts system-wide with dedicated flexibility in adapting individuals to a range of environmental interactions (e.g. through remembered scenarios, feedback and muscular action). The UIPS framework accommodates arguments that only a small fraction of this dynamism operates in conscious learning and that this dynamism operates, both consciously and unconsciously, during the formation of LTM and its spatiotemporal sequencing, including during the linkage of the cognitive aspects of emotion and chemical reward with learning and memory processes. The UIPS framework, however, also allows a formal partitioning of the cognitive structures and interactions involved in learning and memory, as is common in modern

Concluding Remarks

163

science, while supporting the view that learned and remembered concepts are not anatomically isolated or uniquely subject-dependent. The treatment of a human within the UIPS framework, with its separation of conventional learning into memory into three parts—Learning Potential, Memory Potential and Memory Expression—consequently offers a more complex understanding of human learning and memory within a broad context of environmental interaction through the connectivity of information, particularly with regard to the processing that is observed in different time intervals. As such, the description of UIPS supports a consideration that the processes of memory storage over a number of differing time intervals can be separated effectively from both input from and output to the environment, or from any direct behavioural or other observable consequences. This separation is supported by descriptions in both cognitive psychology and integrative biology, as well as in combination and interdisciplinary studies, of variation over time of conventional learning and memory processes (including processes such as thinking) that do not involve observable input or output (e.g. output as motion). Since the UIPS framework supports temporal considerations in learning and memory as seen, for example, in differential development trajectories and lifetime learning, the framework offers advantages in examining the rates at which individuals learn. This book applies the UIPS framework to issues related to scientific integration of educational theories and argues that, where these theories, or the assumptions and analogies upon which they are based, can be expressed in terms of connectivity of matter and energy pathways, there is potential for such integration. As such, the description of educational theories in terms of the single UIPS framework is useful in teaching practices that require a consistent theoretical background. As an example, the UIPS framework is used here to illustrate how the principles of CLT could be integrated scientifically and be amenable to the predictive testing that is integral to modern science. This illustration indicates that, given a broader examination of CLT within the UIPS framework (perhaps using analyses of connectivity), cognitive concepts, such as cognitive load and mental effort, can be made measurable in a scientific way. Such measurement capability may provide information additional to that provided in the subjective assays used commonly in studies utilising CLT. An additional application of the UIPS framework in such examinations may be in the consideration of implicit learning, as well as other types of unconscious learning, that are not utilised currently in CLT. In addition, the examination of CLT within the UIPS framework indicates that the educational principles based in Sweller’s (2004, 2007) natural information processing systems may be generalised as principles that apply to all UIPSs and may, therefore, be useful in examining the relationship of the state and dynamics of UIPSs in relation to environmental input and output. This has application in the consideration of learning and memory in organisms and non-organismal structures—not just in human learning—since the UIPS concept indicates that, considered broadly, learning and memory processes are system-wide in a UIPS, resulting from interacting internal component UIPSs as well as from environmental interaction. This broad sense of information processing and the two-way information transmission between organisms or the structure and environment is used here to delineate novel generalised

164

Concluding Remarks

principles. The two novel principles outlined in this book, as well as others that may be developed in the future, are useful complements to those already developed through examining the educational principles of CLT. In considering a human individual as a UIPS, there may be differing component UIPSs that process information in different ways, but which contribute to an assessable human performance. The contribution of components that function in motivation and emotion and creativity, for example, has been a consideration in examining generalised cognitive processes, where components of the cognitive system are treated effectively as components of an integrated human system. Consideration of interactions of such components, therefore, at various levels of interaction through the lens of the UIPS concept, may be useful in application to systemic approaches to education. The UIPS concept, for example, can be useful in examining educational theories based on descriptions of information pathways and studies of networks as proposed in complexity arguments. In particular, since the UIPS concept is relational and links structures and environment dynamically, it is useful in examining those theories that are based on the application of the concepts of embodiment and enactivation. Since the UIPS framework may be useful as a lens with which to examine information pathways or interactions of component systems that are complex rather than linear, the framework may facilitate moves away from the linear approach of learning hierarchies seen in curricula of the modern industrial world. Such examination may also generate a more detailed account of information being taught and its basis in prior knowledge through a better understanding of the links that occur in environmental information and the actual networks or pathways that are used to store that information over varying time intervals. Such an account may facilitate the delineation of the sometimes-complex pathways involved in human–environmental interaction, including the interaction involved in problem-solving—arguably the main function of learning and memory. In particular, such an account may provide support for the treatment of individuals as learning systems that are adaptive and self-organising as some modern studies suggest. Educational theories and practices need to be able to build memories in accordance with cultural knowledge requirements, particularly as they apply to survival in the modern industrialised world. The success of such theories and practices hinges on their enhancing cultural accumulation, as well as cultural ratcheting, in the time frame of an individual lifetime. The assumptions of science have proven useful in this industrialised world, forming the basis of much of integrative biology and modern industrial development. Indeed, modern science can be argued to be the major driver of developments in technology, particularly as these relate to electronic media and informational connectivity. The consideration of the UIPS concept, therefore, based as it is on the assumptions of modern science, may have some usefulness in examining and developing educational theories and practices that contribute to cultural accumulation in technology-rich industrialised societies. The broad UIPS framework outlined here, and any educational principles based within it, can provide an overall perspective from which to view the system-wide human interaction with the environment upon which institutional education relies in such industrialised societies.

Concluding Remarks

165

This book presents an argument that conventional understanding of learning and memory can be accommodated within the concept of a human UIPS, where each and every information pathway (including those of any component UIPS) is considered as part of the totality of human learning and memory. There are, at present, no educational theories that utilise an estimate of the contribution that is made to learning effectiveness within the human organism by the total of any informational interaction, or that incorporate all of the details of all of the pathways that are involved in communication with the non-human environment. There are no theories either that consider all inputs and outputs from all sources of information for the nervous system. As a result, there is no adequate understanding of environmental informational transmission for the human organism, and no adequate understanding of the contribution to human cognition of such transmission over a variety of time intervals. Although obtaining such an understanding may prove to be a complicated task, modern developments in studies of networks and complex systems in combination with studies within the UIPS framework offer a direction, at least, for future development. The usefulness of the description of a UIPS may be limited by a number of factors, including the logistical difficulty and perhaps the economic cost of documenting even a simple system over a number of differing time periods. However, solutions to this difficulty may lie in examining the matter and energy interactions of component UIPSs at differing scales of complexity and different levels of emergence. The usefulness of the UIPS concept is also limited in that it may not have an application to any examination of educational theories that cannot be clearly described in terms of the matter and energy interactions that form the basis of the UIPS framework. It is useful, however, to use the UIPS description to frame a scientifically based examination of educational research based on network theory and in complexity theory, since such research appears to utilise descriptions of information pathways that can be described in terms of modern science. The main focus of future research using the UIPS concept may be, however, in the examination of the interactions of the component UIPSs of the nervous system and its interactions with other component UIPSs of the human UIPS in the process of environmental interaction through problem-solving. In this way, it may be possible to consider together cognition and behaviour in relation to the environment and the relevance of this consideration to education and cultural accumulation.

Glossary of Terms

Long-Term Memory (LTM): The encoding or storage in memory of environmental information as information that may be recalled and applied on demand for longer, generally indefinite periods of time. Short-Term Memory (STM): The encoding or storage in memory of environmental information as information that may be recalled and applied on demand for short periods of time (e.g. up to 20 s). Working Memory (WM): Involves the recall of concepts from LTM and their comparison with new input, with full consciousness thought to be crucial for WM and any associated attentional processes. Sometimes considered as equivalent to STM. Problem-Solving: The interaction of novel input information with stored information in LTM using WM and attentional processes, with some studies viewing problemsolving as the main function of learning and memory. Cognitive Load Theory (CLT): A theory that accommodates the view of learning and memory as the processing of environmental information while, at the same time, maintaining the conceptualisation of human cognitive architecture in terms of a large LTM constrained by attentional and WM processes that contribute to cognitive load or mental effort. Parieto-Frontal Integration Theory (P-FIT): A theory in neuroscience based on the amount of grey matter (neuronal cell bodies) activated across a number of different brain regions being correlated with test scores from several different assessments of creativity and intelligence. Universal Information Processing System (UIPS): Within a matter and energy universe, any structure or system, including the universe or any spatiotemporal division of the universe. Memory Potential: Within a UIPS, Universal System Memory Potential (Memory Potential) is the potential for certain matter and energy interactions to occur within a UIPS in a given time interval. © Springer Nature Singapore Pte Ltd. 2020 G. Woolcott, Reconceptualising Information Processing for Education, https://doi.org/10.1007/978-981-15-7051-3

167

168

Glossary of Terms

Learning Potential: Within a UIPS, Universal System Learning Potential (Learning Potential) is said to occur if there is a change in Memory Potential due to a communication of environmental information into or out of that UIPS in a given time interval. Memory Expression: Within a UIPS, any observed state of that UIPS in a given time interval is termed as Universal System Memory Expression (Memory Expression). Learning Preparedness Principle: The first novel principle developed within the UIPS framework, the Learning Preparedness Principle, can be described as a chemical (matter) and energetic learning preparedness where there is a minimum condition for internal information, or Memory Potential, of the entire UIPS in order for input and output communication of any information to occur and, therefore, to be considered as Learning Potential. Minimum Information Principle: Set in terms of cognitive psychology, this principle has some equivalence to the Learning Preparedness Principle, with relevance to available knowledge structures for informing (such as schemas) and the capacity of information processing channels. Environmental Connectivity Principle: Refers to the situation where information transmission between the environment and a UIPS can only occur if there is appropriate connectivity of that UIPS with that environment. Information Filtering Principle: Set in terms of cognitive psychology, this principle has some equivalence to the Environmental Connectivity Principle, indicating that there may be a need to qualify this Principle in terms of filters or in terms of how the principle would impact on methods recommended for reducing extraneous cognitive load.