Representation in Cognitive Science Representation in Cognitive Science Nicholas Shea 1 3 Great Clarendon Street, Oxford, OX2 6DP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © Nicholas Shea 2018 The moral rights of the author have been asserted First Edition published in 2018 Impression: 1 Some rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, for commercial purposes, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. 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To Ellie Huffing and puffing with correlation and function might give us a good account of subdoxastic aboutness . [but it is unlikely to work for the content of doxastic states.]* Martin Davies (pers. comm.), developed in Davies (2005) * Not that the antecedent is easy. And even for the subpersonal case we may have to puff on a few more ingredients. But I too am optimistic that we can get a good account. This book aims to show how. Preface The book is in three parts: introduction, exposition, and defence. Part I is introductory and light on argument. Chapter 1 is about others’ views. Chapter 2 is the framework for my own view. I don’t rehearse well-known arguments but simply gesture at the literature. The aim is to demarcate the problem and motivate my own approach. Part II changes gear, into more standard philosophical mode. It aims to state my positive view precisely and to test it against a series of case studies from cognitive science. Part III engages more carefully with the existing literature, showing that the account developed in Part II can deal with important arguments made by previous researchers, and arguing that the framework put forward in Part I has been vindicated. There is a paragraph-by-paragraph summary at the end of the book. Readers who want to go straight to a particular issue may find this a useful guide. It replaces the chapter summaries often found at the end of each chapter of a monograph. The bibli- ography lists the pages where I discuss each reference, so acts as a fine-grained index to particular issues. There is also the usual keyword index at the end. Contents Part I 1. Introduction 3 1.1 A Foundational Question 3 1.2 Homing In on the Problem 8 1.3 Existing Approaches 12 1.4 Teleosemantics 15 1.5 Challenges to Teleosemantics 18 2. Framework 25 2.1 Setting Aside Some Harder Cases 25 2.2 What Should Constrain Our Theorizing? 28 2.3 Externalist Explanandum, Externalist Explanans 31 2.4 Representation Without a Homunculus 36 2.5 What Vehicle Realism Buys 37 2.6 Pluralism: Varitel Semantics 41 Part II 3. Functions for Representation 47 3.1 Introduction 47 3.2 A Natural Cluster Underpins a Proprietary Explanatory Role 48 3.3 Robust Outcome Functions 52 3.4 Stabilized Functions: Three Types 56 (a) Consequence etiology in general, and natural selection 56 (b) Persistence of organisms 57 (c) Learning with feedback 59 (d) A ‘very modern history’ theory of functions 62 3.5 Task Functions 64 3.6 How Task Functions Get Explanatory Purchase 67 (a) Illustrated with a toy system 67 (b) Swamp systems 69 3.7 Rival Accounts 72 3.8 Conclusion 74 4. Correlational Information 75 4.1 Introduction 75 (a) Exploitable correlational information 75 (b) Toy example 80 4.2 Unmediated Explanatory Information 83 (a) Explaining task functions 83 (b) Reliance on explanation 88 (c) Evidential test 89 x contents 4.3 Feedforward Hierarchical Processing 91 4.4 Taxonomy of Cases 94 4.5 One Vehicle for Two Purposes 96 4.6 Representations Processed Differently in Different Contexts 97 (a) Analogue magnitude representations 97 (b) PFC representations of choice influenced by colour and motion 100 4.7 One Representation Processed via Two Routes 103 4.8 Feedback and Cycles 106 4.9 Conclusion 110 5. Structural Correspondence 111 5.1 Introduction 111 5.2 The Cognitive Map in the Rat Hippocampus 113 5.3 Preliminary Definitions 116 5.4 Content-Constituting Structural Correspondence 120 (a) Exploitable structural correspondence 120 (b) Unmediated explanatory structural correspondence 123 5.5 Unexploited Structural Correspondence 126 5.6 Two More Cases of UE Structural Correspondence 132 (a) Similarity structure 132 (b) Causal structure 134 5.7 Some Further Issues 137 (a) Exploiting structural correspondence cannot be assimilated to exploiting correlation 137 (b) Approximate instantiation 140 (c) Evidential test for UE structural correspondence 142 5.8 Conclusion 143 Part III 6. Standard Objections 147 6.1 Introduction 147 6.2 Indeterminacy 148 (a) Aspects of the problem 148 (b) Determinacy of task functions 150 (c) Correlations that play an unmediated role in explaining task functions 151 (d) UE structural correspondence 154 (e) Natural properties 155 (f) Different contents for different vehicles 156 (g) The appropriate amount of determinacy 157 (h) Comparison to other theories 158 6.3 Compositionality and Non-Conceptual Representation 162 6.4 Objection to Relying on (Historical) Functions 166 (a) Swampman 166 (b) Comparison to Millikan and Papineau 169 contents xi 6.5 Norms of Representation and of Function 171 (a) Systematic misrepresentation 171 (b) Psychologically proprietary representation 174 6.6 Conclusion 175 7. Descriptive and Directive Representation 177 7.1 Introduction 177 7.2 An Account of the Distinction 179 7.3 Application to Case Studies 183 (a) UE information 183 (b) UE structural correspondence 185 7.4 Comparison to Existing Accounts 188 7.5 Further Sophistication 192 (a) More complex directive systems 192 (b) Another mode of representing 193 7.6 Conclusion 194 8. How Content Explains 197 8.1 Introduction 197 8.2 How Content Explains 198 (a) Explanatory traction in varitel semantics 198 (b) Non-semantic causal description? 200 (c) Doing without talk of representation 204 (d) Other views about the explanatory purchase of content 205 8.3 Causal Efficacy of Semantic Properties 208 8.4 Why Require Exploitable Relations? 209 8.5 Ambit of Varitel Semantics 210 (a) Representation only if content is explanatory? 210 (b) Are any cases excluded? 213 8.6 Development and Content 216 8.7 Miscellaneous Qualifications 218 8.8 How to Find Out What Is Represented 221 8.9 Differences at the Personal Level 222 Paragraph-by-Paragraph Summary 227 Acknowledgements 267 Figure Credits 269 References 271 Index 285 PART I 1 Introduction 1.1 A Foundational Question 3 1.2 Homing In on the Problem 8 1.3 Existing Approaches 12 1.4 Teleosemantics 15 1.5 Challenges to Teleosemantics 18 1.1 A Foundational Question The mind holds many mysteries. Thinking used to be one of them. Staring idly out of the window, a chain of thought runs through my mind. Concentrating hard to solve a problem, I reason my way through a series of ideas until I find an answer (if I’m lucky). Having thoughts running through our minds is one of the most obvious aspects of the lived human experience. It seems central to the way we behave, especially in the cases we care most about. But what are thoughts and what is this process we call thinking? That was once as mysterious as the movement of the heavens or the nature of life itself. New technology can fundamentally change our understanding of what is possible and what mysterious. For Descartes, mechanical automata were a revelation. These fairground curiosities moved in ways that looked animate, uncannily like the move- ments of animals and even people. A capacity that had previously been linked inextric- ably to a fundamental life force, or to the soul, could now be seen as purely mechanical. Descartes famously argued that this could only go so far. Mechanism would not explain consciousness, nor the capacity for free will. Nor, he thought, could mechan- ism explain linguistic competence. It was inconceivable that a machine could produce different arrangements of words so as to give an appropriately grammatical answer to questions asked of it.1 Consciousness and free will remain baffling. But another machine has made what was inconceivable to Descartes an everyday reality to us. Computers produce appropriately arranged strings of words—Google even annoyingly finishes half-typed sentences—in ways that at least respect the meaning of the words they churn out. Until quite recently a ‘computer’ was a person who did calculations. Now we know that calculations can be done mechanically.