The Mind as a Predictive Modelling Engine: Generative Models, Structural Similarity, and Mental Representation Daniel George Williams Trinity Hall College University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy August 2018 The Mind as a Predictive Modelling Engine: Generative Models, Structural Similarity, and Mental Representation Daniel Williams Abstract I outline and defend a theory of mental representation based on three ideas that I extract from the work of the mid-twentieth century philosopher, psychologist, and cybernetician Kenneth Craik: first, an account of mental representation in terms of idealised models that capitalize on structural similarity to their targets; second, an appreciation of prediction as the core function of such models; and third, a regulatory understanding of brain function. I clarify and elaborate on each of these ideas, relate them to contemporary advances in neuroscience and machine learning, and favourably contrast a predictive model-based theory of mental representation with other prominent accounts of the nature, importance, and functions of mental representations in cognitive science and philosophy. For Marcella Montagnese Preface Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit for the relevant Degree Committee. Acknowledgements This work was supported by the Arts and Humanities Research Council. In addition to the many intellectual debts that I note in the thesis, I would also like to thank: • Richard Holton, whose combination of patience, encouragement, and scepticism made for an ideal supervisor; • Jakob Hohwy for his invaluable feedback and generosity; • Huw Price for his support, and for his appealing combination of naturalism and pragmatism that had a big influence on my philosophical development; • the Cognition and Philosophy Lab at Monash—especially Andrew Corcoran, Stephen Gadsby, Julian Matthews, and Kelsey Palghat—for their extremely useful feedback, and for providing an ideal research community; • Lincoln Colling, a brilliant collaborator; • Juliet Griffin, a force of nature; • the philosophy graduate community at Cambridge; • Tim Crane and Marta Halina for useful advice at different stages of the PhD; • the anonymous reviewers for the helpful and constructive feedback on the papers that I have published whilst a PhD student; • Michael Morris and Sarah Sawyer, whose influence on me at Sussex played a large part in my decision to pursue philosophy further; • My family—mum, dad, Beck, Josh, and Sam—for everything; • and Marcella Montagnese, who made the second half of the PhD roughly 20 times more enjoyable than the first half. Although almost all of the thesis is new, it draws heavily on ideas from the following published articles (cited at relevant points in the text): Gadsby, S., & Williams, D. (2018). Action, affordances, and anorexia: body representation and basic cognition. Synthese. doi: 10.1007/s11229-018-1843-3 Williams, D. (2017). Predictive Processing and the Representation Wars. Minds And Machines, 28(1), 141-172. doi: 10.1007/s11023-017-9441-6 Williams, D. (2018). Hierarchical Bayesian models of delusion. Consciousness And Cognition, 61, 129-147. doi: 10.1016/j.concog.2018.03.003 Williams, D. (2018). Pragmatism and the predictive mind. Phenomenology And The Cognitive Sciences. doi: 10.1007/s11097-017-9556-5 Williams, D. (2018). Predictive coding and thought. Synthese. doi: 10.1007/s11229- 018-1768-x Williams, D. (2018). Predictive minds and small-scale models: Kenneth Craik’s contribution to cognitive science. Philosophical Explorations, 21(2), 245-263. doi: 10.1080/13869795.2018.1477982 Williams, D., & Colling, L. (2017). From symbols to icons: the return of resemblance in the cognitive neuroscience revolution. Synthese, 195(5), 1941-1967. doi: 10.1007/s11229-017-1578-6 List of Figures Figure 1. A map of part of Rochester taken from https://www.google.co.uk/maps (accessed: 21/08/2018). Figure 2. A map of tube connections in London taken from https://tfl.gov.uk/maps/track/tube (accessed: 21/08/2018). Figure 3. A hastily drawn “napkin map.” Created by me. Figure 4. A visual illustration of a graphical model adapted from Pearl (2000). Figure 5. A visual illustration of precision-weighted prediction error minimization. Created by me. Contents 1. Introduction 1 1.1. Introduction 1 1.2. The Philosophy of Mental Representation 1 1.3. Three Questions about Mental Representation 3 1.4. A Brief Overview 4 1.5. Clarifications 6 1.6. Thesis Overview 10 2. The Representation Wars 13 2.1. Introduction 13 2.2. The Existence and Importance of Mental Representations 14 2.3. Three Answers to the Constitutive Question 16 2.4. Format and Content 24 2.5. Constraints on a Theory of Mental Representation 27 2.6. Conclusion 32 3. Craik’s Hypothesis on the Nature of Thought 33 3.1. Introduction 33 3.2. Historical Background 35 3.3. Craik’s Hypothesis on the Nature of Thought 37 3.4. Mental Models 38 3.5. The Predictive Mind 42 3.6. The Cybernetic Brain 48 3.7. Conclusion 52 4. Models as Idealised Structural Surrogates 54 4.1. Introduction 54 4.2. Maps 56 4.3. Orreries 62 4.4. Models in Science 63 4.5. Conclusion 66 5. Could the Mind Be a Modelling Engine? 67 5.1. Introduction 67 5.2. The Challenge to Similarity 68 5.3. Making Sense of Structural Similarity 70 5.4. Target Domain 74 5.5. Exploiting Mental Models 76 5.6. Summary and Conclusion 78 6. Generative Models and the Predictive Mind 80 6.1. Introduction 80 6.2. Generative Models 80 6.3. Graphical Models 88 6.4. The Neocortex as a Hierarchical Prediction Machine 92 6.5. Conclusion 103 7. Representation, Prediction, Regulation 104 7.1. Introduction 104 7.2. Generative Models as Structural Representations 104 7.3. Prediction in the Predictive Mind 110 7.4. The Cybernetic Predictive Mind 117 7.5. Conclusion 124 8. The World Is Not Its Own Best Generative Model 126 8.1. Introduction 126 8.2. The Replacement Hypothesis 126 8.3. The Classical Model 129 8.4. Replacing Mental Representation 130 8.5. The World Is Not Its Own Best Generative Model 137 8.6. Prediction and Representation 145 8.7. Conclusion 147 9. Modelling the Umwelt 149 9.1. Introduction 149 9.2. The Pragmatic Brain 150 9.3. The Challenge to Similarity 152 9.4. Selectivity 154 9.5. Accuracy 156 9.6. Response Dependence 161 9.7. Affordances 163 9.8. Structural Representation and the Manifest Image 167 10. Generative Intentionality 169 10.1. Introduction 169 10.2. The Fodorian Model 171 10.3. Contrast with the Fodorian Model 173 10.4. Content Determination in the Predictive Mind 175 10.5. Conclusion 181 11. Conclusion 182 11.1. Introduction 182 11.2. The Mind as a Predictive Modelling Engine 182 11.3. Three Directions for Future Research 184 11.4. How Many Models? 184 11.5. Explanatory Scope 186 11.6. The Role of Public Representational Systems 188 11.7. Conclusion 189 Page 1 of 206 Chapter 1. Introduction and Overview “The fundamental nature of cognition is rooted in the tricks by which assorted representational schemas give organisms a competitive advantage in predicting” (P.S. Churchland 1986, p.14). (1.)1. Introduction1 In this thesis I outline and defend a theory of mental representation founded on a simple idea. The idea is not original. Its basic tenets were advanced by the Cambridge psychologist, philosopher, and control theorist Kenneth Craik in the 1940s, and it has recently won substantial support from a promising body of research in contemporary cognitive science. In a slogan, the idea is this: the mind is a predictive modelling engine. On this view, mental representations are elements of in-the-head models that re-present the structure of worldly domains in a form that can be exploited for prediction. Explaining what this means, why one should believe it, and what some of its most important implications and limitations are—these are my chief aims in what follows. In this introductory chapter I situate these aims in their appropriate philosophical context and provide a summary of the core structure and contents of forthcoming chapters. (1.)2. The Philosophy of Mental Representation What business does a philosopher have in offering a theory of mental representation? Questions concerning the existence, importance, and nature of mental representations seem like ordinary scientific questions. It is obvious why philosophers of the past have consistently engaged such questions. It is much less obvious what a philosopher can contribute to such questions today— that is, in an era of rapid developments in neuroscience, cognitive psychology, and artificial intelligence research. Nevertheless, an enormous amount of recent philosophical energy has been expended on such questions. Philosophers have advanced theories about the vehicles of mental representation with substantive implications for how the brain works—for example, that the representational system inside our heads must be structured like a language (Fodor 1975; Sterelny 1990; Schneider 2011). They have produced elaborate theories attempting to explain what makes mental representations represent—for example, that states of the brain represent whatever they have been recruited to indicate by evolution or learning (Dretske 1981; Millikan 1984; 1 For clarity, I place chapter number identifications in parentheses and eliminate them from intra-chapter references to sections within the text.