Neural Machines: A Defense of Non-Representationalism in Cognitive Neuroscience

Description

In this book, Matej Kohar demonstrates how the new mechanistic account of explanation can be used to support a non-representationalist view of explanations in cognitive neuroscience, and therefore can bring new conceptual tools to the non-representationalist arsenal. Kohar focuses on the explanatory relevance of representational content in constitutive mechanistic explanations typical in cognitive neuroscience. The work significantly contributes to two areas of literature: 1) the debate between representationalism and non-representationalism, and 2) the literature on mechanistic explanation. Kohar begins with an introduction to the mechanistic theory of explanation, focusing on the analysis of mechanistic constitution as the basis of explanatory relevance in constitutive mechanistic explanation. He argues that any viable analysis of representational contents implies that content is not constitutively relevant to cognitive phenomena. The author also addresses objections against his argument and concludes with an examination of the consequences of his account for both traditional cognitive neuroscience and non-representationalist alternatives. This book is of interest to readers in philosophy of mind, cognitive science and neuroscience. Acknowledgements Contents List of Acronyms Chapter 1: Introduction References Chapter 2: The New Mechanistic Theory of Explanation: A Primer 2.1 The Concept of Mechanism: Mechanisms, Phenomena, and Constitution 2.2 Constructing Mechanistic Models 2.3 Mechanistic Explanation 2.4 Conclusion References Chapter 3: Mechanistic Explanatory Texts 3.1 Craver and Kaplan´s Contrastive Account of Mechanistic Explanation 3.2 Problems with Craver and Kaplan´s Account 3.3 Mechanism Descriptions and Mechanistic Explanatory Texts 3.4 Constructing Mechanistic Explanatory Texts 3.5 Conclusion: Solving the Problems References Chapter 4: Representations and Mechanisms Do Not Mix 4.1 Representations: External and Internal 4.2 Mental vs. Neural Representations 4.3 Content-Determination and the Job-Description Challenge 4.4 Why Contents Are Not Explanatorily Relevant References Chapter 5: Indicator Contents 5.1 What Is Indicator Content? 5.2 Probabilities for Indicator Contents 5.3 Assessing Indicator Contents Based on Frequentist Chances 5.4 Assessing Indicator Content Based on Propensities 5.5 Conclusion References Chapter 6: Structural Contents 6.1 Defining Structural Representation 6.2 Mapping Relations for Structural Representation 6.3 Structural Representation and Locality 6.4 Structural Representations and Mutual Dependence 6.5 Conclusion References Chapter 7: Teleosemantics 7.1 Teleosemantic Analyses of Content 7.2 Teleosemantics with History-Dependent Functions 7.3 Teleosemantics with Synchronic Functions 7.4 Teleosemantics with Cybernetic Norms 7.5 Conclusion References Chapter 8: The Dual-Explananda Defence 8.1 The General Form of the Dual-Explananda Defence 8.2 The Fittingness Explanandum 8.3 The Success Explanandum 8.4 Conclusion References Chapter 9: The Pragmatic Necessity Defence 9.1 Egan´s Deflationary Realism 9.2 Options for a Mechanistic Answer to Deflationary Realism 9.3 Reinterpreting Mathematical Content 9.4 Roles of the Intentional Gloss 9.5 Rejecting Cognitive Contents 9.6 Conclusion References Chapter 10: Conclusions and Future Directions 10.1 Consequences for Mainstream Philosophy of Cognitive Science 10.2 Consequences for Previous Non-representational Theories 10.3 Future Directions References Index