Название: Neural Machines_A Defense of Non-Representationalism in Cognitive NeuroscienceАвтор: Matej Kohar
Издательство: Springer
Год: 2023
Страниц: 199
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB
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.
The goal of cognitive neuroscience is to uncover neural mechanisms responsible for intelligent behaviour in humans and animals. Intelligent behaviour has traditionally been taken to include decision-making, use of language and other high-level cognitive phenomena. Over time, however, the scope of what is meant by intelligent behaviour for the purposes of determining the proper subject matter of cognitive sciences (including cognitive neuroscience) has expanded to include any context-dependent responses to stimuli. Cognitive neuroscience therefore engages in search for neural mechanisms underlying sensory perception, memory, navigation, object-recognition, tracking, avoidance, etc. That is, the scope of cognitive neuroscience covers the search for neural mechanisms all the way from sensory processing, through response selection to motor control. Importantly, the scope of the field is not confined to a single species, such as the human, but includes, at least in principle, also the study of animal cognition – either for its own sake or as a model for the human case, when ethical and/or practical considerations prohibit investigation into the human case directly.
This book is concerned with the nature of explanation in cognitive neuroscience. That is, the question I am tackling is what form (good) explanations in cognitive neuroscience have, which factors are explanatorily relevant, which are not, and how explanations in cognitive neuroscience should be evaluated. This issue is multi-faceted and complex. Therefore, in my analysis I focus on a specific problem, namely, whether good explanations in cognitive neuroscience rely on identifying neural representations. This is a central issue in philosophy of cognitive neuroscience, as representational explanations are widespread. Thus, if it turned out that representational explanations are defective, this would have equally widespread repercussions on the explanatory practices of the field (or, more realistically, on the level of confidence that could be commanded by such defective explanations).
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