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The Computational Mind

This post was co-authored by Matteo Colombo, an Assistant Professor in the Tilburg Center for Logic, Ethics and Philosophy of Science, at Tilburg University in The Netherlands, and Mark Sprevak, Senior Lecturer in the School of Philosophy, Psychology and Language Sciences at the University of Edinburgh. 

They share research interests in philosophy of the cognitive sciences and philosophy of science in general. Here they write about their new co-edited volume “The Routledge Handbook of the Computational Mind”.


The book aims to provide a comprehensive, state-of-the-art treatment of the history, foundations, challenges, applications, and prospects for computational ideas regarding mind, brain, and behaviour. There are thirty-five chapters from contributors across philosophy and the sciences. It is organized into four parts:

1.     History and future prospects of computational approaches

2.     Types of computational approach

3.     Foundations and challenges of computational approaches

4.     Applications to specific parts of psychology

You can read a sample chapter here.

The Handbook displays several common threads. We want to mention three, each reflecting a departure from traditional thinking about the computational mind. 


First, instead of there being “only one game in town” (Fodor, 1975), there are now many different computational approaches to explaining mind, brain, and behaviour. Researchers are moving towards ‘pluralism’ about computational models: the explanatory and practical aims of studying the mind are best pursued with many theories, models, concepts, methods, and sources of evidence from different fields. Traditional dichotomies like representationalism vs anti-representationalism, logicism vs probability, and innate vs learned have become unhelpful as a way of carving out commitments of “the” computational approach. 

Second, recent work on the computational mind reflects broader trends within philosophy of science that should be familiar to those working in philosophy of the special sciences. Examples include the search for models and mechanisms, the role of idealization and approximation in modelling, and the influence of values and social structures in understanding our scientific goals. Such work tends to focus attention on the explanatory role of computational models in actual scientific practice and move attention away from more traditional questions about the metaphysics of mind.

Third, recent years have seen a massive increase in our computing power. Technological change has contributed to advances in machine learning and brain simulation. This has inspired models of brain function based around statistical inference, deep learning, reinforcement learning, predictive processing, and related probabilistic notions. Despite these successes however, the question of how to simulate general human intelligence on a computer remains unanswered.

We see The Routledge Handbook of the Computational Mind as doing three things. First, it offers a “time capsule” of current trends, marking points of departure and continuity with respect to traditional treatments. Second, it informs readers of the accomplishments and challenges of current computational approaches. Third, it is a teaching resource, appropriate for a variety of graduate-level courses in philosophy of mind, cognitive science, computational cognitive neuroscience, AI, and computer science.

We hope you enjoy reading it!

Matteo Colombo
Mark Sprevak



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