Routledge Handbook of the Computational Mind

(with Matteo Colombo)

forthcoming Routledge: London, 592 pp.

Last updated 9 May 2018

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Computational approaches dominate contemporary cognitive science, promising a unified, scientific explanation of how the mind works. However, computational approaches raise major philosophical and scientific questions. In what sense is the mind computational? How do computational approaches explain perception, learning, and decision-making? What kinds of challenges should computational approaches overcome to advance our understanding of mind, brain, and behaviour?

The Routledge Handbook of the Computational Mind is an outstanding overview and exploration of these issues and the first philosophical collection of its kind. Comprising thirty-five chapters by an international team of contributors from different disciplines, the Handbook is organised into four parts:

  • History and future prospects of computational approaches
  • Types of computational approach
  • Foundations and challenges of computational approaches
  • Applications to specific parts of psychology

Essential reading for students and researchers in philosophy of mind, philosophy of psychology, and philosophy of science, The Routledge Handbook of the Computational Mind will also be of interest to those studying computational models in related subjects such as psychology, neuroscience, and computer science.

Contents

Part 1: History and Future Directions

  1. Computational thought from Descartes to Lovelace – Alistair M.C. Isaac

  2. Turing and the first electronic brains: What the papers said – Diane Proudfoot and Jack Copeland

  3. British cybernetics (or ‘The disembodiment of mind’) – Joseph Dewhurst

  4. Cybernetics – Tara Abraham

  5. Turing-equivalent computation at the “conception” of cognitive science – Kenneth Aizawa

  6. Connectionism and post-connectionist models – Cameron Buckner and James Garson

  7. Artificial Intelligence – Murray Shanahan

Part 2: Types of Computing

  1. Classical computational models – Richard Samuels

  2. Explanation and connectionist models – Catherine Stinson

  3. Dynamic information processing – Frank Faries and Anthony Chemero

  4. Probabilistic models – David Danks

  5. Prediction error minimization in the brain – Jakob Hohwy

Part 3: Foundations and Challenges

  1. Triviality arguments about implementation – Mark Sprevak

  2. Computational implementation – J. Brendan Ritchie and Gualtiero Piccinini

  3. Computation and levels in cognitive and neural science – Lotom Elber-Dorozko and Oron Shagrir

  4. Reductive explanation between psychology and neuroscience – Daniel A. Weiskopf

  5. Helmholtz’s vision: Underdetermination, behavior and the brain – Clark Glymour and Reuben Sanchez-Romero

  6. The nature and function of content in computational models – Frances Egan

  7. Maps, models and computational simulations in the mind – William Ramsey

  8. The cognitive basis of computation: Putting computation in its place – Daniel D. Hutto, Erik Myin, Anco Peeters and Farid Zahnoun

  9. Computational explanations and neural coding – Rosa Cao

  10. Computation, consciousness, and “Computation and consciousness” – Colin Klein

  11. Concepts, symbols and computation: An integrative approach – Jenelle Salisbury and Susan Schneider

  12. Embodied cognition – Marcin Miłkowski

  13. Tractability and the computational mind – Jakub Szymanik and Rineke Verbrugge

Part 4: Applications

  1. Computational cognitive neuroscience – Carlos Zednik

  2. Simulation in computational neuroscience – Liz Irvine

  3. Learning and reasoning – Matteo Colombo

  4. Vision – Mazviita Chirimuuta

  5. Perception without computation? – Nico Orlandi

  6. Motor computation – Michael Rescorla

  7. Computational models of emotion – Xiaosi Gu

  8. Computational psychiatry – Matthew Broome and Stefan Brugger

  9. Computational approaches to social cognition – John Michael and Miles MacLeod

  10. Computational theories of group behavior – Bryce Huebner and Joseph Jebari