The Brain as an Input–Output Model of the World

Oron Shagrir*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


An underlying assumption in computational approaches in cognitive and brain sciences is that the nervous system is an input–output model of the world: Its input–output functions mirror certain relations in the target domains. I argue that the input–output modelling assumption plays distinct methodological and explanatory roles. Methodologically, input–output modelling serves to discover the computed function from environmental cues. Explanatorily, input–output modelling serves to account for the appropriateness of the computed function to the explanandum information-processing task. I compare very briefly the modelling explanation to mechanistic and optimality explanations, noting that in both cases the explanations can be seen as complementary rather than contrastive or competing.

Original languageAmerican English
Pages (from-to)53-75
Number of pages23
JournalMinds and Machines
Issue number1
StatePublished - 1 Mar 2018

Bibliographical note

Funding Information:
Acknowledgements I am grateful to Lotem Elber-Dorozko, Jens Harbecke, Shahar Hechtlinger, David Kaplan, Colin Klein, Arnon Levy, Gal Patel and two anonymous referees for their comments. Early versions of the paper were presented at seminars in Macquarie University, Tel-Aviv University, University of Canterbury, University of Otago and at the following conferences: The Aims of Brain Research: Scientific and Philosophical Perspectives (Jerusalem), Conference of the International Association for Computing and Philosophy (Thessaloniki), and the 7th AISB Symposium on Computing and Philosophy (London). I thank the participants for stimulating discussion. This research was supported by a grant from GIF, the German-Israeli Foundation for Scientific Research and Development.

Publisher Copyright:
© 2017, Springer Science+Business Media B.V.


  • Cognitive neuroscience
  • Computational models
  • Mechanistic explanations
  • Modelling
  • Optimality
  • Representation


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