Abstract
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 language | English |
|---|---|
| Pages (from-to) | 53-75 |
| Number of pages | 23 |
| Journal | Minds and Machines |
| Volume | 28 |
| Issue number | 1 |
| DOIs | |
| State | Published - 1 Mar 2018 |
Bibliographical note
Publisher Copyright:© 2017, Springer Science+Business Media B.V.
Keywords
- Cognitive neuroscience
- Computational models
- Mechanistic explanations
- Modelling
- Optimality
- Representation
