Marcelo G. Mattar

Abstract Humans and other animals are driven to acquire information about opportunities in their environments, yet how they evaluate what is worth learning remains unclear. Here we combine artificial neural networks with symbolic regression to extract an expressive yet interpretable model that specifies how human participants evaluate decision-relevant information during choice.

First published as a Review in Advance on April 16, 2026 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), which permits any noncommercial use, sharing, distribution, and reproduction in any medium or format, provided the original author(s) and source are credited; this license does not permit sharing adapted material derived from this article or parts of it.

Abstract Cognition is highly flexible—we perform many different tasks1 and continually adapt our behaviour to changing demands2,3. Artificial neural networks trained to perform multiple tasks will reuse representations4 and computational components5 across tasks. By composing tasks from these subcomponents, an agent can flexibly switch between tasks and rapidly learn new tasks6,7. Yet, whether such compositionality is found in the brain is unclear.