Computational basis of hierarchical and counterfactual information processing.

ABSTRACT: Humans solve complex multistage decision problems using hierarchical and counterfactual strategies. Here we designed a task that reliably engages these strategies and conducted hypothesis-driven experiments to identify the computational constraints that give rise to them. We found three key constraints: a bottleneck in parallel processing that promotes hierarchical analysis, a compensatory but capacity-limited counterfactual process, and working memory noise that reduces counterfactual fidelity. To test whether these strategies are computationally rational-that is, optimal given such constraints-we trained recurrent neural networks under systematically varied limitations. Only recurrent neural networks subjected to all three constraints reproduced human-like behaviour. Further analysis rev

SUBMITTER: Ramadan M 

PROVIDER: S-EPMC12454115 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature