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Computational basis of hierarchical and counterfactual information processing

Bibliographic Data

ID4700837
AuthorsMahdi Ramadan (McGovern Institute for Brain Research), Cheng Tang (0000-0002-9609-9951, McGovern Institute for Brain Research), Nicholas Watters (0000-0002-7757-7700, McGovern Institute for Brain Research), Mehrdad Jazayeri (0000-0002-9764-6961, Howard Hughes Medical Institute, corresponding author)
Year2025
Volume9
Issue9
Pages1913-1927
Publication date2025-06-11
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueNature Human Behaviour (JOURNAL)
Journal identifiersISSN: 2397-3374 • E-ISSN: 2397-3374
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1038/s41562-025-02232-3
PMID40500379
OpenAlexW4411216846
LanguageEN
References cited52

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 revealed that hierarchical, counterfactual and postdictive strategies-typically viewed as distinct-lie along a continuum of rational adaptations. These findings suggest that human decision strategies may emerge from a shared set of computational limitations, offering a unifying framework for understanding the flexibility and efficiency of human cognition

Counterfactual thinking · Advanced Research in Systems and Signal Processing · Computer Science · Mathematics · Psychology · Social Psychology · Artificial Intelligence

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