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What people learn from punishment: A cognitive model.

Created on 05 Aug 2025

Authors

Setayesh Radkani, Joshua B Tenenbaum, Rebecca Saxe

Published in

Proceedings of the National Academy of Sciences of the United States of America. Volume 122. Issue 32. Pages e2500730122. Aug 12, 2025. Epub Aug 04, 2025.

Abstract

Authorities, from parents of toddlers to leaders of formal institutions, use punishment to communicate disapproval and enforce social norms. Ideally, from whether and how severely a transgression is punished, targets and observers infer shared social norms. Yet in light of every punitive choice, observers also evaluate the motives and legitimacy of the authority. Here, we show that the effects of punishment can only be understood by considering these inferences simultaneously. We measured human observers' joint inferences empirically in three preregistered experiments ([Formula: see text]) and developed a rational Bayesian model using an inverse planning framework that captures and explains these inferences and their interactions quantitatively and parsimoniously. When people have different priors about norms or authorities, the model predicted and we experimentally confirmed that observing punishment by the authority can sustain polarization. This work reveals the rational logic behind how people learn from punishment and a key constraint on the function of punishment in establishing shared social norms.

PMID:
40758880
Bibliographic data and abstract were imported from PubMed on 05 Aug 2025.

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