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What people learn from punishment: A cognitive model.

Setayesh Radkani1, Joshua B Tenenbaum1, Rebecca Saxe1

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.

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Summary

Observing punishment helps people learn social norms and authority legitimacy. However, differing beliefs can lead to polarization when punishment is witnessed.

Keywords:
Bayesian inferencecomputational modelinglegitimacymoralitypunishment

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Area of Science:

  • Cognitive Science
  • Social Psychology
  • Behavioral Economics

Background:

  • Authorities use punishment to enforce social norms and communicate disapproval.
  • Punishment ideally informs targets and observers about acceptable behavior.
  • However, observers also assess the punisher's motives and legitimacy.

Purpose of the Study:

  • To investigate how observers simultaneously infer social norms and authority legitimacy from punishment.
  • To develop a quantitative model explaining these joint inferences.
  • To explore how punishment affects norm learning and polarization.

Main Methods:

  • Three preregistered experiments measuring human observers' joint inferences.
  • Development of a rational Bayesian model using an inverse planning framework.
  • Quantitative analysis of inferences and their interactions.

Main Results:

  • Human observers' inferences about norms and authority were measured empirically.
  • A Bayesian model quantitatively captured and explained these joint inferences.
  • Observing punishment sustained polarization when observers had differing priors.

Conclusions:

  • Understanding punishment's effects requires simultaneous consideration of norm and legitimacy inferences.
  • A rational Bayesian model explains the logic of learning from punishment.
  • Punishment can sustain polarization, highlighting a constraint on its role in establishing shared norms.