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Neurocomputational Mechanisms Linking Future Interaction Prospects to Reactive and Proactive Costly Punishment
Chuangbing Huang1,2,3,4,5, Xingmei Zhou6, Feilong Liu1,2,3,4,5
1Key Laboratory of Brain, Cognition, and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, 510631, China.
Neuroscience Bulletin
|June 9, 2026
Summary
Future interactions shape costly punishment in social bonds. People punish unfairness more harshly from non-partners, involving distinct brain mechanisms for reactive and proactive punishment.
Area of Science:
- Neuroscience
- Social Psychology
- Behavioral Economics
Background:
- Costly punishment is crucial for social bonds, encompassing reactive penalties for transgressions and proactive sanctions against fair behaviors.
- A unified neurocomputational framework explaining these punishment forms is currently lacking.
Purpose of the Study:
- To investigate how prospects of future interactions modulate both reactive and proactive costly punishment.
- To establish a neurocomputational framework integrating these regulatory mechanisms.
Main Methods:
- Utilized neuroimaging (fMRI) and computational modeling to analyze participants' punishment decisions.
- Participants acted as second- or third-party punishers, responding to offers from partners with or without future interaction prospects.
Main Results:
- Harsher punishment was imposed on both fair and unfair offers from non-future-interacting individuals, correlating with lower self-reported closeness.
- Heightened aversion to disadvantageous inequity (dorsal anterior cingulate cortex) mediated reactive punishment toward non-future-interacting wrongdoers.
- Punishment of future-interacting transgressors engaged the dorsolateral prefrontal cortex (dlPFC).
- Preference for relative advantage and reduced harm sensitivity (dlPFC, temporoparietal junction) mediated proactive punishment toward non-future-interacting individuals.
- Proactive punishment also demonstrated collective retaliation against others' unfairness.
Conclusions:
- Established a unified neurocomputational framework for costly punishment in social bonds.
- Demonstrated that future interaction prospects significantly regulate reactive and proactive punishment strategies.
- Identified distinct neural correlates (dlPFC, dACC, TPJ) underlying these punishment modulations.
Keywords:
Computational modelingCostly punishmentHarm sensitivityInequity aversionRelative advantagefMRIMore Related Videos
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