Evidence map›Paper›PMID 31519991›Full record

ArticleScientific reports2019

Why we don't always punish: Preferences for non-punitive responses to moral violations.

Joseph Heffner, Oriel FeldmanHall

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. What people learn from punishment: A cognitive model.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Joseph HeffnerDepartment of Cognitive, Linguistic, Psychological Sciences, Brown University, Providence, RI, 02906, USA.ORCID http://orcid.org/0000-0001-6757-3397
Oriel FeldmanHallDepartment of Cognitive, Linguistic, Psychological Sciences, Brown University, Providence, RI, 02906, USA. oriel_feldmanhall@brown.edu.

Funding

The neural basis of sequential control in human and non-human primatesP20GM103645 · NIGMS · BROWN UNIVERSITY · PI WORDEN, MICHAEL S · 2013 to 2022
$22.9M
NIGMS NIH HHS P20 GM103645
6 · The paper itself

Abstract

While decades of research demonstrate that people punish unfair treatment, recent work illustrates that alternative, non-punitive responses may also be preferred. Across five studies (N = 1,010) we examine non-punitive methods for restoring justice. We find that in the wake of a fairness violation, compensation is preferred to punishment, and once maximal compensation is available, punishment is no longer the favored response. Furthermore, compensating the victim-as a method for restoring justice-also generalizes to judgments of more severe crimes: participants allocate more compensation to the victim as perceived severity of the crime increases. Why might someone refrain from punishing a perpetrator? We investigate one possible explanation, finding that punishment acts as a conduit for different moral signals depending on the social context in which it arises. When choosing partners for social exchange, there are stronger preferences for those who previously punished as third-party observers but not those who punished as victims. This is in part because third-parties are perceived as relatively more moral when they punish, while victims are not. Together, these findings demonstrate that non-punitive alternatives can act as effective avenues for restoring justice, while also highlighting that moral reputation hinges on whether punishment is enacted by victims or third-parties.

Indexed as

Choice BehaviorJudgmentMoralsAdultCrimeFemaleHumansMalePunishmentSocial Justice

Identifiers

PMID31519991
PMCPMC6744396

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.