Learning moral values: Another’s desire to punish enhances one’s own punitive behavior.
Open accessAbstract
There is little consensus about how moral values are learned. Using a novel social learning task, we examine whether vicarious learning impacts moral values-specifically fairness preferences-during decisions to restore justice. In both laboratory and Internet-based experimental settings, we employ a dyadic justice game where participants receive unfair splits of money from another player and respond resoundingly to the fairness violations by exhibiting robust nonpunitive, compensatory behavior (baseline behavior). In a subsequent learning phase, participants are tasked with responding to fairness violations on behalf of another participant (a receiver) and are given explicit trial-by-trial feedback about the receiver's fairness preferences (e.g., whether they prefer punishment as a means of restoring justice). This allows participants to update their decisions in accordance with the receiver's feedback (learning behavior). In a final test phase, participants again directly experience fairness violations. After learning about a receiver who prefers highly punitive measures, participants significantly enhance their own endorsement of punishment during the test phase compared with baseline. Computational learning models illustrate the acquisition of these moral values is governed by a reinforcement mechanism, revealing it takes as little as being exposed to the preferences of a single individual to shift one's own desire for punishment when responding to fairness violations. Together this suggests that even in the absence of explicit social pressure, fairness preferences are highly labile. (PsycINFO Database Record
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Based on extant research, the study aimed to achieve a final sample size of N = 40 for all in-lab experiments.
“Based on extant research, we aimed to achieve a final sample size of N = 40 for all in-lab experiments 44 .”
From Crowdsourcing punishment: Individuals reference group preferences to inform their own punitive decisions (2019) · cites this paper as [44]
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- Authors
- FeldmanHall, O., Otto, A. R., & Phelps, E. A.
- Affiliations
- Brown University (US)
- McGill University (CA)
- New York University (US)
- Venue
- Journal of Experimental Psychology: General
- Published
- 2018-08
- Volume
- 147
- Issue
- 8
- Pages
- 1211-1224
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- PMC
- PMC6072600
- OpenAlex
- W2806193542
- Cited by
- 51
- References
- 87
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 3 · 2023: 5 · 2022: 5 · 2021: 11 · 2020: 3 · 2019: 3 · 2018: 6
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Experimental Psychology General (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (R01AG039283)
- Topics
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
- Social Sciences › Sociology and Political Science › Social Power and Status Dynamics
- Keywords
- moral values, moral learning, observational learning, fairness preferences, punishment, restoration of justice, compensatory behavior, reinforcement learning, social learning
- MeSH terms
- Adult, Empathy, Female, Humans, Interpersonal Relations, Male, Middle Aged, Morals, Motivation, Punishment, Social Justice, Feedback, Psychological, Young Adult
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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