Evidence map›Paper›PMID 30337899›Full record

ArticleFrontiers in psychology2018

Both Rewards and Moral Praise Can Increase the Prosocial Decisions: Revealed in a Modified Ultimatum Game Task.

Xiangling Wang, Jiahui Han, Fuhong Li, Bihua Cao

Abstract read
In one paragraph

Article in Frontiers in psychology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

  1. The consequences of AI training on human decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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

4 authors.

Xiangling WangSchool of Psychology, Jiangxi Normal University, Nanchang, China.
Jiahui HanSchool of Psychology, Jiangxi Normal University, Nanchang, China.
Fuhong LiSchool of Psychology, Jiangxi Normal University, Nanchang, China.
Bihua CaoSchool of Psychology, Jiangxi Normal University, Nanchang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlike other creatures, humans developed the ability to cooperate with genetically unrelated strangers and a tendency to comply with social norms. However, humans deviate from social norms in various situations. This study used the modified ultimatum game to explore why humans deviate from social norms and how their prosocial behavior can be promoted. In Study 1, participants were asked to imagine working with an anonymous counterpart to complete a task and obtain a certain amount of money (e.g., ¥10). The computer divided the money randomly in favor of the participant (e.g., 9:1 or 8:2). Participants should decide whether to accept or reject such a self-benefiting division. In the non-risk condition, an absolutely fair redivision of money would take place if participants reject self-benefiting division (e.g., 5:5 or 6:4). By contrast, in the risk condition, other-benefiting redivision of money (e.g., 1:9 or 2:8) would take place if participants rejected the self-benefiting division. Results involving 40 college students showed the main effect of condition. The frequency of accepting self-benefiting division in the risk condition was higher than that in the non-risk condition. As such, compliance with social norms is based on the preservation of material resources. In Study 2, we used economic or moral rewards to compensate for economic loss following compliance with the norm. Results involving 28 college students revealed a significant effect of compensation. The rewards, including moral praise, effectively decreased selfish choices. These findings extend previous studies on social norm compliance by emphasizing the importance of internal, fairness-based balance between material and moral needs, as well as the role of moral praise in promoting prosocial behavior.

Indexed as

compensationfairmoral praiseredivisionsocial norms

Identifiers

PMID30337899
PMCPMC6180182

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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.