A computational account of multiple motives guiding context-dependent prosocial behavior.
Prosocial behaviors play a pivotal role for human societies, shaping critical domains such as healthcare, education, taxation, and welfare. Despite the ubiquity of norms that prescribe prosocial actions, individuals do not consistently adhere to them and often behave selfishly, thereby harming the c...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1013032 |
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| author | Claire Lugrin Jie Hu Christian C Ruff |
| author_facet | Claire Lugrin Jie Hu Christian C Ruff |
| author_sort | Claire Lugrin |
| collection | DOAJ |
| description | Prosocial behaviors play a pivotal role for human societies, shaping critical domains such as healthcare, education, taxation, and welfare. Despite the ubiquity of norms that prescribe prosocial actions, individuals do not consistently adhere to them and often behave selfishly, thereby harming the collective good. Interventions aimed at promoting prosociality would therefore be beneficial but are often ineffective due to a limited understanding of the various motives that govern prosocial behavior across different contexts. Here we present a computational and experimental framework to identify motives underlying individual prosocial choices and to characterize how these vary across contexts with changing social norms. Using a series of experiments in which 575 participants either judge the normative appropriateness of different prosocial actions or choose between prosocial and selfish actions themselves, we first show that while most individuals are consistent in their judgements about the appropriateness of behaviors, the actual prosocial behavior varies strongly across people. We used computational decision models to quantify the conflicting motives underlying judgements and prosocial choices, combined with a clustering approach to characterize different types of individuals whose judgements and choices reflect different motivational profiles. We identified four such types: Unconditionally selfish participants never behave prosocially, Cost-sensitive participants behave selfishly when prosocial actions are costly, Efficiency-sensitive participants choose actions that maximize total wealth, and Harm-sensitive participants prioritize avoiding harming others. When these four types of participants were exposed to different social environments where norms were judged or followed more or less by a group, they responded in fundamentally different ways to this change in others' behavior. Our approach helps us better understand the origins of heterogeneity in prosocial judgments and behaviors and may have implications for policy making and the design of behavioral interventions. |
| format | Article |
| id | doaj-art-90e3dc7036fb41eebdea1f7660d8ce56 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-90e3dc7036fb41eebdea1f7660d8ce562025-08-20T02:22:26ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-04-01214e101303210.1371/journal.pcbi.1013032A computational account of multiple motives guiding context-dependent prosocial behavior.Claire LugrinJie HuChristian C RuffProsocial behaviors play a pivotal role for human societies, shaping critical domains such as healthcare, education, taxation, and welfare. Despite the ubiquity of norms that prescribe prosocial actions, individuals do not consistently adhere to them and often behave selfishly, thereby harming the collective good. Interventions aimed at promoting prosociality would therefore be beneficial but are often ineffective due to a limited understanding of the various motives that govern prosocial behavior across different contexts. Here we present a computational and experimental framework to identify motives underlying individual prosocial choices and to characterize how these vary across contexts with changing social norms. Using a series of experiments in which 575 participants either judge the normative appropriateness of different prosocial actions or choose between prosocial and selfish actions themselves, we first show that while most individuals are consistent in their judgements about the appropriateness of behaviors, the actual prosocial behavior varies strongly across people. We used computational decision models to quantify the conflicting motives underlying judgements and prosocial choices, combined with a clustering approach to characterize different types of individuals whose judgements and choices reflect different motivational profiles. We identified four such types: Unconditionally selfish participants never behave prosocially, Cost-sensitive participants behave selfishly when prosocial actions are costly, Efficiency-sensitive participants choose actions that maximize total wealth, and Harm-sensitive participants prioritize avoiding harming others. When these four types of participants were exposed to different social environments where norms were judged or followed more or less by a group, they responded in fundamentally different ways to this change in others' behavior. Our approach helps us better understand the origins of heterogeneity in prosocial judgments and behaviors and may have implications for policy making and the design of behavioral interventions.https://doi.org/10.1371/journal.pcbi.1013032 |
| spellingShingle | Claire Lugrin Jie Hu Christian C Ruff A computational account of multiple motives guiding context-dependent prosocial behavior. PLoS Computational Biology |
| title | A computational account of multiple motives guiding context-dependent prosocial behavior. |
| title_full | A computational account of multiple motives guiding context-dependent prosocial behavior. |
| title_fullStr | A computational account of multiple motives guiding context-dependent prosocial behavior. |
| title_full_unstemmed | A computational account of multiple motives guiding context-dependent prosocial behavior. |
| title_short | A computational account of multiple motives guiding context-dependent prosocial behavior. |
| title_sort | computational account of multiple motives guiding context dependent prosocial behavior |
| url | https://doi.org/10.1371/journal.pcbi.1013032 |
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