The integration of self-efficacy and response-efficacy in decision making
Abstract The belief that we can exert an influence in our environment is dependent on distinct components of perceived control. Here, we investigate the neural representations that differentially code for self-efficacy (belief in successfully executing a behavior) and response-efficacy (belief that...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85577-z |
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| author | Yun-Yen Yang Mauricio R. Delgado |
| author_facet | Yun-Yen Yang Mauricio R. Delgado |
| author_sort | Yun-Yen Yang |
| collection | DOAJ |
| description | Abstract The belief that we can exert an influence in our environment is dependent on distinct components of perceived control. Here, we investigate the neural representations that differentially code for self-efficacy (belief in successfully executing a behavior) and response-efficacy (belief that the behavior leads to an expected outcome) and how such signals may be integrated to inform decision-making. Participants provided confidence ratings related to executing a behavior (self-efficacy), and the potential for a rewarding outcome (response-efficacy). Computational modeling was used to measure the subjective weight of self-efficacy and response-efficacy while making decisions and to examine the neural mechanisms of perceived control computation. While participants factored in both self-efficacy and response-efficacy during decision-making, we observed that integration of these two components was dependent on neural responses within the vmPFC, OFC and striatum. Further, the dlPFC was observed to assign importance to self-efficacy and response-efficacy in specific trials, while dACC computed the trade-off between both components, taking into account individual differences. These findings highlight the contributions of perceived control components in decision-making, and identify key neural pathways involved in computing perceived control. |
| format | Article |
| id | doaj-art-e0d6160bc3a7416f9d3a9bffcd4b4ec0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e0d6160bc3a7416f9d3a9bffcd4b4ec02025-01-19T12:20:29ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-85577-zThe integration of self-efficacy and response-efficacy in decision makingYun-Yen Yang0Mauricio R. Delgado1Department of Psychology, Rutgers UniversityDepartment of Psychology, Rutgers UniversityAbstract The belief that we can exert an influence in our environment is dependent on distinct components of perceived control. Here, we investigate the neural representations that differentially code for self-efficacy (belief in successfully executing a behavior) and response-efficacy (belief that the behavior leads to an expected outcome) and how such signals may be integrated to inform decision-making. Participants provided confidence ratings related to executing a behavior (self-efficacy), and the potential for a rewarding outcome (response-efficacy). Computational modeling was used to measure the subjective weight of self-efficacy and response-efficacy while making decisions and to examine the neural mechanisms of perceived control computation. While participants factored in both self-efficacy and response-efficacy during decision-making, we observed that integration of these two components was dependent on neural responses within the vmPFC, OFC and striatum. Further, the dlPFC was observed to assign importance to self-efficacy and response-efficacy in specific trials, while dACC computed the trade-off between both components, taking into account individual differences. These findings highlight the contributions of perceived control components in decision-making, and identify key neural pathways involved in computing perceived control.https://doi.org/10.1038/s41598-025-85577-zPerceived controlStriatumvmPFCfMRIChoiceComputational modeling |
| spellingShingle | Yun-Yen Yang Mauricio R. Delgado The integration of self-efficacy and response-efficacy in decision making Scientific Reports Perceived control Striatum vmPFC fMRI Choice Computational modeling |
| title | The integration of self-efficacy and response-efficacy in decision making |
| title_full | The integration of self-efficacy and response-efficacy in decision making |
| title_fullStr | The integration of self-efficacy and response-efficacy in decision making |
| title_full_unstemmed | The integration of self-efficacy and response-efficacy in decision making |
| title_short | The integration of self-efficacy and response-efficacy in decision making |
| title_sort | integration of self efficacy and response efficacy in decision making |
| topic | Perceived control Striatum vmPFC fMRI Choice Computational modeling |
| url | https://doi.org/10.1038/s41598-025-85577-z |
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