Evan Knep, Xinyuan Yan, Cathy S Chen, Suma Jacob, David P Darrow, R Becket Ebitz, Nicola Grissom, Alexander B Herman
{"title":"社会冷漠与非社会探索-利用决策有关。","authors":"Evan Knep, Xinyuan Yan, Cathy S Chen, Suma Jacob, David P Darrow, R Becket Ebitz, Nicola Grissom, Alexander B Herman","doi":"10.1038/s44271-025-00278-7","DOIUrl":null,"url":null,"abstract":"<p><p>How humans resolve the explore-exploit dilemma in decision making is central to how we flexibly interact with both social and non-social aspects of dynamic environments. However, how individual differences in the cognitive computations underlying exploration relate to social and non-social psychological flexibility traits remains unclear. To test this, we probed decision-making strategies in a cognitive flexibility task, a restless three-armed bandit task, and examined how individual differences in cognitive strategy related to social and non-social traits measured by the Broad Autism Phenotype Questionnaire (BAPQ), a well-validated, clinically-relevant, community instrument, in a large (N = 1001) online sample. In contrast to prior links found between exploratory behavior and cognitive rigidity, we found that differences in choice behavior and exploration were primarily associated with social phenotypes as captured by the BAPQ aloof subscale. Higher scores on the BAPQ aloof subscale, indicative of reduced social interest and engagement, were associated with decreased shift rates, increased win-stay/lose-shift behavior, heightened sensitivity to negative outcomes, and reduced exploration. Reinforcement learning (RL) modeling further revealed that reduced exploration in high aloof individuals was driven by lower decision noise rather than increased cognitive rigidity, suggesting that decreased exploratory behavior may reflect a reduced tendency for stochastic exploration rather than an inflexible learning process. Sparse canonical correlation analysis reveals that the strongest loading for these non-social reward-related measures are in fact socially coded items. These results suggest that differences in motivation to seek information, especially in social contexts, may manifest as decreased exploratory behavior in a non-social decision-making task. Our findings additionally highlight the potential for using computational approaches to reveal general cognitive mechanisms underlying social functioning.</p>","PeriodicalId":501698,"journal":{"name":"Communications Psychology","volume":"3 1","pages":"106"},"PeriodicalIF":0.0000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263421/pdf/","citationCount":"0","resultStr":"{\"title\":\"Social aloofness is associated with non-social explore-exploit decisions.\",\"authors\":\"Evan Knep, Xinyuan Yan, Cathy S Chen, Suma Jacob, David P Darrow, R Becket Ebitz, Nicola Grissom, Alexander B Herman\",\"doi\":\"10.1038/s44271-025-00278-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>How humans resolve the explore-exploit dilemma in decision making is central to how we flexibly interact with both social and non-social aspects of dynamic environments. However, how individual differences in the cognitive computations underlying exploration relate to social and non-social psychological flexibility traits remains unclear. To test this, we probed decision-making strategies in a cognitive flexibility task, a restless three-armed bandit task, and examined how individual differences in cognitive strategy related to social and non-social traits measured by the Broad Autism Phenotype Questionnaire (BAPQ), a well-validated, clinically-relevant, community instrument, in a large (N = 1001) online sample. In contrast to prior links found between exploratory behavior and cognitive rigidity, we found that differences in choice behavior and exploration were primarily associated with social phenotypes as captured by the BAPQ aloof subscale. Higher scores on the BAPQ aloof subscale, indicative of reduced social interest and engagement, were associated with decreased shift rates, increased win-stay/lose-shift behavior, heightened sensitivity to negative outcomes, and reduced exploration. Reinforcement learning (RL) modeling further revealed that reduced exploration in high aloof individuals was driven by lower decision noise rather than increased cognitive rigidity, suggesting that decreased exploratory behavior may reflect a reduced tendency for stochastic exploration rather than an inflexible learning process. Sparse canonical correlation analysis reveals that the strongest loading for these non-social reward-related measures are in fact socially coded items. These results suggest that differences in motivation to seek information, especially in social contexts, may manifest as decreased exploratory behavior in a non-social decision-making task. Our findings additionally highlight the potential for using computational approaches to reveal general cognitive mechanisms underlying social functioning.</p>\",\"PeriodicalId\":501698,\"journal\":{\"name\":\"Communications Psychology\",\"volume\":\"3 1\",\"pages\":\"106\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263421/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications Psychology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1038/s44271-025-00278-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications Psychology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s44271-025-00278-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Social aloofness is associated with non-social explore-exploit decisions.
How humans resolve the explore-exploit dilemma in decision making is central to how we flexibly interact with both social and non-social aspects of dynamic environments. However, how individual differences in the cognitive computations underlying exploration relate to social and non-social psychological flexibility traits remains unclear. To test this, we probed decision-making strategies in a cognitive flexibility task, a restless three-armed bandit task, and examined how individual differences in cognitive strategy related to social and non-social traits measured by the Broad Autism Phenotype Questionnaire (BAPQ), a well-validated, clinically-relevant, community instrument, in a large (N = 1001) online sample. In contrast to prior links found between exploratory behavior and cognitive rigidity, we found that differences in choice behavior and exploration were primarily associated with social phenotypes as captured by the BAPQ aloof subscale. Higher scores on the BAPQ aloof subscale, indicative of reduced social interest and engagement, were associated with decreased shift rates, increased win-stay/lose-shift behavior, heightened sensitivity to negative outcomes, and reduced exploration. Reinforcement learning (RL) modeling further revealed that reduced exploration in high aloof individuals was driven by lower decision noise rather than increased cognitive rigidity, suggesting that decreased exploratory behavior may reflect a reduced tendency for stochastic exploration rather than an inflexible learning process. Sparse canonical correlation analysis reveals that the strongest loading for these non-social reward-related measures are in fact socially coded items. These results suggest that differences in motivation to seek information, especially in social contexts, may manifest as decreased exploratory behavior in a non-social decision-making task. Our findings additionally highlight the potential for using computational approaches to reveal general cognitive mechanisms underlying social functioning.