Pub Date : 2025-11-17DOI: 10.1038/s41562-025-02340-0
Anne G. E. Collins
Reinforcement learning (RL) algorithms have had tremendous success accounting for reward-based learning across species, including instrumental learning in contextual bandit tasks, and they capture variance in brain signals. However, reward-based learning in humans recruits multiple processes, including memory and choice perseveration; their contributions can easily be mistakenly attributed to RL computations. Here I investigate how much of reward-based learning behaviour is supported by RL computations in a context where other processes can be factored out. Reanalysis and computational modelling of 7 datasets ( n = 594) in diverse samples show that in this instrumental context, reward-based learning is best explained by a combination of a fast working-memory-based process and a slower habit-like associative process, neither of which can be interpreted as a standard RL-like algorithm on its own. My results raise important questions for the interpretation of RL algorithms as capturing a meaningful process across brain and behaviour.
{"title":"A habit and working memory model as an alternative account of human reward-based learning","authors":"Anne G. E. Collins","doi":"10.1038/s41562-025-02340-0","DOIUrl":"https://doi.org/10.1038/s41562-025-02340-0","url":null,"abstract":"Reinforcement learning (RL) algorithms have had tremendous success accounting for reward-based learning across species, including instrumental learning in contextual bandit tasks, and they capture variance in brain signals. However, reward-based learning in humans recruits multiple processes, including memory and choice perseveration; their contributions can easily be mistakenly attributed to RL computations. Here I investigate how much of reward-based learning behaviour is supported by RL computations in a context where other processes can be factored out. Reanalysis and computational modelling of 7 datasets ( <jats:italic>n</jats:italic> = 594) in diverse samples show that in this instrumental context, reward-based learning is best explained by a combination of a fast working-memory-based process and a slower habit-like associative process, neither of which can be interpreted as a standard RL-like algorithm on its own. My results raise important questions for the interpretation of RL algorithms as capturing a meaningful process across brain and behaviour.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"16 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-17DOI: 10.1038/s41562-025-02348-6
Payam Piray
{"title":"Addressing low statistical power in computational modelling studies in psychology and neuroscience","authors":"Payam Piray","doi":"10.1038/s41562-025-02348-6","DOIUrl":"https://doi.org/10.1038/s41562-025-02348-6","url":null,"abstract":"","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"5 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145532005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1038/s41562-025-02335-x
{"title":"Sniffing dynamics reflect fine differences in perception of odours","authors":"","doi":"10.1038/s41562-025-02335-x","DOIUrl":"https://doi.org/10.1038/s41562-025-02335-x","url":null,"abstract":"","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"90 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1038/s41562-025-02338-8
Ili Ma, Mubashir Sultan, Anastasia Kozyreva, Wouter van den Bos
{"title":"Understanding the impact of misinformation on adolescents","authors":"Ili Ma, Mubashir Sultan, Anastasia Kozyreva, Wouter van den Bos","doi":"10.1038/s41562-025-02338-8","DOIUrl":"https://doi.org/10.1038/s41562-025-02338-8","url":null,"abstract":"","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"25 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1038/s41562-025-02326-y
{"title":"Parallels between human and artificial minds when new learning erases old knowledge","authors":"","doi":"10.1038/s41562-025-02326-y","DOIUrl":"https://doi.org/10.1038/s41562-025-02326-y","url":null,"abstract":"","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"119 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145498180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1038/s41562-025-02312-4
Hugh Riddell, Constantine Sedikides, Hamsini Sivaramakrishnan, Phoebe Wan, Silvio Maltagliati, Ben Jackson, Cecilie Thøgersen-Ntoumani, Daniel F. Gucciardi, Nikos Ntoumanis
There is growing interest in how and why individuals adjust their goals in response to difficulties encountered during goal striving and the outcomes of such adjustments; however, research on these topics is fragmented across theoretical perspectives and life domains. To address this issue, we conducted a systematic search of databases (Web of Science, Scopus, PsycInfo, Business Source Ultimate, Proquest Dissertations and Theses Global, Medline; last updated May 2025) of empirical studies examining antecedents or outcomes of goal adjustment. Studies were eligible if they examined predictors or wellbeing/functional/goal-related outcomes of goal disengagement, goal reengagement, or goal-striving flexibility. We identified 1,421 effect sizes from 235 studies, which we categorized and mapped onto a conceptual model. Further, we used random-effects meta-analyses to examine the strength and direction of associations between model categories and goal adjustment variables. Despite relatively high-quality ratings (assessed using QualSyst), the overall standard of accumulated evidence was determined to be low to moderate due to the reliance on cross-sectional studies, risk of publication bias and high heterogeneity. Nonetheless, we identified associations between multiple antecedent categories and goal disengagement, reengagement and flexibility, as well as associations between these different aspects of goal adjustment and wellbeing, functional and goal-related outcomes. We conclude that different aspects of goal adjustment are predicted by unique combinations of antecedent variables, and predict distinct outcomes. Our conceptual model consolidates the literature on goal adjustment and provides a roadmap for a more systematic investigation of this field going forward.
人们对个人如何以及为什么调整目标以应对目标奋斗过程中遇到的困难以及这种调整的结果越来越感兴趣；然而，对这些主题的研究在理论视角和生活领域是碎片化的。为了解决这一问题，我们对Web of Science、Scopus、PsycInfo、Business Source Ultimate、Proquest Dissertations and Theses Global、Medline等数据库进行了系统的检索，检索了关于目标调整的前因或结果的实证研究。如果研究检测了目标脱离、目标再投入或目标努力灵活性的预测因素或健康/功能/目标相关结果，则该研究是合格的。我们从235项研究中确定了1421个效应值，并将其分类并映射到概念模型中。此外，我们使用随机效应荟萃分析来检验模型类别和目标调整变量之间关联的强度和方向。尽管评分相对较高（使用QualSyst进行评估），但由于依赖于横断面研究、发表偏倚风险和高度异质性，累积证据的总体标准被确定为低至中等。尽管如此，我们确定了多个前因类别与目标脱离、再投入和灵活性之间的关联，以及目标调整与幸福感、功能和目标相关结果的这些不同方面之间的关联。我们得出结论，不同方面的目标调整是由独特的前因变量组合预测，并预测不同的结果。我们的概念模型巩固了关于目标调整的文献，并为该领域今后更系统的研究提供了路线图。
{"title":"A meta-analytic review and conceptual model of the antecedents and outcomes of goal adjustment in response to striving difficulties","authors":"Hugh Riddell, Constantine Sedikides, Hamsini Sivaramakrishnan, Phoebe Wan, Silvio Maltagliati, Ben Jackson, Cecilie Thøgersen-Ntoumani, Daniel F. Gucciardi, Nikos Ntoumanis","doi":"10.1038/s41562-025-02312-4","DOIUrl":"https://doi.org/10.1038/s41562-025-02312-4","url":null,"abstract":"There is growing interest in how and why individuals adjust their goals in response to difficulties encountered during goal striving and the outcomes of such adjustments; however, research on these topics is fragmented across theoretical perspectives and life domains. To address this issue, we conducted a systematic search of databases (Web of Science, Scopus, PsycInfo, Business Source Ultimate, Proquest Dissertations and Theses Global, Medline; last updated May 2025) of empirical studies examining antecedents or outcomes of goal adjustment. Studies were eligible if they examined predictors or wellbeing/functional/goal-related outcomes of goal disengagement, goal reengagement, or goal-striving flexibility. We identified 1,421 effect sizes from 235 studies, which we categorized and mapped onto a conceptual model. Further, we used random-effects meta-analyses to examine the strength and direction of associations between model categories and goal adjustment variables. Despite relatively high-quality ratings (assessed using QualSyst), the overall standard of accumulated evidence was determined to be low to moderate due to the reliance on cross-sectional studies, risk of publication bias and high heterogeneity. Nonetheless, we identified associations between multiple antecedent categories and goal disengagement, reengagement and flexibility, as well as associations between these different aspects of goal adjustment and wellbeing, functional and goal-related outcomes. We conclude that different aspects of goal adjustment are predicted by unique combinations of antecedent variables, and predict distinct outcomes. Our conceptual model consolidates the literature on goal adjustment and provides a roadmap for a more systematic investigation of this field going forward.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"20 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145498178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1038/s41562-025-02327-x
Vivek Sagar, Andrew Sheriff, Qiaohan Yang, Naelly Arriaga, Guangyu Zhou, Gregory Lane, Thorsten Kahnt, Christina Zelano
{"title":"The human brain modulates sniffs according to fine-grained perceptual features of odours","authors":"Vivek Sagar, Andrew Sheriff, Qiaohan Yang, Naelly Arriaga, Guangyu Zhou, Gregory Lane, Thorsten Kahnt, Christina Zelano","doi":"10.1038/s41562-025-02327-x","DOIUrl":"https://doi.org/10.1038/s41562-025-02327-x","url":null,"abstract":"","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"90 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145492613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1038/s41562-025-02318-y
Eleanor Holton, Lukas Braun, Jessica AF Thompson, Jan Grohn, Christopher Summerfield
In artificial neural networks, acquiring new knowledge often interferes with existing knowledge. Here, although it is commonly claimed that humans overcome this challenge, we find surprisingly similar patterns of interference across both types of learner. When learning sequential rule-based tasks (A–B–A), both learners benefit more from prior knowledge when the tasks are similar—but as a result, they also exhibit greater interference when retested on task A. In networks, this arises from reusing previously learned representations, which accelerates new learning at the cost of overwriting prior knowledge. In humans, we also observe individual differences: one group (‘lumpers’) shows more interference alongside better transfer, while another (‘splitters’) avoids interference at the cost of worse transfer. These behavioural profiles are mirrored in neural networks trained in the rich (lumper) or lazy (splitter) regimes, encouraging overlapping or distinct representations respectively. Together, these findings reveal shared computational trade-offs between transferring knowledge and avoiding interference in humans and artificial neural networks.
{"title":"Humans and neural networks show similar patterns of transfer and interference during continual learning","authors":"Eleanor Holton, Lukas Braun, Jessica AF Thompson, Jan Grohn, Christopher Summerfield","doi":"10.1038/s41562-025-02318-y","DOIUrl":"https://doi.org/10.1038/s41562-025-02318-y","url":null,"abstract":"In artificial neural networks, acquiring new knowledge often interferes with existing knowledge. Here, although it is commonly claimed that humans overcome this challenge, we find surprisingly similar patterns of interference across both types of learner. When learning sequential rule-based tasks (A–B–A), both learners benefit more from prior knowledge when the tasks are similar—but as a result, they also exhibit greater interference when retested on task A. In networks, this arises from reusing previously learned representations, which accelerates new learning at the cost of overwriting prior knowledge. In humans, we also observe individual differences: one group (‘lumpers’) shows more interference alongside better transfer, while another (‘splitters’) avoids interference at the cost of worse transfer. These behavioural profiles are mirrored in neural networks trained in the rich (lumper) or lazy (splitter) regimes, encouraging overlapping or distinct representations respectively. Together, these findings reveal shared computational trade-offs between transferring knowledge and avoiding interference in humans and artificial neural networks.","PeriodicalId":19074,"journal":{"name":"Nature Human Behaviour","volume":"120 1","pages":""},"PeriodicalIF":29.9,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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