{"title":"隐藏的奖励:作为主观价值之窗的情感及其预测误差","authors":"Marius C. Vollberg, David Sander","doi":"10.1177/09637214231217678","DOIUrl":null,"url":null,"abstract":"Scientists increasingly apply concepts from reinforcement learning to affect, but which concepts should apply? And what can their application reveal that we cannot know from directly observable states? An important reinforcement learning concept is the difference between reward expectations and outcomes. Such reward prediction errors have become foundational to research on adaptive behavior in humans, animals, and machines. Owing to historical focus on animal models and observable reward (e.g., food or money), however, relatively little attention has been paid to the fact that humans can additionally report correspondingly expected and experienced affect (e.g., feelings). Reflecting a broader “rise of affectivism,” attention has started to shift, revealing explanatory power of expected and experienced feelings—including prediction errors—above and beyond observable reward. We propose that applying concepts from reinforcement learning to affect holds promise for elucidating subjective value. Simultaneously, we urge scientists to test—rather than inherit—concepts that may not apply directly.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hidden Reward: Affect and Its Prediction Errors as Windows Into Subjective Value\",\"authors\":\"Marius C. Vollberg, David Sander\",\"doi\":\"10.1177/09637214231217678\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Scientists increasingly apply concepts from reinforcement learning to affect, but which concepts should apply? And what can their application reveal that we cannot know from directly observable states? An important reinforcement learning concept is the difference between reward expectations and outcomes. Such reward prediction errors have become foundational to research on adaptive behavior in humans, animals, and machines. Owing to historical focus on animal models and observable reward (e.g., food or money), however, relatively little attention has been paid to the fact that humans can additionally report correspondingly expected and experienced affect (e.g., feelings). Reflecting a broader “rise of affectivism,” attention has started to shift, revealing explanatory power of expected and experienced feelings—including prediction errors—above and beyond observable reward. We propose that applying concepts from reinforcement learning to affect holds promise for elucidating subjective value. Simultaneously, we urge scientists to test—rather than inherit—concepts that may not apply directly.\",\"PeriodicalId\":10802,\"journal\":{\"name\":\"Current Directions in Psychological Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2024-01-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Directions in Psychological Science\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1177/09637214231217678\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Directions in Psychological Science","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/09637214231217678","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
Hidden Reward: Affect and Its Prediction Errors as Windows Into Subjective Value
Scientists increasingly apply concepts from reinforcement learning to affect, but which concepts should apply? And what can their application reveal that we cannot know from directly observable states? An important reinforcement learning concept is the difference between reward expectations and outcomes. Such reward prediction errors have become foundational to research on adaptive behavior in humans, animals, and machines. Owing to historical focus on animal models and observable reward (e.g., food or money), however, relatively little attention has been paid to the fact that humans can additionally report correspondingly expected and experienced affect (e.g., feelings). Reflecting a broader “rise of affectivism,” attention has started to shift, revealing explanatory power of expected and experienced feelings—including prediction errors—above and beyond observable reward. We propose that applying concepts from reinforcement learning to affect holds promise for elucidating subjective value. Simultaneously, we urge scientists to test—rather than inherit—concepts that may not apply directly.
期刊介绍:
Current Directions in Psychological Science publishes reviews by leading experts covering all of scientific psychology and its applications. Each issue of Current Directions features a diverse mix of reports on various topics such as language, memory and cognition, development, the neural basis of behavior and emotions, various aspects of psychopathology, and theory of mind. These articles allow readers to stay apprised of important developments across subfields beyond their areas of expertise and bodies of research they might not otherwise be aware of. The articles in Current Directions are also written to be accessible to non-experts, making them ideally suited for use in the classroom as teaching supplements.