{"title":"一刀切:成语计算模型揭示学习和元学习策略的个体差异","authors":"Theodros M. Haile, Chantel S. Prat, Andrea Stocco","doi":"10.1111/tops.12730","DOIUrl":null,"url":null,"abstract":"Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long‐term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT‐R models of performance on the stimulus‐response learning, Reinforcement Learning Working Memory task. The task consisted of short 3‐image, and long 6‐image, feedback‐based learning blocks. A no‐feedback test phase was administered after learning, with an interfering task inserted between learning and test. Our four models included two single‐mechanism RL and LTM models, and two integrated RL‐LTM models: (a) RL‐based meta‐learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL‐LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta‐RL: 13.25%, bias‐RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus‐response task. Finally, long‐term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3‐ vs 6‐image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated‐models, owing to the strength of our idiographic approach.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"One Size Does Not Fit All: Idiographic Computational Models Reveal Individual Differences in Learning and Meta‐Learning Strategies\",\"authors\":\"Theodros M. Haile, Chantel S. Prat, Andrea Stocco\",\"doi\":\"10.1111/tops.12730\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long‐term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT‐R models of performance on the stimulus‐response learning, Reinforcement Learning Working Memory task. The task consisted of short 3‐image, and long 6‐image, feedback‐based learning blocks. A no‐feedback test phase was administered after learning, with an interfering task inserted between learning and test. Our four models included two single‐mechanism RL and LTM models, and two integrated RL‐LTM models: (a) RL‐based meta‐learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL‐LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta‐RL: 13.25%, bias‐RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus‐response task. Finally, long‐term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3‐ vs 6‐image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated‐models, owing to the strength of our idiographic approach.\",\"PeriodicalId\":47822,\"journal\":{\"name\":\"Topics in Cognitive Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Topics in Cognitive Science\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1111/tops.12730\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topics in Cognitive Science","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1111/tops.12730","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
One Size Does Not Fit All: Idiographic Computational Models Reveal Individual Differences in Learning and Meta‐Learning Strategies
Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long‐term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT‐R models of performance on the stimulus‐response learning, Reinforcement Learning Working Memory task. The task consisted of short 3‐image, and long 6‐image, feedback‐based learning blocks. A no‐feedback test phase was administered after learning, with an interfering task inserted between learning and test. Our four models included two single‐mechanism RL and LTM models, and two integrated RL‐LTM models: (a) RL‐based meta‐learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL‐LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta‐RL: 13.25%, bias‐RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus‐response task. Finally, long‐term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3‐ vs 6‐image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated‐models, owing to the strength of our idiographic approach.
期刊介绍:
Topics in Cognitive Science (topiCS) is an innovative new journal that covers all areas of cognitive science including cognitive modeling, cognitive neuroscience, cognitive anthropology, and cognitive science and philosophy. topiCS aims to provide a forum for: -New communities of researchers- New controversies in established areas- Debates and commentaries- Reflections and integration The publication features multiple scholarly papers dedicated to a single topic. Some of these topics will appear together in one issue, but others may appear across several issues or develop into a regular feature. Controversies or debates started in one issue may be followed up by commentaries in a later issue, etc. However, the format and origin of the topics will vary greatly.