{"title":"将元学习模型作为检验认知发展理论的工具。","authors":"Kate Nussenbaum, Catherine A Hartley","doi":"10.1017/S0140525X24000281","DOIUrl":null,"url":null,"abstract":"<p><p>Binz et al. argue that meta-learned models are essential tools for understanding adult cognition. Here, we propose that these models are particularly useful for testing hypotheses about why learning processes change across development. By leveraging their ability to discover optimal algorithms and account for capacity limitations, researchers can use these models to test competing theories of developmental change in learning.</p>","PeriodicalId":8698,"journal":{"name":"Behavioral and Brain Sciences","volume":null,"pages":null},"PeriodicalIF":16.6000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Meta-learned models as tools to test theories of cognitive development.\",\"authors\":\"Kate Nussenbaum, Catherine A Hartley\",\"doi\":\"10.1017/S0140525X24000281\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Binz et al. argue that meta-learned models are essential tools for understanding adult cognition. Here, we propose that these models are particularly useful for testing hypotheses about why learning processes change across development. By leveraging their ability to discover optimal algorithms and account for capacity limitations, researchers can use these models to test competing theories of developmental change in learning.</p>\",\"PeriodicalId\":8698,\"journal\":{\"name\":\"Behavioral and Brain Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.6000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavioral and Brain Sciences\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1017/S0140525X24000281\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BEHAVIORAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavioral and Brain Sciences","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1017/S0140525X24000281","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
Meta-learned models as tools to test theories of cognitive development.
Binz et al. argue that meta-learned models are essential tools for understanding adult cognition. Here, we propose that these models are particularly useful for testing hypotheses about why learning processes change across development. By leveraging their ability to discover optimal algorithms and account for capacity limitations, researchers can use these models to test competing theories of developmental change in learning.
期刊介绍:
Behavioral and Brain Sciences (BBS) is a highly respected journal that employs an innovative approach called Open Peer Commentary. This format allows for the publication of noteworthy and contentious research from various fields including psychology, neuroscience, behavioral biology, and cognitive science. Each article is accompanied by 20-40 commentaries from experts across these disciplines, as well as a response from the author themselves. This unique setup creates a captivating forum for the exchange of ideas, critical analysis, and the integration of research within the behavioral and brain sciences, spanning topics from molecular neurobiology and artificial intelligence to the philosophy of the mind.