Tim Vriens, Mattias Horan, Jacqueline Gottlieb, Massimo Silvetti
{"title":"强化金属学习器作为一种生物学上可信的元学习框架。","authors":"Tim Vriens, Mattias Horan, Jacqueline Gottlieb, Massimo Silvetti","doi":"10.1017/S0140525X24000219","DOIUrl":null,"url":null,"abstract":"<p><p>We argue that the type of meta-learning proposed by Binz et al. generates models with low interpretability and falsifiability that have limited usefulness for neuroscience research. An alternative approach to meta-learning based on hyperparameter optimization obviates these concerns and can generate empirically testable hypotheses of biological computations.</p>","PeriodicalId":8698,"journal":{"name":"Behavioral and Brain Sciences","volume":"47 ","pages":"e168"},"PeriodicalIF":16.6000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The reinforcement metalearner as a biologically plausible meta-learning framework.\",\"authors\":\"Tim Vriens, Mattias Horan, Jacqueline Gottlieb, Massimo Silvetti\",\"doi\":\"10.1017/S0140525X24000219\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We argue that the type of meta-learning proposed by Binz et al. generates models with low interpretability and falsifiability that have limited usefulness for neuroscience research. An alternative approach to meta-learning based on hyperparameter optimization obviates these concerns and can generate empirically testable hypotheses of biological computations.</p>\",\"PeriodicalId\":8698,\"journal\":{\"name\":\"Behavioral and Brain Sciences\",\"volume\":\"47 \",\"pages\":\"e168\"},\"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/S0140525X24000219\",\"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/S0140525X24000219","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
The reinforcement metalearner as a biologically plausible meta-learning framework.
We argue that the type of meta-learning proposed by Binz et al. generates models with low interpretability and falsifiability that have limited usefulness for neuroscience research. An alternative approach to meta-learning based on hyperparameter optimization obviates these concerns and can generate empirically testable hypotheses of biological computations.
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.
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