{"title":"超越可学性：用 DNN 理解人类视觉发展。","authors":"Lei Yuan","doi":"10.1016/j.tics.2024.05.002","DOIUrl":null,"url":null,"abstract":"<p><p>Recently, Orhan and Lake demonstrated the computational plausibility that children can acquire sophisticated visual representations from natural input data without inherent biases, challenging the need for innate constraints in human learning. The findings may also reveal crucial properties of early visual learning and inform theories of human visual development.</p>","PeriodicalId":49417,"journal":{"name":"Trends in Cognitive Sciences","volume":null,"pages":null},"PeriodicalIF":16.7000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beyond learnability: understanding human visual development with DNNs.\",\"authors\":\"Lei Yuan\",\"doi\":\"10.1016/j.tics.2024.05.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Recently, Orhan and Lake demonstrated the computational plausibility that children can acquire sophisticated visual representations from natural input data without inherent biases, challenging the need for innate constraints in human learning. The findings may also reveal crucial properties of early visual learning and inform theories of human visual development.</p>\",\"PeriodicalId\":49417,\"journal\":{\"name\":\"Trends in Cognitive Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.7000,\"publicationDate\":\"2024-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Cognitive Sciences\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1016/j.tics.2024.05.002\",\"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":"Trends in Cognitive Sciences","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1016/j.tics.2024.05.002","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
Beyond learnability: understanding human visual development with DNNs.
Recently, Orhan and Lake demonstrated the computational plausibility that children can acquire sophisticated visual representations from natural input data without inherent biases, challenging the need for innate constraints in human learning. The findings may also reveal crucial properties of early visual learning and inform theories of human visual development.
期刊介绍：
Essential reading for those working directly in the cognitive sciences or in related specialist areas, Trends in Cognitive Sciences provides an instant overview of current thinking for scientists, students and teachers who want to keep up with the latest developments in the cognitive sciences. The journal brings together research in psychology, artificial intelligence, linguistics, philosophy, computer science and neuroscience. Trends in Cognitive Sciences provides a platform for the interaction of these disciplines and the evolution of cognitive science as an independent field of study.
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