{"title":"关于婴儿如何学习动物和非动物因果行动的联想学习理论:对四项经典研究的重新审视。","authors":"Deon T Benton","doi":"10.1037/xge0001656","DOIUrl":null,"url":null,"abstract":"<p><p>Considerable research shows that causal perception emerges between 6 and 10 months of age. Yet, because this research tends to use artificial stimuli, it is unanswered how or through what mechanisms of change human infants learn about the causal properties of real-world categories such as animate entities and inanimate objects. One answer to this question is that this knowledge is innate (i.e., unlearned, evolutionarily ancient, and possibly present at birth) and underpinned by core knowledge and core cognition. An alternative perspective that is tested here through computer simulations is that infants acquire this knowledge via domain-general associative learning. This article demonstrates that associative learning alone-as instantiated in an artificial neural network-is sufficient to explain the data presented in four classic infancy studies: Spelke et al. (1995), Saxe et al. (2005), Saxe et al. (2007), and Markson and Spelke (2006). This work not only advances theoretical perspectives within developmental psychology but also has implications for the design of artificial intelligence systems inspired by human cognitive development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":15698,"journal":{"name":"Journal of Experimental Psychology: General","volume":" ","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An associative-learning account of how infants learn about causal action in animates and inanimates: A critical reexamination of four classic studies.\",\"authors\":\"Deon T Benton\",\"doi\":\"10.1037/xge0001656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Considerable research shows that causal perception emerges between 6 and 10 months of age. Yet, because this research tends to use artificial stimuli, it is unanswered how or through what mechanisms of change human infants learn about the causal properties of real-world categories such as animate entities and inanimate objects. One answer to this question is that this knowledge is innate (i.e., unlearned, evolutionarily ancient, and possibly present at birth) and underpinned by core knowledge and core cognition. An alternative perspective that is tested here through computer simulations is that infants acquire this knowledge via domain-general associative learning. This article demonstrates that associative learning alone-as instantiated in an artificial neural network-is sufficient to explain the data presented in four classic infancy studies: Spelke et al. (1995), Saxe et al. (2005), Saxe et al. (2007), and Markson and Spelke (2006). This work not only advances theoretical perspectives within developmental psychology but also has implications for the design of artificial intelligence systems inspired by human cognitive development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>\",\"PeriodicalId\":15698,\"journal\":{\"name\":\"Journal of Experimental Psychology: General\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Experimental Psychology: General\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1037/xge0001656\",\"RegionNum\":1,\"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":"Journal of Experimental Psychology: General","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/xge0001656","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
An associative-learning account of how infants learn about causal action in animates and inanimates: A critical reexamination of four classic studies.
Considerable research shows that causal perception emerges between 6 and 10 months of age. Yet, because this research tends to use artificial stimuli, it is unanswered how or through what mechanisms of change human infants learn about the causal properties of real-world categories such as animate entities and inanimate objects. One answer to this question is that this knowledge is innate (i.e., unlearned, evolutionarily ancient, and possibly present at birth) and underpinned by core knowledge and core cognition. An alternative perspective that is tested here through computer simulations is that infants acquire this knowledge via domain-general associative learning. This article demonstrates that associative learning alone-as instantiated in an artificial neural network-is sufficient to explain the data presented in four classic infancy studies: Spelke et al. (1995), Saxe et al. (2005), Saxe et al. (2007), and Markson and Spelke (2006). This work not only advances theoretical perspectives within developmental psychology but also has implications for the design of artificial intelligence systems inspired by human cognitive development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
期刊介绍:
The Journal of Experimental Psychology: General publishes articles describing empirical work that bridges the traditional interests of two or more communities of psychology. The work may touch on issues dealt with in JEP: Learning, Memory, and Cognition, JEP: Human Perception and Performance, JEP: Animal Behavior Processes, or JEP: Applied, but may also concern issues in other subdisciplines of psychology, including social processes, developmental processes, psychopathology, neuroscience, or computational modeling. Articles in JEP: General may be longer than the usual journal publication if necessary, but shorter articles that bridge subdisciplines will also be considered.