{"title":"超越归属感:重新思考先天的行为倾向、学习限制和认知能力","authors":"Rodrigo Sosa","doi":"10.1177/10597123221097451","DOIUrl":null,"url":null,"abstract":"Learning is a major determinant of behavioral change for some organisms through their lifecycles. From an associative perspective, learning is assumed to occur whenever organisms experience particular statistical regularities in their environment; specifically, meaningful outcomes that follow certain cues or actions chiefly contribute to behavioral change. However, numerous empirical reports reveal that not all cue–outcome and action–outcome combinations are learned equally well, a phenomenon that is termed belongingness. Those reports are valuable as descriptive-level knowledge, but beg further considerations, like what is the origin, adaptive value of, and underlying mechanisms associated with the predisposition to couple particular events. Contrary to what is often assumed, the mere observation of learning predispositions says little as to whether they arise from genetics, are constrained by hardwired neural circuitries, or have been ecologically advantageous in an evolutionary timescale. The present paper aims to present a number of notions from different research fields outside the hard core of associative learning and, in so doing, provides elements for careful study and conceptualization of this issue. Thereafter, these notions are pooled to understand behavioral variation in a wide array of phenomena, thus, bringing a more informed approach to the nature versus nurture debate.","PeriodicalId":55552,"journal":{"name":"Adaptive Behavior","volume":"1 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beyond belongingness: Rethinking innate behavioral predispositions, learning constraints, and cognitive capacities\",\"authors\":\"Rodrigo Sosa\",\"doi\":\"10.1177/10597123221097451\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Learning is a major determinant of behavioral change for some organisms through their lifecycles. From an associative perspective, learning is assumed to occur whenever organisms experience particular statistical regularities in their environment; specifically, meaningful outcomes that follow certain cues or actions chiefly contribute to behavioral change. However, numerous empirical reports reveal that not all cue–outcome and action–outcome combinations are learned equally well, a phenomenon that is termed belongingness. Those reports are valuable as descriptive-level knowledge, but beg further considerations, like what is the origin, adaptive value of, and underlying mechanisms associated with the predisposition to couple particular events. Contrary to what is often assumed, the mere observation of learning predispositions says little as to whether they arise from genetics, are constrained by hardwired neural circuitries, or have been ecologically advantageous in an evolutionary timescale. The present paper aims to present a number of notions from different research fields outside the hard core of associative learning and, in so doing, provides elements for careful study and conceptualization of this issue. Thereafter, these notions are pooled to understand behavioral variation in a wide array of phenomena, thus, bringing a more informed approach to the nature versus nurture debate.\",\"PeriodicalId\":55552,\"journal\":{\"name\":\"Adaptive Behavior\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2022-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Adaptive Behavior\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1177/10597123221097451\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adaptive Behavior","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/10597123221097451","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Learning is a major determinant of behavioral change for some organisms through their lifecycles. From an associative perspective, learning is assumed to occur whenever organisms experience particular statistical regularities in their environment; specifically, meaningful outcomes that follow certain cues or actions chiefly contribute to behavioral change. However, numerous empirical reports reveal that not all cue–outcome and action–outcome combinations are learned equally well, a phenomenon that is termed belongingness. Those reports are valuable as descriptive-level knowledge, but beg further considerations, like what is the origin, adaptive value of, and underlying mechanisms associated with the predisposition to couple particular events. Contrary to what is often assumed, the mere observation of learning predispositions says little as to whether they arise from genetics, are constrained by hardwired neural circuitries, or have been ecologically advantageous in an evolutionary timescale. The present paper aims to present a number of notions from different research fields outside the hard core of associative learning and, in so doing, provides elements for careful study and conceptualization of this issue. Thereafter, these notions are pooled to understand behavioral variation in a wide array of phenomena, thus, bringing a more informed approach to the nature versus nurture debate.
期刊介绍:
_Adaptive Behavior_ publishes articles on adaptive behaviour in living organisms and autonomous artificial systems. The official journal of the _International Society of Adaptive Behavior_, _Adaptive Behavior_, addresses topics such as perception and motor control, embodied cognition, learning and evolution, neural mechanisms, artificial intelligence, behavioral sequences, motivation and emotion, characterization of environments, decision making, collective and social behavior, navigation, foraging, communication and signalling.
Print ISSN: 1059-7123