{"title":"走向行为的功能分类:基于结果的分类法","authors":"D. E. Mallpress","doi":"10.1177/10597123211040574","DOIUrl":null,"url":null,"abstract":"The classification of behaviour has historically been done using one of the two approaches, either through the hypothetical causes (such as ‘instincts’, ‘drives’ and ‘needs’) or through the cataloguing of the observable form of behaviour using an ethogram. This article offers an alternative framework for classification of behaviour based upon only the behavioural outcomes. The framework is specified from first principles of a state-space approach, allowing us to discuss intermediate outcomes that may have instrumental value. This approach could provide a firmer foundation to consider the hierarchical nature of goals and allows us to address both the ‘how’ and the ‘why’ questions within a single framework. This taxonomy is designed to complement rather than replace existing attempts; the classification of behaviour by outcome is orthogonal to questions of the mechanisms of decision making or of the implementation of actions. This article specifies nine basic classes of behaviour and provides precise definitions for each of these. We then develop a formal language for the description of observed activities, the representation of behavioural hierarchies and for the analysis of possibility sets for achieving future goals. We follow up with some critique and discussion of the problems such a framework poses.","PeriodicalId":55552,"journal":{"name":"Adaptive Behavior","volume":"30 1","pages":"417 - 450"},"PeriodicalIF":1.2000,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards a functional classification of behaviour: a taxonomy based on outcomes\",\"authors\":\"D. E. Mallpress\",\"doi\":\"10.1177/10597123211040574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The classification of behaviour has historically been done using one of the two approaches, either through the hypothetical causes (such as ‘instincts’, ‘drives’ and ‘needs’) or through the cataloguing of the observable form of behaviour using an ethogram. This article offers an alternative framework for classification of behaviour based upon only the behavioural outcomes. The framework is specified from first principles of a state-space approach, allowing us to discuss intermediate outcomes that may have instrumental value. This approach could provide a firmer foundation to consider the hierarchical nature of goals and allows us to address both the ‘how’ and the ‘why’ questions within a single framework. This taxonomy is designed to complement rather than replace existing attempts; the classification of behaviour by outcome is orthogonal to questions of the mechanisms of decision making or of the implementation of actions. This article specifies nine basic classes of behaviour and provides precise definitions for each of these. We then develop a formal language for the description of observed activities, the representation of behavioural hierarchies and for the analysis of possibility sets for achieving future goals. We follow up with some critique and discussion of the problems such a framework poses.\",\"PeriodicalId\":55552,\"journal\":{\"name\":\"Adaptive Behavior\",\"volume\":\"30 1\",\"pages\":\"417 - 450\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2021-09-06\",\"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/10.1177/10597123211040574\",\"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/10.1177/10597123211040574","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}
Towards a functional classification of behaviour: a taxonomy based on outcomes
The classification of behaviour has historically been done using one of the two approaches, either through the hypothetical causes (such as ‘instincts’, ‘drives’ and ‘needs’) or through the cataloguing of the observable form of behaviour using an ethogram. This article offers an alternative framework for classification of behaviour based upon only the behavioural outcomes. The framework is specified from first principles of a state-space approach, allowing us to discuss intermediate outcomes that may have instrumental value. This approach could provide a firmer foundation to consider the hierarchical nature of goals and allows us to address both the ‘how’ and the ‘why’ questions within a single framework. This taxonomy is designed to complement rather than replace existing attempts; the classification of behaviour by outcome is orthogonal to questions of the mechanisms of decision making or of the implementation of actions. This article specifies nine basic classes of behaviour and provides precise definitions for each of these. We then develop a formal language for the description of observed activities, the representation of behavioural hierarchies and for the analysis of possibility sets for achieving future goals. We follow up with some critique and discussion of the problems such a framework poses.
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