{"title":"利用强化学习理论推断黑猩猩在认知任务中的学习来源","authors":"Satoshi Hirata, Yutaka Sakai","doi":"10.1007/s10015-024-00954-7","DOIUrl":null,"url":null,"abstract":"<div><p>Reinforcement learning is a mathematical framework for learning better choices through trial-and-error. Recent studies revealed that reinforcement learning is applicable to animal behavior and cognition. However, applying reinforcement learning to animal behavior sometimes encounters difficulties because the information sources utilized by animals to make choices are often unknown, whereas this is identified as the “state” in the reinforcement learning framework. We sought to identify possible state settings including non-standard formulations suitable for explaining data from past chimpanzee studies. Although chimpanzees’ performance in a serial learning task was inconsistent with standard reinforcement learning formulations, we found that the combination of state-independent choice making and state-dependent evaluation produced consistent results. Exploration of state settings in reinforcement learning may shed new light on animal learning processes.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"29 3","pages":"398 - 403"},"PeriodicalIF":0.8000,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Inferring source of learning by chimpanzees in cognitive tasks using reinforcement learning theory\",\"authors\":\"Satoshi Hirata, Yutaka Sakai\",\"doi\":\"10.1007/s10015-024-00954-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Reinforcement learning is a mathematical framework for learning better choices through trial-and-error. Recent studies revealed that reinforcement learning is applicable to animal behavior and cognition. However, applying reinforcement learning to animal behavior sometimes encounters difficulties because the information sources utilized by animals to make choices are often unknown, whereas this is identified as the “state” in the reinforcement learning framework. We sought to identify possible state settings including non-standard formulations suitable for explaining data from past chimpanzee studies. Although chimpanzees’ performance in a serial learning task was inconsistent with standard reinforcement learning formulations, we found that the combination of state-independent choice making and state-dependent evaluation produced consistent results. Exploration of state settings in reinforcement learning may shed new light on animal learning processes.</p></div>\",\"PeriodicalId\":46050,\"journal\":{\"name\":\"Artificial Life and Robotics\",\"volume\":\"29 3\",\"pages\":\"398 - 403\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2024-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Life and Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10015-024-00954-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Life and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s10015-024-00954-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
Inferring source of learning by chimpanzees in cognitive tasks using reinforcement learning theory
Reinforcement learning is a mathematical framework for learning better choices through trial-and-error. Recent studies revealed that reinforcement learning is applicable to animal behavior and cognition. However, applying reinforcement learning to animal behavior sometimes encounters difficulties because the information sources utilized by animals to make choices are often unknown, whereas this is identified as the “state” in the reinforcement learning framework. We sought to identify possible state settings including non-standard formulations suitable for explaining data from past chimpanzee studies. Although chimpanzees’ performance in a serial learning task was inconsistent with standard reinforcement learning formulations, we found that the combination of state-independent choice making and state-dependent evaluation produced consistent results. Exploration of state settings in reinforcement learning may shed new light on animal learning processes.