{"title":"从迷宫到自动化:动物模型工作记忆研究的现代化。","authors":"Eghlima Razeghian , Ehsan Rezayat","doi":"10.1016/j.bbr.2025.115806","DOIUrl":null,"url":null,"abstract":"<div><div>Working memory (WM) is a core cognitive mechanism necessary for adaptive behavior. In the last few decades, scientists have studied WM using rodent models through traditional and time-consuming approaches, such as the Radial Arm Maze and the T-Maze. While these traditional tools have presented fundamental understanding, their dependence on manual operations restrains experimental precision and scalability. Here, we refine how emerging automated technologies—such as touchscreens, virtual reality (VR), and artificial intelligence (AI)—are inspiring this field by allowing high-throughput testing with improved precision. Further, we present a new framework to evaluate both classic and modern tasks based on their Scalability, Precision, and Neural Compatibility. This evaluation underlines how automation allows the emergence of modern paradigms, such as the Pulse-Based Accumulation Task and the Trial-Unique Nonmatching-to-Location (TUNL) task, offering more precise assessments of WM. Such technological progressions not only boost data quality and mitigate the efforts involved in data collection but also make way for a more unified understanding of the neural processes that underlie working memory.</div></div>","PeriodicalId":8823,"journal":{"name":"Behavioural Brain Research","volume":"496 ","pages":"Article 115806"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From mazes to automation: Modernizing working memory research in animal models\",\"authors\":\"Eghlima Razeghian , Ehsan Rezayat\",\"doi\":\"10.1016/j.bbr.2025.115806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Working memory (WM) is a core cognitive mechanism necessary for adaptive behavior. In the last few decades, scientists have studied WM using rodent models through traditional and time-consuming approaches, such as the Radial Arm Maze and the T-Maze. While these traditional tools have presented fundamental understanding, their dependence on manual operations restrains experimental precision and scalability. Here, we refine how emerging automated technologies—such as touchscreens, virtual reality (VR), and artificial intelligence (AI)—are inspiring this field by allowing high-throughput testing with improved precision. Further, we present a new framework to evaluate both classic and modern tasks based on their Scalability, Precision, and Neural Compatibility. This evaluation underlines how automation allows the emergence of modern paradigms, such as the Pulse-Based Accumulation Task and the Trial-Unique Nonmatching-to-Location (TUNL) task, offering more precise assessments of WM. Such technological progressions not only boost data quality and mitigate the efforts involved in data collection but also make way for a more unified understanding of the neural processes that underlie working memory.</div></div>\",\"PeriodicalId\":8823,\"journal\":{\"name\":\"Behavioural Brain Research\",\"volume\":\"496 \",\"pages\":\"Article 115806\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavioural Brain Research\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0166432825003936\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BEHAVIORAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavioural Brain Research","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0166432825003936","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
From mazes to automation: Modernizing working memory research in animal models
Working memory (WM) is a core cognitive mechanism necessary for adaptive behavior. In the last few decades, scientists have studied WM using rodent models through traditional and time-consuming approaches, such as the Radial Arm Maze and the T-Maze. While these traditional tools have presented fundamental understanding, their dependence on manual operations restrains experimental precision and scalability. Here, we refine how emerging automated technologies—such as touchscreens, virtual reality (VR), and artificial intelligence (AI)—are inspiring this field by allowing high-throughput testing with improved precision. Further, we present a new framework to evaluate both classic and modern tasks based on their Scalability, Precision, and Neural Compatibility. This evaluation underlines how automation allows the emergence of modern paradigms, such as the Pulse-Based Accumulation Task and the Trial-Unique Nonmatching-to-Location (TUNL) task, offering more precise assessments of WM. Such technological progressions not only boost data quality and mitigate the efforts involved in data collection but also make way for a more unified understanding of the neural processes that underlie working memory.
期刊介绍:
Behavioural Brain Research is an international, interdisciplinary journal dedicated to the publication of articles in the field of behavioural neuroscience, broadly defined. Contributions from the entire range of disciplines that comprise the neurosciences, behavioural sciences or cognitive sciences are appropriate, as long as the goal is to delineate the neural mechanisms underlying behaviour. Thus, studies may range from neurophysiological, neuroanatomical, neurochemical or neuropharmacological analysis of brain-behaviour relations, including the use of molecular genetic or behavioural genetic approaches, to studies that involve the use of brain imaging techniques, to neuroethological studies. Reports of original research, of major methodological advances, or of novel conceptual approaches are all encouraged. The journal will also consider critical reviews on selected topics.