{"title":"The Missing Link: Bridging Cognitive Fatigue with Working Memory.","authors":"Brodie E Mangan, Dimitrios Kourtis","doi":"10.1162/JOCN.a.2398","DOIUrl":null,"url":null,"abstract":"<p><p>Cognitive fatigue, a key contributor to failures in high-stakes domains, is poorly understood due to imprecise definitions, inconsistent protocols, and neglect of working memory (WM) mechanisms. We propose that active fatigue, arising from sustained cognitive demands, should be studied through WM frameworks, distinguishing it from passive (low arousal) fatigue. Contemporary WM models identify theta/alpha-gamma oscillatory dynamics as fundamental to WM function, plausibly providing testable markers of fatigue-induced breakdown. Conceptualizing active fatigue as specifically a disruption of WM's oscillatory dynamics provides a framework for the precise identification of its core neurophysiological basis. Specifically, tracking destabilized theta/alpha-gamma coupling and frequency synchrony provides a direct link to observed performance declines, enabling targeted rhythm-specific interventions such as frequency-matched brain stimulation. Current induction tasks rarely sustain optimal cognitive difficulty and are confounded by learning effects, prompting us to develop WAND (working-memory adaptive-fatigue with n-back difficulty), an open-source adaptive fatigue induction n-back suite. WAND reduces learning effects, classifies participant performance, and maintains task performance in the \"optimal challenge zone\"; optional distractor probes and multimodal logging enable robust mechanistic analyses. This approach shifts the field toward mechanistic, intervention-ready insights, enhancing fatigue detection and mitigation through theoretically grounded neural markers and standardized induction protocols.</p>","PeriodicalId":51081,"journal":{"name":"Journal of Cognitive Neuroscience","volume":" ","pages":"1-11"},"PeriodicalIF":3.0000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cognitive Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1162/JOCN.a.2398","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Cognitive fatigue, a key contributor to failures in high-stakes domains, is poorly understood due to imprecise definitions, inconsistent protocols, and neglect of working memory (WM) mechanisms. We propose that active fatigue, arising from sustained cognitive demands, should be studied through WM frameworks, distinguishing it from passive (low arousal) fatigue. Contemporary WM models identify theta/alpha-gamma oscillatory dynamics as fundamental to WM function, plausibly providing testable markers of fatigue-induced breakdown. Conceptualizing active fatigue as specifically a disruption of WM's oscillatory dynamics provides a framework for the precise identification of its core neurophysiological basis. Specifically, tracking destabilized theta/alpha-gamma coupling and frequency synchrony provides a direct link to observed performance declines, enabling targeted rhythm-specific interventions such as frequency-matched brain stimulation. Current induction tasks rarely sustain optimal cognitive difficulty and are confounded by learning effects, prompting us to develop WAND (working-memory adaptive-fatigue with n-back difficulty), an open-source adaptive fatigue induction n-back suite. WAND reduces learning effects, classifies participant performance, and maintains task performance in the "optimal challenge zone"; optional distractor probes and multimodal logging enable robust mechanistic analyses. This approach shifts the field toward mechanistic, intervention-ready insights, enhancing fatigue detection and mitigation through theoretically grounded neural markers and standardized induction protocols.