{"title":"Adopting Stimulus Detection Tasks for Cognitive Workload Assessment: Some Considerations.","authors":"Francesco N Biondi","doi":"10.1177/00187208241228049","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload.</p><p><strong>Background: </strong>Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic.</p><p><strong>Method: </strong>This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation.</p><p><strong>Results: </strong>The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms.</p><p><strong>Conclusion: </strong>There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state.</p><p><strong>Application: </strong>Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2561-2568"},"PeriodicalIF":2.9000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11475934/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Factors","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/00187208241228049","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/21 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload.
Background: Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic.
Method: This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation.
Results: The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms.
Conclusion: There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state.
Application: Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.
目的:本文探讨了在使用刺激检测任务确定操作员工作量时如何正确解释数据的问题:本文探讨了在使用刺激检测任务确定操作员工作量时如何正确解释数据的问题:背景:刺激检测任务是测量操作员状态的一种相对简单且成本低廉的方法。虽然刺激检测任务可能更适合测量高工作负荷条件,但采用这种方法评估低工作负荷可能会出现更多问题:本微型综述详细介绍了常见刺激检测任务的使用及其对人因实践的贡献。本文还借鉴了倒 U 型模型的概念框架,讨论了数据解释问题:结果:本文讨论的证据凸显了刺激检测任务范式的明显局限性:结论:使用像刺激检测任务这样的单维度工具作为确定操作员心理生理状态的主要信息来源存在固有风险:本文向处理刺激检测任务解释难题的人为因素研究人员和从业人员提出了两项建议。
期刊介绍:
Human Factors: The Journal of the Human Factors and Ergonomics Society publishes peer-reviewed scientific studies in human factors/ergonomics that present theoretical and practical advances concerning the relationship between people and technologies, tools, environments, and systems. Papers published in Human Factors leverage fundamental knowledge of human capabilities and limitations – and the basic understanding of cognitive, physical, behavioral, physiological, social, developmental, affective, and motivational aspects of human performance – to yield design principles; enhance training, selection, and communication; and ultimately improve human-system interfaces and sociotechnical systems that lead to safer and more effective outcomes.