Parametrization and validation of the N-SEEV Attention Model for Visual and Auditory scenes

视觉和听觉场景的 N-SEEV 注意力模型的参数化和验证

• 批准号: RGPIN-2022-04852
• 负责人: Tremblay, Sébastien
• 金额: $ 2.84万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763763
• 关键词: Parametrization; validation; SEEV; Attention; Model

The analysis of complex and dynamic environments is cognitively demanding because they require the constant filtering, processing and integrating of a considerable amount of information - much of it irrelevant - in order to understand the interaction between various factors and to gauge the evolution of an unpredictable situation. These environments are conducive to the manifestation of cognitive phenomena that can reduce situation awareness and the quality of decisions (e.g., tunnelling, inattentional blindness). The conceptual framework of NSEEV (noticing; salience, expectancy, eccentricity, value) that offers a model of noticing events in the task environment and predicts supervisory control scanning behavior can be used as a basis for modelling cognitive functioning in critical work environments by offering the possibility to predict the efficiency of information processing, modulation of cognitive load, and distribution of attention in a dynamic workspace (Wickens, 2021). NSEEV provides the theoretical basis necessary to model the cognitive processes associated with several critical work situations; it also provides the parameters to potentially operationalize some premises of the situation awareness concept (frequently used in cognitive ergonomics), and seems promising to interpret the pattern of eye movements that could suggest cognitive vulnerabilities. NSEEV allows for testable predictions that can be assessed within a real-world context. The proposed work will adopt a methodology of microworld, a complex and realistic simulation that retains some of the ecological validity of a time-pressured, multi-tasking work environment, but crucially also allows a high degree of experimental control enabling one to determine cause and effect relationships. A first series of experiments is designed to empirically validate the NSEEV conceptual framework, and also to model cognitive functioning by testing the vulnerability of cognitive processes inherent in complex and dynamic environments (Theme 1). Additionally, the project aims to extend the framework beyond the noticing of visual stimuli to assess whether the identified parameters might impose similar constraints on the deployment of attention in the auditory domain (Theme 2). A final objective is to use the NSEEV framework to guide training to help mitigate some of the cognitive fallibilities that emerge under extreme work conditions (Theme 3). A greater understanding of the fundamental processes involved in the distribution of attention in complex work settings - both auditory and visual - will help inform practical applications of the work. A theoretically driven framework to advise on training procedures would undoubtedly be valuable, and could go some way towards mitigating the vulnerabilities of human cognition that are exposed under conditions of high complexity and workload.

对复杂和动态环境的分析在认知上要求很高，因为它们需要不断过滤、处理和整合大量信息-其中许多信息是无关紧要的-以便了解各种因素之间的相互作用，并衡量不可预测局势的演变。这些环境有利于认知现象的表现，这些认知现象会降低情境意识和决策质量（例如，隧道效应、无意视盲）。NSEEV的概念框架（注意到;显著性、期望、偏心率、价值），其提供了在任务环境中注意事件的模型并预测监督控制扫描行为，通过提供预测信息处理的效率，认知负荷的调节，和注意力在动态工作空间中的分布（Wickens，2021）。NSEEV提供了必要的理论基础，以模拟与几个关键的工作情况下的认知过程，它还提供了参数，可能操作的一些前提的情况下意识的概念（经常使用的认知工效学），似乎有希望解释的模式眼动，可能表明认知的弱点。 NSEEV允许可测试的预测，可以在现实世界的背景下进行评估。拟议的工作将采用微观世界的方法，一个复杂而现实的模拟，保留了一些时间压力，多任务工作环境的生态有效性，但至关重要的是，也允许高度的实验控制，使人们能够确定因果关系。第一系列实验的目的是实证验证NSEEV的概念框架，并通过测试复杂和动态环境中固有的认知过程的脆弱性来模拟认知功能（主题1）。此外，该项目的目的是扩展框架以外的视觉刺激的注意，以评估是否识别的参数可能会施加类似的限制，在听觉域（主题2）的部署注意。最后一个目标是使用NSEEV框架来指导培训，以帮助减轻极端工作条件下出现的一些认知错误（主题3）。对复杂工作环境中注意力分配的基本过程（包括听觉和视觉）有更深入的了解，将有助于为这项工作的实际应用提供信息。一个理论驱动的框架，就培训程序提出建议，无疑是有价值的，并可以在某种程度上减轻人类认知的脆弱性，暴露在高度复杂和工作量的条件下。