Measurement and Analysis of Cognitive Load Associated with Moving Object Classification in Underwater Environments
Measurement and Analysis of Cognitive Load Associated with Moving Object Classification in Underwater Environments
复制标题
水下环境中与运动物体分类相关的认知负荷的测量和分析
DOI：
10.1080/10447318.2023.2171275
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Butail, Sachit
中科院分区：
文献类型：
--
作者：
Bhattacharya, Arunim;Butail, Sachit
野外科学实验中的视觉分析通常涉及对实验图像和视频上的对象进行分类。在这种情况下，开发一个可靠的和独立验证的估计对象分类过程中的心理工作量可以使认知响应任务分配。本研究的目标是量化的认知负荷感知人类从脑电图（EEG）数据在水下物体分类任务，是从公民科学研究的启发。在任务中，参与者被要求在虚拟水下环境的短视频中识别三种可能的入侵鱼类之一。虚拟环境被建模为不同的鱼类行为和环境因素，已知是关键的分类。一个上下文相关的次要任务的目的是提供独立的验证认知负荷的措施。几个既定的认知负荷的措施进行了比较，在不同的权重的头皮位置，并选择与反应时间和次要任务的准确性密切相关的措施进行进一步分析。我们的结果表明，使用阿尔法频率功率差异计算的认知负荷与反应时间和次要任务准确性最相关。当适应环境因素时，使用这种方法计算的认知负荷在环境混浊和鱼类高速移动时较高。这项研究的结果在认知响应的人机交互和开发共享控制策略的人机交互中有应用。
Visual analysis in field science experiments often involves classifying objects on experimental images and videos. In this context, developing a reliable and independently validated estimate of mental workload during object classification can enable cognitively responsive task allocation. The goal of this study is to quantify the cognitive load perceived by humans from electroencephalography (EEG) data during an underwater object classification task that was inspired from citizen science studies. During the task, participants were asked to identify one of three possible invasive fish species in short videos of a virtual underwater environment. The virtual environment was modeled to vary fish behavior and environmental factors that are known to be critical in classification. A contextually-relevant secondary task was designed to provide independent validation of cognitive load measures. Several established measures of cognitive load were compared across different weightings on the scalp positions, and the measure that strongly associated with reaction time and a secondary task accuracy was selected for further analysis. Our results show that cognitive load calculated using the difference in power of alpha frequencies best correlates with reaction time and secondary task accuracy. When fit to the environmental factors, cognitive load calculated using this approach was high when the environment was turbid and the fish moved at high speeds. Results from this study have applications in cognitively-responsive human–computer interaction and in developing shared control strategies in human–robot interaction.
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