Vizer Lisa M, Sears Andrew
UMBC, 100 Hilltop Cir., Baltimore, MD 21250, USA.
Int J Hum Comput Stud. 2017 Aug;104:80-96. doi: 10.1016/j.ijhcs.2017.03.001. Epub 2017 Mar 2.
Although high cognitive demand conditions can impair psychological, physical, and behavioral processes without appropriate management, current measurement methods are too cumbersome for continuous monitoring of cognitive demand, and do not account for individual differences. This research uses keystroke and linguistic markers of typed text to construct individualized models of cognitive demand response to discriminate high and low cognitive demand conditions, the results of which can have implications for design of cognitive demand monitoring systems for personalized health management. We constructed within-subject models of cognitive demand response for nine participants and one between-subjects model based on 20 participants. The AUCs for personalized models ranged from 0.679 to 0.953 (Mean=0.826, SD=0.085), significantly higher than chance (p < 0.0001) and the 0.714 AUC for the generic model (p=0.002). Although the features in each model were different, the most common features across models are rate of negative emotion, lexical diversity, rate of words over six letters, and word count. These results confirm significant individual differences in cognitive demand response and suggest that those developing measurement methods used in a monitoring system should consider adaptation to individual characteristics. Our research operationalizes the effects of cognitive demand on HCI and contributes a unique combination of text and keystroke features used to detect high cognitive demand situations.
尽管在没有适当管理的情况下，高认知需求状况会损害心理、身体和行为过程，但目前的测量方法对于持续监测认知需求来说过于繁琐，且未考虑个体差异。本研究使用按键输入和文本的语言标记来构建认知需求反应的个性化模型，以区分高认知需求和低认知需求状况，其结果可能对个性化健康管理的认知需求监测系统设计具有启示意义。我们为9名参与者构建了认知需求反应的个体内模型，并基于20名参与者构建了一个个体间模型。个性化模型的曲线下面积（AUC）范围为0.679至0.953（平均值 = 0.826，标准差 = 0.085），显著高于随机水平（p < 0.0001）以及通用模型的0.714的AUC（p = 0.002）。尽管每个模型中的特征各不相同，但各模型中最常见的特征是负面情绪发生率、词汇多样性、六个字母以上单词的发生率以及单词数量。这些结果证实了认知需求反应中存在显著的个体差异，并表明开发监测系统中使用的测量方法的人员应考虑适应个体特征。我们的研究将认知需求对人机交互的影响进行了量化，并提供了用于检测高认知需求情况的文本和按键输入特征的独特组合。
