文献类型：期刊文献

英文题名：The Effects of Visual Complexity and Task Difficulty on the Comprehensive Cognitive Efficiency of Cluster Separation Tasks

作者：Guo, Qi[1];Chen, Yan[1]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200030, Peoples R China

年份：2023

卷号：13

期号：10

外文期刊名：BEHAVIORAL SCIENCES

收录：;WOS:【SSCI(收录号:WOS:001096059500001)】；

基金：This research was funded by [National Natural Science Foundation of China] grant number [52205264] and [Shanghai Pujiang Talent Program] grant number [21PJC032], and The APC was funded by [52205264].

语种：英文

外文关键词：visual complexity; task difficulty; cognition efficiency; visualization design

摘要：Cluster separation is required to perform multi-class visual statistics tasks and plays an essential role in information processing in visualization. This cognition behavioral study investigated the cluster separation task and the effects of visual complexity and task difficulty. A total of 32 college students (18 men and 14 women, with ages ranging from 18 to 25 years; mean = 21.2, SD = 3.9) participated in this study. The observers' average response accuracy, reaction time, mental effort, and comprehensive cognitive efficiency were measured as functions of three levels of visual complexity and task difficulty. The levels of visual complexity and task difficulty were quantified via an optimized complexity evaluation method and discrimination judgment task, respectively. The results showed that visual complexity and task difficulty significantly influenced comprehensive cognitive efficiency. Moreover, a strong interaction was observed between the effects of visual complexity and task difficulty. However, there was no positive linear relationship between the mental effort and the complexity level. Furthermore, two-dimensional color x shape redundant coding showed higher cognitive efficiency at low task difficulty levels. In contrast, the one-dimensional color encoding approach showed higher cognitive efficiency at increased task difficulty levels. The findings of this study provide valuable insights into designing more efficient and user-friendly visualization in the future.