How does colour predict multiple object tracking performance? The role of surface feature in attentive tracking

被引:0
|
作者
Wang, Chundi [1 ]
Zhang, Yiyue [1 ]
Zhang, Xuemin [2 ]
Hu, Luming [3 ]
Deng, Hu [4 ]
机构
[1] Beihang Univ, Sch Humanities & Social Sci, Dept Psychol, Beijing, Peoples R China
[2] Beijing Normal Univ, Fac Psychol, Beijing, Peoples R China
[3] Beijing Normal Univ Zhuhai, Sch Arts & Sci, Dept Psychol, Zhuhai, Peoples R China
[4] Peking Univ, Beijing Huilongguan Hosp, Huilongguan Clin Med Sch, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Multiple object tracking; surface feature complexity; target distractor distinctiveness; attentional resources; device type; VISUAL WORKING-MEMORY; FEATURE BINDING; SEARCH; INHIBITION; MODEL;
D O I
10.1080/13506285.2024.2389454
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Simultaneously tracking multiple unique dynamic objects, such as watching soccer games, is common in everyday life. However, the role of intragroup and intergroup features in this cognitive process remains unclear. We set up different colour combinations to investigate the effect of distinctiveness between target and distractor sets (DTD), the complexity of the target set (CT) and distractor set (CD) on multiple object tracking (MOT) on PCs and iPads, and used multiple linear regression to fit the relationship between these factors and tracking performance. The models support our hypothesis, showing that DTD contributes most to the interpretation of tracking performance, followed by CT, while CD has no effect. Furthermore, CT effect is modulated by DTD level. This study demonstrates that DTD and CT jointly contribute to predicting MOT performance regardless of devices. Thus, it would provide the theoretical basis for the future development of MOT-based cognitive applications on mobile devices.
引用
收藏
页码:67 / 81
页数:15
相关论文
共 50 条
  • [21] Effect of feature-changing on multiple-object-tracking
    Shui, RD
    Shen, MW
    INTERNATIONAL JOURNAL OF PSYCHOLOGY, 2004, 39 (5-6) : 50 - 50
  • [22] Can Multiple Object Tracking Predict Laparoscopic Surgical Skills?
    Harenberg, Sebastian
    McCaffrey, Rob
    Butz, Matthew
    Post, Dustin
    Howlett, Joel
    Dorsch, Kim D.
    Lyster, Kish
    JOURNAL OF SURGICAL EDUCATION, 2016, 73 (03) : 386 - 390
  • [23] The effect of visual distinctiveness on multiple object tracking performance
    Howe, Piers D. L.
    Holcombe, Alex O.
    FRONTIERS IN PSYCHOLOGY, 2012, 3
  • [24] A Performance Evaluation Scheme for Multiple Object Tracking with HFSWR
    Wang, Kun
    Zhang, Pengju
    Niu, Jiong
    Sun, Weifeng
    Zhao, Lun
    Ji, Yonggang
    SENSORS, 2019, 19 (06)
  • [26] Benefits and costs of uniqueness in multiple object tracking: The role of object complexity
    Liu, Tianwei
    Chen, Wenfeng
    Liu, Chang Hong
    Fu, Xiaolan
    VISION RESEARCH, 2012, 66 : 31 - 38
  • [27] Automatic Feature-Based Grouping During Multiple Object Tracking
    Erlikhman, Gennady
    Keane, Brian P.
    Mettler, Everett
    Horowitz, Todd S.
    Kellman, Philip J.
    JOURNAL OF EXPERIMENTAL PSYCHOLOGY-HUMAN PERCEPTION AND PERFORMANCE, 2013, 39 (06) : 1625 - 1637
  • [28] Multi-Feature Based Multiple Particle Filters for Object Tracking
    Ma, Ying-dong
    Liu, Yu-chen
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE: TECHNIQUES AND APPLICATIONS, AITA 2016, 2016, : 307 - 314
  • [29] AFMtrack: Attention-Based Feature Matching for Multiple Object Tracking
    Cuong Bui, Duy
    Anh Hoang, Hiep
    Yoo, Myungsik
    IEEE ACCESS, 2024, 12 : 82897 - 82910
  • [30] Dynamic trajectory quantification strategy for multiple object tracking with feature rearrangement
    Zhang, Yuanshu
    Tian, Qing
    Liu, Tianshan
    Kong, Jun
    JOURNAL OF ELECTRONIC IMAGING, 2022, 31 (06)