Multiple object tracking: Anticipatory attention doesn't "bounce"

被引:19
|
作者
Atsma, Jeroen [1 ]
Koning, Arno [1 ]
van Lier, Rob [1 ]
机构
[1] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, NL-6525 ED Nijmegen, Netherlands
来源
JOURNAL OF VISION | 2012年 / 12卷 / 13期
关键词
multiple object tracking (MOT); visual attention; prediction; anticipation; motion; spatiotemporal information; attentional allocation; VISUAL-ATTENTION; MENTAL EXTRAPOLATION; TARGET POSITION; MOTION; VISION; TIME;
D O I
10.1167/12.13.1
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
We investigated motion extrapolation in object tracking in two experiments. In Experiment 1, we used a multiple-object-tracking task (MOT; three targets, three distractors) combined with a probe detection task to investigate the distribution of attention around a target object. We found anisotropic probe detection rates with increased probe detection at locations where a target is heading. In Experiment 2, we introduced a black line (wall) in the center of the screen and block-wise manipulated the object's motion: either objects bounced realistically against the wall or objects went through the wall. Just before a target coincided with the wall, a probe could appear either along the bounce path or along the straight path. In addition to MOT, we included a single-object-tracking task (SOT; one target, five distractors) to control for attentional load. We found that linear extrapolation is dominant (better probe detection along the straight path than bounce path) regardless of attentional load and the motion condition. Anticipation of bouncing behavior did occur but only when attentional load was low. We conclude that attention is not tightly bound to moving target objects but encompasses the object's current position and the area in front of it. Furthermore, under the present experimental conditions, the visuo-attentional system does not seem to anticipate object bounces in the MOT task.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Multiple object tracking based on multi-task learning with strip attention
    Song, Yaoye
    Zhang, Peng
    Huang, Wei
    Zha, Yufei
    You, Tao
    Zhang, Yanning
    IET IMAGE PROCESSING, 2021, 15 (14) : 3661 - 3673
  • [42] Object Tracking Based on Visual Attention
    Lin, Mingqiang
    Dai, Houde
    2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), 2016, : 1846 - 1849
  • [43] Object Tracking Based on Channel Attention
    He, Zhiquan
    Chen, Xuejun
    IEEE ACCESS, 2020, 8 : 17824 - 17832
  • [44] Position Attention Network for Object Tracking
    Du, Mingzhong
    Wei, Longsheng
    Liu, Shiyu
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 6374 - 6379
  • [45] Hyperspectral Attention Network for Object Tracking
    Yu, Shuangjiang
    Ni, Jianjun
    Fu, Shuai
    Qu, Tao
    SENSORS, 2024, 24 (19)
  • [46] Semantic-guided fusion for multiple object tracking and RGB-T tracking
    Liu, Xiaohu
    Luo, Yichuang
    Zhang, Yan
    Lei, Zhiyong
    IET IMAGE PROCESSING, 2023, 17 (11) : 3281 - 3291
  • [47] Evaluating and modeling the effects of brightness on visual attention using multiple object tracking method
    Yazgan, Mehmet Toyanc
    Yagimli, Mustafa
    Ozubko, Jason
    JOURNAL OF INFORMATION DISPLAY, 2024, 25 (03) : 271 - 293
  • [48] Multiple object-tracking isolates feedback-specific load in attention and learning
    Tullo, Domenico
    Perico, Chiara
    Faubert, Jocelyn
    Bertone, Armando
    JOURNAL OF VISION, 2020, 20 (05):
  • [49] Transfer of Learning between Hemifields in Multiple Object Tracking: Memory Reduces Constraints of Attention
    Lapierre, Mark
    Howe, Piers D. L.
    Cropper, Simon J.
    PLOS ONE, 2013, 8 (12):
  • [50] Attention and working memory during multiple object tracking with and without binding of target identities
    Andersen, Soren K.
    Boyanova, Antoniya
    Kucikova, Ludmila
    Katus, Tobias
    PERCEPTION, 2021, 50 (1_SUPPL) : 39 - 39