Across-trial spatial suppression in visual search

被引:7
|
作者
Wang, Lishuang [1 ,2 ,3 ,4 ]
Wang, Benchi [1 ,2 ,3 ,4 ]
Theeuwes, Jan [5 ,6 ]
机构
[1] South China Normal Univ, Minist Educ, Key Lab Brain Cognit & Educ Sci, Guangzhou, Peoples R China
[2] South China Normal Univ, Inst Brain Res & Rehabil, Zhongshan Rd West 55, Guangzhou 510000, Peoples R China
[3] South China Normal Univ, Ctr Studies Psychol Applicat, Guangzhou, Peoples R China
[4] South China Normal Univ, Guangdong Key Lab Mental Hlth & Cognit Sci, Guangzhou, Peoples R China
[5] Vrije Univ Amsterdam, Dept Expt & Appl Psychol, Amsterdam, Netherlands
[6] Vrije Univ Amsterdam, Inst Brain & Behav Amsterdam iBBA, Amsterdam, Netherlands
基金
中国国家自然科学基金;
关键词
Attentional selection; Proactive suppression; Implicit; Priority map; STATISTICAL REGULARITIES; ATTENTIONAL CAPTURE; PRIORITY MAPS; PROBABILITY; MEMORY; COLOR;
D O I
10.3758/s13414-021-02341-x
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
In order to focus on objects of interest, humans must be able to avoid distraction by salient stimuli that are not relevant to the task at hand. Many recent studies have shown that through statistical learning we are able to suppress the location that is most likely to contain a salient distractor. Here we demonstrate a remarkable flexibility in attentional suppression. Participants had to search for a shape singleton while a color distractor singleton was present. Unbeknown to the participant, the color distractor was presented according to a consistent pattern across trials. Our findings show that participants learn this distractor sequence as they proactively suppressed the anticipated location of the distractor on the next trial. Critically, none of the participants were aware of these hidden sequences. We conclude that the spatial priority map is highly flexible, operating at a subconscious level preparing the attentional system for what will happen next.
引用
收藏
页码:2744 / 2752
页数:9
相关论文
共 50 条
  • [1] Across-trial spatial suppression in visual search
    Lishuang Wang
    Benchi Wang
    Jan Theeuwes
    [J]. Attention, Perception, & Psychophysics, 2021, 83 : 2744 - 2752
  • [2] No evidence for spatial suppression due to across-trial distractor learning in visual search
    Li, Ai-Su
    Bogaerts, Louisa
    Theeuwes, Jan
    [J]. ATTENTION PERCEPTION & PSYCHOPHYSICS, 2023, 85 (04) : 1088 - 1105
  • [3] No evidence for spatial suppression due to across-trial distractor learning in visual search
    Ai-Su Li
    Louisa Bogaerts
    Jan Theeuwes
    [J]. Attention, Perception, & Psychophysics, 2023, 85 : 1088 - 1105
  • [4] Statistical Learning of Across-Trial Regularities During Serial Search
    Li, Ai-Su
    Bogaerts, Louisa
    Theeuwes, Jan
    [J]. JOURNAL OF EXPERIMENTAL PSYCHOLOGY-HUMAN PERCEPTION AND PERFORMANCE, 2022, 48 (03) : 262 - 274
  • [5] Task switching and across-trial distance priming are independent
    Liefooghe, Baptist
    Verbruggen, Frederick
    Vandierendonck, Andre
    Fias, Wim
    Gevers, Wim
    [J]. EUROPEAN JOURNAL OF COGNITIVE PSYCHOLOGY, 2007, 19 (01): : 1 - 16
  • [6] Modeling mean estimation tasks in within-trial and across-trial contexts
    Ke Tong
    Chad Dubé
    [J]. Attention, Perception, & Psychophysics, 2022, 84 : 2384 - 2407
  • [7] Modeling across-trial variability in the Wald drift rate parameter
    Helen Steingroever
    Dominik Wabersich
    Eric-Jan Wagenmakers
    [J]. Behavior Research Methods, 2021, 53 : 1060 - 1076
  • [8] Modeling across-trial variability in the Wald drift rate parameter
    Steingroever, Helen
    Wabersich, Dominik
    Wagenmakers, Eric-Jan
    [J]. BEHAVIOR RESEARCH METHODS, 2021, 53 (03) : 1060 - 1076
  • [9] Modeling mean estimation tasks in within-trial and across-trial contexts
    Tong, Ke
    Dube, Chad
    [J]. ATTENTION PERCEPTION & PSYCHOPHYSICS, 2022, 84 (07) : 2384 - 2407
  • [10] Across-Trial Dynamics of Stimulus Priors in an Auditory Discrimination Task
    Hermoso-Mendizabal, Ainhoa
    Hyafil, Alexandre
    Ernesto Rueda-Orozco, Pavel
    Jaramillo, Santiago
    Robbe, David
    de la Rocha, Jaime
    [J]. ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT I, 2016, 9886 : 539 - 539