Global scene layout modulates contextual learning in change detection

被引:11
|
作者
Conci, Markus [1 ]
Mueller, Hermann J. [1 ]
机构
[1] Univ Munich, Dept Psychol, D-80802 Munich, Germany
来源
FRONTIERS IN PSYCHOLOGY | 2014年 / 5卷
关键词
local/global processing; visual attention; change blindness; change detection; natural scenes; contextual learning; MULTIPLE-TARGET LOCATIONS; KANIZSA-FIGURE DETECTION; VISUAL-SEARCH; CHANGE BLINDNESS; NATURALISTIC SCENES; OBJECT; ATTENTION; IDENTIFICATION; MEMORY; SEGMENTATION;
D O I
10.3389/fpsyg.2014.00089
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Change in the visual scene often goes unnoticed a phenomenon referred to as "change blindness." This study examined whether the hierarchical structure, i.e., the global local layout of a scene can influence performance in a one-shot change detection paradigm. To this end, natural scenes of a laid breakfast table were presented, and observers were asked to locate the onset of a new local object. Importantly, the global structure of the scene was manipulated by varying the relations among objects in the scene layouts. The very same items were either presented as global-congruent (typical) layouts or as global-incongruent (random) arrangements. Change blindness was less severe for congruent than for incongruent displays, and this congruency benefit increased with the duration of the experiment. These findings show that global layouts are learned, supporting detection of local changes with enhanced efficiency. However, performance was not affected by scene congruency in a subsequent control experiment that required observers to localize a static discontinuity (i.e., an object that was missing from the repeated layouts). Our results thus show that learning of the global layout is particularly linked to the local objects. Taken together, our results reveal an effect of "global precedence" in natural scenes. We suggest that relational properties within the hierarchy of a natural scene are governed, in particular, by global image analysis, reducing change blindness for local objects through scene learning.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Improving Object Detection Performance Using Scene Contextual Constraints
    Alamri, Faisal
    Pugeault, Nicolas
    [J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2022, 14 (04) : 1320 - 1330
  • [22] Spatial Contextual Information Modulates Affordance Processing and Early Electrophysiological Markers of Scene Perception
    Naveilhan, Clement
    Saulay-Carret, Maud
    Zory, Raphael
    Ramanoel, Stephen
    [J]. JOURNAL OF COGNITIVE NEUROSCIENCE, 2024, 36 (10) : 2084 - 2099
  • [23] Scene Change Detection for Uncompressed Video
    Seeling, Patrick
    [J]. TECHNOLOGICAL DEVELOPMENTS IN EDUCATION AND AUTOMATION, 2010, : 11 - 14
  • [24] Scene change detection in MPEG domain
    Gamaz, N
    Huang, X
    Panchanathan, S
    [J]. 1998 IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS AND INTERPRETATION, 1998, : 12 - 17
  • [25] Saliency Detection on Videos with Scene Change
    Li, Junling
    Meng, Fang
    Mao, Jingbo
    [J]. 2014 INTERNATIONAL CONFERENCE ON AUDIO, LANGUAGE AND IMAGE PROCESSING (ICALIP), VOLS 1-2, 2014, : 506 - 510
  • [26] Adaptive Thresholding for Scene Change Detection
    Hong, Soongi
    Cho, Beobkeun
    Choe, Yoonsik
    [J]. 2013 IEEE THIRD INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - BERLIN (ICCE-BERLIN), 2013,
  • [27] Scene structure enhances change detection
    Varakin, D. Alexander
    Levin, Daniel T.
    [J]. QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2008, 61 (04): : 543 - 551
  • [28] Fuzzy Detection of Change in Spatial Scene
    Salamat, Nadeem
    Zahzah, El-hadi
    [J]. SECOND INTERNATIONAL CONFERENCE ON ADVANCED GEOGRAPHIC INFORMATION SYSTEMS, APPLICATIONS, AND SERVICES: GEOPROCESSING 2010, PROCEEDINGS, 2010, : 9 - 14
  • [29] Learning Object Context for Novel-view Scene Layout Generation
    Qiao, Xiaotian
    Hancke, Gerhard P.
    Lau, Rynson W. H.
    [J]. 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 16969 - 16978
  • [30] Learning to Track Multi-Target Online by Boosting and Scene Layout
    Chen, Guang
    Zhang, Feihu
    Clarke, Daniel
    Knoll, Alois
    [J]. 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1, 2013, : 197 - 202