Relating categorization to set summary statistics perception

被引:0
|
作者
Noam Khayat
Shaul Hochstein
机构
[1] Hebrew University,Life Sciences Institute and Edmond and Lily Safra Center (ELSC) for Brain Research
来源
关键词
Categorization; Prototype; Boundary; Summary statistics; Ensemble; Mean; Range;
D O I
暂无
中图分类号
学科分类号
摘要
Two cognitive processes have been explored that compensate for the limited information that can be perceived and remembered at any given moment. The first parsimonious cognitive process is object categorization. We naturally relate objects to their category, assume they share relevant category properties, often disregarding irrelevant characteristics. Another scene organizing mechanism is representing aspects of the visual world in terms of summary statistics. Spreading attention over a group of objects with some similarity, one perceives an ensemble representation of the group. Without encoding detailed information of individuals, observers process summary data concerning the group, including set mean for various features (from circle size to face expression). Just as categorization may include/depend on prototype and intercategory boundaries, so set perception includes property mean and range. We now explore common features of these processes. We previously investigated summary perception of low-level features with a rapid serial visual presentation (RSVP) paradigm and found that participants perceive both the mean and range extremes of stimulus sets, automatically, implicitly, and on-the-fly, for each RSVP sequence, independently. We now use the same experimental paradigm to test category representation of high-level objects. We find participants perceive categorical characteristics better than they code individual elements. We relate category prototype to set mean and same/different category to in/out-of-range elements, defining a direct parallel between low-level set perception and high-level categorization. The implicit effects of mean or prototype and set or category boundaries are very similar. We suggest that object categorization may share perceptual-computational mechanisms with set summary statistics perception.
引用
收藏
页码:2850 / 2872
页数:22
相关论文
共 50 条
  • [1] Relating categorization to set summary statistics perception
    Khayat, Noam
    Hochstein, Shaul
    ATTENTION PERCEPTION & PSYCHOPHYSICS, 2019, 81 (08) : 2850 - 2872
  • [2] Set Summary Perception, Outlier Pop Out, and Categorization: A Common Underlying Computation?
    Hochstein, Shaul
    Khayat, Noam
    Pavlovskaya, Marina
    Bonneh, Yoram
    Soroker, Nachum
    PERCEPTION, 2019, 48 : 210 - 210
  • [3] Summary Statistics and Material Categorization in the Visual Periphery
    Balas, Benjamin
    Conlin, Catherine
    Shipman, Dylan
    ACM TRANSACTIONS ON APPLIED PERCEPTION, 2017, 14 (02)
  • [4] Summary statistics in auditory perception
    Josh H McDermott
    Michael Schemitsch
    Eero P Simoncelli
    Nature Neuroscience, 2013, 16 : 493 - 498
  • [5] Summary statistics in auditory perception
    McDermott, Josh H.
    Schemitsch, Michael
    Simoncelli, Eero P.
    NATURE NEUROSCIENCE, 2013, 16 (04) : 493 - U169
  • [6] Ensemble summary statistics as a basis for rapid visual categorization
    Utochkin, Igor S.
    JOURNAL OF VISION, 2015, 15 (04):
  • [7] Children's use of visual summary statistics for material categorization
    Balas, Benjamin
    JOURNAL OF VISION, 2017, 17 (12):
  • [8] Summary statistics of edge information predict categorization of naturalistic images
    Groen, I. I.
    Ghebreab, S.
    Lamme, V. A.
    Scholte, H. S.
    PERCEPTION, 2011, 40 : 18 - 18
  • [9] Exploring Users' Perception of Rating Summary Statistics
    Coba, Ludovik
    Zanker, Markus
    Rook, Laurens
    Symeonidis, Panagiotis
    PROCEEDINGS OF THE 26TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'18), 2018, : 353 - 354
  • [10] Ensemble Perception, Summary Statistics, and Perceptual Awareness: A Response
    Cohen, Michael A.
    Dennett, Daniel C.
    Kanwisher, Nancy
    TRENDS IN COGNITIVE SCIENCES, 2016, 20 (09) : 643 - 644