Category-specific learned attentional bias to object parts

被引:0
|
作者
Kao-Wei Chua
Isabel Gauthier
机构
[1] Vanderbilt University,Department of Psychology
来源
关键词
Learned attention; Probability cuing; Attention in complex objects;
D O I
暂无
中图分类号
学科分类号
摘要
Humans can selectively attend to information in visual scenes. Learning from previous experiences plays a role in how visual attention is subsequently deployed. For example, visual search times are faster in areas that are statistically more likely to contain a target (Jiang and Swallow in Cognition, 126(3), 378–390, 2013). Here, we examined whether similar attentional biases can be created for different locations on complex objects as a function of their category, based on a history of these locations containing a target. Subjects performed a visual search task in the context of novel objects called Greebles. The target appeared in one half (e.g., top) of the Greebles 89 % of the time and in the other half (e.g., bottom) 11 % of the time. We found a reaction time advantage when the target was located in a “target-rich” region, even after target location probabilities were equated. This indicates that attentional biases can be associated not only with regions of space but also with specific object features, or at least with locations in an object-based frame of reference.
引用
收藏
页码:44 / 51
页数:7
相关论文
共 50 条
  • [31] The role of premorbid expertise on object identification in a patient with category-specific visual agnosia
    Dixon, MJ
    Desmarais, G
    Gojmerac, C
    Schweizer, TA
    Bub, DN
    COGNITIVE NEUROPSYCHOLOGY, 2002, 19 (05) : 401 - 419
  • [32] Category-Specific Nuance Exploration Network for Fine-Grained Object Retrieval
    Wang, Shijie
    Wang, Zhihui
    Li, Haojie
    Ouyang, Wanli
    THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, : 2513 - 2521
  • [33] Learning Category-Specific Deformable 3D Models for Object Reconstruction
    Tulsiani, Shubham
    Kar, Abhishek
    Carreira, Joao
    Malik, Jitendra
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (04) : 719 - 731
  • [34] Category-specific names test.
    Garrard, P
    EUROPEAN JOURNAL OF COGNITIVE PSYCHOLOGY, 2000, 12 (02): : 285 - 288
  • [35] Category-specific sparing of reading and spelling
    Cappelletti, M
    Kopelman, M
    Butterworth, B
    BRAIN AND LANGUAGE, 2000, 74 (03) : 373 - 376
  • [36] Neural correlates of category-specific knowledge
    Martin, A
    Wiggs, CL
    Ungerleider, LG
    Haxby, JV
    NATURE, 1996, 379 (6566) : 649 - 652
  • [37] Cortical representation of category-specific knowledge
    Martin, A
    JOURNAL OF COGNITIVE NEUROSCIENCE, 1998, 10 : 12 - 12
  • [38] ON THE EXISTENCE OF CATEGORY-SPECIFIC IMPAIRMENTS - A REPLY
    JOB, R
    MIOZZO, M
    SARTORI, G
    QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY SECTION A-HUMAN EXPERIMENTAL PSYCHOLOGY, 1993, 46 (03): : 511 - 516
  • [39] Structural processing and category-specific deficits
    Frederico Marques, J.
    Raposo, Ana
    Almeida, Jorge
    CORTEX, 2013, 49 (01) : 266 - 275
  • [40] Category-specific effects in Welsh mutation
    Hammond, Michael
    Bell, Elise
    Anderson, Skye
    Webb-Davies, Peredur
    Ohalal, Diane
    Carnie, Andrew
    Brooks, Heddwen
    GLOSSA-A JOURNAL OF GENERAL LINGUISTICS, 2020, 5 (01):