Attention-based texture segregation

被引:0
|
作者
Thomas V. Papathomas
Andrei Gorea
Akos Feher
Tiffany E. Conway
机构
[1] Rutgers University,Laboratory of Vision Research
[2] CNRS,Laboratoire de Psychologie Expérimentale
[3] René Descartes University,undefined
来源
关键词
Visual Search; Luminance Contrast; Orientation Contrast; Chromatic Contrast; Texture Segregation;
D O I
暂无
中图分类号
学科分类号
摘要
Luminance- or color-defined ±45°-oriented bars were arranged to yieldsingle-feature ordouble-conjunction texture pairs. In the former, the global edge between two regions is formed by differences in one attribute (orientation, or color, or luminance). In the color/orientation double-conjunction pair, one region has +45° red and −45° green textels, the other −45° red and +45° green textels (the lumi-nance/orientation double-conjunction pair is similar); such a pair contains a single-feature orientation edge in the subset of red (or green) textels, and a color edge in the subset of +45° (or −45°) textels. We studied whether edge detection improved when observers were instructed to attend to such subsets. Two groups of observers participated: in the test group, the stimulus construction was explained to observers, and they were cued to attend to one subset. The control group ran through the same total number of sessions without explanations/cues. The effect of cuing was weak but statistically significant. Feature cuing was more effective for color/orientation than for luminance/orientation conjunctions. Within each stimulus category, performance was nearly the same no matter which subset was attended to. On average, a global performance improvement occurred over time even without cuing, but some observers did not improve with either cuing or practice. We discuss these results in the context of one-versus two-stage segregation theories, as well as by reference to signal enhancement versus noise suppression. We conclude that texture segregation can be improved by attentional strategies aimed to isolate specific stimulus features.
引用
收藏
页码:1399 / 1410
页数:11
相关论文
共 50 条
  • [1] Attention-based texture segregation
    Papathomas, TV
    Gorea, A
    Feher, A
    Conway, TE
    [J]. PERCEPTION & PSYCHOPHYSICS, 1999, 61 (07): : 1399 - 1410
  • [2] Attention and visual texture segregation
    Heinrich, Sven P.
    Andres, Marta
    Bach, Michael
    [J]. JOURNAL OF VISION, 2007, 7 (06):
  • [3] Attention-based similarity
    Stentiford, Fred
    [J]. PATTERN RECOGNITION, 2007, 40 (03) : 771 - 783
  • [4] Attention-based learning
    Kasderidis, S
    Taylor, JG
    [J]. 2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2004, : 525 - 530
  • [5] Spatial attention affects acuity and texture segregation
    Yeshurun, Y.
    Carrasco, M.
    [J]. PERCEPTION, 1998, 27 : 69 - 70
  • [6] Attention-based robot control
    Kasderidis, S
    Taylor, JG
    [J]. KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS, 2003, 2774 : 615 - 621
  • [7] An Attention-based Recommendation Algorithm
    Chu, Yan
    Qi, Shuhao
    Yang, Yue
    Shan, Chenqi
    Wang, Lina
    Wang, Zhengkui
    [J]. 2019 IEEE INTL CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, BIG DATA & CLOUD COMPUTING, SUSTAINABLE COMPUTING & COMMUNICATIONS, SOCIAL COMPUTING & NETWORKING (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2019), 2019, : 1505 - 1510
  • [8] Visual Attention-based Watermarking
    Oakes, Matthew
    Bhowmik, Deepayan
    Abhayaratne, Charith
    [J]. 2011 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2011, : 2653 - 2656
  • [9] Attention-Based Graph Evolution
    Fan, Shuangfei
    Huang, Bert
    [J]. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2020, PT I, 2020, 12084 : 436 - 447
  • [10] Attention-based color correction
    Stentiford, Fred W. M.
    Walker, Matt D.
    [J]. HUMAN VISION AND ELECTRONIC IMAGING XI, 2006, 6057