Visual target detection in a distracting background relies on neural encoding of both visual targets and background

被引:4
|
作者
Luo, Cheng [1 ]
Ding, Nai [1 ,2 ]
机构
[1] Zhejiang Univ, Coll Biomed Engn & Instrument Sci, Minist Educ, Key Lab Biomed Engn, Hangzhou 310027, Peoples R China
[2] Res Ctr Adv Artificial Intelligence Theory, Zhejiang Lab, Hangzhou 311121, Peoples R China
基金
中国国家自然科学基金;
关键词
NATURAL SCENE CATEGORIZATION; MOVEMENT-RELATED POTENTIALS; FEATURE-BASED ATTENTION; SELECTIVE ATTENTION; BRAIN POTENTIALS; P300; COMPONENT; STEADY-STATE; TIME-COURSE; TOP-DOWN; GO;
D O I
10.1016/j.neuroimage.2020.116870
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The ability to detect visual targets in complex background varies across individuals and are affected by factors such as stimulus saliency and top-down attention. Here, we investigated how the saliency of visual background (naturalistic cartoon video vs. blank screen) and top-down attention (single vs. dual tasks) separately affect individual ability to detect visual targets. Behaviorally, we found that target detection accuracy decreased and reaction time elongated when the background was salient or during dual tasking. The EEG response to visual background was recorded using a novel stimulus tagging technique. This response was strongest in occipital electrodes and was sensitive to background saliency but not dual tasking. In contrast, the event-related potential (ERP) evoked by the visual target was strongest in central electrodes, and was affected by both background saliency and dual tasking. With a cartoon background, the EEG responses to visual targets, presented in the central visual field, and the EEG responses to peripheral visual background could both predict individual target detection performance. When these two responses were combined, better prediction was achieved. These results suggest that neural processing of visual targets and background jointly contribute to individual visual target detection performance. © 2020 The Authors
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Searching for camouflaged targets: Effects of target-background similarity on visual search
    Neider, Mark B.
    Zelinsky, Gregory J.
    [J]. VISION RESEARCH, 2006, 46 (14) : 2217 - 2235
  • [2] The moving target detection algorithm based on the improved visual background extraction
    Huang, Wei
    Liu, Lei
    Yue, Chao
    Li, He
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2015, 71 : 518 - 525
  • [3] EFFECTS OF TARGET SIZE AND SHAPE ON VISUAL DETECTION .2. CONTINUOUS FOVEAL TARGETS AT ZERO BACKGROUND LUMINANCE
    SMITH, SW
    BLACKWELL, HR
    [J]. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA, 1957, 47 (01) : 114 - 114
  • [4] VISUAL ASSESSMENT OF CAMOUFLAGED TARGETS WITH DIFFERENT BACKGROUND SIMILARITIES
    Chang, Chi-Chan
    Lee, Yung-Hui
    Lin, Chiuhsiang Joe
    Liu, Bor-Shong
    Shih, Yuh-Chuan
    [J]. PERCEPTUAL AND MOTOR SKILLS, 2012, 114 (02) : 527 - 541
  • [5] VISUAL COURSE CONTROL IN FLIES RELIES ON NEURONAL COMPUTATION OF OBJECT AND BACKGROUND MOTION
    EGELHAAF, M
    HAUSEN, K
    REICHARDT, W
    WEHRHAHN, C
    [J]. TRENDS IN NEUROSCIENCES, 1988, 11 (08) : 351 - 358
  • [6] INTERACTIONS OF SIGNAL AND BACKGROUND VARIABLES IN VISUAL DETECTION
    ESTES, WK
    [J]. PSYCHONOMIC SCIENCE, 1971, 25 (02): : 117 - &
  • [7] VISUAL LOBE AREA FOR SINGLE TARGETS ON A COMPETING HOMOGENEOUS BACKGROUND
    COURTNEY, AJ
    SHOU, CH
    [J]. HUMAN FACTORS, 1985, 27 (06) : 643 - 652
  • [8] Moving Target Detection Based on Improved Three Frame Difference and Visual Background Extractor
    Wu, Siyang
    Chen, Dongfang
    Wane, Xiaofeng
    [J]. 2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI), 2017,
  • [9] Moving target detection based on improved ghost suppression and adaptive visual background extraction
    Liu, Ling
    Chai, Guo-hua
    Qu, Zhong
    [J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY, 2021, 28 (03) : 747 - 759
  • [10] Visual Target Detection and Tracking in UAV EO/IR Videos by Moving Background Subtraction
    Tufano, Francesco
    Angelino, Cesario Vincenzo
    Cicala, Luca
    [J]. ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2016, 2016, 10016 : 547 - 558