Literature DB >> 25341649

Face features and face configurations both contribute to visual crowding.

Hsin-Mei Sun1, Benjamin Balas.   

Abstract

Crowding refers to the inability to recognize an object in peripheral vision when other objects are presented nearby (Whitney & Levi Trends in Cognitive Sciences, 15, 160-168, 2011). A popular explanation of crowding is that features of the target and flankers are combined inappropriately when they are located within an integration field, thus impairing target recognition (Pelli, Palomares, & Majaj Journal of Vision, 4(12), 12:1136-1169, 2004). However, it remains unclear which features of the target and flankers are combined inappropriately to cause crowding (Levi Vision Research, 48, 635-654, 2008). For example, in a complex stimulus (e.g., a face), to what extent does crowding result from the integration of features at a part-based level or at the level of global processing of the configural appearance? In this study, we used a face categorization task and different types of flankers to examine how much the magnitude of visual crowding depends on the similarity of face parts or of global configurations. We created flankers with face-like features (e.g., the eyes, nose, and mouth) in typical and scrambled configurations to examine the impacts of part appearance and global configuration on the visual crowding of faces. Additionally, we used "electrical socket" flankers that mimicked first-order face configuration but had only schematic features, to examine the extent to which global face geometry impacted crowding. Our results indicated that both face parts and configurations contribute to visual crowding, suggesting that face similarity as realized under crowded conditions includes both aspects of facial appearance.

Entities:  

Mesh:

Year:  2015        PMID: 25341649      PMCID: PMC4336613          DOI: 10.3758/s13414-014-0786-0

Source DB:  PubMed          Journal:  Atten Percept Psychophys        ISSN: 1943-3921            Impact factor:   2.199


  40 in total

1.  Seeing sets: representation by statistical properties.

Authors:  D Ariely
Journal:  Psychol Sci       Date:  2001-03

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5.  Multi-feature objects elicit nonconscious priming despite crowding.

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6.  The Psychophysics Toolbox.

Authors:  D H Brainard
Journal:  Spat Vis       Date:  1997

7.  Species sensitivity of early face and eye processing.

Authors:  Roxane J Itier; Patricia Van Roon; Claude Alain
Journal:  Neuroimage       Date:  2010-08-01       Impact factor: 6.556

Review 8.  The role of the occipital face area in the cortical face perception network.

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9.  Crowding changes appearance.

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10.  Perception of face parts and face configurations: an FMRI study.

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  6 in total

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5.  There Is a "U" in Clutter: Evidence for Robust Sparse Codes Underlying Clutter Tolerance in Human Vision.

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6.  Crowding for faces is determined by visual (not holistic) similarity: Evidence from judgements of eye position.

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  6 in total

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