Grid-texture mechanisms in human vision:contrast detection of regular sparse micro-patterns requires specialist templates


Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50–100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.

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Divisions: College of Health & Life Sciences > School of Optometry > Optometry
College of Health & Life Sciences > Clinical and Systems Neuroscience
College of Health & Life Sciences
College of Health & Life Sciences > School of Optometry > Vision, Hearing and Language
College of Health & Life Sciences > School of Optometry > Centre for Vision and Hearing Research
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit Funding: EPSRC (EP/H000038/1), Wellcome Trust (ref: 105624)
Uncontrolled Keywords: General
Publication ISSN: 2045-2322
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://www.natu ... icles/srep29764 (Publisher URL)
PURE Output Type: Article
Published Date: 2016-07-27
Accepted Date: 2016-05-26
Submitted Date: 2015-12-11
Authors: Baker, Daniel H.
Meese, Tim S. (ORCID Profile 0000-0003-3744-4679)



Version: Published Version

License: Creative Commons Attribution

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