Contrast and lustre: a model that accounts for eleven different forms of contrast discrimination in binocular vision

Mark A. Georgeson*, Stuart A. Wallis, Tim S. Meese, Daniel H. Baker

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Our goal here is a more complete understanding of how information about luminance contrast is encoded and used by the binocular visual system. In two-interval forced-choice experiments we assessed observers' ability to discriminate changes in contrast that could be an increase or decrease of contrast in one or both eyes, or an increase in one eye coupled with a decrease in the other (termed IncDec). The base or pedestal contrasts were either in-phase or out-of-phase in the two eyes. The opposed changes in the IncDec condition did not cancel each other out, implying that along with binocular summation, information is also available from mechanisms that do not sum the two eyes' inputs. These might be monocular mechanisms. With a binocular pedestal, monocular increments of contrast were much easier to see than monocular decrements. These findings suggest that there are separate binocular (B) and monocular (L,R) channels, but only the largest of the three responses, max(L,B,R), is available to perception and decision. Results from contrast discrimination and contrast matching tasks were described very accurately by this model. Stimuli, data, and model responses can all be visualized in a common binocular contrast space, allowing a more direct comparison between models and data. Some results with out-of-phase pedestals were not accounted for by the max model of contrast coding, but were well explained by an extended model in which gratings of opposite polarity create the sensation of lustre. Observers can discriminate changes in lustre alongside changes in contrast.
Original languageEnglish
Pages (from-to)98-118
Number of pages21
JournalVision Research
Volume129
Early online date11 Nov 2016
DOIs
Publication statusPublished - Dec 2016

Bibliographical note

© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Funding: BBSRC (BBH00159X1); and EPSRC (EP/H000038/1

Research data: To access the research data underlying this publication, please see http://dx.doi.org/10.17036/3c5e1cfc-5988-4380-843e-dadbfc093e44.

Keywords

  • contrast discrimination
  • binocular vision
  • dichoptic masking
  • contrast matching
  • binocular lustre
  • computational modeling

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