Object Image Size Is a Fundamental Coding Dimension in Human Vision: New Insights and Model

Tim S Meese, Daniel H Baker

Research output: Contribution to journalArticlepeer-review


In previous psychophysical work we found that luminance contrast is integrated over retinal area subject to contrast gain control. If different mechanisms perform this operation for a range of superimposed retinal regions of different sizes, this could provide the basis for size-coding. To test this idea we included two novel features in a standard adaptation paradigm to discount more pedestrian accounts of repulsive size-aftereffects. First, we used spatially jittering luminance-contrast adaptors to avoid simple contour displacement aftereffects. Second, we decoupled adaptor and target spatial frequency to avoid the well-known spatial frequency shift aftereffect. Empirical results indicated strong evidence of a bidirectional size adaptation aftereffect. We show that the textbook population model is inappropriate for our results, and develop our existing model of contrast perception to include multiple size mechanisms with divisive surround-suppression from the largest mechanism. For a given stimulus patch, this delivers a blurred step-function of responses across the population, with contrast and size encoded by the height and lateral position of the step. Unlike for textbook population coding schemes, our human results (N = 4 male, N = 4 female) displayed two asymmetries: (i) size aftereffects were greatest for targets smaller than the adaptor, and (ii) on that side of the function, results did not return to baseline, even when targets were 25% of adaptor diameter. Our results and emergent model properties provide evidence for a novel dimension of visual coding (size) and a novel strategy for that coding, consistent with previous results on contrast detection and discrimination for various stimulus sizes.

Original languageEnglish
Pages (from-to)79-91
Number of pages13
Early online date2 Feb 2023
Publication statusPublished - 15 Mar 2023

Bibliographical note

Copyright © 2023 The Author(s). Published by Elsevier Ltd on behalf of IBRO.
This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)

Funding: This work was supported by an EPSRC project grant (EP/H000038/1)
awarded to Tim S. Meese and Mark A. Georgeson.


  • gain control
  • visual psychophysics
  • computational model
  • size perception
  • adaptation


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