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A neurophysiologically plausible population code model for human contrast discrimination

2009

Article

ei


The pedestal effect is the improvement in the detectability of a sinusoidal grating in the presence of another grating of the same orientation, spatial frequency, and phase—usually called the pedestal. Recent evidence has demonstrated that the pedestal effect is differently modified by spectrally flat and notch-filtered noise: The pedestal effect is reduced in flat noise but virtually disappears in the presence of notched noise (G. B. Henning & F. A. Wichmann, 2007). Here we consider a network consisting of units whose contrast response functions resemble those of the cortical cells believed to underlie human pattern vision and demonstrate that, when the outputs of multiple units are combined by simple weighted summation—a heuristic decision rule that resembles optimal information combination and produces a contrast-dependent weighting profile—the network produces contrast-discrimination data consistent with psychophysical observations: The pedestal effect is present without noise, reduced in broadband noise, but almost disappears in notched noise. These findings follow naturally from the normalization model of simple cells in primary visual cortex, followed by response-based pooling, and suggest that in processing even low-contrast sinusoidal gratings, the visual system may combine information across neurons tuned to different spatial frequencies and orientations.

Author(s): Goris, RLT. and Wichmann, FA. and Henning, GB.
Journal: Journal of Vision
Volume: 9
Number (issue): 7
Pages: 1-22
Year: 2009
Month: July
Day: 31

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1167/9.7.15

Links: Web

BibTex

@article{GorisWH2009,
  title = {A neurophysiologically plausible population code model for human contrast discrimination},
  author = {Goris, RLT. and Wichmann, FA. and Henning, GB.},
  journal = {Journal of Vision},
  volume = {9},
  number = {7},
  pages = {1-22},
  month = jul,
  year = {2009},
  month_numeric = {7}
}