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Related Experiment Videos

How big is a Gabor patch, and why should we care?

R E Fredericksen1, P J Bex, F A Verstraten

  • 1McGill Vision Research, Montreal, PQ, Canada. eric@mach2.hipl.uci.edu

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|January 1, 1997
PubMed
Summary

Perceived size of Gabor patches depends on detection thresholds. Systemic noise significantly impacts perceived size near the detection threshold, influencing contrast threshold measurements.

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Area of Science:

  • Visual perception
  • Computational neuroscience
  • Psychophysics

Background:

  • The perceived size of visual stimuli is crucial for understanding visual processing.
  • Gaussian-windowed stimuli, like Gabor patches, are commonly used in vision research.
  • Previous models have not fully accounted for the role of noise and contrast in perceived size.

Purpose of the Study:

  • To develop and validate a two-parameter model for the perceived size of Gabor patches.
  • To investigate the influence of peak contrast, Gaussian standard deviation, and carrier spatial frequency on perceived size.
  • To elucidate the role of systemic noise and local contrast gain control in determining perceived size.

Main Methods:

  • Psychophysical measurements of perceived size were collected.

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  • A two-parameter model was proposed based on detection threshold for the sinusoidal carrier.
  • Model predictions were compared with experimental data across varying stimulus parameters.

    • Main Results:

    • Perceived size varied with peak contrast, Gaussian standard deviation, and spatial frequency as predicted by the model.
    • At suprathreshold contrasts, perceived size was robust to noise but influenced by local contrast gain control.
    • Near and at threshold, systemic noise played a significant role in determining perceived size.

    Conclusions:

    • The proposed model accurately predicts perceived size of Gabor patches.
    • Contrast threshold measurements with non-flat windowed stimuli are confounded by spatial and temporal integration effects.
    • Understanding noise and contrast dependence is vital for accurate measures of visual integration.