September 2005
Volume 5, Issue 8
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2005
Singularities in the inverse modeling of contrast discrimination and ways to avoid them
Author Affiliations
  • Mikhail Katkov
    Weizmann Institute of Science, Department of Neurobiology, Rehovot, Israel
  • Tal Gan
    Tel-Aviv University, Department of Computer Science, Statistics and Operation Research, Tel-Aviv, Israel
  • Misha Tsodyks
    Weizmann Institute of Science, Department of Neurobiology, Rehovot, Israel
  • Dov Sagi
    Weizmann Institute of Science, Department of Neurobiology, Rehovot, Israel
Journal of Vision September 2005, Vol.5, 455. doi:10.1167/5.8.455
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Mikhail Katkov, Tal Gan, Misha Tsodyks, Dov Sagi; Singularities in the inverse modeling of contrast discrimination and ways to avoid them. Journal of Vision 2005;5(8):455. doi: 10.1167/5.8.455.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

A basic problem in psychophysics is estimating the mean internal response and noise amplitude from sensory discrimination data. However, these components cannot be measured independently and therefore several indirect methods were suggested to resolve this issue. Here we analyze the two-alternative forced-choice method (2AFC), using a signal detection theory approach, and show analytically that some combinations of internal parameters exhibit singularities in the sensitivity to sampling errors, which results in a large range of estimated parameters with a finite number of experimental trials. Four types of singularities were identified. It was found that performances, measured as percent correct discriminations in 2AFC contrast discrimination experiments, are well described by a model with the noise amplitude that is independent of the stimulus intensity (one of the singular models). Thus, the 2AFC contrast discrimination experiment is not suitable for characterization of the contrast perception model. We show that this problem can be avoided using a visual category rating task, with Gabor signals at nine contrast levels as targets. Assuming stable category boundaries, the model parameters, namely, mean internal responses, noise amplitudes and category boundaries were found using a best least square fit to the data. Our findings show that at low contrasts noise amplitude decreases as a function of contrast level, while at higher contrasts the amplitude is independent of the contrast level. The internal responses were found to be best described by a saturating function of contrast. The confidence intervals were estimated using Monte-Carlo simulations of the identification task. The results show that the well-known increase of contrast discrimination thresholds with contrast is due to reduced sensory gain and not due to increasing internal noise.

Katkov, M. Gan, T. Tsodyks, M. Sagi, D. (2005). Singularities in the inverse modeling of contrast discrimination and ways to avoid them [Abstract]. Journal of Vision, 5(8):455, 455a, http://journalofvision.org/5/8/455/, doi:10.1167/5.8.455. [CrossRef] [PubMed]
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×