Nonlinear characterization of a simple process in human vision

Peter Neri*

*Corresponding author for this work

Research output: Contribution to journalLiterature reviewpeer-review

21 Citations (Scopus)


Perceptual processes are often modeled as linear filters followed by a decisional rule. This simple model is central to the understanding of visual processing in humans. Its scope may be extended to capture a wider range of behaviors by the addition of nonlinear operators or kernels, but there is no evidence in human sensory processing that these operators are able to enhance the linear description. We focused on a simple process in human vision, the perception of brightness in a center-surround annular stimulus. We used psychophysical reverse correlation to fully characterize this process up to its second-order nonlinearity. The resulting characterization was then used to reconstruct/predict individual responses by the human observers, a process that was significantly enhanced by the addition of the nonlinear kernels. These results provide direct evidence that behavioral second-order kernels can be successfully derived using reverse correlation, and furthermore that they can be effectively exploited to simulate human vision. We show that the former result does not imply the latter by performing a second series of experiments involving orientation-defined textures, for which no measurable benefit was gained from the addition of second-order kernels.

Original languageEnglish
Article number1
Number of pages29
JournalJournal of Vision
Issue number12
Publication statusPublished - 5 Nov 2009


  • orientation detectors
  • computational modeling
  • signal-detection
  • texture
  • retinal ganglion-cells
  • visual-cortex
  • lightness/brightness perception
  • locust photoreceptors
  • spatial vision
  • simulataneous contrast
  • detection/discrimination
  • receptive-field
  • receptive fields
  • biological-systems
  • reverse-correlation
  • classification images


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