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Peter Neri; Nonlinear characterization of a simple process in human vision. Journal of Vision 2009;9(12):1. doi: 10.1167/9.12.1.
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© ARVO (1962-2015); The Authors (2016-present)
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.
inner (dot) product, outer product, convolution and cross-correlation
input stimulus vector i = ( i 1…, i n) where i t is the stimulus value at time point t
signal and noise components of input stimulus
input stimulus outer product matrix i ⊗ i
system first-order (linear) kernel
system second-order (nonlinear) kernel
standard deviation of external Gaussian noise source
indexing stimulus region p (p = ctr for center, p = sur for surround)
indexing target absent/present (s = 0 or 1) on incorrect/correct (r = 0 or 1) trials
decisional transducer function
observer response/decision (0 correct, 1 incorrect)
time-varying output of the system (before g is applied to yield r)
human-human and model-human consistency
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