Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds.
The neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center-surround organization of receptive fields in early vision.
Zhang
Sheng
S
Vision & Image Understanding Laboratory, Department of Psychology, University of California, Santa Barbara, California 93106-9660, USA.
Abbey
Craig K
CK
Eckstein
Miguel P
MP
D003662:Decision Theory; D006801:Humans; D016014:Linear Models; D008606:Mental Processes / Q000502:physiology*; D008954:Models, Biological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D010364:Pattern Recognition, Visual / Q000502:physiology; D014794:Visual Fields / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*