Spatial context is known to affect how a stimulus is perceived. A variety of illusions spanning all visual modalities have been attributed to contextual processing, but their neural underpinning remains poorly understood. The goal of this thesis is to draft a computational theory of contextual processing across visual modalities that models neural populations in the early visual cortex. We begin by reviewing basic (i.e., non-contextual and feed-forward) cortical computations. We use the example of contour detection as a first illustration of how recurrent network models may bridge the gap from neurophysiology to behavior. Then, we present our main contribution: a novel recurrent network model of classical and extra-classical receptive fields, constrained by the physiology of the primary visual cortex. A key feature is the postulated existence of two spatially-disjoint (near vs. far) extra-classical regions with complementary contributions (facilitatory vs. suppressive) to the classical receptive field. The model accounts for a variety of contextual phenomena across visual modalities; in particular, it explains how center-surround interactions may shift from perceptual attraction to perceptual repulsion in tilt effects and from contrast to assimilation in induction effects. Finally, we discuss how the model reveals a novel taxonomy of contextual phenomena across visual modalities that is based on the different ways a stimulus may activate the extra-classical receptive field (exclusively, competitively, cooperatively) and on the nature of the stimulus (ambiguous vs. non-ambiguous). Overall, the ability of the model to account for the variety and complexity of neurophysiology and psychophysics phenomena provides computational evidence for a novel canonical circuit shared across visual modalities.
Mély, David Alex,
"Circuits and Mechanisms of Contextual Integration in Visual Cortex"
(2016).
Cognitive Sciences Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.7301/Z0R20ZSG
