Nelson and Palmer (2007) concluded that figures/figural properties automatically attract attention, after they found that participants were faster to detect/discriminate targets appearing where a portion of a familiar object was suggested in an otherwise ambiguous display. We investigated whether these effects are truly automatic and whether they generalize to another figural property-convexity. We found that Nelson and Palmer's results do generalize to convexity, but only when participants are uncertain regarding when and where the target will appear. Dependence on uncertainty regarding target location/timing was also observed for familiarity. Thus, although we could replicate and extend Nelson and Palmer's results, our experiments showed that figures do not automatically draw attention. In addition, our research went beyond Nelson and Palmer's, in that we were able to separate figural properties from perceived figures. Because figural properties are regularities that predict where objects lie in the visual field, our results join other evidence that regularities in the environment can attract attention. More generally, our results are consistent with Bayesian theories in which priors are given more weight under conditions of uncertainty.

Vision provides the primary means by which many animals distinguish foreground objects from their background and coordinate locomotion through complex environments. The present thesis focuses on mechanisms within the visual system that afford figure-ground segregation and self-motion perception. These processes are modeled as emergent outcomes of dynamical interactions among neural populations in several brain areas. This dissertation specifies and simulates how border-ownership signals emerge in cortex, and how the medial superior temporal area (MSTd) represents path of travel and heading, in the presence of independently moving objects (IMOs). Neurons in visual cortex that signal border-ownership, the perception that a border belongs to a figure and not its background, have been identified but the underlying mechanisms have been unclear. A model is presented that demonstrates that inter-areal interactions across model visual areas V1-V2-V4 afford border-ownership signals similar to those reported in electrophysiology for visual displays containing figures defined by luminance contrast. Competition between model neurons with different receptive field sizes is crucial for reconciling the occlusion of one object by another. The model is extended to determine border-ownership when object borders are kinetically-defined, and to detect the location and size of shapes, despite the curvature of their boundary contours. Navigation in the real world requires humans to travel along curved paths. Many perceptual models have been proposed that focus on heading, which specifies the direction of travel along straight paths, but not on path curvature. In primates, MSTd has been implicated in heading perception. A model of V1, medial temporal area (MT), and MSTd is developed herein that demonstrates how MSTd neurons can simultaneously encode path curvature and heading. Human judgments of heading are accurate in rigid environments, but are biased in the presence of IMOs. The model presented here explains the bias through recurrent connectivity in MSTd and avoids the use of differential motion detectors which, although used in existing models to discount the motion of an IMO relative to its background, is not biologically plausible. Reported modulation of the MSTd population due to attention is explained through competitive dynamics between subpopulations responding to bottom-up and top- down signals.

Object perception is necessary to our understanding of the visual world, yet its neural mechanism remains poorly understood. The goal of this dissertation is to shed light on this mechanism. Current computational models of object perception suggest that regions on opposite sides of a shared border compete, with the winner perceived as the shaped object and the loser as its locally shapeless background (or ground). Recent behavioral work indicates that the result of this competition is suppression of the ground at the level of object shape--a finding not predicted by models. Here, I present three studies that extend this previous research on ground suppression as a mechanism by which object perception is accomplished. I first show that the amount of suppression applied to the ground depends on the amount of competition for object status (Salvagio, Cacciamani, & Peterson, 2012). I then provide the first neural evidence of ground suppression from shape-level competition at both high and low levels of the visual hierarchy, with the latter arising from top-down feedback (Cacciamani, Scalf, & Peterson, submitted). Finally, I show that semantic information pertaining to the ground is accessed prior to the assignment of object status, but unlike shape information, is not suppressed (Cacciamani, Mojica, Sanguinetti, & Peterson, 2014). Together, the three studies that comprise this dissertation demonstrate that ground suppression arising from shape-level competition underlies object perception. This research contradicts traditional theories stating that objects are processed unidirectionally through the visual system in a single feedforward pass; instead, it supports theories of object perception entailing dynamical feedforward and feedback processes.