Brain Imaging Studies in Pathological Gambling

This article reviews the neuroimaging research on pathological gambling (PG). Because of the similarities between substance dependence and PG, PG research has used paradigms similar to those used in substance use disorder research, focusing on reward and punishment sensitivity, cue reactivity, impulsivity, and decision making. This review shows that PG is consistently associated with blunted mesolimbic-prefrontal cortex activation to nonspecific rewards, whereas these areas show increased activation when exposed to gambling-related stimuli in cue exposure paradigms. Very little is known, and hence more research is needed regarding the neural underpinnings of impulsivity and decision making in PG. This review concludes with a discussion regarding the challenges and new developments in the field of neurobiological gambling research and comments on their implications for the treatment of PG

Robust motion segmentation with unknown correspondences

Part IVMotion segmentation can be addressed as a subspace clustering problem, assuming that the trajectories of interest points are known. However, establishing point correspondences is in itself a challenging task. Most existing approaches tackle the correspondence estimation and motion segmentation problems separately. In this paper, we introduce an approach to performing motion segmentation without any prior knowledge of point correspondences. We formulate this problem in terms of Partial Permutation Matrices (PPMs) and aim to match feature descriptors while simultaneously encouraging point trajectories to satisfy subspace constraints. This lets us handle outliers in both point locations and feature appearance. The resulting optimization problem can be solved via the Alternating Direction Method of Multipliers (ADMM), where each subproblem has an efficient solution. Our experimental evaluation on synthetic and real sequences clearly evidences the benefits of our formulation over the traditional sequential approach that first estimates correspondences and then performs motion segmentation.Pan Ji, Hongdong Li, Mathieu Salzmann, and Yuchao Da

Active Contours for Multi-region Image Segmentation with a Single Level Set Function

Abstract. Segmenting the image into an arbitrary number of parts is at the core of image understanding. Many formulations of the task have been suggested over the years. Among these are axiomatic functionals, which are hard to implement and analyze, while graph-based alternatives impose a non-geometric metric on the problem. We propose a novel approach to tackle the problem of multiple-region segmentation for an arbitrary number of regions. The proposed framework allows generic region appearance models while avoiding metrication errors. Updating the segmentation in this framework is done by level set evolution. Yet, unlike most existing methods, evolution is executed using a single non-negative level set function, through the Voronoi Implicit Interface Method for a multi-phase interface evolution. We apply the proposed framework to synthetic and real images, with various number of regions, and compare it to state-of-the-art image segmentation algorithms.

Noise and the Perceptual Filling-in effect

Nearby collinear flankers increase the false alarm rate (reports of the target being present when it is not) in a Yes-No experiment. This effect has been attributed to “filling-in” of the target location due to increased activity induced by the flankers. According to signal detection theory, false alarms are attributed to noise in the visual nervous system. Here we investigated the effect of external noise on the filling-in effect by adding white noise to a low contrast Gabor target presented between two collinear Gabor flankers at a range of target-flanker separations. External noise modulates the filling-in effect, reducing visual sensitivity (d′) and increasing the filling-in effect (False Alarm rate). We estimated the amount of external noise at which the false alarm rate increases by the √2 (which we refer to as N(FA)). Across flank distances, both the false alarm rate and d′ (with no external noise) are correlated with N(FA). These results are consistent with the notion that nearby collinear flankers add both signal and noise to the target location. The increased signal results in higher d′ values; the increased noise to higher false alarm rates (the filling effect)