Hypervigilance-avoidance pattern in spider phobia

Cognitive-motivational theories of phobias propose that patients' behavior is characterized by a hypervigilance-avoidance pattern. This implies that phobics initially direct their attention towards fear-relevant stimuli, followed by avoidance that is thought to prevent objective evaluation and habituation. However, previous experiments with highly anxious individuals confirmed initial hypervigilance and yet failed to show subsequent avoidance. In the present study, we administered a visual task in spider phobics and controls, requiring participants to search for spiders. Analyzing eye movements during visual exploration allowed the examination of spatial as well as temporal aspects of phobic behavior. Confirming the hypervigilance-avoidance hypothesis as a whole, our results showed that, relative to controls, phobics detected spiders faster, fixated closer to spiders during the initial search phase and fixated further from spiders subsequently.

Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture

We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment.