Eye movement patterns during the recognition of three-dimensional objects: Preferential fixation of concave surface curvature minima

This study used eye movement patterns to examine how high-level shape information is used during 3D object recognition. Eye movements were recorded while observers either actively memorized or passively viewed sets of novel objects, and then during a subsequent recognition memory task. Fixation data were contrasted against different algorithmically generated models of shape analysis based on: (1) regions of internal concave or (2) convex surface curvature discontinuity or (3) external bounding contour. The results showed a preference for fixation at regions of internal local features during both active memorization and passive viewing but also for regions of concave surface curvature during the recognition task. These findings provide new evidence supporting the special functional status of local concave discontinuities in recognition and show how studies of eye movement patterns can elucidate shape information processing in human vision

Artificially created stimuli produced by a genetic algorithm using a saliency model as its fitness function show that Inattentional Blindness modulates performance in a pop-out visual search paradigm

Salient stimuli are more readily detected than less salient stimuli, and individual differences in such detection may be relevant to why some people fail to notice an unexpected stimulus that appears in their visual field whereas others do notice it. This failure to notice unexpected stimuli is termed 'Inattentional Blindness' and is more likely to occur when we are engaged in a resource-consuming task. A genetic algorithm is described in which artificial stimuli are created using a saliency model as its fitness function. These generated stimuli, which vary in their saliency level, are used in two studies that implement a pop-out visual search task to evaluate the power of the model to discriminate the performance of people who were and were not Inattentionally Blind (IB). In one study the number of orientational filters in the model was increased to check if discriminatory power and the saliency estimation for low-level images could be improved. Results show that the performance of the model does improve when additional filters are included, leading to the conclusion that low-level images may require a higher number of orientational filters for the model to better predict participants' performance. In both studies we found that given the same target patch image (i.e. same saliency value) IB individuals take longer to identify a target compared to non-IB individuals. This suggests that IB individuals require a higher level of saliency for low-level visual features in order to identify target patches