Abstract
Humans are good at rapidly detecting animals in natural scenes, and evoked potential studies indicate that the corresponding neural signals emerge in the brain within 100 msec of stimulus onset (Kirchner & Thorpe, 2006). Given this speed, it has been suggested that the cues underling animal detection must be relatively primitive. Here we report on the role and dynamics of four potential cues: luminance, colour, texture and contour shape.
We employed a set of natural images drawn from the Berkeley Segmentation Dataset (BSD, Martin et al, 2001), comprised of 180 test images (90 animal, 90 non-animal) and 45 masking images containing humans. In each trial a randomly-selected test stimulus was briefly displayed, followed by a randomly-selected and block-scrambled masking stimulus. Stimulus duration ranged from 30–120 msec.
Hand-segmentations provided by the BSD allow for relatively independent manipulation of cues. Contour cues can be isolated using line drawings representing segment boundaries. Texture cues can be removed by painting all pixels within each segment with the mean colour of the segment. Shape cues can also be removed by replacing segmented images with Voronoi tessellations based on the centres of mass of the BSD segments.
In this manner, we created nine different stimulus classes involving different combinations of cues, and used these to estimate the dynamics of the mechanisms underlying animal detection in natural scenes. Results suggest that the fastest mechanisms use contour shape as a principal discriminative cue, while slower mechanisms integrate texture cues. Interestingly, dynamics based on machine-generated edge maps are similar to dynamics for hand-drawn contours, suggesting that rapid detection can be based upon contours extracted in bottom-up fashion. Consistent with prior studies, we find little role for luminance and colour cues throughout the time course of visual processing, even though information relevant to the task is available in these signals.
Supported by NSERC and the PREA.