AUTHOR=Druker Michael , Anderson Britt TITLE=Spatial Probability Aids Visual Stimulus Discrimination JOURNAL=Frontiers in Human Neuroscience VOLUME=4 YEAR=2010 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2010.00063 DOI=10.3389/fnhum.2010.00063 ISSN=1662-5161 ABSTRACT=

We investigated whether the statistical predictability of a target's location would influence how quickly and accurately it was classified. Recent results have suggested that spatial probability can be a cue for the allocation of attention in visual search. One explanation for probability cuing is spatial repetition priming. In our two experiments we used probability distributions that were continuous across the display rather than relying on a few arbitrary screen locations. This produced fewer spatial repeats and allowed us to dissociate the effect of a high-probability location from that of short-term spatial repetition. The task required participants to quickly judge the color of a single dot presented on a computer screen. In Experiment 1, targets were more probable in an off-center hotspot of high-probability that gradually declined to a background rate. Targets garnered faster responses if they were near earlier target locations (priming) and if they were near the high-probability hotspot (probability cuing). In Experiment 2, target locations were chosen on three concentric circles around fixation. One circle contained 80% of targets. The value of this ring distribution is that it allowed for a spatially restricted high-probability zone in which sequentially repeated trials were not likely to be physically close. Participant performance was sensitive to the high-probability circle in addition to the expected effects of eccentricity and the distance to recent targets. These two experiments suggest that inhomogeneities in spatial probability can be learned and used by participants on-line and without prompting as an aid for visual stimulus discrimination and that spatial repetition priming is not a sufficient explanation for this effect. Future models of attention should consider explicitly incorporating the probabilities of targets locations and features.