Optimal feature integration in visual search.
Despite embodying fundamentally different assumptions about attentional allocation, a wide range of popular models of attention include a max-of-outputs mechanism for selection. Within these models, attention is directed to the items with the most extreme-value along a perceptual dimension via, for example, a winner-take-all mechanism. From the detection theoretic approach, this MAX-observer can be optimal under specific situations, however in distracter heterogeneity manipulations or in natural visual scenes this is not always the case. We derive a Bayesian maximum a posteriori (MAP)-observer, which is optimal in both these situations. While it retains a form of the max-of-outputs mechanism, it is based on the maximum a posterior probability dimension, instead of a perceptual dimension. To test this model we investigated human visual search performance using a yes/no procedure while adding external orientation uncertainty to distracter elements. The results are much better fitted by the predictions of a MAP observer than a MAX observer. We conclude a max-like mechanism may well underlie the allocation of visual attention, but this is based upon a probability dimension, not a perceptual dimension.
Vincent
Benjamin T
BT
School of Psychology, University of Dundee, Dundee, UK. b.t.vincent@dundee.ac.uk
Baddeley
Roland J
RJ
Troscianko
Tom
T
Gilchrist
Iain D
ID
D001288:Attention / Q000502:physiology*; D001499:Bayes Theorem; D004192:Discrimination, Psychological; D005403:Fixation, Ocular / Q000502:physiology*; D006801:Humans; D010775:Photic Stimulation; D017603:Signal Detection, Psychological / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*