RT Journal Article
A1 Vincent, Benjamin T.
A1 Baddeley, Roland J.
A1 Troscianko, Tom
A1 Gilchrist, Iain D.
T1 Optimal feature integration in visual search
JF Journal of Vision
JO JOV
YR 2009
DO 10.1167/9.5.15
JF Journal of Vision
VO 9
IS 5
SP 15
OP 15
SN 1534-7362
AB  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.
RD 12/10/2019
UL https://doi.org/10.1167/9.5.15