Predictions of a model of spatial attention using sum- and max-pooling functions

Assuming a convergent projection within a hierarchy of processing stages stimuli from different areas of the receptive .eld project onto the same population of cells. Pooling over space a/ects the representation of individual stimuli, and thus its understanding is crucial for attention and ultimately for object recognition. Since attention, in turn, is likely to modify such spatial pooling by changing the competitive weight of individual stimuli, we compare the predictions of sumand max-pooling methods using a model of attention. Both pooling functions can account for data investigating the competition between a pair of stimuli within a V4 receptive .eld; however, our model using sum-pooling predicts a di/erent tuning curve. If we present an additional probe stimulus with the pair, sum-pooling predicts a bottom-up bias of attention, whereas the competition for attention using max-pooling is robust against the additional stimulus. c © 2003 Published by Elsevier B.V.