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Published January 2004 | Accepted Version
Journal Article Open

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

Abstract

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 sum- and 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.

Additional Information

Received 2 August 2002; received in revised form 4 April 2003; accepted 9 September 2003. c2003 Published by Elsevier B.V. This research has been performed at Caltech. I am grateful to John Reynolds for providing the data showing the attention e-ects on V4 cells (Fig. 3A). I am pleased for extensive discussions with Jamie Mazer and Ru,n VanRullen. I thank Christof Koch, Dirk Walther, Brad Motter, Max Riesenhuber and Tomaso Poggio for helpful comments on an earlier manuscript. This work was supported by DFG HA2630/2-1 and in part by the ERC Program of the NSF (EEC-9402726).

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