Probabilistic Models of Attention based on Iconic Representations and Predictive Coding Abstract

We describe two models of attention that utilize probabilistic principles to compute task-relevant variables. In the first model, objects and visual scenes are represented iconically using spatial filters at multiple scales. A maximum likelihood-based approach is used to compute the location of a target in a given scene. The eye movements generated by such a strategy are shown to be similar to human eye movement patterns elicited during visual search in naturalistic scenes. The second model is based on the statistical concept of predictive coding. It assumes that top-down feedback from higher cortical areas conveys predictions of expected activity at lower levels while the errors in prediction are conveyed through feedforward connections. The model explains how multiple objects in a scene can be recognized sequentially without an explicit spotlight of attention. An extension of the model provides an interpretation of object-based versus spatial attention in terms of interactions between “what ” and “where ” networks in the visual pathway