Visual search and other attentionally demanding processes are often guided from the top down when a specific task is given (e.g. Wolfe et al. Vision Research 44, 2004). In the simplified stimuli commonly used in visual search experiments, e.g. red and horizontal bars, the selection of potential features that might be biased for is obvious (by design). In a natural setting with real-world objects, the selection of these features is not obvious, and there is some debate which features can be used for top-down guidance, and how a specific task maps to them (Wolfe and Horowitz, Nat. Rev. Neurosci. 2004).

Learning to detect objects provides the visual system with an effective set of features suitable for the detection task, and with a mapping from these features to an abstract representation of the object.

We suggest a model, in which V4-type features are shared between object detection and top-down attention. As the model familiarizes itself with objects, i.e. it learns to detect them, it acquires a representation for features to solve the detection task. We propose that by cortical feedback connections, top-down processes can re-use these same features to bias attention to locations with higher probability of containing the target object. We propose a model architecture that allows for such processing, and we present a computational implementation of the model that performs visual search in natural scenes for a given object category, e.g. for faces. We compare the performance of our model to pure bottom-up selection as well as to top-down attention using simple features such as hue.