Topic: priority maps

Like many other animals, humans direct their gaze to selectively sample the visual space based on task demands. Visual search, the process of locating an item among several visually presented objects, is a key paradigm in studying visual attention. While much is known about the brain networks underlying visual search, our understanding of the neural computations driving this behavior is limited. Simulating this behavior in-silico can provide a tool for developing and testing hypotheses about these neural computations. To address this gap, we trained an image-computable artificial neural networ

The priority maps framework proposes that attentional priorities are weighted in cognitive/brain maps of the external space. As extensions of saliency maps, a variety of factors known as “attentional signals” would be represented in these so-called priority maps, ranging from low-level features such as physical salience to higher-level features such as social information. According to studies investigating attentional patterns in the presence of social signals, social attentional signals (e.g. faces) would “automatically” overcome the presence of any other signal and win the competition for at