Associative Learning as a Source of Visual Bias
The visual system does not encounter isolated visual features, but sets of features that bind together and constitute complex perceptual events. Visual features are also typically accompanied by the observer's own movements, and it is believed that these movements are also included in the representation of events. In the present thesis, argue that the units of attentional selection are such multi featured events. This means that learning the regularities in the relation among features can guide visual attention toward multiple features that belong to the selected event. Through a series of experiments, I demonstrate how visual bias for colour is influenced by the selection associated features, such as a shape or a manual movement. In all instances, I argue, selecting or inhibiting one feature impacts the treatment of the associated colour feature. In short, feature association can extend the impact of feature based attention across multiple dimensions, which supports the idea that events, not features, are units of attentional selection.