Visual context information constrains what to expect and where to look, facilitating search for objects embedded in complex displays. The original contextual cueing paradigm (Chun & Jiang, 1998) showed that observers implicitly learn the global configuration of targets in artificial visual search tasks and that this context can serve to cue the target location and facilitate search performance in subsequent encounters.

I propose a computational model of this type of learning, and suggest that the majority of spatial contextual cueing effects can be accounted for using a model with only two major constraints: pair-wise learning of target-distractor relations, and a set of spatial constraints.

I then present a series of experiments designed to test the extent of the spatial constraints necessary for modeling contextual cueing, and examine how such a model of contextual cueing can account for the major results in the contextual cueing literature. I next look at the predictions such a model makes about learning in visual search more generally, and present evidence from an interrupted visual search task suggesting that similar constraints control learning during a normal visual search trial.