William H. Alexander
To explain behavioral effects, models of cognitive control frequently rely on task information that the modeler provides. Hard-wired information can include labeling task dimensions as being relevant or irrelevant, defining which task stimuli belong to which task dimensions, or proposing a specific strategy by which control is adjusted during task performance. Although models incorporating hard-wired information of this nature are frequently successful at accounting for observed behavior, their ability to do so often depends on tailoring this information to specific tasks, usually performed in a laboratory setting. Outside of the laboratory, individuals are not usually provided explicit information about how to behave; it thus remains an open question as to how individuals identify, update, and switch task strategies in the real world. Here, we present a new model of cognitive control, learned attention for control (LAC), that not only captures a broad range of control effects but does so using a minimal amount of modeler-supplied information. In a series of simulations, we demonstrate how the LAC model adopts distinct control strategies based on recent trial history and adapts to changing behavioral contexts. The model's ability to do so derives from an ongoing evaluation of how well task stimuli independently predict correct behavior, and the results of this evaluation are used to shift attention among information sources. These results suggest that the reliability of information can serve as a general principle for understanding cognitive control.