Cognitive control strategies derive from dimension reliability

Abstract

To explain behavioral effects, models of cognitive control frequently rely on task information provided by the modeler. ‘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, adapts to changing behavioral contexts, and learns to group related task stimuli under the same abstract dimension. 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 amongst information sources. These results suggest that the reliability of information can serve as a general principle for understanding cognitive control.

Distractor-specific control adaptation in multidimensional environments

“…Our computational model bears resemblance to the recently proposed Learned Attention for Control model (Alexander, 2022). This model learns to deploy attention to dimensions that are useful for responding correctly.…”

“…This introduces a temporal dynamic in which the attentional gain of each dimension is boosted whenever it becomes task-relevant, and then slowly trails off (see Supplementary Figures 1 and 2). The recently proposed Learned Attention for Control model(Alexander, 2022) proposes that such dynamics can be captured by a reinforcement-learning algorithm that deploys attention to dimensions that appear to be useful for responding correctly. Similar to our account, this model assumes independent learning processes for each dimension.…”