Choosing attentional control settings in a dynamically changing environment.

Abstract

Goal-directed attentional control supports efficient visual search by prioritizing relevant stimuli in the environment. Previous research has shown that goal-directed control can be configured in many ways, and often multiple control settings can be used to achieve the same goal. However, little is known about how control settings are selected. We explored the extent to which the configuration of goal-directed control is driven by performance maximization (optimally configuring settings to maximize speed and accuracy) and effort minimization (selecting the least effortful settings). We used a new paradigm, adaptive choice visual search, which allows participants to choose one of two available targets (a red or a blue square) on each trial. Distractor colors vary predictively across trials, such that the optimal target switches back and forth throughout the experiment. Results (N = 43) show that participants chose the optimal target most often, updating to the new target when the environment changed, supporting performance maximization. However, individuals were sluggish to update to the optimal color, consistent with effort minimization. Additionally, we found a surprisingly high rate of nonoptimal choices and switching between targets, which could not be explained by either factor. Analysis of participants' self-reported search strategy revealed substantial individual differences in the control strategies used. In sum, the adaptive choice visual search enables a fresh approach to studying goal-directed control. The results contribute new evidence that control is partly determined by both performance maximization and effort minimization, as well as at least one additional factor, which we speculate to include novelty seeking.