Intentional Learning Establishes Multiple Attentional Sets That Simultaneously Guide Attention

One of the key human cognitive capabilities is to extract regularities from the environment to guide behavior. An attentional set for a target feature can be established through statistical learning of probabilistic target associations; however, whether an array of attentional sets of predictive target features can be established during intentional learning, and how they might guide attention, is not known yet. To address these questions, we had human observers perform a visual search task where we instructed them to try to use color to find their target shape. We structured the task with a fine-grained statistical regularity such that the target shapes appeared in different colors with five unique probabilities (i.e., 33%, 26%, 19%, 12%, and 5%) while we recorded their electroencephalogram. Observers rapidly learned these regularities, evidenced by being faster to report targets that appeared in higher probability colors. These effects were not due to unequal sample sizes or simple feature priming. More importantly, equivalent speeding across a set of high-probability colors suggests that the brain was driving attention to multiple targets simultaneously. Our electrophysiological results showed larger amplitude N2 posterior contralateral component, indexing perceptual attention, and late positive complex (LPC) component, indexing postperceptual processes, for targets paired with high-probability colors. These electrophysiological data suggest that the learned attentional sets change both perceptual selection and how postperceptual decisions are made. In sum, we show that multiple attentional sets can be established during intentional learning that accompanies general task acquisition and that these attentional sets can simultaneously guide attention by enhancing both perceptual attention and postperceptual processes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).