Experience-based and intentional suppression of distracting information

Project Summary Human sensory systems cannot process all available inputs in a structured and meaningful way. Thus, selecting relevant information and filtering out irrelevant and distracting information is critical to survive and thrive. Decades of research on selective attention have investigated the neural mechanisms underlying the ability to focus processing resources on relevant information, demonstrating that processing of information within the focus of attention is enhanced. Much less clear is how task-irrelevant and distracting information is effectively ignored, albeit major theories of attention proposing that efficient filtering of irrelevant information is essential for many aspects of higher-level cognition. Thus, there is a critical need to identify the mechanisms that support the effective ignoring of distracting information. Without such knowledge, models of attention are incomplete, and it will remain difficult to help people avoid distractions in everyday lives. This proposal aims to identify the cortical processes involved in effective distractor suppression, focusing on two modes of attention: Experience-based attention, where based on statistical regularities in the environment processing resources are biased towards or away from relevant or irrelevant information, respectively, and volitional attention, where processing resources are allocated towards relevant or withdrawn from irrelevant information based on an individual’s intentions and explicit task goals. Recent theories of cortical information processing indicate the importance of dissociating between these two types of attention because they each influence information processing in distinct ways. Here, we test the hypothesis that experience-based attention induced via statistical regularities will be more effective relative to volitional attention when ignoring distracting information. Our approach will combine psychophysics, electrophysiological methods (EEG) and computational modeling to determine how experience and intentions influence the temporal dynamics of cortical information processing and how they shape the quality of the perceptual representations of to-be-ignored inputs. Critically, these neural measures will be directly linked to behavioral performance with the goal to identify the neural mechanisms responsible for successful distractor ignoring. Collectively, this work will provide key insights into how different modes of attentional control processes interact to shape perception and behavior, and will more broadly test general models of attention and cognitive control. Furthermore, the results of this proposal have the potential to help support people’s abilities to reduce distraction in everyday tasks, such as driving and at the workplace, and elucidate on why certain populations have particular difficulties in avoiding distractions, thereby enabling more targeted diagnoses and interventions in clinical settings.