# Experience-based and intentional suppression of distracting information

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2024 · $409,896

## Abstract

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are allocated towards relevant or withdrawn from irrelevant information based on an individual’s intentions and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.

## Key facts

- **NIH application ID:** 10906168
- **Project number:** 5R01MH133689-02
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Viola S. Stoermer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $409,896
- **Award type:** 5
- **Project period:** 2023-08-11 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10906168

## Citation

> US National Institutes of Health, RePORTER application 10906168, Experience-based and intentional suppression of distracting information (5R01MH133689-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10906168. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*