Picture yourself observing a speaker while another individual nearby is noisily munching on a bag of chips. To manage this, a person could redirect their focus to minimize those crunching sounds or concentrate their listening on the speaker. Nonetheless, deciphering how human brains accomplish this task has posed a significant challenge.
Now, utilizing a fresh neural network model, researchers at Washington University in St. Louis have acquired a more effective tool to reveal the brain mechanisms involved when individuals must concentrate amidst numerous distractions.
The model, recently featured in Nature Human Behaviour, illustrates that people focus not by intensifying their concentration on one topic, but by disregarding previously distracting inputs.
“Earlier studies indicated that when people face a particularly challenging task, they modify their attention to render them resistant to newly introduced distractions,” explained study author Wouter Kool, an assistant professor of psychological and brain sciences in Arts & Sciences at WashU. “However, it remained ambiguous how they modified their focus.”
It’s not that individuals are incapable of employing both types of attentional control — concentrating and minimizing distractions — but the manner in which they achieve this can be influenced by the complexity of prior tasks.
“In our research, past difficulties clearly influenced their responsiveness to current challenges,” Kool stated, summarizing their insights.
The primary advancement in this research is that typically, scientists investigate this issue using only a single relevant information source alongside one source of distraction.
An example of this traditional research is the Stroop task, which presents a list of color words that may either align or conflict with the color in which the word is displayed.
(Image: Courtesy Wikipedia commons)
By analyzing the reaction time delay when participants are required to identify the color of mismatched words, researchers have developed straightforward neural network models to comprehend how humans focus on tasks. Yet again, that involves just one distraction, one task. Kool’s postdoctoral researcher Davide Gheza aimed to modify the approach.
“We enhanced the sources of distraction, simulating a lively cocktail party or a conference gathering,” Gheza remarked.
In contrast to the Stroop task, which features straightforward colored words, their task necessitates making choices between two intricate stimuli with varying shapes, hues, borders, and directions of motion. This is designed to incorporate a total of four information sources.
By guiding participants through various trials where each of the four sources could serve as either the target task or one of the distractions, “we uncovered substantial evidence that individuals adjusted their attention significantly to the distractor rather than the target,” Kool expressed.
“Individuals finely tune their attention,” he noted. “If something or someone distracted you before, you learn to overlook them in the future, yet you remain receptive to other inputs that may assist in completing the task.”
The forthcoming step will involve testing this model on brain data collected while participants engage in this task inside an MRI scanner. Gheza and Kool believe this will enable them to determine precisely what transpires in people’s brains as they confront and navigate multiple sources of distraction.
Gheza K, Kool W, Distractor-specific control adaptation in multidimensional environments. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-024-02088-z
This research received financial backing from a Multi-University Research Initiative grant (ONR/DoD N00014-23-1-2792) awarded to Kool and by a McDonnell Center for Systems Neuroscience grant given to Gheza.
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