Neural Network Model Can Improve Understanding of Human Attention

Neural Network Model Can Improve Understanding of Human Attention


Researchers at washington university in st. Louis have created a neural network model to undertand the mechanics of how humans concentrate in complex environments. With this task, participants face three distractions from a primary task, Mimicking more natural conditions for human concentration. Intead of Looking at Simple Colored Words (Stroop Task), Participants are required to find a target feature among complex stimuli with varying shapes, colors, borders and motion dires. People Respond Slower when the target is mixed with many distractions. Credit: Washington University in St. Louis / Control and Decision Making Lab

Imagine watching a speaker and another person nearby is loudly crunching from a bag of chips. To deal with this, a person could adjust their attention to downplay that crunch noises or focus their hearing on the speaker. But undersrstanding how human brains do this have been a challenge.

Now, with a new neural network model, researchers at washington university in st. Louis have a better tool to uncover which brain mechanisms are at play when people need to focus amid many distractions.

The model, now Published in Nature human behaviorDemonstrates that people focus not by concentrating extra hard on a subject, but by ignoring inputs that was distracted in the past.

“Previous Work Showed That When People Encounter a very different task, they adjust their attention to make them impervious to new distraction,” Said Study Author WOUTER KOOL, An Assistant Professor of psychological and brain sciences in arts & science at washu. “However, it remained unclear how they adjusted their attention.”

It’s not that people may not do both forms of Attention Control – Concentrate and Reduce Distractions – BUT How They Do that Can Be Affected by the Difafty of Previous Tasks.

“In our work, Previous Difential also also affected their sensitivity to Current Difacity,” Kool Said, Summarizing their findings.

The key innovation in this work is that usually resultars study this problem with only one source of relevant information and one source of distraction information.

An example of this older work is the stroop task, which includes a list of words for colors that can match or not match the color in which the word is printed.

By investment the delay in reaction time when participants are asked to name the color of Mismatching words, resarchers have created simple neural network models to understand how humans focus on tasks. But again, that’s one distraction, one task. Kool’s postdoctoral researcher davide gheza wanted to change it up.

“We increase the sources of distraction, mimicing a cocktail party or a conference meeting,” Gheza said.

In contrast to the Stroop task that involves simple colored words, their task requires to choose among two complex stimuli with varying shapes, colors, borders and motion directions. This is meant to bring four total sources of information.

By running participants through a series of trials where each of the four sources could be eather the target task or one of the distractions, “We found strong evidence thety Attention to the distractor and not so much to the target, “Kool said.

“People tune their Attention very specificly,” He said. “If somenting or someone was distracting before, you learn to ignore them in the future, but you stay open to other inputs that might help complete the task.”

The next step will be testing this model on brain data collected while participants perform this task in an mri scanner. Gheza and kool believe this will help them pin down what specifically is happy’s brains while they encourage and overcome multiple a results of distraction.

More information:
Davide gheza et al, distractor-specific control adaptation in multidimensional environments, Nature human behavior (2025). Doi: 10.1038/s41562-024-02088 -z

Provided by washington university in st. Louis


Citation: Neural Network Model Can Improve Understanding of Human Attention (2025, February 18) Retrieved 19 February 2025 from

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