ArticleFrontiers in human neuroscience2022
A Machine Learning Approach for Detecting Cognitive Interference Based on Eye-Tracking Data.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Machine Learning Integration of Eye-Tracking and Cognitive Screening for Detecting Cognitive Impairment.Journal of eye movement research · 2026Article
- Machine learning-driven model construction for automated classification of cognitive styles.Frontiers in psychology · 2026Article
- Isolating perceptual conflict: a theoretical and methodological analysis of the Task-Irrelevant Paradigm.Frontiers in psychology · 2026Review
- An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.Human factors · 2025Article
- Can Saccade and Vergence Properties Discriminate Stroke Survivors from Individuals with Other Pathologies? A Machine Learning Approach.Brain sciences · 2025Article
- Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.JMIR formative research · 2025Article
- Prediction of Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis using electroencephalography, eye-tracking, and machine learning.Scientific reports · 2024Article
- Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking.NPJ science of learning · 2024Article
- Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review.Frontiers in psychology · 2023Review
- A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data.Frontiers in human neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Stroop test evaluates the ability to inhibit cognitive interference. This interference occurs when the processing of one stimulus characteristic affects the simultaneous processing of another attribute of the same stimulus. Eye movements are an indicator of the individual attention load required for inhibiting cognitive interference. We used an eye tracker to collect eye movements data from more than 60 subjects each performing four different but similar tasks (some with cognitive interference and some without). After the extraction of features related to fixations, saccades and gaze trajectory, we trained different Machine Learning models to recognize tasks performed in the different conditions (i.e., with interference, without interference). The models achieved good classification performances when distinguishing between similar tasks performed with or without cognitive interference. This suggests the presence of characterizing patterns common among subjects, which can be captured by machine learning algorithms despite the individual variability of visual behavior.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.