ArticleJournal of Intelligence2024
Measuring Raven's Progressive Matrices Combining Eye-Tracking Technology and Machine Learning (ML) Models.
Article in Journal of Intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Integrating eye-tracking into psychometric research: computational approaches to explore response burden in questionnaire completion.Frontiers in psychology · 2026Article
- Development and validation of a short form for the Raven's Coloured Progressive Matrices using a machine learning approach.The British journal of developmental psychology · 2025Article
- Assessing the Effect of Artificial Intelligence Anxiety on Turnover Intention: The Mediating Role of Quiet Quitting in Turkish Small and Medium Enterprises.Behavioral sciences (Basel, Switzerland) · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Extended testing time in Raven's Progressive Matrices (RPM) can lead to increased fatigue and reduced motivation, which may impair cognitive task performance. This study explores the application of artificial intelligence (AI) in RPM by combining eye-tracking technology with machine learning (ML) models, aiming to explore new methods for improving the efficiency of RPM testing and to identify the key metrics involved. Using eye-tracking metrics as features, ten ML models were trained, with the XGBoost model demonstrating superior performance. Notably, we further refined the period of interest and reduced the number of metrics, achieving strong performance, with accuracy, precision, and recall all above 0.8, using only 60% of the response time and nine eye-tracking metrics. This study also examines the role of several key metrics in RPM and offers valuable insights for future research.
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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 Socratic graph.