Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven’s Advanced Progressive Matrices: A Data-Driven Perspective
The present study utilized a recursive feature elimination approach in conjunction with a random forest algorithm to assess the efficacy of various features in predicting cognitive strategy usage in Raven’s Advanced Progressive Matrices. In addition to item response accuracy (RA) and response time (...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Journal of Intelligence |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3200/13/2/14 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849718524332736512 |
|---|---|
| author | Yaohui Liu Keren He Kaiwen Man Peida Zhan |
| author_facet | Yaohui Liu Keren He Kaiwen Man Peida Zhan |
| author_sort | Yaohui Liu |
| collection | DOAJ |
| description | The present study utilized a recursive feature elimination approach in conjunction with a random forest algorithm to assess the efficacy of various features in predicting cognitive strategy usage in Raven’s Advanced Progressive Matrices. In addition to item response accuracy (RA) and response time (RT), five key eye-tracking metrics were examined: proportional time on matrix (PTM), latency to first toggle (LFT), rate of latency to first toggle (RLT), number of toggles (NOT), and rate of toggling (ROT). The results indicated that PTM, RLT, and LFT were the three most critical features, with PTM emerging as the most significant predictor of cognitive strategy usage, followed by RLT and LFT. Clustering analysis of these optimal features validated their utility in effectively distinguishing cognitive strategies. The study’s findings underscore the potential of specific eye-tracking metrics as objective indicators of cognitive processing while providing a data-driven method to identify strategies used in complex reasoning tasks. |
| format | Article |
| id | doaj-art-eb28850f4cc64e20a1be1bf7fdb01d72 |
| institution | DOAJ |
| issn | 2079-3200 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Intelligence |
| spelling | doaj-art-eb28850f4cc64e20a1be1bf7fdb01d722025-08-20T03:12:22ZengMDPI AGJournal of Intelligence2079-32002025-01-011321410.3390/jintelligence13020014Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven’s Advanced Progressive Matrices: A Data-Driven PerspectiveYaohui Liu0Keren He1Kaiwen Man2Peida Zhan3School of Psychology, Zhejiang Normal University, Jinhua 321004, ChinaMental Health Education and Development Center, Zhejiang Normal University, Jinhua 321004, ChinaEducational Studies in Psychology, Research Methodology, and Counseling, University of Alabama, Tuscaloosa, AL 35487, USASchool of Psychology, Zhejiang Normal University, Jinhua 321004, ChinaThe present study utilized a recursive feature elimination approach in conjunction with a random forest algorithm to assess the efficacy of various features in predicting cognitive strategy usage in Raven’s Advanced Progressive Matrices. In addition to item response accuracy (RA) and response time (RT), five key eye-tracking metrics were examined: proportional time on matrix (PTM), latency to first toggle (LFT), rate of latency to first toggle (RLT), number of toggles (NOT), and rate of toggling (ROT). The results indicated that PTM, RLT, and LFT were the three most critical features, with PTM emerging as the most significant predictor of cognitive strategy usage, followed by RLT and LFT. Clustering analysis of these optimal features validated their utility in effectively distinguishing cognitive strategies. The study’s findings underscore the potential of specific eye-tracking metrics as objective indicators of cognitive processing while providing a data-driven method to identify strategies used in complex reasoning tasks.https://www.mdpi.com/2079-3200/13/2/14cognitive strategyintelligencematrix reasoningeye movementrandom forest |
| spellingShingle | Yaohui Liu Keren He Kaiwen Man Peida Zhan Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven’s Advanced Progressive Matrices: A Data-Driven Perspective Journal of Intelligence cognitive strategy intelligence matrix reasoning eye movement random forest |
| title | Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven’s Advanced Progressive Matrices: A Data-Driven Perspective |
| title_full | Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven’s Advanced Progressive Matrices: A Data-Driven Perspective |
| title_fullStr | Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven’s Advanced Progressive Matrices: A Data-Driven Perspective |
| title_full_unstemmed | Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven’s Advanced Progressive Matrices: A Data-Driven Perspective |
| title_short | Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven’s Advanced Progressive Matrices: A Data-Driven Perspective |
| title_sort | exploring critical eye tracking metrics for identifying cognitive strategies in raven s advanced progressive matrices a data driven perspective |
| topic | cognitive strategy intelligence matrix reasoning eye movement random forest |
| url | https://www.mdpi.com/2079-3200/13/2/14 |
| work_keys_str_mv | AT yaohuiliu exploringcriticaleyetrackingmetricsforidentifyingcognitivestrategiesinravensadvancedprogressivematricesadatadrivenperspective AT kerenhe exploringcriticaleyetrackingmetricsforidentifyingcognitivestrategiesinravensadvancedprogressivematricesadatadrivenperspective AT kaiwenman exploringcriticaleyetrackingmetricsforidentifyingcognitivestrategiesinravensadvancedprogressivematricesadatadrivenperspective AT peidazhan exploringcriticaleyetrackingmetricsforidentifyingcognitivestrategiesinravensadvancedprogressivematricesadatadrivenperspective |