Using machine learning models to classify user performance in the ruff figurai fluency test from eye-tracking features
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| Status: | |
| Autorzy: | Borys Magdalena, Plechawska-Wójcik Małgorzata, Barakate Sara, Hachmoud Karim, Krukow Paweł |
| Dyscypliny: | |
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| Wersja dokumentu: | Drukowana | Elektroniczna |
| Arkusze wydawnicze: | 0,5 |
| Język: | angielski |
| Strony: | 1 - 4 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 2 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 9th International Conference ELMECO-9 " Electromagnetic Devices and Processes in Environment Protection " with 12th Seminar AoS-12 "Applications of Superconductors" |
| Skrócona nazwa konferencji: | ELMECO & AoS 2017 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 3 grudnia 2017 do 6 grudnia 2017 |
| Miasto konferencji: | Nałęczów |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The Ruff Figurai Fluency Test is a paper and pencil tool to gain information about the nonverbal capacity for such activities as initiation, planning, and divergent reasoning including strategy use. It is applied voluntarily in the form of cognitive test batteries. In this study a computerised version of the Ruff Figural Fluency Test was employed in order to assess user cognitive performance. Sixty-one male participants were examined using the eye-tracking technique to gain the desired data. Different machine learning models were applied in order to classify user performance. The best results (78,7% for the testing dataset) were obtained for Quadratic Discriminant Analysis classifier. |
