Detecting expert’s eye using a multiple-kernel Relevance Vector Machine
Decoding mental states from the pattern of neural activity or overt behavior is an intensely pursued goal. Here we applied machine learning to detect expertise from the oculomotor behavior of novice and expert billiard players during free viewing of a filmed billiard match with no specific task, and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-04-01
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| Series: | Journal of Eye Movement Research |
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| Online Access: | https://bop.unibe.ch/JEMR/article/view/2376 |
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| author | Giuseppe Boccignone Mario Ferraro Sofia Crespi Carlo Robino Claudio de’Sperati |
| author_facet | Giuseppe Boccignone Mario Ferraro Sofia Crespi Carlo Robino Claudio de’Sperati |
| author_sort | Giuseppe Boccignone |
| collection | DOAJ |
| description | Decoding mental states from the pattern of neural activity or overt behavior is an intensely pursued goal. Here we applied machine learning to detect expertise from the oculomotor behavior of novice and expert billiard players during free viewing of a filmed billiard match with no specific task, and in a dynamic trajectory prediction task involving ad-hoc, occluded billiard shots. We have adopted a ground framework for feature space fusion and a Bayesian sparse classifier, namely, a Relevance Vector Machine. By testing different combinations of simple oculomotor features (gaze shifts amplitude and direction, and fixation duration), we could classify on an individual basis which group - novice or expert - the observers belonged to with an accuracy of 82% and 87%, respectively for the match and the shots. These results provide evidence that, at least in the particular domain of billiard sport, a signature of expertise is hidden in very basic aspects of oculomotor behavior, and that expertise can be detected at the individual level both with ad-hoc testing conditions and under naturalistic conditions - and suitable data mining. Our procedure paves the way for the development of a test for the “expert’s eye”, and promotes the use of eye movements as an additional signal source in Brain-Computer-Interface (BCI) systems. |
| format | Article |
| id | doaj-art-c5bbacecce0247e5a0d27f1f4afb68c8 |
| institution | OA Journals |
| issn | 1995-8692 |
| language | English |
| publishDate | 2014-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Eye Movement Research |
| spelling | doaj-art-c5bbacecce0247e5a0d27f1f4afb68c82025-08-20T01:55:02ZengMDPI AGJournal of Eye Movement Research1995-86922014-04-017210.16910/jemr.7.2.3Detecting expert’s eye using a multiple-kernel Relevance Vector MachineGiuseppe Boccignone0Mario Ferraro1Sofia Crespi2Carlo Robino3Claudio de’Sperati4Dip. Informatica, Universit`a di Milano, ItalyDip. Fisica, Universit`a di Torino, ItalyLAPCO - Universit`a Vita-Salute San Raffaele, & CERMAC, Ospedale San Raffaele, ItalyLAPCO - Universit`a Vita-Salute San Raffaele, ItalyLAPCO - Universit`a Vita-Salute San Raffaele, ItalyDecoding mental states from the pattern of neural activity or overt behavior is an intensely pursued goal. Here we applied machine learning to detect expertise from the oculomotor behavior of novice and expert billiard players during free viewing of a filmed billiard match with no specific task, and in a dynamic trajectory prediction task involving ad-hoc, occluded billiard shots. We have adopted a ground framework for feature space fusion and a Bayesian sparse classifier, namely, a Relevance Vector Machine. By testing different combinations of simple oculomotor features (gaze shifts amplitude and direction, and fixation duration), we could classify on an individual basis which group - novice or expert - the observers belonged to with an accuracy of 82% and 87%, respectively for the match and the shots. These results provide evidence that, at least in the particular domain of billiard sport, a signature of expertise is hidden in very basic aspects of oculomotor behavior, and that expertise can be detected at the individual level both with ad-hoc testing conditions and under naturalistic conditions - and suitable data mining. Our procedure paves the way for the development of a test for the “expert’s eye”, and promotes the use of eye movements as an additional signal source in Brain-Computer-Interface (BCI) systems.https://bop.unibe.ch/JEMR/article/view/2376eye movementsexpertisebilliardsmind readingmachine learningfeature fusion |
| spellingShingle | Giuseppe Boccignone Mario Ferraro Sofia Crespi Carlo Robino Claudio de’Sperati Detecting expert’s eye using a multiple-kernel Relevance Vector Machine Journal of Eye Movement Research eye movements expertise billiards mind reading machine learning feature fusion |
| title | Detecting expert’s eye using a multiple-kernel Relevance Vector Machine |
| title_full | Detecting expert’s eye using a multiple-kernel Relevance Vector Machine |
| title_fullStr | Detecting expert’s eye using a multiple-kernel Relevance Vector Machine |
| title_full_unstemmed | Detecting expert’s eye using a multiple-kernel Relevance Vector Machine |
| title_short | Detecting expert’s eye using a multiple-kernel Relevance Vector Machine |
| title_sort | detecting expert s eye using a multiple kernel relevance vector machine |
| topic | eye movements expertise billiards mind reading machine learning feature fusion |
| url | https://bop.unibe.ch/JEMR/article/view/2376 |
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