Abstract
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.
| Original language | English |
|---|---|
| Article number | 3 |
| Journal | Journal of Eye Movement Research |
| Volume | 7 |
| Issue number | 2 |
| Publication status | Published - 2014 |
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Keywords
- Billiards
- Expertise
- Eye movements
- Feature fusion
- Machine learning
- Mind reading
- Relevance vector machine
ASJC Scopus subject areas
- Ophthalmology
- Sensory Systems
Cite this
Detecting expert's eye using a multiple-kernel Relevance Vector Machine. / Boccignone, Giuseppe; Ferraro, Mario; Crespi, Sofia; Robino, Carlo; De'Sperati, Claudio.
In: Journal of Eye Movement Research, Vol. 7, No. 2, 3, 2014.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Detecting expert's eye using a multiple-kernel Relevance Vector Machine
AU - Boccignone, Giuseppe
AU - Ferraro, Mario
AU - Crespi, Sofia
AU - Robino, Carlo
AU - De'Sperati, Claudio
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Billiards
KW - Expertise
KW - Eye movements
KW - Feature fusion
KW - Machine learning
KW - Mind reading
KW - Relevance vector machine
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M3 - Article
AN - SCOPUS:84901201535
VL - 7
JO - Journal of Eye Movement Research
JF - Journal of Eye Movement Research
SN - 1995-8692
IS - 2
M1 - 3
ER -