Title |
Predicting Cognitive State from Eye Movements
|
---|---|
Published in |
PLOS ONE, May 2013
|
DOI | 10.1371/journal.pone.0064937 |
Pubmed ID | |
Authors |
John M. Henderson, Svetlana V. Shinkareva, Jing Wang, Steven G. Luke, Jenn Olejarczyk |
Abstract |
In human vision, acuity and color sensitivity are greatest at the center of fixation and fall off rapidly as visual eccentricity increases. Humans exploit the high resolution of central vision by actively moving their eyes three to four times each second. Here we demonstrate that it is possible to classify the task that a person is engaged in from their eye movements using multivariate pattern classification. The results have important theoretical implications for computational and neural models of eye movement control. They also have important practical implications for using passively recorded eye movements to infer the cognitive state of a viewer, information that can be used as input for intelligent human-computer interfaces and related applications. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 12% |
United States | 4 | 12% |
Italy | 2 | 6% |
India | 1 | 3% |
Germany | 1 | 3% |
South Africa | 1 | 3% |
Netherlands | 1 | 3% |
Ireland | 1 | 3% |
Norway | 1 | 3% |
Other | 1 | 3% |
Unknown | 17 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 25 | 74% |
Scientists | 5 | 15% |
Practitioners (doctors, other healthcare professionals) | 4 | 12% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 3% |
France | 3 | 1% |
United Kingdom | 2 | <1% |
Austria | 1 | <1% |
Hungary | 1 | <1% |
China | 1 | <1% |
Belgium | 1 | <1% |
Japan | 1 | <1% |
Luxembourg | 1 | <1% |
Other | 0 | 0% |
Unknown | 201 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 61 | 28% |
Researcher | 31 | 14% |
Student > Master | 26 | 12% |
Student > Doctoral Student | 13 | 6% |
Professor | 12 | 5% |
Other | 46 | 21% |
Unknown | 30 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 67 | 31% |
Computer Science | 35 | 16% |
Neuroscience | 19 | 9% |
Agricultural and Biological Sciences | 12 | 5% |
Engineering | 12 | 5% |
Other | 33 | 15% |
Unknown | 41 | 19% |