| Title |
GazeParser: an open-source and multiplatform library for low-cost eye tracking and analysis
|
|---|---|
| Published in |
Behavior Research Methods, December 2012
|
| DOI | 10.3758/s13428-012-0286-x |
| Pubmed ID | |
| Authors |
Hiroyuki Sogo |
| Abstract |
Eye movement analysis is an effective method for research on visual perception and cognition. However, recordings of eye movements present practical difficulties related to the cost of the recording devices and the programming of device controls for use in experiments. GazeParser is an open-source library for low-cost eye tracking and data analysis; it consists of a video-based eyetracker and libraries for data recording and analysis. The libraries are written in Python and can be used in conjunction with PsychoPy and VisionEgg experimental control libraries. Three eye movement experiments are reported on performance tests of GazeParser. These showed that the means and standard deviations for errors in sampling intervals were less than 1 ms. Spatial accuracy ranged from 0.7° to 1.2°, depending on participant. In gap/overlap tasks and antisaccade tasks, the latency and amplitude of the saccades detected by GazeParser agreed with those detected by a commercial eyetracker. These results showed that the GazeParser demonstrates adequate performance for use in psychological experiments. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 2% |
| United States | 2 | 2% |
| Italy | 1 | <1% |
| Japan | 1 | <1% |
| Portugal | 1 | <1% |
| Unknown | 101 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 21% |
| Researcher | 20 | 19% |
| Student > Master | 15 | 14% |
| Professor > Associate Professor | 9 | 8% |
| Professor | 5 | 5% |
| Other | 19 | 18% |
| Unknown | 17 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 27 | 25% |
| Computer Science | 18 | 17% |
| Engineering | 18 | 17% |
| Medicine and Dentistry | 7 | 6% |
| Agricultural and Biological Sciences | 4 | 4% |
| Other | 13 | 12% |
| Unknown | 21 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 September 2016.
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#16,046,765
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Outputs from Behavior Research Methods
#1,468
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Outputs of similar age
#178,565
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Outputs of similar age from Behavior Research Methods
#8
of 22 outputs
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