| Title |
iTemplate: A template-based eye movement data analysis approach
|
|---|---|
| Published in |
Behavior Research Methods, February 2018
|
| DOI | 10.3758/s13428-018-1015-x |
| Pubmed ID | |
| Authors |
Naiqi G. Xiao, Kang Lee |
| Abstract |
Current eye movement data analysis methods rely on defining areas of interest (AOIs). Due to the fact that AOIs are created and modified manually, variances in their size, shape, and location are unavoidable. These variances affect not only the consistency of the AOI definitions, but also the validity of the eye movement analyses based on the AOIs. To reduce the variances in AOI creation and modification and achieve a procedure to process eye movement data with high precision and efficiency, we propose a template-based eye movement data analysis method. Using a linear transformation algorithm, this method registers the eye movement data from each individual stimulus to a template. Thus, users only need to create one set of AOIs for the template in order to analyze eye movement data, rather than creating a unique set of AOIs for all individual stimuli. This change greatly reduces the error caused by the variance from manually created AOIs and boosts the efficiency of the data analysis. Furthermore, this method can help researchers prepare eye movement data for some advanced analysis approaches, such as iMap. We have developed software (iTemplate) with a graphic user interface to make this analysis method available to researchers. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Guinea | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Members of the public | 1 | 33% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 22% |
| Researcher | 4 | 17% |
| Student > Postgraduate | 4 | 17% |
| Professor > Associate Professor | 2 | 9% |
| Professor | 1 | 4% |
| Other | 2 | 9% |
| Unknown | 5 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 7 | 30% |
| Engineering | 2 | 9% |
| Social Sciences | 2 | 9% |
| Economics, Econometrics and Finance | 1 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 9 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 08 December 2018.
All research outputs
#4,150,416
of 25,382,440 outputs
Outputs from Behavior Research Methods
#499
of 2,526 outputs
Outputs of similar age
#85,787
of 447,797 outputs
Outputs of similar age from Behavior Research Methods
#9
of 31 outputs
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We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.