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iMAP and iMAP2 produce erroneous statistical maps of eye-movement differences
1Research Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, Gower Street, London WC1E 6BT, UK. i.mcmanus@ucl.ac.uk
The iMAP and iMAP2 programs may produce erroneous results for eye-movement analysis. A modified statistical method suggests previous research on fixation patterns might be flawed.
Area of Science:
- Cognitive Psychology
- Behavioral Research Methods
- Data Analysis
Background:
- The iMAP and iMAP2 programs offer a method for analyzing eye-movement data, including heat maps and significance tests.
- These tests aim to account for spatial autocorrelation in fixation locations for group comparisons.
Purpose of the Study:
- To critically evaluate the statistical significance tests implemented in the iMAP and iMAP2 programs.
- To identify potential errors in the algorithms used for analyzing eye-movement data and group differences.
Main Methods:
- The study re-evaluated the significance tests in iMAP/iMAP2 using a conventional randomization test.
- A modified statistical technique, incorporating a t-test, error functions, and spatial autocorrelation, was developed and applied.
- Simulated data with and without true differences were analyzed using both iMAP/iMAP2 and the modified method.
Main Results:
- The iMAP/iMAP2 significance tests produced erroneous results, claiming significant differences even with random group allocation.
- The modified statistical technique found no significant differences between example participant groups.
- Simulations showed iMAP/iMAP2 consistently found significant differences, while the modified method only did for larger sample sizes.
Conclusions:
- The statistical algorithms in iMAP/iMAP2 are inappropriate, leading to potentially erroneous conclusions in eye-movement research.
- Previous studies relying on iMAP/iMAP2 for group comparisons of fixation patterns may need re-evaluation.
- A modified statistical approach offers a more reliable method for analyzing eye-movement data with spatial autocorrelation.
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