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Temporal sequences quantify the contributions of individual fixations in complex perceptual matching tasks
Thomas Busey1, Chen Yu, Dean Wyatte
1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA. busey@indiana.edu
Cognitive Science
|March 16, 2013
Summary
Machine translation techniques applied to eye-tracking data reveal expert fingerprint examiners identify more corresponding areas than novices. This helps pinpoint informative fixations in visual matching tasks.
Area of Science:
- Cognitive Psychology
- Computer Vision
- Human-Computer Interaction
Background:
- Perceptual matching tasks, like fingerprint examination, require identifying correspondences across views.
- Eye-tracking data offers insights into visual search strategies but distinguishing informative fixations is challenging.
Purpose of the Study:
- To apply machine translation techniques to eye-tracking data from a fingerprint matching task.
- To identify informative fixations relevant to perceptual comparison tasks.
Main Methods:
- Gaze data from experts and novices performing a fingerprint matching task were analyzed.
- Machine translation algorithms were used to estimate assumed corresponding regions based on temporal gaze sequences.
Main Results:
- Experts and novices exhibited similar surface-level behaviors (e.g., fixation count, duration).
- The machine translation approach identified more corresponding areas in expert data.
- Fixations linked to high-probability mappings between prints were deemed more useful.
Conclusions:
- Machine translation techniques can reveal underlying expert strategies in perceptual matching tasks.
- This method helps differentiate informative fixations from general gaze behavior.
- The approach offers a novel way to analyze eye-tracking data in complex visual comparison tasks.
