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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Visual Data Mining: An Exploratory Approach to Analyzing Temporal Patterns of Eye Movements.

Chen Yu¹, Daniel Yurovsky¹, Tian Linger Xu¹

¹Psychological and Brain Sciences and Cognitive Science Program Indiana University.

Infancy : the Official Journal of the International Society on Infant Studies

|July 23, 2020

Summary

Scientists can better understand infant development by using interactive data visualization to analyze complex eye movement data. This human-in-the-loop approach combines visual exploration with existing knowledge for efficient discovery.

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Area of Science:

Developmental Psychology
Cognitive Science
Human-Computer Interaction

Background:

Infant eye movements are crucial for understanding early development and learning.
Analyzing dense gaze data from eye-tracking systems presents significant challenges.

Purpose of the Study:

To present an interactive approach for making sense of complex infant gaze data.
To integrate domain knowledge with information visualization and visual data mining.

Main Methods:

Leveraging the human visual system's computational power.
Employing a human-in-the-loop knowledge discovery process.
Iteratively examining data through visualization to identify patterns and link them with hypotheses.

Main Results:

Demonstrating an effective and efficient method for discovering patterns in gaze data.
Illustrating how interactive exploration leads to new findings in infant behavior.

Conclusions:

Combining bottom-up visualization with top-down theoretical knowledge via visual data mining facilitates discovery.
This interactive, human-centered approach enhances the analysis of complex behavioral data.