[Evaluation algorithm for eye movement patterns during a problem solving task]

Area of Science:

• Cognitive Science
• Neuroscience
• Psychology

Context:

• Cognitive processes are intrinsically linked to eye movements, specifically fixations on visual points.
• Analyzing sequences of fixations is crucial for understanding cognitive functions.
• Identifying patterns in eye movements can reveal underlying thought processes.

Purpose:

• To present a novel method for the automatic recognition of fixation point patterns in eye movement data.
• To demonstrate the method's utility in analyzing cognitive processes during problem-solving tasks.

Summary:

• The study introduces a time series analysis variant for nominal data to detect recurring fixation point patterns.
• This method analyzes eye movement data, specifically fixations, to infer cognitive connections and discoveries.
• The technique was validated using eye-tracking data from subjects solving Raven's Test items.

Impact:

• Provides a tool for objective analysis of cognitive processes through eye movement patterns.
• Enhances understanding of the interplay between visual attention and cognitive task performance.
• Applicable to various non-numerical data analysis scenarios in cognitive research.