Eye-tracking-based hidden Markov modeling for revealing within-item cognitive strategy switching.

Area of Science:

• Cognitive psychology
• Psychometrics
• Neuroscience

Background:

• Understanding cognitive strategies is key to advanced problem-solving research.
• Existing methods primarily capture between-item cognitive strategy switching (CSS), neglecting within-item dynamics.
• Dynamic identification of within-item CSS is currently lacking.

Purpose of the Study:

• To introduce a novel method for dynamically identifying and revealing within-item cognitive strategy switching (CSS).
• To quantify within-item CSS using eye-tracking data and hidden Markov modeling.
• To analyze the relationship between within-item CSS and problem-solving performance.

Main Methods:

• Introduced the concept of an 'eye movement snippet' as a basic unit for within-item CSS analysis.
• Developed a new eye-tracking process measure quantifying the sequence length of alternatives viewed.
• Applied hidden Markov modeling to eye-tracking data from a matrix reasoning test.

Main Results:

• Identified two primary strategies: constructive matching and response elimination.
• Found a high probability of switching from constructive matching to response elimination, but not vice versa.
• More difficult items and frequent strategy switching correlated with decreased planning time, increased total response time, and more incorrect answers.

Conclusions:

• The proposed eye-tracking method effectively identifies within-item cognitive strategies and their switching in matrix reasoning.
• Within-item CSS patterns varied across the test and were linked to intelligence levels and total test time.
• This approach offers a dynamic window into cognitive strategy use during complex problem-solving.