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Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye-Tracking and Machine Learning
Jean-Pierre Thibaut1, Yannick Glady1, Robert M French1
1University of Bourgogne, Dijon, France.
Cognitive Science
|November 18, 2022
Summary
Adults use common search strategies for analogical reasoning but adapt them based on problem complexity. Eye-tracking reveals how search patterns, including distractors, change with task difficulty.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Artificial Intelligence
Background:
- Analogical reasoning is crucial for complex problem-solving.
- Understanding the cognitive processes involved in analogical reasoning is an ongoing challenge.
- Previous models suggest a systematic search of semantic space.
Purpose of the Study:
- To investigate the time course of information integration during analogical reasoning using eye-tracking.
- To determine if search strategies vary across different analogy types and complexities.
- To examine how task difficulty influences search adaptations.
Main Methods:
- Eye-tracking was used to monitor participants' visual search patterns during analogical reasoning tasks.
- Participants completed various formats and complexities of analogies.
- Machine learning, specifically Support Vector Machines (SVMs), analyzed eye-tracking data to identify predictive search transitions.
Main Results:
- Common search patterns were observed, with adaptations in looking times and saccades based on problem specifics.
- Participants generally focused on source-domain relations to generalize to the target domain.
- Search included both related and unrelated distractors, influenced by trial difficulty, with SVMs identifying discriminating transition types.
Conclusions:
- Analogical reasoning involves adaptable search strategies within semantic space.
- Task difficulty and analogy type modulate visual search patterns.
- Eye-tracking and machine learning provide insights into the dynamic processes of analogical problem-solving.
