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# Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye-Tracking and Machine Learning Approach

## Details

**Authors** Thibaut et al.

**Year** 2022

**Publisher** Cognitive Science

**Discipline** Cognitive Science

[Read it at the publisher](https://doi.org/10.1111/cogs.13208) 
10.1111/cogs.13208

## In authors' words

### Abstract

Starting with the hypothesis that analogical reasoning consists of a search of semantic space, we used eye‐tracking to study the time course of information integration in adults in various formats of analogies. The two main questions we asked were whether adults would follow the same search strategies for different types of analogical problems and levels of complexity and how they would adapt their search to the difficulty of the task. We compared these results to predictions from the literature. Machine learning techniques, in particular support vector machines (SVMs), processed the data to find out which sets of transitions best predicted the output of a trial (error or correct) or the type of analogy (simple or complex). Results revealed common search patterns, but with local adaptations to the specifics of each type of problem, both in terms of looking‐time durations and the number and types of saccades. In general, participants organized their search around source‐domain relations that they generalized to the target domain. However, somewhat surprisingly, over the course of the entire trial, their search included, not only semantically related distractors, but also unrelated distractors, depending on the difficulty of the trial. An SVM analysis revealed which types of transitions are able to discriminate between analogy tasks. We discuss these results in light of existing models of analogical reasoning.

### What they set out to do (purpose)

To trace the time course of information integration during analogical reasoning across different analogy formats and complexity levels, and test it against predictions from existing computational models.

### What they found (results)

Eye-tracking across two experiments, with support vector machines classifying gaze-transition patterns, found a common search strategy across analogy formats and difficulty levels, locally adapted to each problem type in both fixation durations and the number and type of saccades; transitions involving distractors best separated correct from error trials.

## Commentary

### In short

Analogy extracts a relation from a source pair (action) and projects that relational structure onto a target pair (reaction) as a directed mapping.

**Patterns it shows** R

**Added** 2026-08-01

**How to cite this** Thibaut et al. (2022). Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye-Tracking and Machine Learning Approach. Cognitive Science.