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Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye‐Tracking and Machine Learning Approach
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 strategi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9786648/ https://www.ncbi.nlm.nih.gov/pubmed/36399055 http://dx.doi.org/10.1111/cogs.13208 |
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author | Thibaut, Jean‐Pierre Glady, Yannick French, Robert M. |
author_facet | Thibaut, Jean‐Pierre Glady, Yannick French, Robert M. |
author_sort | Thibaut, Jean‐Pierre |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9786648 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97866482022-12-27 Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye‐Tracking and Machine Learning Approach Thibaut, Jean‐Pierre Glady, Yannick French, Robert M. Cogn Sci Extended Articles 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. John Wiley and Sons Inc. 2022-11-18 2022-11 /pmc/articles/PMC9786648/ /pubmed/36399055 http://dx.doi.org/10.1111/cogs.13208 Text en © 2022 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS). https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Extended Articles Thibaut, Jean‐Pierre Glady, Yannick French, Robert M. Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye‐Tracking and Machine Learning Approach |
title | Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye‐Tracking and Machine Learning Approach |
title_full | Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye‐Tracking and Machine Learning Approach |
title_fullStr | Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye‐Tracking and Machine Learning Approach |
title_full_unstemmed | Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye‐Tracking and Machine Learning Approach |
title_short | Understanding the What and When of Analogical Reasoning Across Analogy Formats: An Eye‐Tracking and Machine Learning Approach |
title_sort | understanding the what and when of analogical reasoning across analogy formats: an eye‐tracking and machine learning approach |
topic | Extended Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9786648/ https://www.ncbi.nlm.nih.gov/pubmed/36399055 http://dx.doi.org/10.1111/cogs.13208 |
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