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Updated: Oct 31, 2025
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
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Predicting patterns of similarity among abstract semantic relations
Nicholas Ichien1, Hongjing Lu1, Keith J Holyoak1
1Department of Psychology.
Summary
Computational models using distributional semantics can predict human judgments of relational similarity. Cognitive capacity influences the ability to discern abstract semantic relations, showing these models capture key aspects of meaning.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Psycholinguistics
Background:
- Distributional semantic models effectively predict similarity judgments for individual concepts.
- Extending these models to capture relational similarity between concepts remains a challenge.
Purpose of the Study:
- To investigate whether distributional semantics can predict human judgments of similarity among abstract semantic relations.
- To explore the role of cognitive capacity in discriminating between different types of relations.
Main Methods:
- Combined an individual-differences approach with computational modeling.
- Utilized distributional semantics to represent word meanings.
- Developed a model to learn explicit representations of semantic relations.
Main Results:
- Cognitive capacity was a significant predictor of individual differences in discriminating relations.
- A computational model using distributional semantics best predicted human judgments of relational similarity.
- Both group-level and individual judgments aligned with the model's predictions.
Conclusions:
- Abstract semantic relations are not explicitly encoded in individual word meanings.
- Distributional semantics provides a foundation for deriving aspects of relational similarity.
- Computational models can capture human understanding of abstract semantic relations.
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