Related Experiment Video
Updated: Jan 9, 2026
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
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Similarity as likelihood ratio: Coupling representations from machine learning (and other sources) with cognitive
1Department of Psychology, University at Albany, State University of New York, 1400 Washington Ave., Albany, NY, 12222, USA. gregcox7@gmail.com.
Psychonomic Bulletin & Review
|December 9, 2025
Summary
This study introduces Similarity as Likelihood Ratio (SALR), a novel method to link machine learning vector similarity with psychological similarity. SALR enhances cognitive models by better explaining human performance on complex tasks.
Area of Science:
- Cognitive Science
- Machine Learning
- Psychology
Background:
- Similarity is fundamental to memory and perception theories.
- Machine learning generates high-dimensional vector representations for complex items like text and images.
- Bridging vector similarity and psychological similarity is crucial for cognitive modeling.
Purpose of the Study:
- Introduce Similarity as Likelihood Ratio (SALR), a mathematical transformation.
- Operationalize vector similarity (cosine) into a likelihood ratio.
- Integrate machine learning representations into cognitive models.
Main Methods:
- Developed SALR, transforming cosine similarity into a likelihood ratio.
- Applied SALR to diffusion decision models.
- Used SALR to analyze human response speed and accuracy.
Main Results:
- SALR successfully computes diffusion model drift rates using machine learning similarity.
- Demonstrated SALR's ability to account for human response times and accuracy.
- Enabled inferences on individual item representation and information encoding.
Conclusions:
- SALR provides a practical method for incorporating machine learning representations into cognitive models.
- SALR offers theoretical insights into human processing of complex, naturalistic items.
- This approach enhances understanding of perception and memory through computational modeling.
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