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The relative psychometric function: A general analysis framework for relating psychological processes
Brian Maniscalco1, Olenka Graham Castaneda1, Brian Odegaard2
1Department of Cognitive Sciences, University of California, Irvine.
Psychological Review
|June 15, 2026
Summary
A new method, relative psychometric function (RPF) analysis, quantifies relationships between psychological processes. This approach reveals how factors like dot density alter the link between task performance and subjective confidence.
Area of Science:
- Cognitive Psychology
- Psychophysics
- Neuroscience
Background:
- Psychophysics traditionally links objective stimuli to subjective perception.
- Existing methods struggle to quantify relationships *among* psychological processes.
- Understanding performance-subjective experience links is crucial for cognitive science.
Purpose of the Study:
- To introduce a novel psychophysical method, relative psychometric function (RPF) analysis.
- To quantitatively characterize nonlinear relationships between psychological processes.
- To investigate how these relationships vary with experimental conditions.
Main Methods:
- Developed relative psychometric function (RPF) analysis.
- Applied RPF analysis to human psychophysical data (random dot kinematograms).
- Manipulated motion coherence, dot density, and collected confidence ratings.
Main Results:
- RPF analysis revealed systematic changes in performance-subjective measure relationships.
- Dot density and task design significantly altered links between performance and confidence/metacognitive sensitivity.
- Demonstrated the flexibility of RPF analysis across various psychological measures.
Conclusions:
- RPF analysis offers a powerful tool for understanding dynamic psychological process interactions.
- This method can uncover how experimental manipulations influence the interplay between performance and subjective experience.
- An open-source RPF toolbox is provided to facilitate community adoption.
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