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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
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Modeling perceptual confidence and the confidence forced-choice paradigm
Pascal Mamassian1, Vincent de Gardelle2
1Laboratoire des Systemes Perceptifs, Departement d'Etudes Cognitives, Ecole Normale Superieure, PSL University, CNRS.
Psychological Review
|July 29, 2021
Summary
This study introduces a generative model for perceptual confidence, explaining how confidence judgments arise from evidence. The model distinguishes between confidence noise and boost, aiding in understanding decision-making processes.
Area of Science:
- Cognitive Psychology
- Computational Neuroscience
- Decision Science
Background:
- Perceptual confidence reflects the certainty of perceptual decisions.
- Understanding confidence generation is crucial for modeling decision-making.
Purpose of the Study:
- To present a generative model of perceptual confidence.
- To investigate the roles of confidence noise and confidence boost.
- To address confidence parameter indeterminacy and introduce new confidence measures.
Main Methods:
- Developed a generative model with parameters for confidence noise and boost.
- Estimated confidence parameters across multiple stimulus strengths.
- Applied the model to the confidence forced-choice paradigm.
Main Results:
- Demonstrated recovery of confidence parameters, distinguishing primary and secondary information sources.
- Introduced a novel measure of confidence efficiency.
- Estimated a type of confidence bias.
Conclusions:
- The generative model successfully explains confidence judgments.
- The model allows for the disentanglement of confidence parameters.
- The confidence forced-choice paradigm facilitates objective confidence estimation.
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