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Recognition memory models and binary-response ROCs: a comparison by minimum description length.

David Kellen¹, Karl Christoph Klauer, Arndt Bröder

¹Institut für Psychologie, Albert-Ludwigs-Universität Freiburg, Germany. david.kellen@psychologie.uni-freiburg.de

Psychonomic Bulletin & Review

|March 19, 2013

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This meta-analysis of recognition memory data favors a discrete-state model, suggesting a combination of detection and guessing states. This finding aids in understanding memory processes through receiver operating characteristic (ROC) analysis.

Area of Science:

Cognitive Psychology
Psychometric Modeling

Background:

Receiver Operating Characteristic (ROC) data is crucial for model comparison in recognition memory research.
Previous meta-analyses have provided insights but require expansion with more data and models.

Purpose of the Study:

To conduct an updated meta-analysis of binary-response ROC data.
To incorporate a broader range of candidate models and modern model selection techniques.
To evaluate model performance using the normalized maximum likelihood (NML) index.

Main Methods:

Meta-analysis of existing binary-response ROC datasets.
Application of model selection methods based on the minimum description length framework.
Utilizing the normalized maximum likelihood (NML) index to assess model fit and complexity.

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Main Results:

The study analyzed a comprehensive dataset of ROC data, including more data than previous meta-analyses.
A wide array of candidate models were evaluated using advanced statistical techniques.
NML analysis indicated a preference for a specific type of cognitive model.

Conclusions:

The findings suggest that a discrete-state model, incorporating both detection and guessing states, best explains the analyzed ROC data.
This research refines our understanding of recognition memory processes.
The normalized maximum likelihood (NML) approach offers a robust method for model comparison in this domain.