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Recognition memory models and binary-response ROCs: A comparison by minimum description length
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DOI：10.3758/s13423-013-0407-2.png)
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Abstract
En 中文
Model comparison in recognition memory has frequently relied on receiver operating characteristics (ROC) data. We present a meta-analysis of binary-response ROC data that builds on previous such meta-analyses and extends them in several ways. Specifically, we include more data and consider a much more comprehensive set of candidate models. Moreover, we bring to bear modern developments in model selection on the current selection problem. The new methods are based on the minimum description length framework, leading to the normalized maximum likelihood (NML) index for assessing model performance, taking into account differences between the models in flexibility due to functional form. Overall, NML results for individual ROC data indicate a preference for a discrete-state model that assumes a mixture of detection and guessing states.
Keywords:
Model selection
Minimum description length
Normalized maximum likelihood
Recognition memory
Signal detection
Discrete-state models
Hybrid models
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