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Testing Signal-Detection Models of Yes/No and Two-Alternative Forced-Choice Recognition Memory
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BibTeX
@MISC{Jang_testingsignal-detection,
author = {Yoonhee Jang and John T. Wixted and David E. Huber},
title = {Testing Signal-Detection Models of Yes/No and Two-Alternative Forced-Choice Recognition Memory},
year = {}
}
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Abstract
The current study compared 3 models of recognition memory in their ability to generalize across yes/no and 2-alternative forced-choice (2AFC) testing. The unequal-variance signal-detection model assumes a continuous memory strength process. The dual-process signal-detection model adds a thresholdlike recollection process to a continuous familiarity process. The mixture signal-detection model assumes a continuous memory strength process, but the old item distribution consists of a mixture of 2 distributions with different means. Prior efforts comparing the ability of the models to characterize data from both test formats did not consider the role of parameter reliability, which can be critical when comparing models that differ in flexibility. Parametric bootstrap simulations revealed that parameter regressions based on separate fits of each test type only served to identify the least flexible model. However, simultaneous fits of receiver-operating characteristic data from both test types with goodness-of-fit adjusted with Akaike’s information criterion (AIC) successfully recovered the true model that generated the data. With AIC and simultaneous fits to real data, the unequal-variance signal-detection model was found to provide the best account across yes/no and 2AFC testing.
Keyphrases
two-alternative forced-choice recognition memory signal-detection model simultaneous fit continuous memory strength process test type unequal-variance signal-detection model different mean separate fit real data test format information criterion 2-alternative forced-choice continuous familiarity process receiver-operating characteristic data parameter reliability current study dual-process signal-detection model parameter regression thresholdlike recollection process flexible model parametric bootstrap simulation old item distribution recognition memory prior effort mixture signal-detection model true model
