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Title: Exploring signal detection models of recognition memory

Abstract: Computational models are frequently applied to behavioral data to summarize or describe the data. In the case of recognition memory, the unequal-variance Gaussian signal detection model is arguably the most popular mathematical model. While it was seen as merely mathematically convenient when first applied to recognition memory, it is now often discussed in terms of the theoretical inferences it allows about the latent processes underlying the observed behavior. In two related projects, we explore alternatives to the U-V Gaussian model, and discuss the implications for theoretical inference.

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