Does variability in recognition memory scale with mean memory strength or encoding variability in the UVSD model?

Abstract

The unequal variance signal detection (UVSD) model of recognition memory assumes that the variance of memory strength for studied items (σo) is typically greater than that of non-studied items. It has been proposed that this old item variance effect is caused by many factors that affect memory strength at encoding, which add variable amounts of strength to a baseline amount. However, Spanton and Berry (2020) failed to find evidence for this encoding variability hypothesis and instead found that estimates of σo tended to be strongly positively correlated with estimates of mean memory strength (d) across participants, suggesting that σo may simply scale with d. The present experiments examined the effects of encoding variability and scaling on old item variance by creating conditions in which mean memory strength and encoding variability was either low or high in 2 × 2 factorial designs. In Experiment 1, overall strength determined estimates of σo, with no effect of encoding variability. The same effect of overall strength was found in Experiment 2; there was also a significant effect of encoding variability, although this manipulation also affected overall memory strength and was therefore partially confounded. We conclude that there is strong evidence that mean memory strength at least partially determines estimates of old item variance, and that satisfactory explanations of the old item variance effect should account for this.

Disentangling Different Aspects of Between-Item Similarity Unveils Evidence Against the Ensemble Model of Lineup Memory

“…More specifically, if the assumptions regarding variances and/or covariances (as specified in Proposition 2) are relaxed, the model can no longer be decisively ruled out based on the occurrence of the critical effect alone. Moreover, an unequal-variance assumption, in particular, is rather prominent in Gaussian SDT models of recognition memory (Jang et al, 2009;Mickes et al, 2007;Wixted, 2007;Starns et al, 2012;Spanton & Berry, 2021;but see ; and should therefore be considered in the present work. Hence, it seems worthwhile to complement our argument with an additional quantitative model comparison between the unequal-variance Gaussian IOM and the unequal-variance Gaussian EM in order to ensure that our criticism of the EM framework is indeed warranted.…”