Recognition memory zROC slopes for items with correct versus incorrect source decisions discriminate the dual process and unequal variance signal detection models.

Abstract

We tested the dual process and unequal variance signal detection models by jointly modeling recognition and source confidence ratings. The 2 approaches make unique predictions for the slope of the recognition memory zROC function for items with correct versus incorrect source decisions. The standard bivariate Gaussian version of the unequal variance model predicts little or no slope difference between the source-correct and source-incorrect functions. We also developed a "bounded" version of this model that did not permit below-chance source discrimination in any region of the evidence space. The bounded version predicts that the source-correct function should have a lower slope than the source-incorrect function. A bivariate version of the dual process signal detection model can predict slope differences in either direction, but it must predict a u-shaped source zROC function if the source-correct slope is lower than the source-incorrect slope. Across 4 experiments, results consistently showed that the recognition memory zROC function had a lower slope for items attributed to the correct source than items attributed to the incorrect source, and the source zROC function for words recognized with high confidence was linear. Only the bounded version of the unequal variance model successfully predicted the full pattern of results.

Behavioural pattern separation is strongly associated with familiarity-based decisions

“…Furthermore, an ROC curve was generated by plotting confidence ratings given to the targets against false alarm rate for the lures (when participants gave old responses to lures). Finally, we transformed response proportions to z-scores as well to analyse whether zROCs are linear (see Macmillan & Creelman, 2005;Starns, Rotello, & Hautus, 2014; for an overview, see also Heathcote, 2003). ROC and zROC curves were fitted to data of all participants (i.e., we cumulated response frequencies for all subjects within each stimulus type) and to individual data as well.…”

“…Plotting z-transformed hit rate against z-transformed false alarm rate across multiple levels of bias is another way to analyse confidence ratings (see e.g., Macmillan & Creelman, 2005;Starns et al, 2014). The dual-process model predicts a linear zROC when only familiarity contributes to performance and a curvilinear zROC when recognition memory decisions are accompanied by recollection, such as for correct source memory judgments (Yonelinas, 1999).…”

A Dynamic Approach to Recognition Memory

“…The process dissociation procedure also relies on the strong assumption that the two processes are stochastically independent of one another; when the assumption is violated, estimates obtained from this procedure are uninterpretable (Curran & Hintzman, 1995;Hillstrom & Logan, 1997). Indeed, it has long been established that there is a correlation between the probability of correct recall and that of correct recognition (Tulving & Wiseman, 1975;Kahana, Rizzuto, & Schneider, 2005), strongly arguing against the process dissociation assumption of independence (item and source memory performance are also correlated; Starns, Rotello, & Hautus, 2014). Finally, even if the assumption of the process dissociation procedure are satisfied, it is insufficient for identifying the relevant processing components (Humphreys, Dennis, Chalmers, & Finnigan, 2000).…”

Item strength influences source confidence and alters source memory zROC slopes.

“…Indeed, if one considers only the memory processes assumed by each model, then a Gaussian model predicts linear zROCs, whereas the DPSD model predicts a shape. However, the converging criteria response bias produces -shaped functions even with Gaussian distributions of memory evidence (Starns et al, 2014). Thus, the shape cannot be unambiguously interpreted in terms of memory processes.…”