Differentiation and response bias in episodic memory: Evidence from reaction time distributions.

Abstract

In differentiation models, the processes of encoding and retrieval produce an increase in the distribution of memory strength for targets and a decrease in the distribution of memory strength for foils as the amount of encoding increases. This produces an increase in the hit rate and decrease in the false-alarm rate for a strongly encoded compared with a weakly encoded list, consistent with empirical data. Other models assume that the foil distribution is unaffected by encoding manipulations or the foil distribution increases as a function of target strength. They account for the empirical data by adopting a stricter criterion for strongly encoded lists relative to weakly encoded lists. The differentiation and criterion shift explanations have been difficult to discriminate with accuracy measures alone. In this article, reaction time distributions and accuracy measures are collected in a list-strength paradigm and in a response bias paradigm in which the proportion of test items that are targets is manipulated. Diffusion model analyses showed that encoding strength is primarily accounted for by changes in the rate of accumulation of evidence (i.e., drift rate) for both targets and foils and manipulating the proportion of targets is primarily accounted for by changes in response bias (i.e., starting point). The diffusion model analyses is interpreted in terms of predictions of the differentiation models in which subjective memory strength is mapped directly onto drift rate and criterion placement is mapped onto starting point. Criterion shift models require at least 2 types of shifts to account for these findings.

The strength-based mirror effect in subjective strength ratings: The evidence for differentiation can be produced without differentiation

“…A general strategy for supporting the differentiation account over the decision-process account has been to find a measure of memory strength that is not affected by decision biases, and this property has been claimed for both subjective strength ratings (Criss, 2009) and diffusion model drift rates (Criss, 2010). By demonstrating significant strength effects following mixed study lists, our findings suggest that both of these measures can be influenced by decision processes (the present experiments; Starns et al, in press).…”

“…Models in this class share a property called differentiation, whereby strengthening a memory trace makes it less confusable with the traces of other items (McClelland & Chappell, 1998;Shiffrin & Steyvers, 1997; see also . This account has been most thoroughly explored using the retrieving effectively from memory model (REM; Criss, 2006Criss, , 2009Criss, , 2010. In REM, items are represented by vectors of feature values (with length w).…”

Output interference in recognition memory

“…If recall also plays a role, interference is due to competition between traces: the chances of sampling and retrieving the desired memory trace are higher if there are fewer competing similar traces (either due to item similarity, context similarity, or both). stored about an item decreases the similarity of non-target traces (i.e., differentiation, see Criss, 2006Criss, , 2009Criss, , 2010.…”

Distinctiveness Benefits Novelty (and Not Familiarity), but Only Up to a Limit: The Prior Knowledge Perspective

“…This difference could be the result of different levels of differentiation for familiar and novel items. According to differentiation models, strengthening the representations of separate items (e.g., by repeating them) increases their distinguishability, and thus their retrievability (as evaluated behaviorally, computationally, and neuronally; Criss, 2010;Criss, Wheeler, & McClelland, 2013;Shiffrin, Ratcliff, & Clark, 1990). Sufficiently high preexperimental differentiation between the familiar items could explain their insensitivity to the list-based distinctiveness manipulation.…”