Models that allow us to perceive the world more accurately also allow us to remember past events more accurately via differentiation

Abstract

Differentiation is a theory that originally emerged from the perception literature and proposes that with experience, the representation of stimuli becomes more distinct from or less similar to the representation of other stimuli. In recent years, the role of differentiation has played a critical role in models of memory. Differentiation mechanisms have been implemented in episodic memory models by assuming that information about new experiences with a stimulus in a particular context accumulates in a single memory trace and these updated memory traces become more distinct from the representations of other stimuli. A key implication of such models is that well encoded events are less confusable with other events. This prediction is particularly relevant for two important phenomena. One is the role of encoding strength on memory. The strength based mirror effect is the finding of higher hit rates and lower false alarm rates for a list composed of all strongly encoded items compared to a list composed of all weakly encoded items. The other is output interference, the finding that accuracy decreases across a series of test trials. Results from four experiments show a tight coupling between these two empirical phenomena such that strongly encoded target items are less prone to interference. By proposing a process model and evaluating the predictions of the model, we show how a single theoretical principle, differentiation, provides a unified explanation for these effects.

Spaced Learning Enhances Episodic Memory by Increasing Neural Pattern Similarity Across Repetitions

“…These findings could also well account for the differential effect of encoding variability on memory performance. According to the differentiation model of memory (Kılıç et al, 2017), repeated exposure to an item results in the storage of additional information in the single memory trace established during the first exposure. As the memory trace is updated, its similarity to other items decreases and becomes more distinguishable during retrieval.…”

Robust Modeling in Cognitive Science

“…Some practices for prediction tests are well established, like cross-validation and accumulative prediction error (Shiffrin et al, 2008;Wagenmakers, Grünwald, & Steyvers, 2006). There are fewer examples of generalization tests in cognitive modeling (but see Criss, Malmberg, & Shiffrin, 2011, Guan, Lee, & Vandekerckhove, 2015, and Kılıç, Criss, Malmberg, & Shiffrin, 2017, for some recent examples). The "Serial Position Effects in Free Recall" casestudy box provides one concrete example.…”

Information and Processes Underlying Semantic and Episodic Memory Across Tasks, Items, and Individuals

“…Strength of semantic features must also play a role at retrieval, in that it is associated with discriminability in single-item recognition (i.e., not just recognition of studied words, but the ability to reject an unstudied but semantically distinctive word). Feature distinctiveness and the resulting differentiation between event memories is a core component of likelihoodbased models of episodic memory (McClelland & Chappell, 1998;Shiffrin & Steyvers, 1997) and has been found to be critical for explaining a variety of memory phenomena across a variety of stimuli (not just words; Cox & Shiffrin, 2017;Kılıç, Criss, Malmberg, & Shiffrin, 2017;Nosofsky & Zaki, 2003;Steyvers & Malmberg, 2003).…”