Semantic similarity dissociates shortfrom long-term recency effects: testing a neurocomputational model of list memory

The finding that recency effects can occur not only in immediate free recall (i.e., short-term recency) but also in the continuous-distractor task (i.e., long-term recency) has led many theorists to reject the distinction between short- and long-term memory stores. Recently, we have argued that long-term recency effects do not undermine the concept of a short-term store, and we have presented a neurocomputational model that accounts for both short- and long-term recency and for a series of dissociations between these two effects. Here, we present a new dissociation between short- and long-term recency based on semantic similarity, which is predicted by our model. This dissociation is due to the mutual support between associated items in the short-term store, which takes place in immediate free recall and delayed free recall but not in continuous-distractor free recall

Spontaneous Gender Categorization in Masking and Priming Studies: Key for Distinguishing Jane from John Doe but Not Madonna from Sinatra

Facial recognition is key to social interaction, however with unfamiliar faces only generic information, in the form of facial stereotypes such as gender and age is available. Therefore is generic information more prominent in unfamiliar versus familiar face processing? In order to address the question we tapped into two relatively disparate stages of face processing. At the early stages of encoding, we employed perceptual masking to reveal that only perception of unfamiliar face targets is affected by the gender of the facial masks. At the semantic end; using a priming paradigm, we found that while to-be-ignored unfamiliar faces prime lexical decisions to gender congruent stereotypic words, familiar faces do not. Our findings indicate that gender is a more salient dimension in unfamiliar relative to familiar face processing, both in early perceptual stages as well as later semantic stages of person construal

Why has research in face recognition progressed so slowly? The importance of variability

Despite many years of research, there has been surprisingly little progress in our understanding of how faces are identified. Here I argue that there are two contributory factors: (a) Our methods have obscured a critical aspect of the problem, within-person variability; and (b) research has tended to conflate familiar and unfamiliar face processing. Examples of procedures for studying variability are given, and a case is made for studying real faces, of the type people recognize every day. I argue that face recognition (specifically identification) may only be understood by adopting new techniques that acknowledge statistical patterns in the visual environment. As a consequence, some of our current methods will need to be abandoned

Short-term memory after all: comment on Sederberg, Howard, and Kahana (2008)

P. B. Sederberg, M. W. Howard, and M. J. Kahana (see record 2008-14936-014) have proposed an updated version of the temporal-context model (TCM-A). In doing so, they accepted the challenge of developing a single-store model to account for the dissociations between short- and long-term recency effects that were reviewed by E. J. Davelaar, Y. Goshen-Gottstein, A. Ashkenazi, H. J. Haarmann, and M. Usher (2005). In this commentary, the authors argue that the success of TCM-A in addressing the dissociations is dependent not only on an episodic encoding matrix but--critically--also on its implicit use of a short-term memory store--albeit exponential rather than buffer-like. The authors also highlight some difficulties of TCM-A in accounting for these dissociations, and they argue that TCM-A fails to account for critical data--the presentation-rate effect--that dissociates exponential and buffer-like models

Postscript: through TCM, STM shines bright

We find the reply by Kahana, Sederberg, and Howard (see record 2008-14936-014) helpful in clarifying the temporal-context model (TCM) function, in particular with regard to the elimination of the recency effect by a difficult distractor under parameters that still enable long-term contiguity effects to emerge. We agree with Kahana et al. that what matters most to the understanding of memory is the testing of models against actual data, while attempting to maintain the criterion of parsimony. We welcome, therefore, the challenge offered by this exchange, which has produced quite a number of novel predictions (see below). Still, we are not convinced that TCM has been successful in offering a satisfactory account for memory dissociations between long- and short-term recency, that it is able to flexibly discriminate and recall items from different lists, or that it is more parsimonious than is our dual-store model. Our arguments have implications for the wider debate about short-term memory (STM) and long-term memory (LTM)

The demise of short-term memory revisited: empirical and computational investigations of recency effects

In the single-store model of memory, the enhanced recall for the last items in a free-recall task (i.e., the recency effect) is understood to reflect a general property of memory rather than a separate short-term store. This interpretation is supported by the finding of a long-term recency effect under conditions that eliminate the contribution from the short-term store. In this article, evidence is reviewed showing that recency effects in the short and long terms have different properties, and it is suggested that 2 memory components are needed to account for the recency effects: an episodic contextual system with changing context and an activation-based short-term memory buffer that drives the encoding of item-context associations. A neurocomputational model based on these 2 components is shown to account for previously observed dissociations and to make novel predictions, which are confirmed in a set of experiments