Asymmetric Weights and Retrieval Practice in an Autoassociative Neural Network Model of Paired-Associate Learning

Rizzuto and Kahana (2001) applied an autoassociative Hopfield network to a paired-associate word learning experiment in which (1) participants studied word pairs (e.g., ABSENCE-HOLLOW), (2) were tested in one direction (ABSENCE-?) on a first test, and (3) were tested in the same direction again or in the reverse direction (?-HOLLOW) on a second test. The model contained a correlation parameter to capture the dependence between forward versus backward learning between the two words of a word pair, revealing correlation values close to 1.0 for all participants, consistent with neural network models that use the same weight for communication in both directions between nodes. We addressed several limitations of the model simulations and proposed two new models incorporating retrieval practice learning (e.g., the effect of the first test on the second) that fit the accuracy data more effectively, revealing substantially lower correlation values (average of .45 across participants, with zero correlation for some participants). In addition, we analyzed recall latencies, finding that second test recall was faster in the same direction after a correct first test. Only a model with stochastic retrieval practice learning predicted this effect. In conclusion, recall accuracy and recall latency suggest asymmetric learning, particularly in light of retrieval practice effects.

Optimizing Design Efficiency of Free Recall Events for fMRI

Free recall is a fundamental paradigm for studying memory retrieval in the context of minimal cue support. Accordingly, free recall has been extensively studied using behavioral methods. However, the neural mechanisms that support free recall have not been fully investigated due to technical challenges associated with probing individual recall events with neuroimaging methods. Of particular concern is the extent to which the uncontrolled latencies associated with recall events can confer sufficient design efficiency to permit neural activation for individual conditions to be distinguished. The present study sought to rigorously assess the feasibility of testing individual free recall events with fMRI. We used both theoretically and empirically derived free recall latency distributions to generate simulated fMRI data sets and assessed design efficiency across a range of parameters that describe free recall performance and fMRI designs. In addition, two fMRI experiments empirically assessed whether differential neural activation in visual cortex at onsets determined by true free recall performance across different conditions can be resolved. Collectively, these results specify the design and performance parameters that can provide comparable efficiency between free recall designs and more traditional jittered event-related designs. These findings suggest that assessing BOLD response during free recall using fMRI is feasible, under certain conditions, and can serve as a powerful tool in understanding the neural bases of memory search and overt retrieval.

Digit Span in Individuals With Down Syndrome and in Typically Developing Children

This study explored factors influencing digit span performance in individuals with Down syndrome. The following questions were asked: Is there a deficit in the phonological loop, either in articulatory rehearsal (measured in speaking rate and recall latency) or in the passive store (measured in recall duration)? Is reduced auditory short-term memory associated with a language production deficit? Thirty five adolescents with trisomy 21 Down syndrome were compared to 35 mental-age-matched and 35 language-production-matched controls. There was no group difference in speaking rate. The DS group had shorter digit spans than the MA controls. Language production level accounted for substantial variance in digit span in individuals with Down syndrome.

Finding Choice Alternatives in Memory: Probability Models of Brand Name Recall

Recalling brand names is an important aspect of consumer choice in many situations. The authors develop a general Markov model that relates probabilistic aspects of recall to consumer and marketing mix variables. Then they illustrate how parameters can be estimated from recall data for three special cases of the model: The first is a “baseline” version of the model that can be used to assess the extent to which categories of brands can be automatically included in or excluded from memory search; the second is a zero-order model that can be used to estimate the effects of brand variables and individual usage rates on recall latency; and the third is a first-order model that uses aggregate recall data to assess market structure. Finally, the authors demonstrate how the model can be used in numerical analyses to evaluate awareness-building strategies.