A Multiple-Trace Memory Model Exhibiting Realistic Retrieval Dynamics

A Multiple-Trace Memory Model Exhibiting Realistic Retrieval Dynamics Collin Green (cbgreen@ucla.edu) Aniket Kittur (nkittur@ucla.edu) Department of Psychology, 1285 Franz Hall University of California, Los Angeles Los Angeles, CA 90095 retrieved. The use of these elements allows for a distinction between item accessibility and item storage, a key insight of the New Theory of Disuse (NTD) (Bjork & Bjork, 1992). The model was inspired by and provides a potential algo- rithmic basis for the NTD and the complex memory dynam- ics it explains. Abstract A process model of human memory dynamics is proposed as an implementation of Kittur, Green, & Bjork’s (2004) mathematical model. Both models are based on an ideal in- formation processing approach, in which an item’s accessibility is based on the predicted future need of that item. The proposed model is an adaptation of the multiple-trace ar- chitecture of Hintzman’s MINERVA2 model (Hintzman 1984; 1986; 1988). We present simulations of complex spacing and practice dynamics encompassing the mechanics of Bjork and Bjork’s (1992) New Theory of Disuse, which accounts for di- verse phenomena such as massed vs. spaced practice and spontaneous recovery. In addition, we show how the model explains and simulates time-dependent serial position effects (such as the shift from recency to primacy with delay and time-invariant recency effects). The model’s potential as a tool for exploring the relationship between the content of items in memory and more general memory dynamics is also discussed. The New Theory of Disuse The NTD accounts for a variety of effects in the human memory literature. The NTD includes the following as- sumptions about memory (see Bjork & Bjork, 1992): 1) Memory items are associated with two distinct “strengths”: a storage strength (SS) and a retrieval strength (RS). SS indicates how well-learned an item is (that is, the accumulated history of an item is reflected in its SS). RS, on the other hand, indicates how readily accessible an item is for retrieval. RS alone determines the probability that an item will be successfully recalled from memory. SS does not directly influence memory performance, but has impor- tant implications for memory dynamics over time 1 . 2) SS does not decrease. SS grows during study or re- trieval events as a decelerating function of the current SS. That is, all else being equal, items with low SS benefit from study or retrieval events more than items with high SS. The total storage strength across all items in memory is therefore unbounded. Changes in SS are dependent on both RS and SS. An item gains SS as a decelerating function of its cur- rent SS, and as a decelerating function of its current RS. 3) RS increases and decreases. As with SS, an item gains RS as a result of study or retrieval events. When the item is not being studied or retrieved, such as when other items are being attended, RS decreases. As a result, gains in RS for one item necessarily result in a loss of RS for the other (un- studied) items in memory, though these are not necessarily changes of the same magnitude. Changes in RS are depend- ent on both RS and SS: Gain in RS due to a retrieval or study event is a decelerating function of current RS, and an increasing function of current SS. Conversely, RS loss in is faster the larger the current RS is, and slower with larger SS. 4) Generally, retrieval events are more potent than study events. Increments in both SS and RS are larger when an item is retrieved versus when it is studied. Memory as a System for Predicting Need Kittur, Green, & Bjork (2004) described a mathematical model of memory dynamics inspired by Bayesian statistics. The model is driven by the assumption that memory ap- proximates an ideal information processor, keeping memory items accessible to the degree that they are likely to be needed in the future (see Anderson, 1989 for further ration- ale on this approach). The predicted future need for an item is computed by the model based on the pattern of past re- trievals for that item and the time since it was last retrieved. This is best illustrated by analogy. Imagine that Book A has been checked out of a library once a month for the past year. Book B, on the other hand, has been checked out every week for the last month but never prior to that. If the librarian was forced to choose which of the two books should be kept readily available, the best choice would change over time. Initially, the librarian would probably keep Book B more readily retrievable as it has been needed frequently in the recent past (possibly, an instructor has assigned reading from this book for a class project); however, after a month has passed with neither book being required, the librarian would likely decide that Book A should be more accessible, given its history of be- ing required at regular, if infrequent, intervals. The Kittur, Green, & Bjork (2004) model functions in a similar way. It calculates the probability that an item will be needed given three key pieces of information: the aver- age interval between past retrievals; the number of times the item was retrieved in the past; and the time since it was last The two-strength theory espoused by NTD and implemented in MNEM is an important difference between it and other related need-based models, such as Anderson’s ACT-R (1989). We are currently exploring testable differences between the models.