What makes distributed practice effective?

Abstract

The advantages provided to memory by the distribution of multiple practice or study opportunities are among the most powerful effects in memory research. In this paper, we critically review the class of theories that presume contextual or encoding variability as the sole basis for the advantages of distributed practice, and recommend an alternative approach based on the idea that some study events remind learners of other study events. Encoding variability theory encounters serious challenges in two important phenomena that we review here: superadditivity and nonmonotonicity. The bottleneck in such theories lies in the assumption that mnemonic benefits arise from the increasing independence, rather than interdependence, of study opportunities. The reminding model accounts for many basic results in the literature on distributed practice, readily handles data that are problematic for encoding variability theories, including superadditivity and nonmonotonicity, and provides a unified theoretical framework for understanding the effects of repetition and the effects of associative relationships on memory.Recent years have seen a resurgence of interest in the advantages provided to memory by the distribution of multiple practice or study events. This interest owes in part to the ramifications such results have for developing effective training, educational, and athletic regimens, and to the impressive success researchers have had in importing interventions out of the laboratory and into training settings, the classroom, and the practice field. The effects of distributing practice are extremely robust and cross-cutting-the advantages are evident in basic memory tasks using words (Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006;Janiszewski, Noel, & Sawyer, 2003) and pictures (Hintzman & Rogers, 1973), in motor skill acquisition (Lee & Genovese, 1988), and with more complex educationally relevant materials (Krug, Davis, & Glover, 1990). Almost every major textbook on cognitive psychology and on human memory discusses the advantages of distributed learning, and such effects are among the keystone results that contending theoretical models of learning and memory must dutifully explain.The results from experiments on distributed practice have informed memory theory principally insofar as those theories successfully handle the difference between massed and spaced practice conditions (the spacing effect; Ebbinghaus, 1885) or between differentially spaced conditions (the lag effect; Melton, 1967), as well as how they account for differences arising from intentionality of learning during study or from different memory tests (Challis, 1993;Greene, 1989;Russo, Parkin, Taylor, & Wilks, 1998). Here we make the case that the superadditivity effect (Begg & Green, 1988;Watkins & Kerkar, 1985;Glanzer, 1969;Waugh, 1963), which concerns the relationship between memory for single events and memory for multiple events, and the nonmonotonicity effect, which concerns the shape of the lag function, are empirical

Pushing typists back on the learning curve: Memory chunking in the hierarchical control of skilled typewriting.

“…When the typing materials are repeated in consecutive trials, a memory trace from the previous trial is still in short-term memory on the current trial when the same typing material is encoded into short-term memory. With longer lags between repetitions, the memory trace in short-term memory is different from the typing material on the current trial, so chunking will not occur unless an earlier study episode is retrieved from long-term memory (Benjamin & Tullis, 2010;Ross, 1984). Concurrent memory load might have prevented such retrieval of an earlier episode.…”

“…Consistent with this idea, we found that memory chunks under a concurrent memory load were consolidated in long-term memory and utilized for later use (Experiment 3). It has been suggested that the spacing effect requires retrieval of a prior study state from long-term memory (Benjamin & Tullis, 2010;Thios & D'Agosino, 1976;Ross, 1984;Verkoeijen, Rikers, & Schmidt, 2005). This study state retrieval integrates a greater number of contextual cues into the memory trace of the study item.…”

“…This provides contiguity, which is essential for learning (e.g., Hebb learning; Hebb, 1961). It has been suggested that the spacing effect depends on retrieval of prior presentations of the study materials (i.e., study state retrieval; Benjamin & Tullis, 2010;Greene, 1989). With …”

Effects of learning experience on forgetting rates of item and associative memories

“…Because the forgetting rate is more related to the recollection process, DL leads to slower forgetting than ML. The reason for the spacing effect may be related to encoding variability (Glenberg 1979;Janiszewski et al 2003;Cepeda et al 2006) and reactivation process (Benjamin and Tullis 2010). DL6 may improve memory by increasing the probability of successful trace retrieval (Estes 1955;Glenberg 1979;Cepeda et al 2006).…”

On the importance of looking back: The role of recursive remindings in recency judgments and cued recall

“…Results similar to those reported by Asch et al have been reported by others and described as evidence of the importance of detecting repetitions along with retrieval processes for finding a facilitative effect of spacing repetitions (e.g., Appleton-Knapp, Bjork, & Wickens, 2005;Bellezza, Winkler, & Andrasik, 1975, Exp. 3;Benjamin & Tullis, 2010;Braun & Rubin, 1998, Exp. 2).…”