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Although memory retrieval often enhances subsequent memory, Peterson and Mulligan (2013) reported conditions under which retrieval produces poorer subsequent recall--the negative testing effect. The item-specific--relational account proposes that the effect occurs when retrieval disrupts interitem organizational processing relative to the restudy condition. Rawson et al. (2015), in contrast, failed to replicate the negative testing effect despite repeated high-powered attempts. This article examines the discrepant results, ruling out differences in procedures, and concludes that differences in participant population produced the varying outcome. Specifically, participants from the University of North Carolina (UNC) and Kent State University (KSU) completed the same version of the negative-testing paradigm and were assessed on several measures of cognitive ability (working memory capacity, Raven's progressive matrices, and SAT or ACT score). For the UNC sample, free recall scores and the amount of category clustering (a measure of organizational processing) was greater in the restudy than retrieval condition (i.e., the negative testing effect was found); for the KSU sample, there was no difference on either measure. Furthermore, in the restudy condition, recall and clustering was greater for UNC than KSU students whereas in the retrieval condition, there was no effect of site on either measure. As expected, measures of cognitive ability were greater for the UNC than KSU sample. The results indicate that the negative testing effect is replicable but is subject to limitation related to the participant population. An analysis in terms of the relationship between cognitive ability and memory predicted this pattern of results.

Recent research on testing effects (i.e., practice tests are more effective than restudy for enhancing subsequent memory) has focused on explaining when and why testing enhances memory. Of particular interest for present purposes, Zaromb and Roediger (2010) reported evidence that testing effects in part reflect enhanced relational processing, which refers to the encoding of similarity among to-be-learned items. The "multifactor account" of testing effects (Peterson & Mulligan, 2013) further distinguishes between processing of cue-target relations ("intraitem relational processing") and processing of relations shared by targets from different items ("interitem relational processing"). The intriguing claim of this account is that testing enhances intraitem relational processing at the expense of interitem relational processing. Confirming predictions of this account, Peterson and Mulligan (2013) found negative testing effects on final free recall and on a measure of interitem relational processing (the same measures on which Zaromb and Roediger found positive testing effects). The original intent of the current research was to resolve this theoretical debate by replicating and extending the findings of Peterson and Mulligan (2013) to identify the locus of the apparent inconsistency in the outcomes reported in these 2 studies. However, 5 high-powered experiments affording 8 comparisons of testing versus restudy did not replicate the negative testing effect on final memory performance nor on most measures of interitem relational processing. Thus, the weight of the evidence supports the conclusion that testing does not impair relational processing.

The current research evaluated the extent to which the grain size of recall practice for lengthy text material affects recall during practice and subsequent memory. The "grain size hypothesis" states that a smaller vs. larger grain size will increase retrieval success during practice that in turn will enhance subsequent memory for lengthy text material. Participants were prompted to recall directly after studying each section (section recall) or after all sections had been studied (whole-text recall) during practice, and then all participants completed a final test after a delay. Results across 7 experiments (including 587 participants and 1,394 recall protocols) partially disconfirmed the predictions of the grain size hypothesis: Although the smaller grain size produced sizable recall advantages during practice as expected ("ds" from 1.02 to 1.87 across experiments), the advantage was substantially or completely attenuated across a delay. Experiments 2-7 falsified several plausible methodological and theoretical explanations for the fragility of the effect, indicating that it was not due to particular text materials, retrieval from working memory during practice, the length of the retention interval, the spacing between study and practice recall, a disproportionate increase in recall of unimportant details, or a deficit in integration of ideas across text sections. In sum, results conclusively establish an initially sizable but mysteriously fragile effect of grain size, for which an explanation remains elusive.

Research suggests exposure to misinformation continues to impact belief and reasoning, even if that misinformation has been corrected (referred to as the Continued Influence Effect, CIE). The present experiment explores two potentially important factors that may impact the effect: (1) learner age and (2) length of delay between retraction and final test. During initial learning, participants (both young and older adults) read six scenarios in which a critical piece of misinformation was either retracted or not retracted. Following no delay, a short (10 min) delay, or a long (2 days) delay, participants then answered inferential reasoning questions about the previously studied scenarios to evaluate how (if at all) the prior retraction impacts reliance on misinformation. Outcomes help us to understand the ways in which misinformation (even following retraction) impacts reasoning, an issue of exceeding importance as the proliferation of fake news shows no signs of slowing. [ABSTRACT FROM AUTHOR]

Summary: Research suggests exposure to misinformation continues to impact belief and reasoning, even if that misinformation has been corrected (referred to as the Continued Influence Effect, CIE). The proposed experiment explores two potentially important factors that may impact the effect: (a) learner age and (b) length of delay between retraction and final test. During initial learning, participants (both young and older adults) will read six scenarios in which a critical piece of misinformation is either retracted or not retracted. Following no delay, a short (10 min) delay, or a long (2 days) delay, participants will then answer inferential reasoning questions about the previously‐studied scenarios to evaluate how (if at all) the prior retraction impacts reliance on misinformation. Outcomes will better help us to understand the ways in which misinformation (even following retraction) impacts reasoning, an issue of exceeding importance as the proliferation of fake news shows no signs of slowing. [ABSTRACT FROM AUTHOR]

Students are expected to learn key-term definitions across many different grade levels and academic disciplines. Thus, investigating ways to promote understanding of key-term definitions is of critical importance for applied purposes. A recent survey showed that learners report engaging in collaborative practice testing when learning key-term definitions, with outcomes also shedding light on the way in which learners report engaging in collaborative testing in real-world contexts (Wissman & Rawson, 2016, Memory, 24, 223-239). However, no research has directly explored the effectiveness of engaging in collaborative testing under representative conditions. Accordingly, the current research evaluates the costs (with respect to efficiency) and the benefits (with respect to learning) of collaborative testing for key-term definitions under representative conditions. In three experiments ( ns = 94, 74, 95), learners individually studied key-term definitions and then completed retrieval practice, which occurred either individually or collaboratively (in dyads). Two days later, all learners completed a final individual test. Results from Experiments 1-2 showed a cost (with respect to efficiency) and no benefit (with respect to learning) of engaging in collaborative testing for key-term definitions. Experiment 3 evaluated a theoretical explanation for why collaborative benefits do not emerge under representative conditions. Collectively, outcomes indicate that collaborative testing versus individual testing is less effective and less efficient when learning key-term definitions under representative conditions. [ABSTRACT FROM AUTHOR]

Engaging in collaborative retrieval practice increases performance on subsequent memory tests taken individually (Blumen & Stern, 2011). However, the basis of these post-collaborative benefits is largely unexplained. Thus, the primary goal of the current research was to investigate the cognitive mechanisms underlying post-collaborative benefits vis-à-vis the theoretical framework of distinctiveness theory, which postulates that two processes influence memory. Relational processing refers to the encoding of similarity among a set of items, whereas item-specific processing refers to the encoding of information that differentiates items from one another. The central claim of distinctiveness theory is that memory is enhanced when differences among individual items are processed in the context of the similarity among those items (referred to as distinctive processing ). The basic design of all three experiments was similar (i.e., close replications with intent to provide multiple estimates of effect sizes on which to base conclusions): Learners were asked to study lists of exemplars from taxonomic categories, followed by recall practice that occurred either collaboratively or individually. All learners completed individual final tests, immediately after practice and/or after a delay. Of greatest theoretical interest for diagnosing the extent to which post-collaborative benefits reflect distinctive processing, we examined indicators of relational processing (clustering and category access) and item-specific processing (items recalled per category and recognition performance). Results across the three experiments established that distinctive processing (i.e., enhancements in both relational and item-specific processing) contributes to post-collaborative benefits across both short and long retention intervals. [ABSTRACT FROM AUTHOR]