Interim tests of previously studied information can potentiate subsequent learning of new information, in part, because retrieval-based processes help to reduce proactive interference from previously learned information. We hypothesized that an effect similar to this forward testing effect would also occur when making judgments of (prior) learning (JOLs). Previous research showed that making JOLs likely prompts covert retrieval attempts and thereby enhances memory, specifically when providing only parts of previously studied information. This study examined the forward effect of different types of JOLs (i.e., with complete or partial prior study information available) on subsequent learning of new materials, compared to restudy and retrieval practice. In a between-subjects design, participants (N = 161) consecutively studied five lists of 20 words with the aim to recall as many of them on a final cumulative recall test. After the presentation of each of the first four lists, participants either restudied the list, made JOLs with complete words, made JOLs with word stems, or they were tested on word stems. Compared to restudy, practicing retrieval and making JOLs with word stems, but not JOLs with complete words, facilitated the List-5 interim recall performance and attenuated the number of intrusions from prior lists. The findings suggest that, similar to overt retrieval, making JOLs with incomplete information can enhance new learning to the extent that it elicits covert retrieval attempts.

Indicators of affect dynamics (IADs) capture temporal dependencies and instability in affective trajectories over time. However, the relevance of IADs for the prediction of time?invariant outcomes (e.g., depressive symptoms) was recently challenged due to results suggesting low predictive utility beyond intraindividual means and variances. We argue that these results may in part be explained by mathematical redundancies between IADs and static variability. In two extensive simulation studies we investigate the accuracy and power for detecting non-null relations between IADs and an outcome variable in different relevant settings, illustrating the effect of the length of a time series as well as of the presence of missing values or measurement error. We show that, if uncertainty in individual IAD estimates is not accounted for, relations between IADs (i.e., autoregressive effects) and a time-invariant outcome are underestimated even in large samples and propose the use of a latent multilevel one-step approach. In an empirical application we illustrate that the different modeling approaches can lead to different substantive conclusions regarding the role of negative affect inertia in the prediction of depressive symptoms.

We investigated immediate learning benefits of retrieval tasks when followed by a self-regulated relearning opportunity, compared to a restudy task. We aimed to unravel the underlying metacognitive and motivational mechanisms. In a mixed-factorial design ( N = 104), we manipulated review task (retrieval vs. restudy) as a within-subjects factor in two experimental sessions, and task granularity (coarse- vs. fine-grained) as a between-subjects factor. The retrieval task led to an immediate learning benefit compared to a restudy task in Session 1, but not across sessions. The review-task order influenced the emergence of an immediate retrieval-practice effect. Mediation analyses for Session 1 revealed that decreased levels of both judgments of learning (JOL) and self-efficacy partially mediated the retrieval-task effect on the learning outcome. Fine-grained tasks increased JOL regulation accuracy; however, this increase did not translate into better relearning. Retrieval tasks can improve learning outcomes, also after short delays, specifically when relearning opportunities are provided.