Judgments of Learning Reactivity on Item-Specific and Relational Processing
Bibliographic record
Abstract
Judgments of learning (JOLs) reactivity refers to the finding that the mere solicitation of JOLs modifies subsequent memory performance. One theoretical explanation is the item-specific processing hypothesis, which posits that item-level JOLs redound to the benefit of later memory performance because they enhance item-specific processing. The current study was designed to test this account. We factorially manipulated the organization (blocked vs. randomized) of categorized lists and JOL condition (item-JOLs, list-JOLs, no-JOLs) between participants, and fit the dual-retrieval model to free recall data to pinpoint the underlying memory processes that were affected by JOL solicitation. Our results showed that item-level JOLs produced positive reactivity for randomized but not for blocked categorized lists. Moreover, we found that the positive JOL reactivity for randomized categorized lists was tied to a familiarity judgment process that is associated with gist processing, rather than to item-specific recollective processes. Thus, our results pose a challenge to the item-specific processing explanation of JOL reactivity. We argue that JOL reactivity is not restricted to item-specific processing; instead, whether JOLs predominantly engage participants with item-specific or relational processing depends on the interaction between learning stimuli and JOLs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".