Perceptual blurring and recognition memory: A desirable difficulty effect revealed
Bibliographic record
Abstract
Recent research in the area of desirable difficulty—defined as processing difficulty at either encoding or retrieval that improves long-term retention—has demonstrated that perceptually blurring an item makes processing less fluent, but does not improve remembering (Yue et al., 2013). This result led us to examine more closely perceptual blurring as a potential desirable difficulty. In Experiment 1, better recognition of blurry than clear words was observed, a result that contrasts with those reported by Yue et al. This result was replicated in Experiment 2, in which both mixed-list and pure-list designs were used. The following experiments were conducted to determine when blurring does and does not result in enhanced remembering. The desirable difficulty effect observed in Experiments 1 and 2 was replicated in Experiments 3A, 3B, and 3C, despite varying encoding intent during study, context reinstatement at the time of test, study list length, and the nature of the distractor task between study and test phases. It was only in Experiments 4A and 4B that a null effect of perceptual blurring on remembering was found. These experiments demonstrated that (1) the level of blurring used is critical, with a lower blurring level producing results similar to Yue et al. (2013), and (2) the introduction of judgments of learning at the time of study eliminated the benefit of blurring on remembering. These results extend the desirable difficulty principle to encoding manipulations involving perceptual blurring, and identify judgments of learning at encoding as a powerful moderator of this particular desirable difficulty effect.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".