The Production Effect Becomes Spatial
Bibliographic record
Abstract
In the verbal domain, it is well established that words read aloud are better remembered than their silently read counterparts. It has been hypothesized that this production effect stems from the addition of distinctive features, with the caveat that the processing that generates added features interferes with rehearsal. Here, we tested the idea that a similar trade-off is found in the visuospatial domain. In all experiments, a short series of single dots sequentially appeared at various locations on a screen. Participants produced the items by clicking on them at presentation, watched the items appear quietly, or produced an irrelevant click after each item to better even out rehearsal opportunities between produced and control conditions. In Experiment 1, the dots appeared within a visible grid and an order reconstruction task was used. Experiment 2 also called upon reconstruction, but with the grid removed. In Experiments 3, a recall task was used. The results show that producing items hindered performance compared to the control condition. Conversely, production improved performance compared to the control condition where rehearsal was hindered. This is the first demonstration of a visuospatial production effect. The key findings were successfully modeled by the Revised Feature Model (RFM).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".