Intentional forgetting reduces color-naming interference: Evidence from item-method directed forgetting.
Bibliographic record
Abstract
In an item-method-directed forgetting task, Chinese words were presented individually, each followed by an instruction to remember or forget. Colored probe items were presented following each memory instruction requiring a speeded color-naming response. Half of the probe items were novel and unrelated to the preceding study item, whereas the remaining half of the probe items were a repetition of the preceding study item. Repeated probe items were either identical to the preceding study item (E1, E2), a phonetic reproduction of the preceding study item (E3), or perceptually matched to the preceding study item (E4). Color-naming interference was calculated by subtracting color-naming reaction times made in response to a string of meaningless symbols from that of the novel and repeated conditions. Across all experiments, participants recalled more to-be-remembered (TBR) than to-be-forgotten (TBF) study words. More importantly, Experiments 1 and 2 found that color-naming interference was reduced for repeated TBF words relative to repeated TBR words. Experiments 3 and 4 further found that this effect occurred at the perceptual rather than semantic level. These findings suggest that participants may bias processing resources away from the perceptual representation of to-be-forgotten information.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".