Forgetting by any other name: The effect of instruction framing on item-method directed forgetting
Bibliographic record
Abstract
In a typical item-method directed forgetting task, study words are presented one at a time, each followed by an instruction to Remember or Forget. Subsequent recognition shows a directed forgetting effect, with better recognition of to-be-remembered words than to-be-forgotten words. This study determined whether recognition depends only on the intention to remember or forget or also on the words used to frame the trial-by-trial instructions or task. In Experiments 1 and 3, participants were instructed, trial-by-trial, to Remember and Forget, Remember and Don't Remember, Don't Forget and Forget, or were told that some words were Important and that others were Not Important. There was no compelling evidence that the directed forgetting effect was altered by the specific words used as these trial-by-trial instructions. However, in Experiment 2, a smaller directed forgetting effect occurred when the task was framed as requiring participants to Remember unless instructed otherwise, compared to when it was framed as requiring participants to Forget unless instructed otherwise. These findings emphasize the freedom that researchers have for deciding how to frame trial-by-trial instructions and the caution they must use in deciding how to frame the task itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".