The representational consequences of intentional forgetting: Impairments to both the probability and fidelity of long-term memory.
Bibliographic record
Abstract
We investigated whether intentional forgetting impacts only the likelihood of later retrieval from long-term memory or whether it also impacts the fidelity of those representations that are successfully retrieved. We accomplished this by combining an item-method directed forgetting task with a testing procedure and modeling approach inspired by the delayed-estimation paradigm used in the study of visual short-term memory (STM). Abstract or concrete colored images were each followed by a remember (R) or forget (F) instruction and sometimes by a visual probe requiring a speeded detection response (E1-E3). Memory was tested using an old-new (E1-E2) or remember-know-no (E3) recognition task followed by a continuous color judgment task (E2-E3); a final experiment included only the color judgment task (E4). Replicating the existing literature, more "old" or "remember" responses were made to R than F items and RTs to postinstruction visual probes were longer following F than R instructions. Color judgments were more accurate for successfully recognized or recollected R than F items (E2-E3); a mixture model confirmed a decrease to both the probability of retrieving the F items as well as the fidelity of the representation of those F items that were retrieved (E4). We conclude that intentional forgetting is an effortful process that not only reduces the likelihood of successfully encoding an item for later retrieval, but also produces an impoverished memory trace even when those items are retrieved; these findings draw a parallel between the control of memory representations within working and long-term memory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".