A computational model of item-based directed forgetting.
Bibliographic record
Abstract
Montagliani and Hockley (2019) presented evidence that item-method directed forgetting not only leads to worse recognition of forget-cued targets than remember-cued targets but also better rejection of foils associated with forget-cued targets than remember-cued targets. Based on that result, they proposed that participants elaboratively encode more category-level information about R-cued targets. We present a retrieval-based explanation of the result within an instance-based memory model. The model imports word representations from two distributional semantic models, latent semantic analysis (LSA) and random permutation model (RPM), into an instance-based model of memory, MINERVA 2. The model reproduced Montagliani and Hockley's results without requiring assumptions about elaborated encoding of category-level information at study. The simulations demonstrate that whereas Montagliani and Hockley's findings are consistent with an account grounded in elaborated encoding of words at study, the results do not force that conclusion. Instead, better encoding of remember-cued targets at study establishes the conditions for retrieval-time effects at test to produce a corresponding influence on false recognition for category-related foils. Our model can be used as a formal tool to think about and study the incidental consequences of item directed forgetting in recognition memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".