Putting retrieval-induced forgetting in context: An inhibition-free, context-based account.
Bibliographic record
Abstract
We present a new theoretical account of retrieval-induced forgetting (RIF) together with new experimental evidence that fits this account and challenges the dominant inhibition account. RIF occurs when the retrieval of some material from memory produces later forgetting of related material. The inhibition account asserts that RIF is the result of an inhibition mechanism that acts during retrieval to suppress the representations of interfering competitors. This inhibition is enduring, such that the suppressed material is difficult to access on a later test and is, therefore, recalled more poorly than baseline material. Although the inhibition account is widely accepted, a growing body of research challenges its fundamental assumptions. Our alternative account of RIF instead emphasizes the role of context in remembering. According to this context account, both of 2 tenets must be met for RIF to occur: (a) A context change must occur between study and subsequent retrieval practice, and (b) the retrieval practice context must be the active context during the final test when testing practiced categories. The results of 3 experiments, which directly test the divergent predictions of the 2 accounts, support the context account but cannot be explained by the inhibition account. In an extensive discussion, we survey the literature on RIF and apply our context account to the key findings, demonstrating the explanatory power of context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".