The multiple encoding benefit: encoding specificity does not hinder the retrieval generalizability of visual long-term memory
Bibliographic record
Abstract
A robust method for enhancing visual long-term memory (VLTM) retrieval is to encode visual information over multiple opportunities, known as the multiple encoding benefit (MEB). An aspect of the MEB that may be overlooked is the potential detriments of encoding specificity. According to the encoding specificity principle, memory performance is best when the context at encoding matches that at retrieval. In a typical MEB experiment, visual information is not only encoded repeatedly in the same context but also retrieved in the same context. This raises the possibility that the MEB is contingent upon the context match between repeated encoding and retrieval. If so, the MEB may not extend to a new retrieval context, thus limiting the generalizability of memory retrieval. To examine the impact of encoding specificity on retrieval generalizability, we had participants encode a set of real-world objects presented on one of three nature scenes which served as the encoding context. Some objects were presented three times on the same scene (consistent encoding), while others were presented three times, each on a different scene (variable encoding). For each of the encoding conditions, we tested participant’s VLTM recognition by having them retrieve items in the same encoding context or in a brand-new context (a fourth scene). When comparing both encoding styles, we found that neither was more detrimental or effective for retrieval generalization. That is, regardless of consistent or variable encoding, VLTM performance was similar when items were retrieved in a new context. Furthermore, when comparing retrieval following consistent encoding, we found that performance was similar for items retrieved in the original encoding context and those retrieved in a new context. Therefore, encoding specificity in the MEB does not hinder retrieval generalizability and further demonstrates the enduring benefit of multiple encoding opportunities.
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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 | 0.011 |
| 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".