The multiple encoding benefit: contributions from the number of encoding opportunities amplifies benefits from the length of encoding duration in visual long-term memory
Bibliographic record
Abstract
Despite the virtually unlimited capacity of visual long-term memory (VLTM) (e.g., Brady et al., 2008), not all visual information that we wish to remember gets encoded into VLTM. One robust way to enhance VLTM encoding is to encode the visual information over multiple opportunities; known as the multiple encoding benefit (MEB). However, it is unclear whether it is the number of encoding opportunities or the total encoding duration that underlies the MEB, because as the number of encoding opportunities increases, so does the encoding duration. Thus, we dissociated the contributions of the number of encoding opportunities and the encoding duration and measured their impacts on objective memory recall precision as well as subjective memory recall confidence. Specifically, we had participants encode a series of 360 pictures of real-world objects presented in a solid colour drawn from a 360° colour wheel (Brady et al., 2013). During the serial presentations, baseline pictures were presented once for 500ms, while some were presented once for 1000ms, and other pictures were presented twice for 500ms each with variable lags between the two presentations. Here we found that while elongating encoding duration benefited both objective and subjective memory recall performance, there was also a unique benefit of additional encoding opportunities on both measures of memory performance. Importantly, the magnitude of this additional benefit amplified as a function of the lag between the two encoding opportunities. Therefore, the MEB is not just due to the increase in total encoding duration but is driven by the increase in the 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".