We tallied the votes: No survival advantage in visual long-term memory
Bibliographic record
Abstract
It has long been known that depth of processing at encoding predicts later memory performance. One well-established encoding manipulation in the long-term memory (LTM) literature is survival processing, where LTM is significantly enhanced for objects that have been rated for relevance in survival scenarios compared to rating items for pleasantness. In LTM, this survival advantage has been found with object words as stimuli and surprise free recall tests; would a similar survival advantage be found for visual objects in visual long-term memory (VLTM)? To answer this question, participants rated coloured real-world object images in one of three conditions: softness, pleasantness, or relevance in a specified survival context. They then completed a surprise colour recall test, where they were shown greyscale versions of the object images and indicated each object’s previous colour on a colour wheel. Mixture modelling was used to analyze the responses. Unlike the findings from LTM tasks, no survival advantage was found; objects rated for relevance in the survival scenario did not demonstrate greater resolution (i.e., precision, indicated by smaller standard deviation values of responses from the correct object colour) in comparison to the pleasantness or softness conditions. These results suggest that the survival advantage cannot be extended from its current status in the memory literature to that of VLTM. While encoding objects into LTM in a survival scenario context enhances retention, encoding objects this way into VLTM does not enhance the resolution nor the availability of the memory representation.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".