Even affective changes induced by the global health crisis are insufficient to perturb the hyper-stability of visual long-term memory
Bibliographic record
Abstract
Past studies of emotion and mood on memory have mostly focused on the learning of emotional material in the laboratory or on the consequences of a punctate catastrophic event. However, the influence of a long-lasting global condition on memory and learning has not been studied. The COVID-19 pandemic unfortunately offered a unique situation to observe the effects of prolonged, negative events on human memory for visual information. One thousand online subjects were asked to remember the details of real-world photographs of objects to enable fine-grained visual discriminations from novel within-category foils. Visual memory performance was invariant across time, regardless of the infection rate in the local or national population, or the subjects' self-reported affective state using the Positive and Negative Affect Schedule (PANAS). Thus, visual memory provides the human brain with storage that is particularly resilient to changes in emotional state, even when those changes are experienced for months longer than any imaginable laboratory procedure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".