Survival processing effect in memory under semantic divided attention.
Bibliographic record
Abstract
Past research demonstrated enhanced memory for information encoded with relevance to a survival scenario compared to a control scenario, an effect referred to as the survival processing effect in memory. This effect has been explained by a proximate mechanism hypothesis (i.e., survival processing enables deep elaborative processing that promotes memory). In support of this hypothesis, past research found that, during encoding, the survival processing effect was largely intact under a perceptual or low-load secondary task condition but eliminated under a high-load secondary task condition. To test semantic encoding as a possible proximate mechanism, the current study assesses the impact of high-load and low-load divided attention tasks that require semantic processing of digits on the survival processing effect. Seventy-two young adults rated words for their relevance to two survival scenarios (i.e., grassland and mountain) and one non-survival control scenario (i.e., cruise), while completing a concurrent high-load or low-load semantic digit-monitoring task. No survival processing effect was found in either condition. The results suggest that semantic encoding probably serves as a proximate mechanism for the survival processing effect in memory. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".