Raw data and analysis code from Avoidance-motivational intensity modulated the effect of negative emotion on working memory
Bibliographic record
Abstract
Although many studies have explored the association between negative emotion and working memory, the findings remain controversial. The present study investigated the role of avoidance-motivational intensity in modulating the effect of negative emotion on different processes (maintenance versus manipulation) of verbal and spatial working memory. Two experiments employed the modified delayed match-to-sample paradigms to separate the two processes of verbal and spatial working memory under different emotional states, respectively. In Experiment 1, participants were asked to perform the delayed match-to-sample task with or without reordering the characters (manipulation process of verbal working memory). In Experiment 2, mental rotation was used as the manipulation process of spatial working memory. The results showed that negative emotion only affected the manipulation process, but not the maintenance process. Relative to neutral and low avoidance-motivated negative conditions, the manipulation processes of both types of working memory were impaired under the high avoidance-motivated negative condition. No significant difference was observed between low avoidance-motivated negative condition and neutral condition. Our results are discussed in relation to efficiency processing theory and motivational dimensional model of affect. We conclude that negative emotional states with high avoidance-motivational intensity impair the manipulation process of verbal and spatial working memory.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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".