Divided attention at encoding or retrieval interferes with emotionally enhanced memory for words
Bibliographic record
Abstract
Emotional information is typically better remembered than neutral information. We asked whether emotional, compared to neutral, words were less vulnerable to the detrimental effects of divided attention. In two experiments, undergraduate students intentionally encoded words of intermixed valence (neutral, negative, or positive) and arousal (neutral, high, or low). Following a filled delay, memory was assessed with a free recall test. In Experiment 1, participants encoded visually-presented words under either full attention (FA; no distracting task) or divided attention (DA; concurrently making animacy decisions to auditorily-presented distractor words) in a counterbalanced, within-subjects design. As expected following FA at encoding, recall was significantly enhanced for negative compared to neutral words. Following DA at encoding, recall was significantly impaired across all valences. Critically, DA at encoding also eliminated the memory benefit for negative information: recall of negative words was no longer significantly different from neutral or positive words. In Experiment 2, we manipulated attention at retrieval rather than encoding. Remarkably, results from Experiment 1 were replicated: DA eliminated the well-known emotionality boost for negative words. In both experiments, memory for positive words did not significantly differ from neutral. Findings suggest that DA during either encoding or retrieval can interfere with the specific mechanisms by which negative emotion typically improves 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".