Divided attention at encoding or retrieval interferes with emotionally enhanced memory for words
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it