The effect of vocal emotion identification on word repetition and recall accuracy in noise
Bibliographic record
Abstract
Purpose: The aim of the study was to determine if memory for words spoken in noise depends on vocal emotion (fear, neutral, pleasant surprise, or sadness) to a greater extent if the listeners are engaged in an emotion identification task (current experiment) compared to only repeating the words (previous experiment). Hypotheses: It was hypothesized that there would be an effect of emotion on repetition and recall and that this effect would be greater for repetition and recall accuracy when listeners were engaged in a task that drew attention to the identification of the vocal emotion. Performance was expected to be better for words portraying arousing emotions (fear and pleasant surprise) than for words spoken with sad or neutral emotion. Methods: Participants listened to 100 sentences spoken in four different emotion conditions. All words were semantically neutral. Participants were instructed to 1) repeat the word presented, 2) identify the emotion in which it was spoken, 3) judge whether the word began with the first or second half of the alphabet, and 4) after each set, recall as many words as possible that had been heard in the set. Results: Repetition accuracy was higher for words spoken to portray fear than sadness, but pleasant surprise was the same as neutral. Similarly, recall accuracy was highest for words spoken to portray fear, and lowest for sadness. The addition of the emotion identification task did not alter performance. Emotion identification was highest for pleasant surprise and lowest for neutral.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".