Effects of Vocal Emotion on Memory in Younger and Older Adults
Bibliographic record
Abstract
BACKGROUND/STUDY CONTEXT: Emotional content can enhance memory for visual stimuli, and older adults often perform better if stimuli portray positive emotion. Vocal emotion can enhance the accuracy of word repetition in noise when vocal prosody portrays attention-capturing emotions such as fear and pleasant surprise. In the present study, the authors examined the effect of vocal emotion on the accuracy of repetition and recall in younger and older adults when words are presented in quiet or in a background of competing babble. METHODS: Younger and older adults (Mage = 20 and 72 years, respectively) participated. Lists of 100 items (carrier phrase plus target word) were presented in recall sets of increasing size. Word repetition accuracy was tested after each item and recall after each trial in each set size. In Experiment 1, one list spoken in a neutral voice and another with emotion (fear, pleasant surprise, sad, neutral) were presented in quiet (n = 24 per group). In Experiment 2, participants (n = 12 per group) were presented the emotional list in noise. RESULTS: In quiet, word repetition accuracy was near perfect for both groups and did not vary systematically with set size for the list spoken in a neutral voice; however, for the emotional list, repetition was less accurate, especially for the older group. Recall in quiet was higher for younger than older adults; collapsed over groups, recall was higher for the neutral than for the emotional list and it decreased with increasing set size. In noise, emotion-specific effects emerged; word repetition for the older group and word recall for both groups (more for younger than older) was best for fear or pleasant surprise and worst for sad. CONCLUSION: In quiet, vocal emotion reduced the word repetition accuracy of the older group and recall accuracy for both groups. In noise, there were emotion-specific effects on the repetition accuracy of older adults and the recall accuracy of both groups. Both groups, but especially the younger group, performed better for items portraying fear or pleasant surprise and worse for items portraying sadness or neutral emotion. The emotion-specific effects on word repetition cascade to recall, especially in older listeners.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".