Proceedings Article—ICMPC 2018: Acetaminophen Blunts Emotional Responses to Music
Bibliographic record
Abstract
Warrenburg, L. A., & Way, B. (2018). Acetaminophen blunts emotional responses to music. In Proceedings of the 15th International Conference for Music Perception and Cognition (pp. 483-488). Montréal, Canada. The capacity of listeners to perceive or experience emotions in response to music depends on many factors including dispositional traits, empathy, and musical enculturation. Emotional responses are also known to be mediated by pharmacological factors, including both legal and illegal drugs. Existing research has established that acetaminophen, a common over-the-counter pain medication, blunts emotional responses (e.g., Durso, Luttrell, & Way, 2015). The current study extends this research by examining possible effects of acetaminophen on both perceived and felt responses to emotionally-charged sound stimuli. Additionally, it tests whether acetaminophen effects are specific for particular emotions (e.g. sadness, fear) or whether acetaminophen blunts emotional responses in general. The experiment employs a randomized, double-blind, parallel-group, placebo-controlled design. Participants are randomly assigned to ingest acetaminophen or a placebo. Then, they are asked to complete two experimental blocks regarding musical and non-musical sounds. The first block asks participants to judge the extent to which a sound conveys a certain affect (on a Likert scale). The second block aims to examine a listener’s emotional responses to sound stimuli. The study is currently in progress; here, preliminary results are reported for 19 participants of a planned 200 cohort. In light of the fact that some 50 million Americans take acetaminophen each week, if the final results prove consistent with existing research on the emotional blunting of acetaminophen, this suggests that future studies in music and emotion might consider controlling for the pharmacological state of participants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.005 |
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".