The Role of Vocal Affect in Persuasion: The CIVA Model
Bibliographic record
Abstract
Abstract Past research has largely focused on how emotional expressions provide information about the speaker’s emotional state, but has generally neglected vocal affect’s influence over communication effectiveness. This is surprising given that other nonverbal behaviors often influence communication between individuals. In the present theory paper, we develop a novel perspective called the Contextual Influences of Vocal Affect (CIVA) model to predict and explain the psychological processes by which vocal affect may influence communication through three broad categories of process: emotion origin/construal, changing emotions, and communication source inferences. We describe research that explores potential moderators (e.g., affective/cognitive message types, message intensity), and mechanisms (e.g., emotional assimilation, attributions, surprise) shaping the effects of vocally expressed emotions on communication. We discuss when and why emotions expressed through the voice can influence the effectiveness of communication. CIVA advances theoretical and applied psychology by providing a clear theoretical account of vocal affect’s diverse impacts on communication.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".