Influence of social and semantic contexts on phonetic encoding in naturalistic conversations
Bibliographic record
Abstract
ABSTRACT Social interactions occupy a significant part of life, and understanding others’ conversations is key to navigating our social world. While the role of semantics in speech comprehension is well-established at the word or sentence level, its influence on larger conversational scales, alongside social context, is less understood. The present study examined how semantic and social contexts modulate phonetic encoding during natural conversations using a speech-in-noise paradigm. Participants listened to AI-generated dialogues (two speakers) or monologues (one speaker) in an intact or sentence-scrambled order. Each trial contained five sentences, with the fifth sentence embedded in multi-talker babble noise. The same sentence was then repeated without noise, with one word either altered or unchanged. Healthy adults identified whether the sentence matched the in-noise version. Through several online experiments (N=211), both social and semantic contexts showed influences on speech-in-noise processing, with improved performance for dialogues over monologues and for intact over sentence-scrambled conversations. These results suggest that both semantic and social factors shape speech comprehension, emphasizing their role in auditory cognition. This finding raises important questions about predictive and other mechanisms involved in processing complex, multi-sentence conversations, underscoring the critical role of social interaction in 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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".