Influence of social and semantic contexts on phonetic encoding in naturalistic conversations
Bibliographic record
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 time 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".