Word frequency and cognitive effort in turns-at-talk: turn structure affects processing load in natural conversation
Bibliographic record
Abstract
Frequency distributions are known to widely affect psycholinguistic processes. The effects of word frequency in turns-at-talk, the nucleus of social action in conversation, have, by contrast, been largely neglected. This study probes into this gap by applying corpus-linguistic methods on the conversational component of the British National Corpus (BNC) and the Freiburg Multimodal Interaction Corpus (FreMIC). The latter includes continuous pupil size measures of participants of the recorded conversations, allowing for a systematic investigation of patterns in the contained speech and language on the one hand and their relation to concurrent processing costs they may incur in speakers and recipients on the other hand. We test a first hypothesis in this vein, analyzing whether word frequency distributions within turns-at-talk are correlated with interlocutors' processing effort during the production and reception of these turns. Turns are found to generally show a regular distribution pattern of word frequency, with highly frequent words in turn-initial positions, mid-range frequency words in turn-medial positions, and low-frequency words in turn-final positions. Speakers' pupil size is found to tend to increase during the course of a turn at talk, reaching a climax toward the turn end. Notably, the observed decrease in word frequency within turns is inversely correlated with the observed increase in pupil size in speakers, but not in recipients, with steeper decreases in word frequency going along with steeper increases in pupil size in speakers. We discuss the implications of these findings for theories of speech processing, turn structure, and information packaging. Crucially, we propose that the intensification of processing effort in speakers during a turn at talk is owed to an informational climax, which entails a progression from high-frequency, low-information words through intermediate levels to low-frequency, high-information words. At least in English conversation, interlocutors seem to make use of this pattern as one way to achieve efficiency in conversational interaction, creating a regularly recurring distribution of processing load across speaking turns, which aids smooth turn transitions, content prediction, and effective information transfer.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".