Analysis of physiological measures around conversational state changes
Bibliographic record
Abstract
The goal of this work is to extend the use of physiological measures of listening effort to interactive conversation. The initial work here investigates variations in head movement, eye gaze, and pupil dilation around conversational state changes during task-based conversations. Here, conversational state changes are defined as the points in time at which speakers start and stop talking. Windows around each of these types of state changes are analyzed for systematic differences of these parameters, which could be indicative of changes in attention and/or differences in speech production versus perception. Additionally, we calculate state change response functions, derived from a multivariate regression that maps from the state changes to the measured parameters and extracted features. The predictive power of these functions is explored, alongside comparisons of various considerations in their derivations. Our findings, based on data collected from 12 sets of interactive conversations taking place in varying levels of noise and simulated hearing loss, offer insight into how physiological responses during complex interactions can be measured and interpreted to infer when and where effort is directed throughout conversation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".