Visual scanning patterns of a talking face when evaluating phonetic information in a native and non-native language
Bibliographic record
Abstract
When comprehending speech, listeners can use information encoded in visual cues from a face to enhance auditory speech comprehension. For example, prior work has shown that the mouth movements reflect articulatory features of speech segments and durational information, while pitch and speech amplitude are primarily cued by eyebrow and head movements. Little is known about how the visual perception of segmental and prosodic speech information is influenced by linguistic experience. Using eye-tracking, we studied how perceivers' visual scanning of different regions on a talking face predicts accuracy in a task targeting both segmental versus prosodic information, and also asked how this was influenced by language familiarity. Twenty-four native English perceivers heard two audio sentences in either English or Mandarin (an unfamiliar, non-native language), which sometimes differed in segmental or prosodic information (or both). Perceivers then saw a silent video of a talking face, and judged whether that video matched either the first or second audio sentence (or whether both sentences were the same). First, increased looking to the mouth predicted correct responses only for non-native language trials. Second, the start of a successful search for speech information in the mouth area was significantly delayed in non-native versus native trials, but just when there were only prosodic differences in the auditory sentences, and not when there were segmental differences. Third, (in correct trials) the saccade amplitude in native language trials was significantly greater than in non-native trials, indicating more intensely focused fixations in the latter. Taken together, these results suggest that mouth-looking was generally more evident when processing a non-native versus native language in all analyses, but fascinatingly, when measuring perceivers' latency to fixate the mouth, this language effect was largest in trials where only prosodic information was useful for the task.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".