Speaker-Specific Cues Influence Semantic Disambiguation
Bibliographic record
Abstract
Addressees use information from specific speakers' previous discourse to make predictions about incoming linguistic material and to restrict the choice of potential interpretations. In this way, speaker specificity has been shown to be an influential factor in language processing across several domains e.g., spoken word recognition, sentence processing, and pragmatics. However, its influence on semantic disambiguation has received little attention to date. Using an exposure-test design and visual world eye tracking, we examined the effect of speaker-specific literal vs. nonliteral style on the disambiguation of metaphorical polysemes such as 'fork', 'head', and 'mouse'. Eye movement data revealed that when interpreting polysemous words with a literal and a nonliteral meaning, addressees showed a late-stage preference for the literal meaning in response to a nonliteral speaker. We interpret this as reflecting an indeterminacy in the intended meaning in this condition, as well as the influence of meaning dominance cues at later stages of processing. Response data revealed that addressees then ultimately resolved to the literal target in 90% of trials. These results suggest that addressees consider a range of senses in the earlier stages of processing, and that speaker style is a contextual determinant in semantic processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".