Dissociating Frontotemporal Contributions to Semantic Ambiguity Resolution in Spoken Sentences
Bibliographic record
Abstract
Comprehension of sentences containing semantically ambiguous words requires listeners to select appropriate interpretations, maintain linguistic material in working memory, and to reinterpret sentences that have been misinterpreted. All these functions appear to involve frontal cortical regions. Here, we attempt to differentiate these functions by varying the relative timing of an ambiguous word and disambiguating information in spoken sentences. We compare the location, magnitude, and timing of evoked activity using a fast-acquisition semisparse functional magnetic resonance imaging sequence. The left inferior frontal gyrus (LIFG) shows a strong response to sentences that are initially ambiguous (disambiguated by information that occurs either soon after the ambiguity or that is delayed until the end of the sentence). Response profiles indicate that activity, in both anterior and posterior LIFG regions, is triggered both by the ambiguous word and by the subsequent disambiguating information. The LIFG also responds to ambiguities that are preceded by disambiguating context. These results suggest that the LIFG subserves multiple cognitive processes including selecting an appropriate meaning and reinterpreting sentences that have been misparsed. In contrast, the left inferior temporal gyrus responds to the disambiguating information but not to the ambiguous word itself and may be involved in reprocessing sentences that were initially misinterpreted.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".