Age-Related Differences in the Online Processing of Spoken Semantic Context and the Effect of Semantic Competition: Evidence From Eye Gaze
Bibliographic record
Abstract
Purpose The study examined age-related differences in the use of semantic context and in the effect of semantic competition in spoken sentence processing. We used offline (response latency) and online (eye gaze) measures, using the "visual world" eye-tracking paradigm. Method Thirty younger and 30 older adults heard sentences related to one of four images presented on a computer monitor. They were asked to touch the image corresponding to the final word of the sentence (target word). Three conditions were used: a nonpredictive sentence, a predictive sentence suggesting one of the four images on the screen (semantic context), and a predictive sentence suggesting two possible images (semantic competition). Results Online eye gaze data showed no age-related differences with nonpredictive sentences, but revealed slowed processing for older adults when context was presented. With the addition of semantic competition to context, older adults were slower to look at the target word after it had been heard. In contrast, offline latency analysis did not show age-related differences in the effects of context and competition. As expected, older adults were generally slower to touch the image than younger adults. Conclusions Traditional offline measures were not able to reveal the complex effect of aging on spoken semantic context processing. Online eye gaze measures suggest that older adults were slower than younger adults to predict an indicated object based on semantic context. Semantic competition affected online processing for older adults more than for younger adults, with no accompanying age-related differences in latency. This supports an early age-related inhibition deficit, interfering with processing, and not necessarily with response execution.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".