Effects of Aging and Noise on Real-Time Spoken Word Recognition: Evidence From Eye Movements
Bibliographic record
Abstract
PURPOSE: To use eye tracking to investigate age differences in real-time lexical processing in quiet and in noise in light of the fact that older adults find it more difficult than younger adults to understand conversations in noisy situations. METHOD: Twenty-four younger and 24 older adults followed spoken instructions referring to depicted objects, for example, "Look at the candle." Eye movements captured listeners' ability to differentiate the target noun (candle) from a similar-sounding phonological competitor (e.g., candy or sandal). Manipulations included the presence/absence of noise, the type of phonological overlap in target-competitor pairs, and the number of syllables. RESULTS: Having controlled for age-related differences in word recognition accuracy (by tailoring noise levels), similar online processing profiles were found for younger and older adults when targets were discriminated from competitors that shared onset sounds. Age-related differences were found when target words were differentiated from rhyming competitors and were more extensive in noise. CONCLUSIONS: Real-time spoken word recognition processes appear similar for younger and older adults in most conditions; however, age-related differences may be found in the discrimination of rhyming words (especially in noise), even when there are no age differences in word recognition accuracy. These results highlight the utility of eye movement methodologies for studying speech processing across the life span.
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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 | 0.000 | 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 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".