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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".