THE IMPACT OF NOISE AND WORKING MEMORY ON ONLINE PROCESSING OF SPOKEN WORDS: EYETRACKING EVIDENCE
Bibliographic record
Abstract
Among the complaints of older adults is a difficulty in speech recognition, especially in noisy backgrounds. This difficulty can interfere with maintenance of health and quality of life and can potentially affect the rate of cognitive decline. A central research question in speech recognition in older adults is the extent to which difficulties stem from bottom-up, sensory declines that degrade the speech input, and to what extent they stem from an age-related reduction in working memory. We used eye-tracking as an on-line measure of spoken word recognition. Listeners hear spoken instructions that relate to an object presented in the visual display, while their eye movements are recorded. For example, hearing “touch the candle,” with four objects displayed: candle, candy, dog and bicycle. As the speech signal unfolds, several alternatives are activated in response to phonemic information, i.e., CAND leads to candy and candle. In order to successfully achieve word identification, one has to inhibit phonological alternatives. Using eye-tracking, we tracked, in real-time, as the listener shifts his or her focus between candle and candy. We manipulated working memory load by using the digit pre-load task, where participants have to retain either one (low-load) or four (high-load) spoken digits for the duration of a spoken word recognition trial. We will present three separate studies. The data show that both noise and working memory can delay speech processing. With younger adults, data suggest that the two effects may interact. Preliminary data with older adults will be discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".