One Size Does Not Fit All: Examining the Effects of Working Memory Capacity on Spoken Word Recognition in Older Adults Using Eye Tracking
Bibliographic record
Abstract
Difficulties understanding speech form one of the most prevalent complaints among older adults. Successful speech perception depends on top-down linguistic and cognitive processes that interact with the bottom-up sensory processing of the incoming acoustic information. The relative roles of these processes in age-related difficulties in speech perception, especially when listening conditions are not ideal, are still unclear. In the current study, we asked whether older adults with a larger working memory capacity process speech more efficiently than peers with lower capacity when speech is presented in noise, with another task performed in tandem. Using the Eye-tracking of Word Identification in Noise Under Memory Increased Load (E-WINDMIL) an adapted version of the "visual world" paradigm, 36 older listeners were asked to follow spoken instructions presented in background noise, while retaining digits for later recall under low (single-digit) or high (four-digits) memory load. In critical trials, instructions (e.g., "point at the candle") directed listeners' gaze to pictures of objects whose names shared onset or offset sounds with the name of a competitor that was displayed on the screen at the same time (e.g., candy or sandal). We compared listeners with different memory capacities on the time course for spoken word recognition under the two memory loads by testing eye-fixations on a named object, relative to fixations on an object whose name shared phonology with the named object. Results indicated two trends. (1) For older adults with lower working memory capacity, increased memory load did not affect online speech processing, however, it impaired offline word recognition accuracy. (2) The reverse pattern was observed for older adults with higher working memory capacity: increased task difficulty significantly decreases online speech processing efficiency but had no effect on offline word recognition accuracy. Results suggest that in older adults, adaptation to adverse listening conditions is at least partially supported by cognitive reserve. Therefore, additional cognitive capacity may lead to greater resilience of older listeners to adverse listening conditions. The differential effects documented by eye movements and accuracy highlight the importance of using both online and offline measures of speech processing to explore age-related changes in speech perception.
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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 | 0.004 |
| 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 | 0.001 | 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".