The lexical bias in older adults’ compensation to altered auditory feedback
Bibliographic record
Abstract
Older adults show a larger lexical bias when categorizing speech sounds (i.e., Ganong effect), such that they are even more biased than younger adults to categorize ambiguous tokens as real words rather than non-words (Mattys and Scharenborg, 2014). Using an altered auditory feedback paradigm, we sought to investigate whether this larger perceptual bias would be reflected in older adults’ compensation to perturbations of their own speech. Groups of older (n = 27) and younger adults (n = 35) produced monosyllabic words and non-words containing the vowel /ε/. Altered auditory feedback lowered the first formant (F1) of the vowel towards an F1 characteristic of /ɪ/. This real-time frequency manipulation shifted the perceived lexical status of the stimuli (i.e., words were shifted towards non-words, and non-words towards words). Younger adults compensated more to non-words that were shifted towards real words (e.g., kess-kiss) than to real words that were shifted towards non-words (e.g., chest-chist), consistent with previous findings (Bourguignon et al., 2014). However, older adults did not show the same pattern of compensation as younger adults, suggesting that different mechanisms are involved in older adults’ lexical bias. A combination of age-related cognitive and sensory changes likely influences the effect of lexical status on sensorimotor adaptation in older adults.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".