Lexical status influences response variability in older adults’ speech perception
Bibliographic record
Abstract
Speech sound categorization is a building block of downstream language processes, however, speech categorization can itself be influenced by lexical knowledge: Speakers tend to resolve ambiguous speech sounds in favor of real-word rather than non-word interpretations (Ganong,1980). Some studies have shown that older adults exhibit an increased lexical bias (e.g., Mattys and Scharenborg, 2014). However, previous work establishing this link has relied on an experimental paradigm in which participants are forced to make a binary choice, a task that may be overly sensitive to higher-level strategies and can obscure the distinction between responses that are highly variable and highly gradient (Apfelbaum et al., 2022). We thus asked if the link between aging and lexical bias persists in a more sensitive paradigm, in which participants indicated the extent to which a stimulus matches one of two categories using a Visual Analogue Scale (VAS). We performed an online study (n = 60) in which older and younger adults responded to a nine-step continua between either two words, or a word and a non-word (à la Ganong, 1980). Preliminary results suggest that while older adults are not necessarily more lexically biased generally, those with increased response variability do show a stronger lexical bias.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".