Inhibitory and lexical frequency effects in younger and older adults’ spoken word recognition
Bibliographic record
Abstract
Older adults are known to have more difficulty recognizing words with dense phonological neighbourhoods (Sommers & Danielson, 1999), suggesting an increased role of inhibition in older adults’ spoken word recognition. Revill and Spieler (2012) found that older adults are particularly susceptible to frequency effects, and will look more to high frequency items compared to younger adults. We aim to replicate and extend the findings of Revill and Spieler (2012) by investigating the role of inhibition along with frequency for resolving lexical competition in both older and younger adults. Older (n = 16) and younger (n = 18) adults completed a visual word paradigm eyetracking task that used high and low frequency targets paired with competitors of opposing frequency, and a Simon task as a measure of inhibition. We find that older adults with poorer inhibition are more distracted by competitors than those with better inhibition and younger adults. This effect is larger for high frequency competitors compared to low. These results have implications for the changing role of inhibition in resolving lexical competition across the adult lifespan and support the idea that decreased inhibition in older adults contributes to increased lexical competition and stronger frequency effects in word recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".