Tip-of-the-tongue states reveal age differences in the syllable frequency effect.
Bibliographic record
Abstract
Syllable frequency has been shown to facilitate production in some languages but has yielded inconsistent results in English and has never been examined in older adults. Tip-of-the-tongue (TOT) states represent a unique type of production failure where the phonology of a word is unable to be retrieved, suggesting that the frequency of phonological forms, like syllables, may influence the occurrence of TOT states. In the current study, we investigated the role of first-syllable frequency on TOT incidence and resolution in young (18-26 years of age), young-old (60-74 years of age), and old-old (75-89 years of age) adults. Data from 3 published studies were compiled, where TOTs were elicited by presenting definition-like questions and asking participants to respond with "Know," "Don't Know," or "TOT." Young-old and old-old adults, but not young adults, experienced more TOTs for words beginning with low-frequency first syllables relative to high-frequency first syllables. Furthermore, age differences in TOT incidence occurred only for words with low-frequency first syllables. In contrast, when a prime word with the same first syllable as the target was presented during TOT states, all age groups resolved more TOTs for words beginning with low-frequency syllables. These findings support speech production models that allow for bidirectional activation between conceptual, lexical, and phonological forms of words. Furthermore, the age-specific effects of syllable frequency provide insight into the progression of age-linked changes to phonological processes. (PsycINFO Database Record (c) 2010 APA, all rights reserved).
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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 | 0.001 | 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) | 0.002 | 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".