The Role of Lexical Status and Individual Differences for Perceptual Learning in Younger and Older Adults
Bibliographic record
Abstract
Purpose: This study examined whether older adults remain perceptually flexible when presented with ambiguities in speech in the absence of lexically disambiguating information. We expected older adults to show less perceptual learning when top-down information was not available. We also investigated whether individual differences in executive function predicted perceptual learning in older and younger adults. Method: Younger (n = 31) and older adults (n = 27) completed 2 perceptual learning tasks composed of a pretest, exposure, and posttest phase. Both learning tasks exposed participants to clear and ambiguous speech tokens, but crucially, the lexically guided learning task provided disambiguating lexical information whereas the distributional learning task did not. Participants also performed several cognitive tasks to investigate individual differences in working memory, vocabulary, and attention-switching control. Results: We found that perceptual learning is maintained in older adults, but that learning may be stronger in contexts where top-down information is available. Receptive vocabulary scores predicted learning across both age groups and in both learning tasks. Conclusions: Implicit learning is maintained with age across different learning conditions but remains stronger when lexically biasing information is available. We find that receptive vocabulary is relevant for learning in both types of learning tasks, suggesting the importance of vocabulary knowledge for adapting to ambiguities in speech.
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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 | 0.001 | 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".