The Effects of Semantic Context and the Type and Amount of Acoustic Distortion on Lexical Decision by Younger and Older Adults
Bibliographic record
Abstract
PURPOSE: In this study, the authors investigated how acoustic distortion affected younger and older adults' use of context in a lexical decision task. METHOD: The authors measured lexical decision reaction times (RTs) when intact target words followed acoustically distorted sentence contexts. Contexts were semantically congruent, neutral, or incongruent. Younger adults (n = 216) were tested on three distortion types: low-pass filtering, time compression, and masking by multitalker babble, using two amounts of distortion selected to control for word recognition accuracy. Older adults (n = 108) were tested on two amounts of time compression and one low-pass filtering condition. RESULTS: For both age groups, there was robust facilitation by congruent contexts but minimal inhibition by incongruent contexts. Facilitation decreased as distortion increased. Older listeners had slower RTs than younger listeners, but this difference was smaller in congruent than in neutral or incongruent conditions. After controlling for word recognition accuracy, older listeners' RTs were slower in time-compressed than in low-pass filtering conditions, but younger listeners performed similarly in both conditions. CONCLUSIONS: These RT results highlight the interdependence between bottom-up sensory and top-down semantic processing. Consistent with previous findings based on accuracy measures, compared with younger adults, older adults were disproportionately slowed when speech was time compressed but more facilitated by congruent contexts.
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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 | 0.003 |
| 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) | 0.001 | 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".