Age-related differences in inhibitory control predict audiovisual speech perception.
Bibliographic record
Abstract
Audiovisual (AV) speech perception is the process by which auditory and visual sensory signals are integrated and used to understand what a talker is saying during face-to-face communication. This form of communication is markedly superior to speech perception in either sensory modality alone. However, there are additional lexical factors that are affected by age-related cognitive changes that may contribute to differences in AV perception. In the current study, we extended an existing model of spoken word identification to the AV domain, and examined the cognitive factors that contribute to age-related and individual differences in AV perception of words varying in lexical difficulty (i.e., on the basis of competing items). Young (n = 49) and older adults (n = 50) completed a series of cognitive inhibition tasks and a spoken word identification task. The words were presented in auditory-only, visual-only, and AV conditions, and were equally divided into lexically hard (words with many competitors) and lexically easy (words with few competitors). Overall, young adults demonstrated better inhibitory abilities and higher identification performance than older adults. However, whereas no relationship was observed between inhibitory abilities and AV word identification performance in young adults, there was a significant relationship between Stroop interference and AV identification of lexically hard words in older adults. These results are interpreted within the framework of existing models of spoken-word recognition with implications for how cognitive deficits in older adults contribute to speech perception.
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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 | 0.000 |
| 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".