Different Responses to Altered Auditory Feedback in Younger and Older Adults Reflect Differences in Lexical Bias
Bibliographic record
Abstract
Purpose Previous work has found that both young and older adults exhibit a lexical bias in categorizing speech stimuli. In young adults, this has been argued to be an automatic influence of the lexicon on perceptual category boundaries. Older adults exhibit more top-down biases than younger adults, including an increased lexical bias. We investigated the nature of the increased lexical bias using a sensorimotor adaptation task designed to evaluate whether automatic processes drive this bias in older adults. Method A group of older adults ( n = 27) and younger adults ( n = 35) participated in an altered auditory feedback production task. Participants produced target words and nonwords under altered feedback that affected the 1st formant of the vowel. There were 2 feedback conditions that affected the lexical status of the target, such that target words were shifted to sound more like nonwords (e.g., less-liss) and target nonwords to sound more like words (e.g., kess-kiss). Results A mixed-effects linear regression was used to investigate the magnitude of compensation to altered auditory feedback between age groups and lexical conditions. Over the course of the experiment, older adults compensated (by shifting their production of 1st formant) more to altered auditory feedback when producing words that were shifted toward nonwords ( less-liss) than when producing nonwords that were shifted toward words ( kess-kiss). This is in contrast to younger adults who compensated more to nonwords that were shifted toward words compared to words that were shifted toward nonwords. Conclusion We found no evidence that the increased lexical bias previously observed in older adults is driven by a greater sensitivity to top-down lexical influence on perceptual category boundaries. We suggest the increased lexical bias in older adults is driven by postperceptual processes that arise as a result of age-related cognitive and sensory changes.
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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 | 0.000 | 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) | 0.002 | 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".