Auditory Stimulus Processing and Task Learning Are Adequate in Dyslexia, but Benefits From Regularities Are Reduced
Bibliographic record
Abstract
Purpose: The reading deficit for people with dyslexia is typically associated with linguistic, memory, and perceptual-discrimination difficulties, whose relation to reading impairment is disputed. We proposed that automatic detection and usage of serial sound regularities for individuals with dyslexia is impaired (anchoring deficit hypothesis), leading to the formation of less reliable sound predictions. Agus, Carrión-Castillo, Pressnitzer, and Ramus, (2014) reported seemingly contradictory evidence by showing similar performance by participants with and without dyslexia in a demanding auditory task that contained task-relevant regularities. To carefully assess the sensitivity of participants with dyslexia to regularities of this task, we replicated their study. Method: Thirty participants with and 24 without dyslexia performed the replicated task. On each trial, a 1-s noise stimulus was presented. Participants had to decide whether the stimulus contained repetitions (was constructed from a 0.5-s noise segment repeated twice) or not. It is implicit in this structure that some of the stimuli with repetitions were themselves repeated across trials. We measured the ability to detect within-noise repetitions and the sensitivity to cross-trial repetitions of the same noise stimuli. Results: We replicated the finding of similar mean performance. However, individuals with dyslexia were less sensitive to the cross-trial repetition of noise stimuli and tended to be more sensitive to repetitions in novel noise stimuli. Conclusion: These findings indicate that online auditory processing for individuals with dyslexia is adequate but their implicit retention and usage of sound regularities is indeed impaired.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".