Investigating Speech Perception and Individual Variation in Cognitive-Behavioural Traits
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition associated with differences in cognition, communication and language processing. These differences may be explained by their distinct cognitive and perceptual processing styles compared to those of neurotypical individuals. Previous studies have shown that individual differences in traits linked to autism, as measured by the Autism Spectrum Quotient (AQ), influence speech perception and the ability to adapt to contextual variability in speech signals (Stewart & Ota, 2008; Yu, 2010). The present study examined how variation in AQ traits is associated with behavioural and neural responses to ambiguous speech sounds. Fifty-two participants completed a two-alternative forced choice (2AFC) task, categorizing a continuum of fricative sounds between /s/ and /ʃ/ in different vowel contexts (/a/ and /u/). Electroencephalography (EEG) was also recorded during a passive oddball paradigm to measure mismatch negativity (MMN) brain responses to such vowel-fricative-vowel syllables. Results from the behavioural task showed strong categorical perception across participants, with vowel context reliably influencing categorization patterns. Comparison of the highest and lowest AQ quartiles showed that Low AQ participants categorized stimuli more often as /ʃ/ overall. Analysis of the 50% cross over point (PSE) indicated that High AQ participants shifted toward /ʃ/ earlier than the Low AQ group, particularly in the /u/ context. Neural responses showed that participants exhibited reliable event-related potential (ERP) responses in the MMN time window, although polarity was positive rather than negative. Group-level differences emerged in both ERP amplitude and latency, with High AQ individuals showing stronger but slower responses to stimuli in the /u/ context. These findings highlight how individual differences can shape both perceptual and neural mechanisms of speech processing.
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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 | 0.002 |
| 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 | 0.000 | 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".