Analyzing the effects of high autistic traits on neural markers of learning and memory: An EEG approach analysis
Bibliographic record
Abstract
• High autistic traits participants perform as well as low autistic traits participants on learning, memory, and decision-making tasks. • High autistic traits participants perceived their task performance as worse than low autistic traits participants. • Effects of group were present for frontal theta during both tasks. • There were no component or correlational differences observed between groups. A body of electroencephalographic (EEG) research demonstrates that executive functioning (EF) differences exist in autistic people. Here, we aimed to investigate how and to what extent these EF differences appear in people with high autistic traits in contrast to a low autistic traits comparison. The present study used a series of EEG markers (frontal theta power, frontal beta power, the reward positivity ERP component, and the P300 ERP component) to examine potential differences in EF over the course of gambling and oddball tasks. Qualitative research measures to include the perspectives of the autistic people who took part in the study were also used. While frontal theta and beta power differed between groups, we observed no significant component or correlational differences. However, it was found that high autistic traits participants perceived their task performance as worse than low autistic traits participants despite task performance being equal across groups. EF differences as measured by frontal theta and beta power were observed across groups. Self-perception of task performance may differ in high autistic traits participants when asked to complete tasks under a time constraint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".