Autistic Trait Expression Does Not Affect Müller-Lyer Susceptibility Across Forced-Choice Tasks
Bibliographic record
Abstract
The Müller-Lyer Illusion (ML) is known to bias perception of size estimation. Previous studies have reported mixed findings regarding a possible reduction in susceptibility to the ML that is associated with autistic traits. The present study examined ML bias with three perceptual tasks involving neurotypical participants (n=30) assessed for autistic trait expression by the Autism-Quotient (AQ) and Systemizing-Quotient (SQ) questionnaires. Figures were presented in three different forced-choice conditions: concurrently (Task1), successively with a discrete response slide (Task2), or successively with responses made upon presentation of the second figure (Task3). Participants identified via keypress the figure with the longer central shaft. Questionnaire scores were found to not significantly correlate with any of the outcome measures of selection accuracy nor Response Time (RT). However, task procedure did affect illusion susceptibility where biased figure selections were significantly greater in Task1 relative to Task2 (M=9.33; p
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".