Mind wandering during five minutes of rest: Autistic traits, visual thought, and thinking about others
Bibliographic record
Abstract
Neurotypicals mind wander between 20% and 50% of their waking hours, during which they engage in mental tasks such as problem-solving, planning, mulling current concerns, and self-reflection. Despite extensive literature dedicated to mind wandering (MW) in neurotypicals, very little is known about what Autistic individuals or those high in autistic traits might think about while MW. Also unclear is whether they are predisposed to thinking in visual rather than verbal form. To investigate the effects of autistic traits on MW, we asked 92 participants to sit in a quiet room for five minutes with their eyes closed. Following the resting period, participants completed the 10-item Autism quotient (AQ-10) and the Amsterdam Resting-State Questionnaire (ARSQ), which measures 10 factors of MW. The ARSQ factor Theory of Mind (ToM-A) was modestly and negatively correlated with AQ-10 score, driven by fewer reports of thinking about others. Notably, neither correlational nor group-level analyses provided evidence that autistic traits were linked with placing oneself in others' shoes or Visual Thought. For both high and low AQ-10 scorers, Bayesian analyses indicated extreme evidence for a positive correlation between thinking about others and Visual Thought, and moderate evidence for a positive correlation between thinking about others and thinking about the self. These exploratory findings contribute to the limited literature on MW content in individuals high in autistic traits and provide directions for future research with larger, more diverse samples. • Autistic traits were negatively correlated with thinking about other people. • No correlation was found between autistic traits and perspective-taking. • Autistic traits were not related to visual or verbal thinking. • Visual thought was positively correlated with thinking about others. • Self-related thoughts were positively correlated with thoughts about others.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".