Differences in ongoing thought between autistic and non-autistic adults
Bibliographic record
Abstract
Autistic people may be distinguishable from non-autistic individuals in the content and modality of their thoughts. Such differences potentially underlie both psychological vulnerability and strengths, motivating the need to better understand autistic thought patterns. In non-clinical undergraduates, a recent study found that autistic traits were associated with thinking more in words than images. However, it is unclear whether such differences in thought are present in clinically diagnosed autistic individuals. The current study applied the same methods (multidimensional experience sampling during an N-back task) to examine ongoing thought in autistic and non-autistic adults. We found that autistic individuals showed less variability in the modality of their thoughts between easy and difficult task contexts. While both non-autistic and autistic participants tended to report thinking more in words during the difficult task context, the difference between conditions was significantly smaller for the autistic group. In addition, autistic individuals showed a weaker coupling between task performance and off-task social thinking, a finding that may be related to differences in social processing during the off-task state. Overall, our results provide a clinical replication and extension of previous work, highlighting the differential effects of changing external context on internal mental states in autism.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".