Cognitive flexibility in autism: How task predictability and sex influence performances
Bibliographic record
Abstract
While cognitive flexibility challenges are frequently reported in autistic individuals, inconsistencies in the findings prompt further investigation into the factors influencing this flexibility. We suggest that unique aspects of the predictive brain in autistic individuals might contribute to these challenges, potentially varying by sex. Our study aimed to test these hypotheses by examining cognitive flexibility under different predictability conditions in a sample including a similar number of males and females. We conducted an online study with 263 adults (127 with an autism diagnosis), where participants completed a flexibility task under varying levels of predictability (unpredictable, moderately predictable, and predictable). Our results indicate that as task predictability increases, performance improves; however, the response time gap between autistic and non-autistic individuals also widens. Moreover, we observe significant differences between autistic males and females, which differ from non-autistic individuals, highlighting the need to consider sex differences in research related to the cognition of autistic individuals. Overall, our findings contribute to a better understanding of cognitive flexibility and sex differences in autism in light of predictive brain theories and suggest avenues for further research.
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 | 0.007 |
| 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 | 0.000 |
| 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".