Weak Action Predictions in Autism
Bibliographic record
Abstract
Humans are adept at predicting the actions of others by interpreting subtle preparatory movements, a skill crucial for successful social interactions. This study investigated whether autistic individuals, who face challenges in social interactions, exhibit reduced efficiency in predicting others' actions. Additionally, we examined whether autistic individuals demonstrate improved understanding of the actions of other autistic individuals. To address these questions, we used a competitive-reaching task in which an "attacker" was directed by an auditory cue to move toward one of two possible targets. A "blocker" anticipates the attacker’s trajectory to reach the same target as quickly as possible. Using motion-tracking technology, we measured the blockers' finger reaction time (fRT), the time interval between the attacker’s movement initiation and the blocker’s response, as well as the blockers' movement velocity. Results reveal that autistic blockers have longer fRTs and slower velocities compared to non-autistic blockers, indicating a deficit in action prediction. This impairment is consistent regardless of the attacker’s identity, challenging the hypothesis that autistic individuals are better at predicting the actions of others autistic individuals. Importantly, a control experiment measured the blockers' reaction time in response to an auditory cue rather than to another person's action. This experiment revealed no differences between autistic and non-autistic blockers, suggesting that the observed differences in the main task were specific to action prediction. Taken together, these findings provide novel evidence of reduced efficiency in integrating perceptual cues critical for predicting others' actions, offering insights into the mechanisms underlying social interaction challenges 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".