Getting Off to a Shaky Start: Specificity in Planning and Feedforward Control During Sensorimotor Learning in Autism Spectrum Disorder
Bibliographic record
Abstract
Whilst autistic individuals develop new internal action models during sensorimotor learning, the acquired movements are executed less accurately and with greater variability. Such movement profiles are related to differences in sensorimotor integration and/or altered feedforward/feedback sensorimotor control. We investigated the processes underlying sensorimotor learning in autism by quantifying accuracy and variability, relative timing, and feedforward and feedback control. Although autistic individuals demonstrated significant sensorimotor learning across trials, which was facilitated by processing knowledge-of-results feedback, motor execution was less accurate than non-autistic individuals. Kinematic analysis indicated that autistic individuals showed significantly greater spatial variability at peak acceleration, but comparable spatial variability at peak velocity. These kinematic markers suggest that autistic movement profiles are driven by specific differences in sensorimotor control processes (i.e., internal action models) associated with planning and regulating the forces required to execute the movement. The reduction of variability at peak velocity indicates intact early feedback-based sensorimotor control in autism. Understanding how feedforward and feedback-based control processes operate provides an opportunity to explore how these control processes influence the acquisition of socio-motor actions in autism. Autism Res 2020, 13: 423-435. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autistic adults successfully learned a new movement skill by physically practising it, and using feedback about how well they had done to become more accurate. When looking at the movements in detail, autistic adults were more variable than non-autistic adults when planning (e.g., how much force to use), and performing, the movement. These differences impact how autistic individuals learn different types of movement skills, which might influence how other behaviours (e.g., imitation) are acquired that support social interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".