Developmental Coordination Disorder (DCD, or dyspraxia) is a congenital disorder impacting motor skills in 5-6% of school-age children, often leading to difficulties in physical, social, and emotional development. Traditional diagnostic tools, while effective, can be resource-intensive, stress-inducing, and time consuming. There is potential for the motion tracking capabilities of consumer immersive Virtual Reality (VR) technologies to occupy the ‘space’ between survey-based screening and formal motor assessment used to diagnose this population. Here, we describe the process for the initial alpha development of motor assessment tasks in VR based on established tasks from literature, and our engagement with dyspraxic adults and children to co-develop a tool which could lay the foundation for a more accessible, objective, and engaging alternative to traditional diagnostic and screening tools.

Inspired by the observation that humans naturally adjust finger configurations based on object size and weight, we present ThumbShift, a novel haptic controller that physically moves and rotates the user's thumb to render subtle shifts in finger collaboration and affect whole-hand grasp perception. Unlike prior work focused on grasp type or global haptic feedback, our approach uniquely targets finger collaboration variation through localised, real-time finger repositioning during grasp - enabling Dynamic Digit Positioning (DDP) to modulate haptic and perceptual experience. Results of our user studies show that while size perception changes only slightly, by about 5%, in a two-alternative forced choice (2AFC) task, perceived weight shifts significantly—by approximately 19%—in magnitude estimation tasks. We also report on the influence of mass centre position which extends the weight perception changing ability to about 56%, and how finger force distribution works in altering users' perception. These findings demonstrate that dynamic thumb movement can reconfigure force distribution across the hand and substantially alter haptic experience. By highlighting the underexplored role of digit motion in object perception, our work opens new directions for perception-aware haptic devices in VR, AR, and physical interaction design.

Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. To disentangle their subtly different predictions about the nature of atypicalities in autistic perception, we performed computational modelling of two sensorimotor tasks: the predictive use of manual gripping forces during object lifting and anticipatory eye movements during a naturalistic interception task. In contrast to some accounts, we found no evidence of chronic atypicalities in the use of priors or weighting of sensory information during object lifting. Differences in prior beliefs, rates of belief updating, and the precision weighting of prediction errors were, however, observed for anticipatory eye movements. Most notably, we observed autism-related difficulties in flexibly adapting learning rates in response to environmental change (i.e., volatility). These findings suggest that atypical encoding of precision and context-sensitive adjustments provide a better explanation of autistic perception than generic attenuation of priors or persistently high precision prediction errors. Our results did not, however, support previous suggestions that autistic people perceive their environment to be persistently volatile.