Figure 14:
Quantifying noise levels in the self-generated kinesthetic reafferent feedback derived from motor output variability of goal-directed visuo-motor actions. (A) Pointing task to assess signatures of motor noise across neurodevelopment. The arrow indicates the proposed taxonomy of neuromotor control across different levels of function in the nervous systems. (B) Typical development (TD) signatures of voluntary (instructed) and spontaneous (uninstructed) hand-retracting movements during pointing. Speed profiles are derived from hand-pointing trajectories in panel A forward to the target (first bell-shaped) and away from the target toward a resting position during decision making in a cognitive match-to-sample task. The resting hand raises speed continuously until it reaches a peak, then decelerates to touch the target on a touch monitor that registers the touch. The hand leaves the monitor and accelerates away from the target, toward the body, to reach the resting state again. Midway along the retracting hand trajectory, the hand reaches its second peak and then decelerates again to rest. Trials are recorded at the child's own pace. The target appears in trial 1 and disappears once the child touches it, then appears again, implicitly instructing the child to touch it again. There are 100 trials presented as a heat map with global peaks highlighted in yellow. They are stacked in the order of occurrence and aligned to the touch. The typical behavior of the TD children is highly periodic and well structured. The third plot at the bottom shows the small (local) speed peaks of the resting state, followed by the first ballistic phase of the motion with well-aligned peaks of the maximal speed of the hand on its way to touch the target, then the ballistic phase toward the target, followed by small speed peaks while resting briefly at the target-stratifying ASD subtypes in Wu, Jose, Nurnberger, and Torres (2018). The ballistic phase returning the hand toward the body follows (with no peaks); then the peaks of the speed maxima automatically align again from trial to trial. Another ballistic phase retracting the hand ensues, and the hand lands to a resting state, with the presence once again of the small speed peaks at rest. (B) The participant with ASD (similar age and sex to TD child) depicts very different patterns, consisting of highly disorganized motions, with (involuntary, unintended) random noise output by his system while performing these goal-directed reaches. Notice the absence of a pattern in the forward phase of the pointing to the target and the emergence of some structure in the return motions. Further, notice the presence of random noise (also modeled and empirically characterized in Wu et al., 2018, using a Poisson process. (C) The micromovement spikes reveal states of maturation in TD from 3 years of age to college age, in contrast to its absence in ASD 3 to 25 years old (Torres, Brincker, et al., 2013), suggesting the importance of tracking the shifts in probability space with age and treating autism as a lifelong condition. This contrasts with assuming a theoretical distribution under the one-size-fits-all model of autism ADOS-driven research today.

Quantifying noise levels in the self-generated kinesthetic reafferent feedback derived from motor output variability of goal-directed visuo-motor actions. (A) Pointing task to assess signatures of motor noise across neurodevelopment. The arrow indicates the proposed taxonomy of neuromotor control across different levels of function in the nervous systems. (B) Typical development (TD) signatures of voluntary (instructed) and spontaneous (uninstructed) hand-retracting movements during pointing. Speed profiles are derived from hand-pointing trajectories in panel A forward to the target (first bell-shaped) and away from the target toward a resting position during decision making in a cognitive match-to-sample task. The resting hand raises speed continuously until it reaches a peak, then decelerates to touch the target on a touch monitor that registers the touch. The hand leaves the monitor and accelerates away from the target, toward the body, to reach the resting state again. Midway along the retracting hand trajectory, the hand reaches its second peak and then decelerates again to rest. Trials are recorded at the child's own pace. The target appears in trial 1 and disappears once the child touches it, then appears again, implicitly instructing the child to touch it again. There are 100 trials presented as a heat map with global peaks highlighted in yellow. They are stacked in the order of occurrence and aligned to the touch. The typical behavior of the TD children is highly periodic and well structured. The third plot at the bottom shows the small (local) speed peaks of the resting state, followed by the first ballistic phase of the motion with well-aligned peaks of the maximal speed of the hand on its way to touch the target, then the ballistic phase toward the target, followed by small speed peaks while resting briefly at the target-stratifying ASD subtypes in Wu, Jose, Nurnberger, and Torres (2018). The ballistic phase returning the hand toward the body follows (with no peaks); then the peaks of the speed maxima automatically align again from trial to trial. Another ballistic phase retracting the hand ensues, and the hand lands to a resting state, with the presence once again of the small speed peaks at rest. (B) The participant with ASD (similar age and sex to TD child) depicts very different patterns, consisting of highly disorganized motions, with (involuntary, unintended) random noise output by his system while performing these goal-directed reaches. Notice the absence of a pattern in the forward phase of the pointing to the target and the emergence of some structure in the return motions. Further, notice the presence of random noise (also modeled and empirically characterized in Wu et al., 2018, using a Poisson process. (C) The micromovement spikes reveal states of maturation in TD from 3 years of age to college age, in contrast to its absence in ASD 3 to 25 years old (Torres, Brincker, et al., 2013), suggesting the importance of tracking the shifts in probability space with age and treating autism as a lifelong condition. This contrasts with assuming a theoretical distribution under the one-size-fits-all model of autism ADOS-driven research today.

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