Abstract: A characteristic of visuomotor tracking of non-regular oscillating stimuli are high-frequency jittery corrective motions, oscillating around the tracked stimuli. However, the properties of these corrective jitter responses are not well understood. For example, does the jitter response show an idiosyncratic signature? What is the relationship between stimuli properties and jitter properties? Is the jitter response similar across effectors with different inertial properties? To answer these questions, we measured participants' jitter frequencies in two tracking tasks in the arm and the finger. Thirty participants tracked the same set of eleven non-regular oscillating stimuli, vertically moving on a screen, once with forward-backward arm movements (holding a tablet stylus) and once with upward-downward index finger movements (with a motion tracker attached). Participants' jitter frequencies and tracking errors varied systematically as a function of stimuli frequency and amplitude. Additionally, there were clear individual differences in average jitter frequencies between participants, ranging from 0.7 to 1.15 Hz, similar to values reported previously. A comparison of individual jitter frequencies in the two tasks showed a strong correlation between participants' jitter frequencies in the finger and the arm, despite the very different inertial properties of the two effectors. This result suggests that the corrective jitter response stems from common neural processes.

Abstract: To evaluate normal and impaired control of anticipatory grip force (GF) modulation, we compared GF production during horizontal arm movements in healthy and post-stroke subjects, and, based on a physiologically feasible dynamic model, determined referent control variables underlying the GF-arm motion coordination in each group. 63% of 13 healthy and 48% of 13 stroke subjects produced low sustained initial force (< 10 N) and increased GF prior to arm movement. Movement-related GF increases were higher during fast compared to self-paced arm extension movements only in the healthy group. Differences in the patterns of anticipatory GF increases before the arm movement onset between groups occurred during fast extension arm movement only. In the stroke group, longer delays between the onset of GF change and elbow motion were related to clinical upper limb deficits. Simulations showed that GFs could emerge from the difference between the actual and the referent hand aperture (Ra) specified by the CNS. Similarly, arm movement could result from changes in the referent elbow position (Re) and could be affected by the co-activation (C) command. A subgroup of stroke subjects, who increased GF before arm movement, could specify different patterns of the referent variables while reproducing the healthy typical pattern of GF-arm coordination. Stroke subjects, who increased GF after arm movement onset, also used different referent strategies than controls. Thus, altered anticipatory GF behavior in stroke subjects may be explained by deficits in referent control.

Abstract: Natural human behavior arises from continuous interactions between the cognitive and motor domains. However, assessments of cognitive abilities are typically conducted using pen and paper tests, i.e., in isolation from “real life” cognitive-motor behavior and in artificial contexts. In the current study, we aimed to assess cognitive-motor task performance in a more naturalistic setting while recording multiple motor and eye tracking signals. Specifically, we aimed to (i) delineate the contribution of cognitive and motor components to overall task performance and (ii) probe for a link between cognitive-motor performance and pupil size. To that end, we used a virtual reality (VR) adaptation of a well-established neurocognitive test for executive functions, the 'Color Trails Test' (CTT). The VR-CTT involves performing 3D reaching movements to follow a trail of numbered targets. To tease apart the cognitive and motor components of task performance, we included two additional conditions: a condition where participants only used their eyes to perform the CTT task (using an eye tracking device), incurring reduced motor demands, and a condition where participants manually tracked visually-cued targets without numbers on them, incurring reduced cognitive demands. Our results from a group of 30 older adults (>65) showed that reducing cognitive demands shortened completion times more extensively than reducing motor demands. Conditions with higher cognitive demands had longer target search time, as well as decreased movement execution velocity and head-hand coordination. We found larger pupil sizes in the more cognitively demanding conditions, and an inverse correlation between pupil size and completion times across individuals in all task conditions. Lastly, we found a possible link between VR-CTT performance measures and clinical signatures of participants (fallers versus non-fallers). In summary, performance and pupil parameters were mainly dependent on task cognitive load, while maintaining systematic interindividual differences. We suggest that this paradigm opens the possibility for more detailed profiling of individual cognitive-motor performance capabilities in older adults and other at-risk populations.