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: Acquisition of motor skills often involves the concatenation of single movements into sequences. Along the course of learning, sequential performance becomes progressively faster and smoother, presumably by optimization of both motor planning and motor execution. Following its encoding during training, “how-to” memory undergoes consolidation, reflecting transformations in performance and its neurobiological underpinnings over time. This offline post-training memory process is characterized by two phenomena: reduced sensitivity to interference and the emergence of delayed, typically overnight, gains in performance. Here, using a training protocol that effectively induces motor sequence memory consolidation, we tested temporal and kinematic parameters of performance within (online) and between (offline) sessions, and their sensitivity to retroactive interference. One group learned a given finger-to-thumb opposition sequence (FOS), and showed robust delayed (consolidation) gains in the number of correct sequences performed at 24 h. A second group learned an additional (interference) FOS shortly after the first and did not show delayed gains. Reduction of touch times and inter-movement intervals significantly contributed to the overall offline improvement of performance overnight. However, only the offline inter-movement interval shortening was selectively blocked by the interference experience. Velocity and amplitude, comprising movement time, also significantly changed across the consolidation period but were interference-insensitive. Moreover, they paradoxically canceled out each other. Current results suggest that shifts in the representation of the trained sequence are subserved by multiple processes: from distinct changes in kinematic characteristics of individual finger movements to high-level, temporal reorganization of the movements as a unit. Each of these processes has a distinct time course and a specific susceptibility to retroactive interference. This multiple-component view may bridge the gap in understanding the link between the behavioral changes, which define online and offline learning, and the biological mechanisms that support those changes.