Serial processing in human movement production

Doeringer, Joseph A.; Hogan, Neville

doi:10.1016/s0893-6080(98)00083-5

Cited by 42 publications

(25 citation statements)

References 64 publications

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“…This strategy of control (Evarts et al, 1971;Evarts, 1975; is consistent with a number of hypotheses (Doeringer & Hogan, 1998;Iqbal & Roy, 2009) and experiments Kornhuber (1973) about the CNS being involved in the production of "ramp-type" signals generated in the basal ganglia and the reticular formation that propagate to the spinal cord. It is also consistent with other findings that long loop reflexes may not be involved in postural adjustment mer.ccsenet.org Mechanical Engineering Research Vol.…”

Section: Introductionsupporting

confidence: 76%

Toward a Computational Model of the Upper Extremity

Hemami¹

2017

MER

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A basic 22-segment model of the upper extremity is formulated that can allow computational testing of hypotheses about the control and coordination of the upper extremity by the central nervous system. The formulation allows for further analytical, anatomical, physiological, and bio-mechanical expansion and improvement of the model. It allows for inclusion of all passive structures: ligaments, membranes, soft tissues, and cartilages. The formulation is based on the state space formulation of the Newton-Euler method applied to multi-body systems. Extensive use is made of three-segment rigid body modules, constraints, reduction of dimensionality, projection, and matrices of large dimensions.An example, gliding motion of a rigid body on a circular surface (as in wiping a dish with a pre-specified force of contact) shows the application of some of the concepts and feasibility of the developed routines. The control is based on analogous strategies in living systems where co-activation of agonist-antagonist muscular systems and precise reference inputs implement the desirable trajectories of motion and where an integral feedback of the force implements the desired forces of contact.

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Section: Introductionsupporting

confidence: 76%

Toward a Computational Model of the Upper Extremity

Hemami¹

2017

MER

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“…The seminal pulse-tracking experiments of Vince (1948), later confirmed by Navas and Stark (1968), imply the existence of the "psychological refractory period" (PRP) previously reported in the context of double-response time experiments by Telford (1931). As discussed by Smith (1967) the "single-channel hypothesis" of Welford (1967) provides an explanation of this effect which is discussed in a wider context by Doeringer and Hogan (1998). An alternative explanation can be made in terms of competing resources (McLeod 1977;Navon and Miller 2002).…”

Section: Introductionmentioning

confidence: 88%

Intermittent control: a computational theory of human control

et al. 2011

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The paradigm of continuous control using internal models has advanced understanding of human motor control. However, this paradigm ignores some aspects of human control, including intermittent feedback, serial ballistic control, triggered responses and refractory periods. It is shown that event-driven intermittent control provides a framework to explain the behaviour of the human operator under a wider range of conditions than continuous control. Continuous control is included as a special case, but sampling, system matched hold, an intermittent predictor and an event trigger allow serial open-loop trajectories using intermittent feedback. The implementation here may be described as "continuous observation, intermittent action". Beyond explaining unimodal regulation distributions in common with continuous control, these features naturally explain refractoriness and bimodal stabilisation distributions observed in double stimulus tracking experiments and quiet standing, respectively. Moreover, given that human control systems contain significant time delays, a biological-cybernetic rationale favours intermittent over continuous control: intermittent predictive control is computationally less demanding than continuous predictive control. A standard continuous-time predictive control model of the human operator is used as the underlying design method for an event-driven intermittent controller. It is shown that when event thresholds are small and sampling is regular, the intermittent controller can masquerade as the underlying continuous-time controller and thus, under these conditions, the continuous-time and intermittent controller cannot be distinguished. This explains why the intermittent control hypothesis is consistent with the continuous control hypothesis for certain experimental conditions.

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“…Complex movements have been decomposed into submovements as an analysis tool (Morasso and Mussa-Ivaldi, 1982;Flash and Henis, 1991;Berthier, 1996;Burdet and Milner, 1998) with apparent success. Although the existence of submovements has not been demonstrated unequivocally, they account for many patterns in human movement (Doeringer and Hogan, 1998;Hogan et al, 1999). Krebs et al (1998b) report that movements made by patients recovering from stroke become smoother as recovery proceeds.…”

mentioning

confidence: 99%

Movement Smoothness Changes during Stroke Recovery

Rohrer¹,

Fasoli²,

Krebs³

et al. 2002

J. Neurosci.

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662

610

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Smoothness is characteristic of coordinated human movements, and stroke patients' movements seem to grow more smooth with recovery. We used a robotic therapy device to analyze five different measures of movement smoothness in the hemiparetic arm of 31 patients recovering from stroke. Four of the five metrics showed general increases in smoothness for the entire patient population. However, according to the fifth metric, the movements of patients with recent stroke grew less smooth over the course of therapy. This pattern was reproduced in a computer simulation of recovery based on submovement blending, suggesting that progressive blending of submovements underlies stroke recovery.

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Serial processing in human movement production

Cited by 42 publications

References 64 publications

Toward a Computational Model of the Upper Extremity

Toward a Computational Model of the Upper Extremity

Intermittent control: a computational theory of human control

Movement Smoothness Changes during Stroke Recovery

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