Intermittent Control in Man and Machine

Gawthrop, P.J.; Gollee, H.; Loram, Ian D.

doi:10.1201/b19013-14

Cited by 9 publications

(31 citation statements)

References 112 publications

(168 reference statements)

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“…Our wholly deterministic explanation (IC) uses the previously published intermittent control model, with prediction error thresholds and sampling delay to generate aperiodic sampling of system states (position, velocity, joystick) (Gawthrop & Wang, ; Gawthrop et al . , ). This intermittent control model (IC) is based upon the same continuous control model above (CC), and uses two additional processes, a hold and a trigger‐switch (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The sampling delay enforces a maximum speed–minimum accuracy limit since it defines the minimum time for the observer to settle before sampling system states (Gawthrop et al . ). When the sampling delay is small, triggering is determined by the thresholds and is typically aperiodic.…”

Section: Methodsmentioning

confidence: 97%

“…C ) using the same control parameters as the underlying continuous controller (Gawthrop & Wang, ; Gawthrop et al . , ). Hence the same controller parameters are appropriate for both continuous and intermittent control models (Gollee et al .…”

Section: Methodsmentioning

confidence: 99%

“…A quadratic switching function of the form e p T Q t e p > 1, with Q t a positive semi‐definite matrix, can be defined as an event trigger to reset the hold state x h to the observer state x o (Gawthrop & Wang, ; Gawthrop et al . , ). In our case only the elements of e p corresponding to the velocity ( e p vel ) and position ( e p pos ) states are considered, and Q t is a diagonal matrix with two positive elements, θ vel (velocity threshold) and θ pos (position threshold), forming the axes of an elliptic switching surface,

{((), \frac{e_{normalp}^{pos}}{θ^{pos}})}^{2} + {((), \frac{e_{normalp}^{vel}}{θ^{vel}})}^{2} > 1 .

…”

Section: Appendix: Description Of Modelsmentioning

confidence: 99%

“…); and (3) we used the intermittent open loop predictive control model of Gawthrop et al . (, ), with thresholds on the prediction error of the states (IC). The default hypothesis would be that a continuous linear mechanism, with added noise (CCN) provides a better explanation of the complete power spectrum (linear component plus non‐linear remnant) than a wholly deterministic intermittent control mechanism (IC).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Visuo‐manual tracking: does intermittent control with aperiodic sampling explain linear power and non‐linear remnant without sensorimotor noise?

Gollee

Gawthrop

Lakie

et al. 2017

The Journal of Physiology

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Key points A human controlling an external system is described most easily and conventionally as linearly and continuously translating sensory input to motor output, with the inevitable output remnant, non‐linearly related to the input, attributed to sensorimotor noise.Recent experiments show sustained manual tracking involves repeated refractoriness (insensitivity to sensory information for a certain duration), with the temporary 200–500 ms periods of irresponsiveness to sensory input making the control process intrinsically non‐linear.This evidence calls for re‐examination of the extent to which random sensorimotor noise is required to explain the non‐linear remnant.This investigation of manual tracking shows how the full motor output (linear component and remnant) can be explained mechanistically by aperiodic sampling triggered by prediction error thresholds.Whereas broadband physiological noise is general to all processes, aperiodic sampling is associated with sensorimotor decision making within specific frontal, striatal and parietal networks; we conclude that manual tracking utilises such slow serial decision making pathways up to several times per second. AbstractThe human operator is described adequately by linear translation of sensory input to motor output. Motor output also always includes a non‐linear remnant resulting from random sensorimotor noise from multiple sources, and non‐linear input transformations, for example thresholds or refractory periods. Recent evidence showed that manual tracking incurs substantial, serial, refractoriness (insensitivity to sensory information of 350 and 550 ms for 1st and 2nd order systems respectively). Our two questions are: (i) What are the comparative merits of explaining the non‐linear remnant using noise or non‐linear transformations? (ii) Can non‐linear transformations represent serial motor decision making within the sensorimotor feedback loop intrinsic to tracking? Twelve participants (instructed to act in three prescribed ways) manually controlled two systems (1st and 2nd order) subject to a periodic multi‐sine disturbance. Joystick power was analysed using three models, continuous‐linear‐control (CC), continuous‐linear‐control with calculated noise spectrum (CCN), and intermittent control with aperiodic sampling triggered by prediction error thresholds (IC). Unlike the linear mechanism, the intermittent control mechanism explained the majority of total power (linear and remnant) (77–87% vs. 8–48%, IC vs. CC). Between conditions, IC used thresholds and distributions of open loop intervals consistent with, respectively, instructions and previous measured, model independent values; whereas CCN required changes in noise spectrum deviating from broadband, signal dependent noise. We conclude that manual tracking uses open loop predictive control with aperiodic sampling. Because aperiodic sampling is inherent to serial decision making within previously identified, specific frontal, striatal and parietal networks we suggest that these structures are intimately inv...

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

{((), \frac{e_{normalp}^{pos}}{θ^{pos}})}^{2} + {((), \frac{e_{normalp}^{vel}}{θ^{vel}})}^{2} > 1 .

…”

Section: Appendix: Description Of Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Visuo‐manual tracking: does intermittent control with aperiodic sampling explain linear power and non‐linear remnant without sensorimotor noise?

Gollee

Gawthrop

Lakie

et al. 2017

The Journal of Physiology

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Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering

2018

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A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework’s main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control.

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Steering is initiated based on error accumulation.

Goodridge¹,

Mole²,

Billington³

et al. 2022

Journal of Experimental Psychology: Human Perception and Perfor

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Vehicle control by humans is possible because the central nervous system is capable of using visual information to produce complex sensorimotor actions. Drivers must monitor errors and initiate steering corrections of appropriate magnitude and timing to maintain a safe lane position. The perceptual mechanisms determining how a driver processes visual information and initiates steering corrections remain unclear. Previous research suggests two potential alternative mechanisms for responding to errors: (i) perceptual evidence (error) satisficing fixed constant thresholds (Threshold), or (ii) the integration of perceptual evidence over time (Accumulator). To distinguish between these mechanisms an experiment was conducted using a computer-generated steering correction paradigm. Drivers (N=20) steered towards an intermittently appearing 'road-line' that varied in position and orientation with respect to the driver's position and trajectory. One key prediction from a Threshold framework is a fixed absolute error response across conditions regardless of the rate of error development, whereas the Accumulator framework predicts that drivers would respond to larger absolute errors when the error signal develops at a faster rate. Results were consistent with an Accumulator framework, thus we propose that models of steering should integrate perceived control error over time in order to accurately capture human perceptual performance.

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Intermittent Control in Man and Machine

Cited by 9 publications

References 112 publications

Visuo‐manual tracking: does intermittent control with aperiodic sampling explain linear power and non‐linear remnant without sensorimotor noise?

Visuo‐manual tracking: does intermittent control with aperiodic sampling explain linear power and non‐linear remnant without sensorimotor noise?

Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering

Steering is initiated based on error accumulation.

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