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

Abstract

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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“…Due to this split in the experimental data between two separate research articles with different hypotheses, no power analysis was conducted for the current article. However, the sample size for the current experiment is substantially larger than comparable and recently published manual tracking and modeling studies which used between 10 and 22 participants (Gollee et al 2017 ; Inoue and Sakaguchi 2014 ; Stepp and Turvey 2017 ).…”

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

“…As discussed by Benderius ( 2014 ) and Gollee et al. ( 2017 ), such an underlying control intermittency might potentially be able to account for much of the nonlinear “remnant” that is left unexplained by the continuous model. In other words, although presented here mainly as a first, minimal illustration of the proposed modelling framework, this simple model of visuo-motor tracking could potentially offer many of the same advantages over conventional, continuous models as other intermittent models of this task (Gawthrop et al.…”

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