Sustained sensorimotor control as intermittent decisions about prediction errors: Computational framework and application to ground vehicle steering
For researchers in human motor control and robotics, this framework offers a neurobiologically plausible model of intermittent control that integrates prediction and evidence accumulation, though it is an incremental extension of existing intermittent control models.
The paper proposes a computational framework for human sensorimotor control, applied to car steering, showing that intermittent control with prediction-error accumulation better explains steering adjustments than continuous control. Empirical data from a driving simulator supports the framework, with stepwise adjustments predicted by visual cues and evidence accumulation explaining duration-amplitude covariability.
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 observations from a driving simulator provide support for some of the framework assumptions: 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 steering adjustments, than as continuous control. Furthermore, the amplitudes of individual steering adjustments are well predicted by a compound visual cue signalling steering error, and even better so if also adjusting for predictions of how the same cue is affected by previous control. Finally, evidence accumulation is shown to explain observed covariability between inter-adjustment durations and adjustment amplitudes, seemingly better so than the type of threshold mechanisms that are typically assumed in existing models of intermittent control.