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

## TL;DR

This paper introduces a computational framework for human sensorimotor control emphasizing intermittency, using prediction primitives and evidence accumulation, with empirical support from driving simulator experiments showing discrete steering adjustments.

## Contribution

It extends existing models by proposing a neurobiologically plausible mechanism for intermittent control based on prediction primitives and evidence accumulation, applied to vehicle steering.

## Key findings

- Human steering is better modeled as discrete adjustments than continuous control.
- Steering adjustment amplitudes are predicted by visual cues and their predictions.
- Evidence accumulation explains variability in adjustment timing and size.

## Abstract

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.