Great expectations: a predictive processing account of automobile driving

TLDR

Predictive processing proposes that cognition and behavior arise from minimizing prediction errors, framing the brain as a statistical organ that continuously predicts sensory input, a theory influential in neuroscience yet largely untested in applied human‑factor contexts. This paper pioneers applying predictive‑processing concepts to automobile driving to understand driving behavior. The authors demonstrate that a predictive‑processing framework offers a novel, unifying perspective on diverse driving phenomena, reconciling previously disparate human‑factor models.

Abstract

Predictive processing has been proposed as a unifying framework for understanding brain function, suggesting that cognition and behaviour can be fundamentally understood based on the single principle of prediction error minimisation. According to predictive processing, the brain is a statistical organ that continuously attempts get a grip on states in the world by predicting how these states cause sensory input and minimising the deviations between the predicted and actual input. While these ideas have had a strong influence in neuroscience and cognitive science, they have so far not been adopted in applied human factors research. The present paper represents a first attempt to do so, exploring how predictive processing concepts can be used to understand automobile driving. It is shown how a framework based on predictive processing may provide a novel perspective on a range of driving phenomena and offer a unifying framework for traditionally disparate human factors models.