Visuomotor coordination on the road: Low-dimensional representations reveal adaptive, context-dependent reductions in the dimensionality of natural driving behavior

The brain’s ability to transform complex, high-dimensional sensory and motor inputs into coordinated, goal-directed behavior remains a central challenge in neuroscience research. Current research suggests that behavior is generated through patterns within a low-dimensional structure. What visuomotor patterns underlie complex, unconstrained behaviors such as driving, and how stereotypical they are, remain poorly understood. Driving involves complex perception-action interactions, which we hypothesize are specific to the driver’s role and adapt to task demands. Here, we unfold these dynamics using an immersive virtual drive that recreates ten hazardous on-road events. We collected eye, head, and vehicle movements from participants assigned to either a manual or an automated driving condition. From the behavioral movements of 284 drivers, we constructed a common time-resolved behavioral state space based on collective variance, computed via principal component analysis across participants. The obtained low-dimensional representation thus captures generalized visuomotor coordination strategies. We then characterized the evolving low-dimensional structure with cosine similarity and various measures of effective dimensionality. We tested whether leading components contained condition-specific information using discriminant analysis. Throughout the entire drive, few components accounted for most of the variance in behavior, and effective dimensionality decreased reliably around critical events, indicating tighter coordination between perceptual and motor variables. During periods of low effective dimensionality, the contributions of eye, head, steering, and vehicle heading reorganized toward leading dimensions in a task-dependent manner. Across events, between-subject similarity was partially stable and stronger among manual drivers. This reorganization also made behaviors more distinct across driving conditions, enabling better classification of driving modes using only the first two components. These results show that naturalistic driving is supported by shared low-dimensional visuomotor coordination strategies that are flexibly reshaped by contextual demands, and provide a principled behavioral framework for linking neural manifolds, human driver models, and the design of adaptive automated vehicles.Author summary: Human behavior is complex. Driving, for instance, involves actively turning our head and eyes to see what matters most while translating those movements into appropriate actions with our hands and feet. Research using constrained repeated actions suggests that these seemingly complex behaviors are highly coordinated and can be described by a relatively small number of variables. We want to know whether this holds during natural, unconstrained tasks. We asked people to complete a virtual drive comprising ten hazards, such as animals and pedestrians on the road. Some participants drove the car, others sat in a self-driving car. We recorded where people looked, how they moved their heads, and steered the vehicle. Then, we used statistical methods to reduce the data’s dimensionality and looked for patterns that could explain all these signals together. We found that a small number of patterns were enough to describe behavior and became even simpler and more linked around hazards, differentiating automated from manual driving behavior. Our results suggest that even complex driving behavior follows a few basic coordination strategies specific to task demands. These basic dimensions contain important details that are unique to certain groups, ultimately helping us better understand and compare human behavior.