Madrid-Carvajal, J.; Derakhshan, S.; König, P.

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 autonomous 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 manifold thus represents generalized visuomotor coordination strategies. We then characterized the evolving low-dimensional structure with cosine similarity and various entropy-based measures of effective dimensionality. We tested whether early components contained condition-specific information using discriminant analysis. Across the entire drive, a 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 early dimensions in a task-dependent manner. As a result, behaviors became 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 autonomous vehicles.