Fisher-Rao Distance Detects Shifts in Kinematic Profiles under Cognitive Load
Motor control research involves the study of movement kinematics derived from the positional trajectories that complex motions describe. In natural, unconstrained motions requiring cognitive and memory processes in real time, the temporal speed profiles are not bell-shaped, may have multiple maxima and the peaks distribution is best fit by the continuous gamma family with two parameters, the shape and the scale. As the stochastic processes described by complex motion trajectories are non-stationary, the gamma shape and scale parameters describing them span stochastic trajectories. These points live on a curved surface where Euclidean distance depends on the arbitrary choice of parameterization. We adopt the Fisher-Rao distance (the geodesic length on the gamma manifold) as a coordinate-free metric for movement fluctuation signatures and present a robust numerical solver that converges across the full range of empirically observed parameters. We demonstrate the metric on a tablet-based digitized Trail Making Test in healthy adults, comparing micromovement fluctuations under low and high cognitive load. Load displaced every participant's fluctuation signature but with no shared direction. Instead, participants converged toward a common operating regime, with those farthest from it at baseline moving the most. Cognitive load thus contracts individuality in motor fluctuations rather than shifting the population uniformly.