In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbancerejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise) explains observed performance improvements.

In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a continuous trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward elements of subjects' controllers. We find that handedness does not affect the learned controller and that controllers transfer between hands. Observed improvements in time-domain tracking performance may be attributed to adaptation of feedback to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise)

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Despite the growing prevalence of adaptive systems in daily life, methods for analysis and synthesis of these systems are limited. Here we find theoretical obstacles to creating optimization-based algorithms that co-adapt with people in the presence of dynamic machines. These theoretical limitations motivate us to conduct human subjects experiments with adaptive interfaces, where we find an interface that decreases human effort while improving closed-loop system performance during interaction with a machine that has complex dynamics. Finally, we conduct computational simulations and find a parsimonious model for the human's adaptation strategy in our experiments, providing a hypothesis that can be tested in future studies. Our results highlight major gaps in understanding of co-adaptation in dynamic human-machine interfaces that warrant further investigation. New theory and algorithms are needed to ensure interfaces are safe, accessible, and useful.