In daily activities, humans rely on visual information to plan hand movements, making the extraction of task-relevant information through eye gaze a key aspect of motor control. Behavioral studies have revealed characteristic saccade-pursuit patterns, likely governed by shared neural circuits, which enable an efficient reduction of task-related uncertainty. However, a unifying computational principle explaining the emergence of these patterns in continuous tasks such as reading or driving is still lacking. Here we propose a dual stochastic model predictive control formulation of active vision, in which eye movements are continuously controlled to minimize task-relevant uncertainty and build an internal model used for hand movement planning. Through experiments manipulating the amount, density, and difficulty of future visual information, we show how eye movement patterns adapt to the information context while maintaining an invariant extraction horizon. A saccade-pursuit pattern naturally emerges from the model, which accurately predicts both eye and hand movement features observed in experiments. These results provide a principled framework for understanding the continuous regulation of human eye movements and open new perspectives for applications in robotic assistance and active perception.
Zhao, J., VERDEL, D., Tan, Y., Burdet, E.
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