Theories of visually guided action are characterized as closed-loop control in the presence of reliable sources of visual information, and predictive control to compensate for visuomotor delay and temporary occlusion. However, prediction is not well understood. To investigate, a series of studies was designed to characterize the role of predictive strategies in humans as they perform visually guided actions, and to guide the development of computational models that capture these strategies. During data collection, subjects were immersed in a virtual reality (VR) system and were tasked with using a paddle to intercept a virtual ball. To force subjects into a predictive mode of control, the ball was occluded or made invisible for a portion of its 3D parabolic trajectory. The subjects gaze, hand and head movements were recorded during the performance. To improve the quality of gaze estimation, new algorithms were developed for the measurement and calibration of spatial and temporal errors of an eye tracking system. The analysis focused on the subjects gaze and hand movements reveal that, when the temporal constraints of the task did not allow the subjects to use closed-loop control, they utilized a short-term predictive strategy. Insights gained through behavioral analysis were formalized into computational models of visual prediction using machine learning techniques. In one study, LSTM recurrent neural networks were utilized to explain how information is integrated and used to guide predictive movement of the hand and eyes. In a subsequent study, subject data was used to train an inverse reinforcement learning (IRL) model that captures the full spectrum of strategies from closed-loop to predictive control of gaze and paddle placement. A comparison of recovered reward values between occlusion and no-occlusion conditions revealed a transition from online to predictive control strategies within a single course of action. This work has shed new insights on predictive strategies that guide our eye and hand movements.
Imaging Science (Ph.D.)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Gabriel J. Diaz
Jeff B. Pelz
Binaee, Kamran, "Study of Human Hand-Eye Coordination Using Machine Learning Techniques in a Virtual Reality Setup" (2019). Thesis. Rochester Institute of Technology. Accessed from
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