Adaptations of lateral hand movements to early and late visual occlusion in catching

Recent studies suggested that the control of hand movements in catching involves continuous vision-based adjustments. More insight into these adjustments may be gained by examining the effects of occluding different parts of the ball trajectory. Here, we examined the effects of such occlusion on lateral hand movements when catching balls approaching from different directions, with the occlusion conditions presented in blocks or in randomized order. The analyses showed that late occlusion only had an effect during the blocked presentation, and early occlusion only during the randomized presentation. During the randomized presentation movement biases were more leftward if the preceding trial was an early occlusion trial. The effect of early occlusion during the randomized presentation suggests that the observed leftward movement bias relates to the rightward visual acceleration inherent to the ball trajectories used, while its absence during the blocked presentation seems to reflect trial-by-trial adaptations in the visuomotor gain, reminiscent of dynamic gain control in the smooth pursuit system. The movement biases during the late occlusion block were interpreted in terms of an incomplete motion extrapolation--a reduction of the velocity gain--caused by the fact that participants never saw the to-be-extrapolated part of the ball trajectory. These results underscore that continuous movement adjustments for catching do not only depend on visual information, but also on visuomotor adaptations based on non-visual information.

How position, velocity, and temporal information combine in the prospective control of catching:data and model

The cerebral cortex contains circuitry for continuously computing properties of the environment and one's body, as well as relations among those properties. The success of complex perceptuomotor performances requires integrated, simultaneous use of such relational information. Ball catching is a good example as it involves reaching and grasping of visually pursued objects that move relative to the catcher. Although integrated neural control of catching has received sparse attention in the neuroscience literature, behavioral observations have led to the identification of control principles that may be embodied in the involved neural circuits. Here, we report a catching experiment that refines those principles via a novel manipulation. Visual field motion was used to perturb velocity information about balls traveling on various trajectories relative to a seated catcher, with various initial hand positions. The experiment produced evidence for a continuous, prospective catching strategy, in which hand movements are planned based on gaze-centered ball velocity and ball position information. Such a strategy was implemented in a new neural model, which suggests how position, velocity, and temporal information streams combine to shape catching movements. The model accurately reproduces the main and interaction effects found in the behavioral experiment and provides an interpretation of recently observed target motion-related activity in the motor cortex during interceptive reaching by monkeys. It functionally interprets a broad range of neurobiological and behavioral data, and thus contributes to a unified theory of the neural control of reaching to stationary and moving targets.

A dynamical neural network for hitting an approaching object

Besides making contact with an approaching ball at the proper place and time, hitting requires control of the effector velocity at contact. A dynamical neural network for the planning of hitting movements was derived in order to account for both these requirements. The model in question implements continuous required velocity control by extending the Vector Integration To Endpoint model while providing explicit control of effector velocity at interception. It was shown that the planned movement trajectories generated by the model agreed qualitatively with the kinematics of hitting movements as observed in two recent experiments. Outstanding features of this comparison concerned the timing and amplitude of the empirical backswing movements, which were largely consistent with the predictions from the model. Several theoretical implications as well as the informational basis and possible neural underpinnings of the model were discussed.