Work Package 1

UCAM has completed two studies of inertial sensing using the novel WristBot manipulandum (Figure). To manipulate an object skilfully, the motor system must have knowledge of its dynamics, specifying the mapping between applied force and object motion. An important question relates to the mechanisms by which object dynamics are represented: do humans learn the dynamics of individual objects or do they develop general models of dynamics for classes of objects that are then specifically parameterized for individual objects. We addressed this issue using a task in which subjects manipulated virtual hammers simulated with a novel robotic interface. Our results suggest that when viewing a hammer, subjects can recall a general model of hammer dynamics that enables them to generate forces in directions appropriate for the visual orientation of the hammer. Subsequent experience of the dynamics when manipulating the hammer allows subjects to learn its specific inertial parameters. Interestingly, there is limited generalization of the inertial parameters when the orientation of the hammer relative to the hand is changed. These results provide evidence for two distinct levels of representation of objects dynamics: general models that capture the dynamic structure of classes of objects and specific parameters associated with individual objects which are represented locally for specific contexts.

Work Package 3

UCAM has completed analysis if the statistics of hand movement has now been published as Ingram et al. The statistics of natural hand movements. Experimental brain research (2008) vol. 188 (2) pp. 223-36. Humans constantly use their hands to interact with the environment and they engage spontaneously in a wide variety of manual activities during everyday life. In contrast, laboratory-based studies of hand function have used a limited range of predefined tasks. The natural movements made by the hand during everyday life have thus received little attention. Here we developed a portable recording device that can be worn by subjects to track movements of their right hand as they go about their daily routine outside of a laboratory setting. We analyse the kinematic data using various statistical methods. Principal component analysis of the joint angular velocities showed that the first two components were highly conserved across subjects, explained 60% of the variance and were qualitatively similar to those reported in previous studies of reach-to-grasp movements. To examine independence of the digits we developed a measure based on the degree to which the movements of each digit could be linearly predicted from the movements of the other four digits. Our independence measure was highly correlated with results from previous studies of the hand, including the estimated size of the digit representations in primary motor cortex and other laboratory measures of digit individuation. Specifically the thumb was found to be the most independent of the digits and the index finger was the most independent of the fingers. These results support and extend laboratory-based studies of the human hand.