When lifting or moving a novel object, humans are routinely able to quickly characterize the nature of the unknown load and swiftly achieve the desired movement trajectory. It appears that both tactile and proprioceptive feedback systems help humans develop an accurate prediction of load properties and determine how associated limb segments behave during voluntary movements. While various types of limb movement information, such as position, velocity, acceleration, and manipulating forces, can be detected using human tactile and proprioceptive systems, we know little about how the central nervous system decodes these various types of movement data, and in which order or priority they are used when developing predictions of joint motion during novel object manipulation. In this study, we tested whether the ability to predict motion is different between position- (elastic), velocity- (viscous), and acceleration-dependent (inertial) loads imposed using a multiaxial haptic robot. Using this protocol, we can learn if the prediction of the motion model is optimized for one or more of these types of mechanical load. We examined ten neurologically intact subjects. Our key findings indicated that inertial and viscous loads showed the fastest adaptation speed, whereas elastic loads showed the slowest adaptation speed. Different speeds of adaptation were observed across different magnitudes of the load, suggesting that human capabilities for predicting joint motion and manipulating loads may vary systematically with different load types and load magnitudes. Our results imply that human capabilities for load manipulation seems to be most sensitive to and potentially optimized for inertial loads.