Effects of magnitude and phase cues on human motor adaptation

Abstract

Recent findings have shown that humans can adapt their internal control model to account for the changing dynamics of systems they manipulate. In this paper, we explore the effects of magnitude and phase cues on human motor adaptation. In our experiments, participants excite virtual second-order systems at resonance via a two-degree of freedom haptic interface, with visual and visual plus haptic feedback conditions. Then, we change the virtual system parameters and observe the resulting motor adaptation in catch trials. Through four experimental conditions we demonstrate the effects of magnitude and phase cues on human motor adaptation. First, we show that humans adapt to a nominal virtual system resonant frequency. Second, humans shift to higher and lower natural frequencies during catch trials regardless of feedback modality and force cues. Third, participants can detect changes in natural frequency when gain, magnitude, and phase cues are manipulated independently. Fourth, participants are able to detect changes in natural frequency when the feedback (visual or visual plus haptic) is delayed such that the phase shift between the nominal system and catch trial system is zero. The persistent ability of participants to perform system identification of the dynamic systems which they control, regardless of the cue that is conveyed, demonstrates the human's versatility with regard to manual control situations. We intend to further investigate human motor adaptation and the time for adaptation in order to improve the efficacy of shared control methodologies for training and rehabilitation in haptic virtual environments. KEYWORDS:Rhythmic motion, motor adaptation, internal models, catch trials. INTRODUCTIONHumans frequently perform motor tasks that require interactions with external dynamic systems, such as driving a car or wielding a tool. Such systems may be underactuated or may have higher order control mappings, thereby requiring training in order for the human to learn proper control of the system [1,2]. For rhythmic tasks such as pumping a swing or bouncing a ball, the perception of the dynamic behavior of the external system directly affects the control input planned and executed by the user [3]. However, psychophysical analysis of actively controlled dynamic systems, which may shed light on the mechanisms used by humans to execute motor tasks, has received little attention. A broader understanding of human motor control could directly benefit researchers who develop training protocols or simulations to teach new motor skills.Virtual environments have been explored as a means to teach new motor skills in domains such as surgery, assembly, and pilot training. Haptic guidance schemes have been incorporated in virtual environments to improve performance and to reduce training duration and user workload. Virtual fixtures, record-and-play, shared control, and error-based guidance schemes have shown potential to improve user performance during task completion and to accelerate learning rates, by guiding the...

Rate of human motor adaptation under varying system dynamics

“…Systems with intermediate gain values were not significantly different from each other in terms of adaptation rates; however, there exists an overall trend for the mean values indicating a monotonic decrease of adaptation rates as the gain is increased. The fact that the change in gain does not effect L ∞ values is in agreement with the recent findings in the literature [6] which state that humans can robustly identify the natural frequency of a second order system and excite it at this natural frequency even when the magnitude cues are changed. On the other hand, the adaptation rate is significantly affected in such a way that the rate of adaptation decreases as the gain is increased.…”

“…A catch-trial experiment was conducted to test the effect of system parameters on rate of human adaptation. To do so, participants were overtrained with a nominal system of 1 Hz natural frequency and their adaptation to 1.4 Hz target natural frequency was tested, so that the experiments are compatible with the existing literature on human motor control of second order dynamic systems [6,7]. Details of the experiment design are presented in Figure 2 and Table 1.…”

“…They observed that participants tuned their control parameters as a general feedback strategy. Similarly, in our earlier study [6], we explored the effects of magnitude and phase cues on human motor adaptation and showed the persistent ability of humans to perform system identification of the dynamic systems which they control, regardless of the cue that was conveyed. In related studies, we have showed that the performance of the manual control task was influenced by the participant's ability to perform system identification in order to excite the virtual dynamic system near its resonant frequency [9,5] and determined the just-noticeable-difference (JND) for natural frequency of virtual second-order dynamic systems to be in 4%-9% range for 1 Hz and 2 Hz reference natural frequencies with visual-, haptic, and visual+haptic feedback [7].…”