Separable Representations for Duration and Distance in Virtual Movements.

To navigate through the environment, humans must be able to measure both the distance traveled in space, and the interval elapsed in time. Yet, how the brain holds both of these metrics simultaneously is less well known. One possibility is that participants measure how far and how long they have traveled relative to a known reference point. To measure this, we had human participants (n = 24) perform a distance estimation task in a virtual environment in which they were cued to attend to either the spatial or temporal interval traveled while responses were measured with multiband fMRI. We observed that both dimensions evoked similar frontoparietal networks, yet with a striking rostrocaudal dissociation between temporal and spatial estimation. Multivariate classifiers trained on each dimension were further able to predict the temporal or spatial interval traveled, with centers of activation within the SMA and retrosplenial cortex for time and space, respectively. Furthermore, a cross-classification approach revealed the right supramarginal gyrus and occipital place area as regions capable of decoding the general magnitude of the traveled distance. Altogether, our findings suggest the brain uses separate systems for tracking spatial and temporal distances, which are combined together along with dimension-nonspecific estimates.

Using principles of motor control to analyze performance of human machine interfaces.

There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.

Using Principles of Motor Control to Analyze Performance of Human Machine Interfaces.

There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of a end effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.

Dissociable effects of emotional stimuli on electrophysiological indices of time and decision-making.

Previous research has demonstrated that emotional faces affect time perception, however, the underlying mechanisms are not fully understood. Earlier attempts focus on effects at the different stages of the pacemaker-accumulator model (clock, memory, and/or decision-making) including, an increase in pacemaker rate or accumulation rate via arousal or attention, respectively, or by biasing decision-making. A visual temporal bisection task with sub-second intervals was conducted in two groups to further investigate these effects; one group was strictly behavioral whereas the second included a 64-channel electroencephalogram (EEG). To separate the influence of face and timing responses, participants timed a visual stimulus, temporally flanked (before and after) by two faces, either negative or neutral, creating three trial-types: Neg→Neut, Neut→Neg, or Neut→Neut. We found a leftward shift in bisection point (BP) in Neg→Neut relative to Neut→Neut suggests an overestimation of the temporal stimulus when preceded by a negative face. Neurally, we found the face-responsive N170 was larger for negative faces and the N1 and contingent negative variation (CNV) were larger when the temporal stimulus was preceded by a negative face. Additionally, there was an interaction effect between condition and response for the late positive component of timing (LPCt) and a significant difference between response (short/long) in the neutral condition. We concluded that a preceding negative face affects the clock stage leading to more pulses being accumulated, either through attention or arousal, as indexed by a larger N1, CNV, and N170; whereas viewing a negative face after impacted decision-making mechanisms, as evidenced by the LPCt.

How movements shape the perception of time.

In order to keep up with a changing environment, mobile organisms must be capable of deciding both where and when to move. This precision necessitates a strong sense of time, as otherwise we would fail in many of our movement goals. Yet, despite this intrinsic link, only recently have researchers begun to understand how these two features interact. Primarily, two effects have been observed: movements can bias time estimates, but they can also make them more precise. Here we review this literature and propose that both effects can be explained by a Bayesian cue combination framework, in which movement itself affords the most precise representation of time, which can influence perception in either feedforward or active sensing modes.