Paul R. Davidson

Widespread access to predictive models in the motor system: a short review

Recent behavioural and computational studies suggest that access to internal predictive models of arm and object dynamics is widespread in the sensorimotor system. Several systems, including those responsible for oculomotor and skeletomotor control, perceptual processing, postural control and mental imagery, are able to access predictions of the motion of the arm. A capacity to make and use predictions of object dynamics is similarly widespread. Here, we review recent studies looking at the predictive capacity of the central nervous system which reveal pervasive access to forward models of the environment.

EEG-Based Lapse Detection With High Temporal Resolution

A warning system capable of reliably detecting lapses in responsiveness (lapses) has the potential to prevent many fatal accidents. We have developed a system capable of detecting lapses in real-time with second-scale temporal resolution. Data was from 15 subjects performing a visuomotor tracking task for two 1-hour sessions with concurrent electroencephalogram (EEG) and facial video recordings. The detector uses a neural network with normalized EEG log-power spectrum inputs from two bipolar EEG derivations, though we also considered a multichannel detector. Lapses, identified using a combination of video rating and tracking behavior, were used to train our detector. We compared detectors employing tapped delay-line linear perceptron, tapped delay-line multilayer perceptron (TDL-MLP), and long short-term memory (LSTM) recurrent neural networks operating continuously at 1 Hz. Using estimates of EEG log-power spectra from up to 4 s prior to a lapse improved detection compared with only using the most recent estimate. We report the first application of a LSTM to an EEG analysis problem. LSTM performance was equivalent to the best TDL-MLP network but did not require an input buffer. Overall performance was satisfactory with area under the curve from receiver operating characteristic analysis of 0.84 plusmn 0.02 (mean plusmn SE) and area under the precision-recall curve of 0.41 plusmn 0.08

Motor learning and prediction in a variable environment.

Traditional studies of motor learning and prediction have focused on how subjects perform a single task. Recent advances have been made in our understanding of motor learning and prediction by investigating the way we learn variable tasks, which change either predictably or unpredictably over time. Similarly, studies have examined how variability in our own movements affects motor learning.

Frequent lapses of responsiveness during an extended visuomotor tracking task in non-sleep-deprived subjects

We investigated the occurrence of lapses of responsiveness (lapses) in 15 non‐sleep‐deprived subjects performing a 1D continuous tracking task during normal working hours. Tracking behaviour, facial video, and electroencephalogram (EEG) were recorded simultaneously during two 1‐h sessions. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 ± 12.9 lapses per hour (mean ± SE) and a lapse duration of 3.4 ± 0.5 s. We also found that subjects’ performance improved towards the end of the 1‐h long session, even though no external temporal cues were available. Spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. In conclusion, lapses are a frequent phenomenon in normal subjects – even when not sleep‐deprived – engaged in an extended monotonous continuous visuomotor task. This is of particular importance to the transport sector in which there is a need to maintain sustained attention for extended periods of time and in which lapses can lead to multiple‐fatality accidents.

Internal models underlying grasp can be additively combined

Our ability to additively combine two learned internal models was investigated by studying the forces people generate when lifting objects with a precision grip. Subjects were required to alternately lift two objects of identical physical appearance but differing weight. Grip force scaling prior to lift-off was used to estimate the output of the internal model associated with each object. Appropriate internal models were formed when alternately lifting two objects of different weight. The objects were then combined by stacking them one upon the other, and the combined object was lifted. Results show that subjects can additively combine internal models of object dynamics but the sum is biased by a default estimate of the object’s weight.

Detecting Behavioral Microsleeps from EEG Power Spectra

EEG spectral power has been shown to correlate with level of arousal and alertness in humans. In this paper, we assess its usefulness in the detection of behavioral microsleeps (BMs). Eight non-sleep-deprived normal subjects performed two 1-hour sessions of a continuous tracking task while EEG and facial video were recorded. BMs were identified independent of tracking performance by a human rater by viewing the video recordings. Spectral power, normalized spectral power, and power ratios in the standard EEG bands were calculated using the Burg method on 16 bipolar derivations to form an EEG feature matrix. PCA was used to reduce the dimensionality of the feature matrix and linear discriminant analysis used to form a classifier for each subject. The 8 classifiers were combined using stacked generalization to create an overall detection model and N-fold cross-validation used to determine its performance (Phi=0.30plusmn0.05, meanplusmnSE). While modest, the detection of BMs at such a high temporal resolution (1 s) has not been achieved previously other than by our group

Detection of adaptive inverse models in the human motor system

Abstract This study aimed to find evidence for the formation of an internal inverse model of a novel visuomotor relationship for feedforward control in the brain. An experiment was carried out involving 20 normal adult subjects who performed a pursuit random tracking task with a steering wheel for input. During learning the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Results showed a transfer of learning from the unblanked runs to the blanked runs for a static nonlinear system (linear trend RMS error F (1,19)=5.05, p =0.037) thereby demonstrating adaptive feedforward control in the nervous system. No such transfer was observed for a dynamic linear system, indicating a dominant adaptive feedback control component. Results are consistent with inverse modeling and suggest a combination of feedforward and feedback adaptive control in the brain.

Common Encoding of Novel Dynamic Loads Applied to the Hand and Arm

In manual action, the relationship between a given motor command and the ensuing movement depends on the dynamics of both the arm and hand-held objects. Skilled performance relies on the brain learning both these dynamics, and previous studies have examined how people adapt to novel loads applied to either the hand or the arm. In this study, we ask whether these different kinds of load are represented independently as a result of changes in cutaneous feedback and hand-arm coordination. We used a robotic apparatus that could either apply forces to an object held in the subjects hand or directly to the segments of the arm. We tested whether subjects could retain learning of a force field applied to the hand after subsequently experiencing the opposing field applied to the arm (or vice versa), or whether retrograde interference would be observed. In separate experiments, we used force fields and torque fields that were linearly related to either hand or joint velocities, respectively. Our finding of complete interference between opposing fields suggests that loads applied to the arm and hand are not represented independently by the sensorimotor system. This interference occurred despite markedly different cutaneous inputs that were directly related to the movement task. This result suggests that the brain represents dynamics independently of these sensory inputs. In addition, we found that the rate at which subjects adapted to a given force field, specified either in hand or joint coordinates, was independent of whether the forces were applied to the hand or arm segments.

Simulating closed- and open-loop voluntary movement: a nonlinear control-systems approach

In many recent human motor control models, including feedback-error learning and adaptive model theory (AMT), feedback control is used to correct errors while an inverse model is simultaneously tuned to provide accurate feedforward control. This popular and appealing hypothesis, based on a combination of psychophysical observations and engineering considerations, predicts that once the tuning of the inverse model is complete the role of feedback control is limited to the correction of disturbances. This hypothesis was tested by looking at the open-loop behavior of the human motor system during adaptation. An experiment was carried out involving 20 normal adult subjects who learned a novel visuomotor relationship on a pursuit tracking task with a steering wheel for input. During learning, the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Open-loop behavior was not consistent with a progressive transfer from closed to open-loop control. Our recently developed computational model of the brain-a novel nonlinear implementation of AMT-was able to reproduce the observed closed- and open-loop results. In contrast, other control-systems models exhibited only minimal feedback control following adaptation, leading to incorrect open-loop behavior. This is because our model continues to use feedback to control slow movements after adaptation is complete. This behavior enhances the internal stability of the inverse model. In summary, our computational model is currently the only motor control model able to accurately simulate the closed- and open-loop characteristics of the experimental response trajectories.

Automated video-based measurement of eye closure for detecting behavioral microsleep

A device capable of continuously monitoring an individuals levels of alertness in real-time is highly desirable for preventing drowsiness and microsleep related accidents. This paper presents a development of non-intrusive and light-insensitive video-based system that uses computer-vision methods to measure facial metric for identifying visible facial signs of drowsiness and behavioral microsleep. The developed system uses a remotely placed camera with a near-infrared illumination to acquire the video. The computer-vision methods are then applied to sequentially localize face, eyes, and eyelids positions to measure ratio of eye closure. The system was evaluated in frontal images of nine subjects with varying facial structures and exhibiting several ratio of eye closure and eye gaze under fully dark and ambient lighting conditions. The preliminary results showed promising results with sufficient accuracy to distinguish between fully closed, half closed, and fully open eyes.