Heiko Maurer

Brain negativity as an indicator of predictive error processing: the contribution of visual action effect monitoring

The error (related) negativity (Ne/ERN) is an event-related potential in the electroencephalogram (EEG) correlating with error processing. Its conditions of appearance before terminal external error information suggest that the Ne/ERN is indicative of predictive processes in the evaluation of errors. The aim of the present study was to specifically examine the Ne/ERN in a complex motor task and to particularly rule out other explaining sources of the Ne/ERN aside from error prediction processes. To this end, we focused on the dependency of the Ne/ERN on visual monitoring about the action outcome after movement termination but before result feedback (action effect monitoring). Participants performed a semi-virtual throwing task by using a manipulandum to throw a virtual ball displayed on a computer screen to hit a target object. Visual feedback about the ball flying to the target was masked to prevent action effect monitoring. Participants received a static feedback about the action outcome (850 ms) after each trial. We found a significant negative deflection in the average EEG curves of the error trials peaking at ~250 ms after ball release, i.e., before error feedback. Furthermore, this Ne/ERN signal did not depend on visual ball-flight monitoring after release. We conclude that the Ne/ERN has the potential to indicate error prediction in motor tasks and that it exists even in the absence of action effect monitoring.NEW & NOTEWORTHY In this study, we are separating different kinds of possible contributors to an electroencephalogram (EEG) error correlate (Ne/ERN) in a throwing task. We tested the influence of action effect monitoring on the Ne/ERN amplitude in the EEG. We used a task that allows us to restrict movement correction and action effect monitoring and to control the onset of result feedback. We ascribe the Ne/ERN to predictive error processing where a conscious feeling of failure is not a prerequisite.

Neural correlates of error prediction in a complex motor task

The goal of the study was to quantify error prediction processes via neural correlates in the Electroencephalogram (EEG). Access to such a neural signal will allow to gain insights into functional and temporal aspects of error perception in the course of learning. We focused on the error negativity (Ne) or error-related negativity (ERN) as a candidate index for the prediction processes. We have used a virtual goal-oriented throwing task where participants used a lever to throw a virtual ball displayed on a computer monitor with the goal of hitting a virtual target as often as possible. After one day of practice with 400 trials, participants performed another 400 trials on a second day with EEG measurement. After error trials (i.e., when the ball missed the target), we found a sharp negative deflection in the EEG peaking 250 ms after ball release (mean amplitude: t = −2.5, df = 20, p = 0.02) and another broader negative deflection following the first, reaching from about 300 ms after release until unambiguous visual knowledge of results (KR; hitting or passing by the target; mean amplitude: t = −7.5, df = 20, p < 0.001). According to shape and timing of the two deflections, we assume that the first deflection represents a predictive Ne/ERN (prediction based on efferent commands and proprioceptive feedback) while the second deflection might have arisen from action monitoring.

Verbal-Motor Attention-Focusing Instructions Influence Kinematics and Performance on a Golf-Putting Task

ABSTRACT The authors examined how varying the content of verbal-motor instructions and requesting an internal versus external focus influenced the kinematics and outcome of a golf putting task. On Day 1, 30 novices performed 120 trials with the instruction to focus attention either on performing a pendulum-like movement (internal) or on the desired ball path (external). After 20 retention trials on Day 2, they performed 20 transfer trials with the opposite instruction. Group differences for retention and a group by block interaction showed that external instruction enhanced movement outcome. Kinematic data indicated that specific instruction content influenced outcomes by eliciting changes in movement execution. Switching from the external to the internal focus instruction resulted in a more pendulum-like movement.

Timing accuracy in self-timed movements related to neural indicators of movement initiation.

Timely movement initiation is crucial in quick reactions or when a series of movements has to be strung together in a timed fashion to create a coordinated sequence. Stochastic neural variability can lead to misinitiation errors as reaction time studies suggest. Higher reaction times occur when preparatory neural activity reaches an initiation threshold later relative to shorter reaction times. Whether this also applies to self-timed movements is harder to scrutinize because they lack an external event that could serve as a reference for timing accuracy estimations. By example of a self-timed goal-oriented throwing task, we used a method that synchronizes the throwing movements by their kinematic profiles to assess relative timing differences in throwing release. We determined neural preparatory processes of the release using the movement-related electrophysiological Bereitschaftspotential (BP). By analyzing differences in shape and timing of the BP in delayed and non-delayed throws, two variables could be extracted that are related to timing differences on the kinematic level. First, temporal deviations in BP curves partly meet the kinematic deviations. Second, delayed releases were preceded by a short flattening of the BP curves prior to release. Thus, temporal and shape deviations in the neural movement initiation are assumed to delay self-timed movements.

The Influence of Movement Initiation Deficits on the Quantification of Retention in Parkinson’s Disease

In patients with an impaired motor system, like Parkinson’s disease (PD), deficits in motor learning are expected and results of various studies seem to confirm these expectations. However, most studies in this regard are behaviorally based and quantify learning by performance changes between at least two points in time, e.g., baseline and retention. But, performance in a retention test is also dependent on other factors than learning. Especially in patients, the functional capacity of the control system might be altered unspecific to a certain task and learning episode. The aim of the study is to test whether characteristic temporal deficits exist in PD patients that affect retention performance. We tested the confounding effects of typical PD motor control deficits, here movement initiation deficits, on retention performance in the motor learning process. 12 PD patients and 16 healthy control participants practiced a virtual throwing task over 3 days with 24 h rest between sessions. Retention was tested comparing performance before rest with performance after rest. Movement initiation deficits were quantified by the timing of throwing release that should be affected by impairments in movement initiation. To scrutinize the influence of the initiation deficits on retention performance we gave participants a specific initiation intervention prior to practice on one of the three practice days. We found that only for the PD patients, post-rest performance as well as release timing was better with intervention as compared to without intervention. Their performance could be enhanced through a tuning of release initiation. Thus, we suggest that in PD patients, performance decline after rest that might be easily interpreted as learning deficits could rather result from disease-related deficiencies in motor control.

Accuracy of Motor Error Predictions for Different Sensory Signals

Detecting and evaluating errors in action execution is essential for learning. Through complex interactions of the inverse and the forward model, the human motor system can predict and subsequently adjust ongoing or subsequent actions. Inputs to such a prediction are efferent and afferent signals from various sources. The aim of the current study was to examine the impact of visual as well as a combination of efferent and proprioceptive input signals to error prediction in a complex motor task. Predicting motor errors has been shown to be correlated with a neural signal known as the error-related negativity (Ne/ERN). Here, we tested how the Ne/ERN amplitude was modulated by the availability of different sensory signals in a semi-virtual throwing task where the action outcome (hit or miss of the target) was temporally delayed relative to movement execution allowing participants to form predictions about the outcome prior to the availability of knowledge of results. 19 participants practiced the task and electroencephalogram was recorded in two test conditions. In the Visual condition, participants received only visual input by passively observing the throwing movement. In the EffProp condition, participants actively executed the task while visual information about the real and the virtual effector was occluded. Hence, only efferent and proprioceptive signals were available. Results show a significant modulation of the Ne/ERN in the Visual condition while no effect could be observed in the EffProp condition. In addition, amplitudes of the feedback-related negativity in response to the actual outcome feedback were found to be inversely related to the Ne/ERN amplitudes. Our findings indicate that error prediction is modulated by the availability of input signals to the forward model. The observed amplitudes were found to be attenuated in comparison to previous studies, in which all efferent and sensory inputs were present. Furthermore, we assume that visual signals are weighted higher than proprioceptive signals, at least in goal-oriented tasks with visual targets.

Analysis of timing variability in human movements by aligning parameter curves in time

The analysis of timing in human movements requires a reference with which timing can be quantified. In reactive movements this reference is given by the stimulus. However, many movements do not respond to such an external event. In throwing, for instance, the hand opening for release has to be timed to an acceleration of the throwing arm. A common approach to analyzing release-timing variability is to choose a landmark in the movement that is supposed to have a fixed temporal relation to the release. Such distinct landmarks, however, are not always well definable. Therefore, the present article describes an alternative approach analyzing timing variability on the basis of the alignment of different trials relative to their kinematic shape, by shifting the trials in the time domain. The basic assumption behind this approach is that single throwing movements are one instance of an acquired movement template, and thus show a considerable similarity. In contrast, the location of the temporal moment of release varies from trial to trial, generating imprecision regarding the release timing. In trials synchronized with respect to the release, this variability can be assessed by shifting the kinematic profiles of the throwing movements in time such that they superimpose as closely as possible. As a result, the corresponding time shifts for all trials represent a measure of the release time deviations across trials, and the standard deviation of these deviations represents the timing variability. Aside from timing analyses in such movements as throwing, the approach can be applied to very different tasks with timing demands—for example, to neurophysiological signals.

Artificial Neural Networks Predicting the Outcome of a Throwing Task – Effects of Input Quantity and Quality

Internal forward models are used to explain motor prediction processes in motor control and learning e.g. predicting an upcoming miss in a throwing task before the knowledge of results is available. In this study we used artificial neural networks (ANN) to model such movement outcome prediction processes. Additionally, we varied the inputs of four different multilayer perceptrons (MLP) with respect to the quantity and the reliability (quality) of input parameters to account for perceptual noise. The results show that ANNs are able to learn the non-linear input-output mapping underlying the throwing task even with few input variables (velocity and angle at ball release). Results improve when providing additional information about the ball flight (prediction accuracy increases from RMSE = 7.9 mm to RMSE = 3.9 mm). However, when a model is provided with noisy inputs only, model training and prediction suffers substantially (RMSE = 53.8 mm). Yet, additional reliable information about the ball flight (in addition to noisy velocity and angle) leads to very high model prediction accuracy again (RMSE = 4.1 mm). In a nutshell, ANNs can be used to model internal forward model predictions, but the availability of reliable input information is essential at least to some extent.