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 fr...

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Main Authors: Michael Joch, Mathias Hegele, Heiko Maurer, Hermann Müller, Lisa K. Maurer
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-08-01
Series:Frontiers in Psychology
Subjects:
EEG
Online Access:https://www.frontiersin.org/article/10.3389/fpsyg.2018.01376/full
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spelling doaj-221aca31e2c64dc3837637398a5d193b2020-11-24T20:57:42ZengFrontiers Media S.A.Frontiers in Psychology1664-10782018-08-01910.3389/fpsyg.2018.01376355837Accuracy of Motor Error Predictions for Different Sensory SignalsMichael JochMathias HegeleHeiko MaurerHermann MüllerLisa K. MaurerDetecting 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.https://www.frontiersin.org/article/10.3389/fpsyg.2018.01376/fullEEGerror negativityfeedback-related negativityerror predictionreinforcement learningforward model
collection DOAJ
language English
format Article
sources DOAJ
author Michael Joch
Mathias Hegele
Heiko Maurer
Hermann Müller
Lisa K. Maurer
spellingShingle Michael Joch
Mathias Hegele
Heiko Maurer
Hermann Müller
Lisa K. Maurer
Accuracy of Motor Error Predictions for Different Sensory Signals
Frontiers in Psychology
EEG
error negativity
feedback-related negativity
error prediction
reinforcement learning
forward model
author_facet Michael Joch
Mathias Hegele
Heiko Maurer
Hermann Müller
Lisa K. Maurer
author_sort Michael Joch
title Accuracy of Motor Error Predictions for Different Sensory Signals
title_short Accuracy of Motor Error Predictions for Different Sensory Signals
title_full Accuracy of Motor Error Predictions for Different Sensory Signals
title_fullStr Accuracy of Motor Error Predictions for Different Sensory Signals
title_full_unstemmed Accuracy of Motor Error Predictions for Different Sensory Signals
title_sort accuracy of motor error predictions for different sensory signals
publisher Frontiers Media S.A.
series Frontiers in Psychology
issn 1664-1078
publishDate 2018-08-01
description 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.
topic EEG
error negativity
feedback-related negativity
error prediction
reinforcement learning
forward model
url https://www.frontiersin.org/article/10.3389/fpsyg.2018.01376/full
work_keys_str_mv AT michaeljoch accuracyofmotorerrorpredictionsfordifferentsensorysignals
AT mathiashegele accuracyofmotorerrorpredictionsfordifferentsensorysignals
AT heikomaurer accuracyofmotorerrorpredictionsfordifferentsensorysignals
AT hermannmuller accuracyofmotorerrorpredictionsfordifferentsensorysignals
AT lisakmaurer accuracyofmotorerrorpredictionsfordifferentsensorysignals
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