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Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring
Response inhibition has been widely explored using the stop signal paradigm in the laboratory setting. However, the mechanism that demarcates attentional capture from the motor inhibition process is still unclear. Error monitoring is also involved in the stop signal task. Error responses that do not...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070019/ https://www.ncbi.nlm.nih.gov/pubmed/33918711 http://dx.doi.org/10.3390/brainsci11040478 |
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author | Nguyen, Trung Van Balachandran, Prasad Muggleton, Neil G. Liang, Wei-Kuang Juan, Chi-Hung |
author_facet | Nguyen, Trung Van Balachandran, Prasad Muggleton, Neil G. Liang, Wei-Kuang Juan, Chi-Hung |
author_sort | Nguyen, Trung Van |
collection | PubMed |
description | Response inhibition has been widely explored using the stop signal paradigm in the laboratory setting. However, the mechanism that demarcates attentional capture from the motor inhibition process is still unclear. Error monitoring is also involved in the stop signal task. Error responses that do not complete, i.e., partial errors, may require different error monitoring mechanisms relative to an overt error. Thus, in this study, we included a “continue go” (Cont_Go) condition to the stop signal task to investigate the inhibitory control process. To establish the finer difference in error processing (partial vs. full unsuccessful stop (USST)), a grip-force device was used in tandem with electroencephalographic (EEG), and the time-frequency characteristics were computed with Hilbert–Huang transform (HHT). Relative to Cont_Go, HHT results reveal (1) an increased beta and low gamma power for successful stop trials, indicating an electrophysiological index of inhibitory control, (2) an enhanced theta and alpha power for full USST trials that may mirror error processing. Additionally, the higher theta and alpha power observed in partial over full USST trials around 100 ms before the response onset, indicating the early detection of error and the corresponding correction process. Together, this study extends our understanding of the finer motor inhibition control and its dynamic electrophysiological mechanisms. |
format | Online Article Text |
id | pubmed-8070019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80700192021-04-26 Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring Nguyen, Trung Van Balachandran, Prasad Muggleton, Neil G. Liang, Wei-Kuang Juan, Chi-Hung Brain Sci Article Response inhibition has been widely explored using the stop signal paradigm in the laboratory setting. However, the mechanism that demarcates attentional capture from the motor inhibition process is still unclear. Error monitoring is also involved in the stop signal task. Error responses that do not complete, i.e., partial errors, may require different error monitoring mechanisms relative to an overt error. Thus, in this study, we included a “continue go” (Cont_Go) condition to the stop signal task to investigate the inhibitory control process. To establish the finer difference in error processing (partial vs. full unsuccessful stop (USST)), a grip-force device was used in tandem with electroencephalographic (EEG), and the time-frequency characteristics were computed with Hilbert–Huang transform (HHT). Relative to Cont_Go, HHT results reveal (1) an increased beta and low gamma power for successful stop trials, indicating an electrophysiological index of inhibitory control, (2) an enhanced theta and alpha power for full USST trials that may mirror error processing. Additionally, the higher theta and alpha power observed in partial over full USST trials around 100 ms before the response onset, indicating the early detection of error and the corresponding correction process. Together, this study extends our understanding of the finer motor inhibition control and its dynamic electrophysiological mechanisms. MDPI 2021-04-09 /pmc/articles/PMC8070019/ /pubmed/33918711 http://dx.doi.org/10.3390/brainsci11040478 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nguyen, Trung Van Balachandran, Prasad Muggleton, Neil G. Liang, Wei-Kuang Juan, Chi-Hung Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring |
title | Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring |
title_full | Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring |
title_fullStr | Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring |
title_full_unstemmed | Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring |
title_short | Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring |
title_sort | dynamical eeg indices of progressive motor inhibition and error-monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070019/ https://www.ncbi.nlm.nih.gov/pubmed/33918711 http://dx.doi.org/10.3390/brainsci11040478 |
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