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Using subjective expectations to model the neural underpinnings of proactive inhibition
Proactive inhibition – the anticipation of having to stop a response – relies on objective information contained in cue‐related contingencies in the environment, as well as on the subjective interpretation derived from these cues. To date, most studies of brain areas underlying proactive inhibition...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6618303/ https://www.ncbi.nlm.nih.gov/pubmed/30556927 http://dx.doi.org/10.1111/ejn.14308 |
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author | Pas, Pascal Plessis, Stefan Du van den Munkhof, Hanna E. Gladwin, Thomas E. Vink, Matthijs |
author_facet | Pas, Pascal Plessis, Stefan Du van den Munkhof, Hanna E. Gladwin, Thomas E. Vink, Matthijs |
author_sort | Pas, Pascal |
collection | PubMed |
description | Proactive inhibition – the anticipation of having to stop a response – relies on objective information contained in cue‐related contingencies in the environment, as well as on the subjective interpretation derived from these cues. To date, most studies of brain areas underlying proactive inhibition have exclusively considered the objective predictive value of environmental cues, by varying the probability of stop‐signals. However, by only taking into account the effect of different cues on brain activation, the subjective component of how cues affect behavior is ignored. We used a modified stop‐signal response task that includes a measurement for subjective expectation, to investigate the effect of this subjective interpretation. After presenting a cue indicating the probability that a stop‐signal will occur, subjects were asked whether they expected a stop‐signal to occur. Furthermore, response time was used to retrospectively model brain activation related to stop‐expectation. We found more activation during the cue period for 50% stop‐signal probability, when contrasting with 0%, in the mid and inferior frontal gyrus, inferior parietal lobe and putamen. When contrasting expected vs. unexpected trials, we found modest effects in the mid frontal gyrus, parietal, and occipital areas. With our third contrast, we modeled brain activation during the cue with trial‐by‐trial variances in response times. This yielded activation in the putamen, inferior parietal lobe, and mid frontal gyrus. Our study is the first to use the behavioral effects of proactive inhibition to identify the underlying brain regions, by employing an unbiased task‐design that temporally separates cue and response. |
format | Online Article Text |
id | pubmed-6618303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-66183032019-07-22 Using subjective expectations to model the neural underpinnings of proactive inhibition Pas, Pascal Plessis, Stefan Du van den Munkhof, Hanna E. Gladwin, Thomas E. Vink, Matthijs Eur J Neurosci Cognitive Neuroscience Proactive inhibition – the anticipation of having to stop a response – relies on objective information contained in cue‐related contingencies in the environment, as well as on the subjective interpretation derived from these cues. To date, most studies of brain areas underlying proactive inhibition have exclusively considered the objective predictive value of environmental cues, by varying the probability of stop‐signals. However, by only taking into account the effect of different cues on brain activation, the subjective component of how cues affect behavior is ignored. We used a modified stop‐signal response task that includes a measurement for subjective expectation, to investigate the effect of this subjective interpretation. After presenting a cue indicating the probability that a stop‐signal will occur, subjects were asked whether they expected a stop‐signal to occur. Furthermore, response time was used to retrospectively model brain activation related to stop‐expectation. We found more activation during the cue period for 50% stop‐signal probability, when contrasting with 0%, in the mid and inferior frontal gyrus, inferior parietal lobe and putamen. When contrasting expected vs. unexpected trials, we found modest effects in the mid frontal gyrus, parietal, and occipital areas. With our third contrast, we modeled brain activation during the cue with trial‐by‐trial variances in response times. This yielded activation in the putamen, inferior parietal lobe, and mid frontal gyrus. Our study is the first to use the behavioral effects of proactive inhibition to identify the underlying brain regions, by employing an unbiased task‐design that temporally separates cue and response. John Wiley and Sons Inc. 2019-01-01 2019-06 /pmc/articles/PMC6618303/ /pubmed/30556927 http://dx.doi.org/10.1111/ejn.14308 Text en 2018 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Cognitive Neuroscience Pas, Pascal Plessis, Stefan Du van den Munkhof, Hanna E. Gladwin, Thomas E. Vink, Matthijs Using subjective expectations to model the neural underpinnings of proactive inhibition |
title | Using subjective expectations to model the neural underpinnings of proactive inhibition |
title_full | Using subjective expectations to model the neural underpinnings of proactive inhibition |
title_fullStr | Using subjective expectations to model the neural underpinnings of proactive inhibition |
title_full_unstemmed | Using subjective expectations to model the neural underpinnings of proactive inhibition |
title_short | Using subjective expectations to model the neural underpinnings of proactive inhibition |
title_sort | using subjective expectations to model the neural underpinnings of proactive inhibition |
topic | Cognitive Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6618303/ https://www.ncbi.nlm.nih.gov/pubmed/30556927 http://dx.doi.org/10.1111/ejn.14308 |
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