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The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades
The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain err...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5555715/ https://www.ncbi.nlm.nih.gov/pubmed/28767650 http://dx.doi.org/10.1371/journal.pcbi.1005692 |
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author | Aponte, Eduardo A. Schöbi, Dario Stephan, Klaas E. Heinzle, Jakob |
author_facet | Aponte, Eduardo A. Schöbi, Dario Stephan, Klaas E. Heinzle, Jakob |
author_sort | Aponte, Eduardo A. |
collection | PubMed |
description | The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence. First, we provide a formal likelihood function of actions (pro- and antisaccades) and reaction times based on previously published models. Second, we introduce the Stochastic Early Reaction, Inhibition, and late Action model (SERIA), a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process. Third, we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials. Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process. Moreover, we show that the early decision process postulated by the SERIA model is, to a large extent, insensitive to the cue presented in a single trial. Finally, we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type (pro- or antisaccade) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades. |
format | Online Article Text |
id | pubmed-5555715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-55557152017-08-28 The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades Aponte, Eduardo A. Schöbi, Dario Stephan, Klaas E. Heinzle, Jakob PLoS Comput Biol Research Article The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence. First, we provide a formal likelihood function of actions (pro- and antisaccades) and reaction times based on previously published models. Second, we introduce the Stochastic Early Reaction, Inhibition, and late Action model (SERIA), a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process. Third, we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials. Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process. Moreover, we show that the early decision process postulated by the SERIA model is, to a large extent, insensitive to the cue presented in a single trial. Finally, we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type (pro- or antisaccade) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades. Public Library of Science 2017-08-02 /pmc/articles/PMC5555715/ /pubmed/28767650 http://dx.doi.org/10.1371/journal.pcbi.1005692 Text en © 2017 Aponte et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Aponte, Eduardo A. Schöbi, Dario Stephan, Klaas E. Heinzle, Jakob The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades |
title | The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades |
title_full | The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades |
title_fullStr | The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades |
title_full_unstemmed | The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades |
title_short | The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades |
title_sort | stochastic early reaction, inhibition, and late action (seria) model for antisaccades |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5555715/ https://www.ncbi.nlm.nih.gov/pubmed/28767650 http://dx.doi.org/10.1371/journal.pcbi.1005692 |
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