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Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model

Human observers automatically extract temporal contingencies from the environment and predict the onset of future events. Temporal predictions are modeled by the hazard function, which describes the instantaneous probability for an event to occur given it has not occurred yet. Here, we tackle the qu...

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Detalles Bibliográficos
Autores principales: Herbst, Sophie K., Fiedler, Lorenz, Obleser, Jonas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society for Neuroscience 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938715/
https://www.ncbi.nlm.nih.gov/pubmed/29740594
http://dx.doi.org/10.1523/ENEURO.0017-18.2018
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author Herbst, Sophie K.
Fiedler, Lorenz
Obleser, Jonas
author_facet Herbst, Sophie K.
Fiedler, Lorenz
Obleser, Jonas
author_sort Herbst, Sophie K.
collection PubMed
description Human observers automatically extract temporal contingencies from the environment and predict the onset of future events. Temporal predictions are modeled by the hazard function, which describes the instantaneous probability for an event to occur given it has not occurred yet. Here, we tackle the question of whether and how the human brain tracks continuous temporal hazard on a moment-to-moment basis, and how flexibly it adjusts to strictly implicit variations in the hazard function. We applied an encoding-model approach to human electroencephalographic data recorded during a pitch-discrimination task, in which we implicitly manipulated temporal predictability of the target tones by varying the interval between cue and target tone (i.e. the foreperiod). Critically, temporal predictability either was driven solely by the passage of time (resulting in a monotonic hazard function) or was modulated to increase at intermediate foreperiods (resulting in a modulated hazard function with a peak at the intermediate foreperiod). Forward-encoding models trained to predict the recorded EEG signal from different temporal hazard functions were able to distinguish between experimental conditions, showing that implicit variations of temporal hazard bear tractable signatures in the human electroencephalogram. Notably, this tracking signal was reconstructed best from the supplementary motor area, underlining this area’s link to cognitive processing of time. Our results underline the relevance of temporal hazard to cognitive processing and show that the predictive accuracy of the encoding-model approach can be utilized to track abstract time-resolved stimuli.
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spelling pubmed-59387152018-05-08 Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model Herbst, Sophie K. Fiedler, Lorenz Obleser, Jonas eNeuro New Research Human observers automatically extract temporal contingencies from the environment and predict the onset of future events. Temporal predictions are modeled by the hazard function, which describes the instantaneous probability for an event to occur given it has not occurred yet. Here, we tackle the question of whether and how the human brain tracks continuous temporal hazard on a moment-to-moment basis, and how flexibly it adjusts to strictly implicit variations in the hazard function. We applied an encoding-model approach to human electroencephalographic data recorded during a pitch-discrimination task, in which we implicitly manipulated temporal predictability of the target tones by varying the interval between cue and target tone (i.e. the foreperiod). Critically, temporal predictability either was driven solely by the passage of time (resulting in a monotonic hazard function) or was modulated to increase at intermediate foreperiods (resulting in a modulated hazard function with a peak at the intermediate foreperiod). Forward-encoding models trained to predict the recorded EEG signal from different temporal hazard functions were able to distinguish between experimental conditions, showing that implicit variations of temporal hazard bear tractable signatures in the human electroencephalogram. Notably, this tracking signal was reconstructed best from the supplementary motor area, underlining this area’s link to cognitive processing of time. Our results underline the relevance of temporal hazard to cognitive processing and show that the predictive accuracy of the encoding-model approach can be utilized to track abstract time-resolved stimuli. Society for Neuroscience 2018-05-08 /pmc/articles/PMC5938715/ /pubmed/29740594 http://dx.doi.org/10.1523/ENEURO.0017-18.2018 Text en Copyright © 2018 Herbst et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle New Research
Herbst, Sophie K.
Fiedler, Lorenz
Obleser, Jonas
Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model
title Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model
title_full Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model
title_fullStr Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model
title_full_unstemmed Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model
title_short Tracking Temporal Hazard in the Human Electroencephalogram Using a Forward Encoding Model
title_sort tracking temporal hazard in the human electroencephalogram using a forward encoding model
topic New Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938715/
https://www.ncbi.nlm.nih.gov/pubmed/29740594
http://dx.doi.org/10.1523/ENEURO.0017-18.2018
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