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Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus
The distributed nature of information processing in the brain creates a complex variety of decision making behavior. Likewise, computational models of saccadic decision making behavior are numerous and diverse. Here we present a generative model of saccadic action selection in the context of competi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381059/ https://www.ncbi.nlm.nih.gov/pubmed/30814938 http://dx.doi.org/10.3389/fnsys.2019.00003 |
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author | Coe, Brian C. Trappenberg, Thomas Munoz, Douglas P. |
author_facet | Coe, Brian C. Trappenberg, Thomas Munoz, Douglas P. |
author_sort | Coe, Brian C. |
collection | PubMed |
description | The distributed nature of information processing in the brain creates a complex variety of decision making behavior. Likewise, computational models of saccadic decision making behavior are numerous and diverse. Here we present a generative model of saccadic action selection in the context of competitive decision making in the superior colliculus (SC) in order to investigate how independent neural signals may converge to interact and guide saccade selection, and to test if systematic variations can better replicate the variability in responses that are part of normal human behavior. The model was tasked with performing pro- and anti-saccades in order to replicate specific attributes of healthy human saccade behavior. Participants (ages 18–39) were instructed to either look toward (pro-saccade, well-practiced automated response) or away from (anti-saccade, combination of inhibitory and voluntary responses) a peripheral visual stimulus. They generated express and regular latency saccades in the pro-saccade task. In the anti-saccade task, correct reaction times were longer and participants occasionally looked at the stimulus (direction error) at either express or regular latencies. To gain a better understanding of the underlying neural processes that lead to saccadic action selection and response inhibition, we implemented 8 inputs inspired by systems neuroscience. These inputs reflected known sensory, automated, voluntary, and inhibitory components of cortical and basal ganglia activity that coalesces in the intermediate layers of the SC (SCi). The model produced bimodal reaction time distributions, where express and regular latency saccades had distinct modes, for both correct pro-saccades and direction errors in the anti-saccade task. Importantly, express and regular latency direction errors resulted from interactions of different inputs in the model. Express latency direction errors were due to a lack of pre-emptive fixation and inhibitory activity, which aloud sensory and automated inputs to initiate a stimulus-driven saccade. Regular latency errors occurred when the automated motor signals were stronger than the voluntary motor signals. While previous models have emulated fewer aspects of these behavioral findings, the focus of the simulations here is on the interaction of a wide variety of physiologically-based information integration producing a richer set of natural behavioral variability. |
format | Online Article Text |
id | pubmed-6381059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63810592019-02-27 Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus Coe, Brian C. Trappenberg, Thomas Munoz, Douglas P. Front Syst Neurosci Neuroscience The distributed nature of information processing in the brain creates a complex variety of decision making behavior. Likewise, computational models of saccadic decision making behavior are numerous and diverse. Here we present a generative model of saccadic action selection in the context of competitive decision making in the superior colliculus (SC) in order to investigate how independent neural signals may converge to interact and guide saccade selection, and to test if systematic variations can better replicate the variability in responses that are part of normal human behavior. The model was tasked with performing pro- and anti-saccades in order to replicate specific attributes of healthy human saccade behavior. Participants (ages 18–39) were instructed to either look toward (pro-saccade, well-practiced automated response) or away from (anti-saccade, combination of inhibitory and voluntary responses) a peripheral visual stimulus. They generated express and regular latency saccades in the pro-saccade task. In the anti-saccade task, correct reaction times were longer and participants occasionally looked at the stimulus (direction error) at either express or regular latencies. To gain a better understanding of the underlying neural processes that lead to saccadic action selection and response inhibition, we implemented 8 inputs inspired by systems neuroscience. These inputs reflected known sensory, automated, voluntary, and inhibitory components of cortical and basal ganglia activity that coalesces in the intermediate layers of the SC (SCi). The model produced bimodal reaction time distributions, where express and regular latency saccades had distinct modes, for both correct pro-saccades and direction errors in the anti-saccade task. Importantly, express and regular latency direction errors resulted from interactions of different inputs in the model. Express latency direction errors were due to a lack of pre-emptive fixation and inhibitory activity, which aloud sensory and automated inputs to initiate a stimulus-driven saccade. Regular latency errors occurred when the automated motor signals were stronger than the voluntary motor signals. While previous models have emulated fewer aspects of these behavioral findings, the focus of the simulations here is on the interaction of a wide variety of physiologically-based information integration producing a richer set of natural behavioral variability. Frontiers Media S.A. 2019-02-13 /pmc/articles/PMC6381059/ /pubmed/30814938 http://dx.doi.org/10.3389/fnsys.2019.00003 Text en Copyright © 2019 Coe, Trappenberg and Munoz. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Coe, Brian C. Trappenberg, Thomas Munoz, Douglas P. Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus |
title | Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus |
title_full | Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus |
title_fullStr | Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus |
title_full_unstemmed | Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus |
title_short | Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus |
title_sort | modeling saccadic action selection: cortical and basal ganglia signals coalesce in the superior colliculus |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381059/ https://www.ncbi.nlm.nih.gov/pubmed/30814938 http://dx.doi.org/10.3389/fnsys.2019.00003 |
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