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The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes
Predictive coding models can simulate known perceptual or neuronal phenomena, but there have been fewer attempts to identify a reliable neural signature of predictive coding for complex stimuli. In a pair of studies, we test whether the N300 component of the event-related potential, occurring 250–35...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8171016/ https://www.ncbi.nlm.nih.gov/pubmed/34296175 http://dx.doi.org/10.1093/texcom/tgab030 |
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author | Kumar, Manoj Federmeier, Kara D Beck, Diane M |
author_facet | Kumar, Manoj Federmeier, Kara D Beck, Diane M |
author_sort | Kumar, Manoj |
collection | PubMed |
description | Predictive coding models can simulate known perceptual or neuronal phenomena, but there have been fewer attempts to identify a reliable neural signature of predictive coding for complex stimuli. In a pair of studies, we test whether the N300 component of the event-related potential, occurring 250–350-ms poststimulus-onset, has the response properties expected for such a signature of perceptual hypothesis testing at the level of whole objects and scenes. We show that N300 amplitudes are smaller to representative (“good exemplars”) compared with less representative (“bad exemplars”) items from natural scene categories. Integrating these results with patterns observed for objects, we establish that, across a variety of visual stimuli, the N300 is responsive to statistical regularity, or the degree to which the input is “expected” (either explicitly or implicitly) based on prior knowledge, with statistically regular images evoking a reduced response. Moreover, we show that the measure exhibits context-dependency; that is, we find the N300 sensitivity to category representativeness when stimuli are congruent with, but not when they are incongruent with, a category pre-cue. Thus, we argue that the N300 is the best candidate to date for an index of perceptual hypotheses testing for complex visual objects and scenes. |
format | Online Article Text |
id | pubmed-8171016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-81710162021-07-21 The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes Kumar, Manoj Federmeier, Kara D Beck, Diane M Cereb Cortex Commun Original Article Predictive coding models can simulate known perceptual or neuronal phenomena, but there have been fewer attempts to identify a reliable neural signature of predictive coding for complex stimuli. In a pair of studies, we test whether the N300 component of the event-related potential, occurring 250–350-ms poststimulus-onset, has the response properties expected for such a signature of perceptual hypothesis testing at the level of whole objects and scenes. We show that N300 amplitudes are smaller to representative (“good exemplars”) compared with less representative (“bad exemplars”) items from natural scene categories. Integrating these results with patterns observed for objects, we establish that, across a variety of visual stimuli, the N300 is responsive to statistical regularity, or the degree to which the input is “expected” (either explicitly or implicitly) based on prior knowledge, with statistically regular images evoking a reduced response. Moreover, we show that the measure exhibits context-dependency; that is, we find the N300 sensitivity to category representativeness when stimuli are congruent with, but not when they are incongruent with, a category pre-cue. Thus, we argue that the N300 is the best candidate to date for an index of perceptual hypotheses testing for complex visual objects and scenes. Oxford University Press 2021-04-21 /pmc/articles/PMC8171016/ /pubmed/34296175 http://dx.doi.org/10.1093/texcom/tgab030 Text en © The Author(s) 2020. Published by Oxford University Press. https://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/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Kumar, Manoj Federmeier, Kara D Beck, Diane M The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes |
title | The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes |
title_full | The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes |
title_fullStr | The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes |
title_full_unstemmed | The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes |
title_short | The N300: An Index for Predictive Coding of Complex Visual Objects and Scenes |
title_sort | n300: an index for predictive coding of complex visual objects and scenes |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8171016/ https://www.ncbi.nlm.nih.gov/pubmed/34296175 http://dx.doi.org/10.1093/texcom/tgab030 |
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