Online tracking of the contents of conscious perception using real-time fMRI
Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physi...
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| Published in: | Frontiers in neuroscience Vol. 8; p. 116 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Frontiers Research Foundation
23.05.2014
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| ISSN: | 1662-453X, 1662-4548, 1662-453X |
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| Abstract | Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physical stimulus intermittently elicits categorically different conscious object percepts. In this study, we simulated an outline object that is moving behind a narrow slit. With such displays, the physically identical stimulus can elicit categorically different percepts that either correspond closely to the physical stimulus (vertically moving line segments) or represent a hypothesis about the underlying cause of the physical stimulus (a horizontally moving object that is partly occluded). In the latter case, the brain must construct an object from the input sequence. Combining rtfMRI with machine learning techniques we show that it is possible to determine online the momentary state of a subject's conscious percept from time resolved BOLD-activity. In addition, we found that feedback about the currently decoded percept increased the decoding rates compared to prior fMRI recordings of the same stimulus without feedback presentation. The analysis of the trained classifier revealed a brain network that discriminates contents of conscious perception with antagonistic interactions between early sensory areas that represent physical stimulus properties and higher-tier brain areas. During integrated object percepts, brain activity decreases in early sensory areas and increases in higher-tier areas. We conclude that it is possible to use BOLD responses to reliably track the contents of conscious visual perception with a relatively high temporal resolution. We suggest that our approach can also be used to investigate the neural basis of auditory object formation and discuss the results in the context of predictive coding theory. |
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| AbstractList | Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physical stimulus intermittently elicits categorically different conscious object percepts. In this study, we simulated an outline object that is moving behind a narrow slit. With such displays, the physically identical stimulus can elicit categorically different percepts that either correspond closely to the physical stimulus (vertically moving line segments) or represent a hypothesis about the underlying cause of the physical stimulus (a horizontally moving object that is partly occluded). In the latter case, the brain must construct an object from the input sequence. Combining rtfMRI with machine learning techniques we show that it is possible to determine online the momentary state of a subject’s conscious percept from time resolved BOLD-activity. In addition, we found that feedback about the currently decoded percept increased the decoding rates compared to prior fMRI recordings of the same stimulus without feedback presentation. The analysis of the trained classifier revealed a brain network that discriminates contents of conscious perception with antagonistic interactions between early sensory areas that represent physical stimulus properties and higher-tier brain areas. During integrated object percepts, brain activity decreases in early sensory areas and increases in higher-tier areas. We conclude that it is possible to use BOLD responses to reliably track the contents of conscious visual perception with a relatively high temporal resolution. We suggest that our approach can also be used to investigate the neural basis of auditory object formation and discuss the results in the context of predictive coding theory. Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physical stimulus intermittently elicits categorically different conscious object percepts. In this study, we simulated an outline object that is moving behind a narrow slit. With such displays, the physically identical stimulus can elicit categorically different percepts that either correspond closely to the physical stimulus (vertically moving line segments) or represent a hypothesis about the underlying cause of the physical stimulus (a horizontally moving object that is partly occluded). In the latter case, the brain must construct an object from the input sequence. Combining rtfMRI with machine learning techniques we show that it is possible to determine online the momentary state of a subject's conscious percept from time resolved BOLD-activity. In addition, we found that feedback about the currently decoded percept increased the decoding rates compared to prior fMRI recordings of the same stimulus without feedback presentation. The analysis of the trained classifier revealed a brain network that discriminates contents of conscious perception with antagonistic interactions between early sensory areas that represent physical stimulus properties and higher-tier brain areas. During integrated object percepts, brain activity decreases in early sensory areas and increases in higher-tier areas. We conclude that it is possible to use BOLD responses to reliably track the contents of conscious visual perception with a relatively high temporal resolution. We suggest that our approach can also be used to investigate the neural basis of auditory object formation and discuss the results in the context of predictive coding theory.Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physical stimulus intermittently elicits categorically different conscious object percepts. In this study, we simulated an outline object that is moving behind a narrow slit. With such displays, the physically identical stimulus can elicit categorically different percepts that either correspond closely to the physical stimulus (vertically moving line segments) or represent a hypothesis about the underlying cause of the physical stimulus (a horizontally moving object that is partly occluded). In the latter case, the brain must construct an object from the input sequence. Combining rtfMRI with machine learning techniques we show that it is possible to determine online the momentary state of a subject's conscious percept from time resolved BOLD-activity. In addition, we found that feedback about the currently decoded percept increased the decoding rates compared to prior fMRI recordings of the same stimulus without feedback presentation. The analysis of the trained classifier revealed a brain network that discriminates contents of conscious perception with antagonistic interactions between early sensory areas that represent physical stimulus properties and higher-tier brain areas. During integrated object percepts, brain activity decreases in early sensory areas and increases in higher-tier areas. We conclude that it is possible to use BOLD responses to reliably track the contents of conscious visual perception with a relatively high temporal resolution. We suggest that our approach can also be used to investigate the neural basis of auditory object formation and discuss the results in the context of predictive coding theory. |
| Author | Fendrich, Robert Hinrichs, Hermann Reichert, Christoph Bernarding, Johannes Rieger, Jochem W. Tempelmann, Claus |
| AuthorAffiliation | 4 Department of Psychological and Brain Sciences, Dartmouth College Hanover, NH, USA 2 Department of Knowledge and Language Processing, Otto-von-Guericke University Magdeburg, Germany 7 German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Germany 6 Department of Behavioral Neurology, Leibniz Institute for Neurobiology Magdeburg, Germany 10 Research Center for Neurosensory Sciences, Carl-von-Ossietzky University Oldenburg, Germany 3 Forschungscampus STIMULATE Magdeburg, Germany 9 Department of Applied Neurocognitive Psychology, Carl-von-Ossietzky University Oldenburg, Germany 1 Department of Neurology, University Medical Center A.ö.R. Magdeburg, Germany 5 Institute for Biometry and Medical Informatics, Medical Faculty, Otto-von-Guericke University Magdeburg, Germany 8 Center for Behavioral Brain Sciences Magdeburg, Germany |
| AuthorAffiliation_xml | – name: 4 Department of Psychological and Brain Sciences, Dartmouth College Hanover, NH, USA – name: 10 Research Center for Neurosensory Sciences, Carl-von-Ossietzky University Oldenburg, Germany – name: 8 Center for Behavioral Brain Sciences Magdeburg, Germany – name: 5 Institute for Biometry and Medical Informatics, Medical Faculty, Otto-von-Guericke University Magdeburg, Germany – name: 9 Department of Applied Neurocognitive Psychology, Carl-von-Ossietzky University Oldenburg, Germany – name: 3 Forschungscampus STIMULATE Magdeburg, Germany – name: 1 Department of Neurology, University Medical Center A.ö.R. Magdeburg, Germany – name: 2 Department of Knowledge and Language Processing, Otto-von-Guericke University Magdeburg, Germany – name: 6 Department of Behavioral Neurology, Leibniz Institute for Neurobiology Magdeburg, Germany – name: 7 German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Germany |
| Author_xml | – sequence: 1 givenname: Christoph surname: Reichert fullname: Reichert, Christoph – sequence: 2 givenname: Robert surname: Fendrich fullname: Fendrich, Robert – sequence: 3 givenname: Johannes surname: Bernarding fullname: Bernarding, Johannes – sequence: 4 givenname: Claus surname: Tempelmann fullname: Tempelmann, Claus – sequence: 5 givenname: Hermann surname: Hinrichs fullname: Hinrichs, Hermann – sequence: 6 givenname: Jochem W. surname: Rieger fullname: Rieger, Jochem W. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24904260$$D View this record in MEDLINE/PubMed |
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| Keywords | object integration real-time fMRI slit viewing anorthoscopic ambiguous stimulus bistable perception |
| Language | English |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Neuroscience. Reviewed by: Peter Christiaan Klink, Royal Netherlands Academy of Arts and Sciences, Netherlands; Hirohito M. Kondo, NTT Corporation, Japan Edited by: Susann Deike, Leibniz Institute for Neurobiology, Germany |
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| SubjectTerms | ambiguous stimulus anorthoscopic Aperture bistable perception Brain mapping Coding theory Feedback Functional magnetic resonance imaging Internet Learning algorithms Neural coding Neuroimaging Neurosciences NMR Nuclear magnetic resonance object integration Psychology real-time fMRI slit viewing Temporal perception Visual perception |
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| Title | Online tracking of the contents of conscious perception using real-time fMRI |
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