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Investigating How Conscious Experience Shapes the Distributed Face Processing Network.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Investigating How Conscious Experience Shapes the Distributed Face Processing Network./
Author:	Goold, Jessica Emeline.
Description:	1 online resource (104 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Neurosciences. -
Online resource:	click for full text (PQDT)
ISBN:	9780355225808

Investigating How Conscious Experience Shapes the Distributed Face Processing Network.
Goold, Jessica Emeline.

Investigating How Conscious Experience Shapes the Distributed Face Processing Network. - 1 online resource (104 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--Dartmouth College, 2017.

Includes bibliographical references

Multiple brain regions interact, forming networks to process critical object categories. In this thesis, I will address two questions to further our understanding of the dynamics of the distributed face processing network. 1) How may experience shape the learning of face category boundary? And 2) How do network dynamics differ when emotional faces are viewed with and without awareness? To investigate the first question, two-toned images were used with either categorical or feature face learning between subjects. Interestingly, categorical learning shaped the multivoxel pattern in only the left fusiform face area (lFFA). To investigate the second question, dynamic faces (happy or angry) and objects were presented either explicitly or implicitly, utilizing continuous flash suppression, to participants. Dynamic causal modeling (DCM), multivariate pattern analysis (MVPA), and representational similarity analysis (RSA) were implemented to understand the relationship between core face processing areas as well as amygdala, anterior temporal lobule (ATL) and inferior frontal gyrus (IFG). DCM results revealed preference for a hierarchical model from early visual areas through both the ventral and dorsal streams over the subcortical model through the amygdala to the core face processing regions during explicit processing. However, for implicit processing, data from most subjects preferred the subcortical model. MVPA revealed significant decoding of category information (face vs. object) in all the core face processing areas and the right ATL and left IFG, but not emotion information in any areas. Interestingly, for implicit face processing, category information could only be decoded by the right occipital face area and right superior temporal sulcus, while emotion could be decoded from the right FFA. Finally, an RSA across these regions with MVPA showed tighter clustering of face processing areas in explicit processing as compared to implicit processing. Overall, the results of this dissertation reveal the necessity of experience and awareness to shape and optimally engage the distributed face processing network. Interestingly, even without visual awareness, core face processing areas in the right hemisphere are significantly engaged, suggesting the implicit processing of faces as a critical social cue.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355225808Subjects--Topical Terms:

593561
Neurosciences.
Index Terms--Genre/Form:

554714
Electronic books.

Investigating How Conscious Experience Shapes the Distributed Face Processing Network.
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520 $a Multiple brain regions interact, forming networks to process critical object categories. In this thesis, I will address two questions to further our understanding of the dynamics of the distributed face processing network. 1) How may experience shape the learning of face category boundary? And 2) How do network dynamics differ when emotional faces are viewed with and without awareness? To investigate the first question, two-toned images were used with either categorical or feature face learning between subjects. Interestingly, categorical learning shaped the multivoxel pattern in only the left fusiform face area (lFFA). To investigate the second question, dynamic faces (happy or angry) and objects were presented either explicitly or implicitly, utilizing continuous flash suppression, to participants. Dynamic causal modeling (DCM), multivariate pattern analysis (MVPA), and representational similarity analysis (RSA) were implemented to understand the relationship between core face processing areas as well as amygdala, anterior temporal lobule (ATL) and inferior frontal gyrus (IFG). DCM results revealed preference for a hierarchical model from early visual areas through both the ventral and dorsal streams over the subcortical model through the amygdala to the core face processing regions during explicit processing. However, for implicit processing, data from most subjects preferred the subcortical model. MVPA revealed significant decoding of category information (face vs. object) in all the core face processing areas and the right ATL and left IFG, but not emotion information in any areas. Interestingly, for implicit face processing, category information could only be decoded by the right occipital face area and right superior temporal sulcus, while emotion could be decoded from the right FFA. Finally, an RSA across these regions with MVPA showed tighter clustering of face processing areas in explicit processing as compared to implicit processing. Overall, the results of this dissertation reveal the necessity of experience and awareness to shape and optimally engage the distributed face processing network. Interestingly, even without visual awareness, core face processing areas in the right hemisphere are significantly engaged, suggesting the implicit processing of faces as a critical social cue.
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