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Modeling Affective States : = Applications for Psychology and Neuroscience Research.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Modeling Affective States :/
其他題名:	Applications for Psychology and Neuroscience Research.
作者:	Mattek, Alison.
面頁冊數:	1 online resource (57 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Experimental psychology. -
電子資源:	click for full text (PQDT)
ISBN:	9780438029095

Modeling Affective States : = Applications for Psychology and Neuroscience Research.
Mattek, Alison.

Modeling Affective States :Applications for Psychology and Neuroscience Research. - 1 online resource (57 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

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

Includes bibliographical references

A central question in psychology involves the classification of psychological states (e.g., pleasure, depression, etc.) using behavioral and/or physiological measures. In order to tackle this question, we have to first ask ourselves how to characterize the structure of the output classes---that is, how are psychological states such as pleasure, depression, and fear related to one another, and is it possible to model this similarity structure? Decades of work has suggested it is possible to reduce the dimensionality of this space to two primary dimensions---valence (unpleasant versus pleasant affect) and arousal (high versus low intensity). Still, the optimal rotation of this low dimensional solution has not been resolved due to the non-orthogonality of these psychological dimensions. In this thesis, I propose a new model that mathematically formalizes the non-orthogonality of these affective state variables (valence and arousal). This model captures more variance in behavioral ratings of affective dimensions (>90%) compared to existing alternative models (~60%). When applied to an functional Magnetic Resonance Imaging (fMRI) dataset, this model can effectively separate blood-oxygen level dependent (BOLD) responses to valence versus arousal and approximately doubles the amount of variance explained in these BOLD responses compared to existing alternative approaches. Possible convergences between the proposed model and existing models of clinical disorders are discussed.

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

Mode of access: World Wide Web

ISBN: 9780438029095Subjects--Topical Terms:

1180476
Experimental psychology.
Index Terms--Genre/Form:

554714
Electronic books.

Modeling Affective States : = Applications for Psychology and Neuroscience Research.
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500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: George Wolford.
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504 $a Includes bibliographical references
520 $a A central question in psychology involves the classification of psychological states (e.g., pleasure, depression, etc.) using behavioral and/or physiological measures. In order to tackle this question, we have to first ask ourselves how to characterize the structure of the output classes---that is, how are psychological states such as pleasure, depression, and fear related to one another, and is it possible to model this similarity structure? Decades of work has suggested it is possible to reduce the dimensionality of this space to two primary dimensions---valence (unpleasant versus pleasant affect) and arousal (high versus low intensity). Still, the optimal rotation of this low dimensional solution has not been resolved due to the non-orthogonality of these psychological dimensions. In this thesis, I propose a new model that mathematically formalizes the non-orthogonality of these affective state variables (valence and arousal). This model captures more variance in behavioral ratings of affective dimensions (>90%) compared to existing alternative models (~60%). When applied to an functional Magnetic Resonance Imaging (fMRI) dataset, this model can effectively separate blood-oxygen level dependent (BOLD) responses to valence versus arousal and approximately doubles the amount of variance explained in these BOLD responses compared to existing alternative approaches. Possible convergences between the proposed model and existing models of clinical disorders are discussed.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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655 7 $a Electronic books. $2 local $3 554714
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