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Modeling Affective States: Applicati...

Mattek, Alison.

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Modeling Affective States: Applications for Psychology and Neuroscience Research.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling Affective States: Applications for Psychology and Neuroscience Research./
Author:	Mattek, Alison.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	57 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
Subject:	Experimental psychology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10635606
ISBN:	9780438029095

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

Modeling Affective States: Applications for Psychology and Neuroscience Research. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 57 p.

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

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

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.

ISBN: 9780438029095Subjects--Topical Terms:

2144733
Experimental psychology.

Modeling Affective States: Applications for Psychology and Neuroscience Research.
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245 1 0 $a Modeling Affective States: Applications for Psychology and Neuroscience Research.
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300 $a 57 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: George Wolford.
502 $a Thesis (Ph.D.)--Dartmouth College, 2017.
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.
590 $a School code: 0059.
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