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Decoding Ambiguity in the Facial Expressions of Others : = Neural Underpinnings of Affective Valence Computation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Decoding Ambiguity in the Facial Expressions of Others :/
Reminder of title:	Neural Underpinnings of Affective Valence Computation.
Author:	Kim, Justin M.
Description:	1 online resource (138 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
Subject:	Psychology. -
Online resource:	click for full text (PQDT)
ISBN:	9780355156263

Decoding Ambiguity in the Facial Expressions of Others : = Neural Underpinnings of Affective Valence Computation.
Kim, Justin M.

Decoding Ambiguity in the Facial Expressions of Others :Neural Underpinnings of Affective Valence Computation. - 1 online resource (138 pages)

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

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

Includes bibliographical references

Resolving ambiguity in the face of uncertainty is a critical socioemotional process that requires the ability to assess the situation at hand while taking context into consideration. Deciphering the meaning of the facial expressions of others is a prime example. Surprised facial expressions are a particularly potent example in this respect because of their inherent ambiguity---these expressions can signal either positive (e.g., I got the job!) or negative (e.g., My wallet is not in my pocket!) outcomes. Surprised faces offer a unique means to investigate how bottom-up processing of specific cues (i.e., stimulus-driven features of the face) are dynamically processed and integrated with top-down processing of complementary information (i.e., contextual information) to arrive at a final interpretation. Critically, some individuals will be more attuned to stimulus-driven features versus contextual information. Thus, the current thesis examines the individual differences in the sensitivity to features versus contexts to ultimately dictate the output of valence computations. Study 1 demonstrated stimulus-driven features of surprised expressions lead to varying degrees of emotional ambiguity (i.e., some surprised expressions are judged to be more positive or negative than others). I then used a machine learning classifier to identify the stimulus-driven features that shaped these consistent interpretations of affective valence. Study 2 used functional neuroimaging to show that neural responses of the amygdala tracked this perceived affective valence of surprised facial expressions based on these stimulus-driven features. Study 3 introduces the concept of context sensitivity and feature sensitivity, and explored their psychometric properties. Study 4 utilized diffusion tensor imaging to highlight the microstructural substrates of feature/context sensitivity in an amygdala-prefrontal circuitry. Study 5 examined the neural representation of affective valence computed by combining stimulus-driven features and contextual information. Collectively, these studies show that in addition to the effects of features and context per se, individual differences in sensitivity to bottom-up versus top-down information are reflected in an amygdala-prefrontal circuitry. Understanding this neural mechanism may further inform the psychopathology of mood and anxiety disorders, which are characterized by an imbalance within an amygdala-prefrontal circuitry.

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

Mode of access: World Wide Web

ISBN: 9780355156263Subjects--Topical Terms:

555998
Psychology.
Index Terms--Genre/Form:

554714
Electronic books.

Decoding Ambiguity in the Facial Expressions of Others : = Neural Underpinnings of Affective Valence Computation.
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520 $a Resolving ambiguity in the face of uncertainty is a critical socioemotional process that requires the ability to assess the situation at hand while taking context into consideration. Deciphering the meaning of the facial expressions of others is a prime example. Surprised facial expressions are a particularly potent example in this respect because of their inherent ambiguity---these expressions can signal either positive (e.g., I got the job!) or negative (e.g., My wallet is not in my pocket!) outcomes. Surprised faces offer a unique means to investigate how bottom-up processing of specific cues (i.e., stimulus-driven features of the face) are dynamically processed and integrated with top-down processing of complementary information (i.e., contextual information) to arrive at a final interpretation. Critically, some individuals will be more attuned to stimulus-driven features versus contextual information. Thus, the current thesis examines the individual differences in the sensitivity to features versus contexts to ultimately dictate the output of valence computations. Study 1 demonstrated stimulus-driven features of surprised expressions lead to varying degrees of emotional ambiguity (i.e., some surprised expressions are judged to be more positive or negative than others). I then used a machine learning classifier to identify the stimulus-driven features that shaped these consistent interpretations of affective valence. Study 2 used functional neuroimaging to show that neural responses of the amygdala tracked this perceived affective valence of surprised facial expressions based on these stimulus-driven features. Study 3 introduces the concept of context sensitivity and feature sensitivity, and explored their psychometric properties. Study 4 utilized diffusion tensor imaging to highlight the microstructural substrates of feature/context sensitivity in an amygdala-prefrontal circuitry. Study 5 examined the neural representation of affective valence computed by combining stimulus-driven features and contextual information. Collectively, these studies show that in addition to the effects of features and context per se, individual differences in sensitivity to bottom-up versus top-down information are reflected in an amygdala-prefrontal circuitry. Understanding this neural mechanism may further inform the psychopathology of mood and anxiety disorders, which are characterized by an imbalance within an amygdala-prefrontal circuitry.
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