Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.690715
Title: Functional integration of neural signals during person perception
Author: Greven, Inez Margot
ISNI:       0000 0004 5915 1831
Awarding Body: Prifysgol Bangor University
Current Institution: Bangor University
Date of Award: 2016
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Abstract:
In every day social interactions,it is important to know who other people are and how we might expect them to behave. Neuroscientific research has identified neuroanatomically distinct networks involved in perceiving a person’s physical features and reasoning about their trait characteristics. While it has been demonstrated that both these networks are engaged when linking multiple features of a person ogether, the neural networks integration under these circumstances has mostly been overlooked. Over four empirical chapters, this thesis aims to understand how functional integration between distinct cognitive and neural systems supports person perception during social interactions. The first empirical chapter (Chapter 3) investigates how physical features are linked to social knowledge, similarly to how we form impressions when we initially meet someone. While in this chapter social knowledge was inferred from descriptions of the person’s behaviour, Chapter 4 aimed to investigate how social signals are extracted from the visual image of the body alone. Chapter 5 investigated functional integration during the perception of bodies that cued recall of social knowledge. Finally, Chapter 6 differentiated between affective valences of trait-­‐based judgments. Taken together, the findings presented in this thesis highlight the importance of an integrative perspective when investigating the role of functionally segregated brain regions in a large interconnected network. This view advocates the use offunctional connectivity measures when investigating the role of person perception nodes in socially complex settings.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.690715  DOI: Not available
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