Giannakopoulou, Afroditi (2024) Understanding Emotions: The Evolution of Affective Neuroscience and a Network-Based Taxonomy of Emotion. Advisor: Ricciardi, Prof. Emiliano. Coadvisor: Cecchetti, Prof. Luca . pp. 235. [IMT PhD Thesis]
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Text (Doctoral thesis)
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Abstract
Emotion, as a field of study, has evolved significantly over the centuries, encompassing various perspectives from theology, philosophy, medicine, psychology, and neuroscience. Historically, terms like 'passions,' 'affections,' and 'emotions' have carried different connotations and have been studied through different lenses, influenced by societal norms and scientific advancements. The affective lexicon has seen a transition across different languages and cultural contexts, contributing to the development of a global scientific community and a more nuanced understanding of emotional phenomena. The first part provides a comprehensive overview of the evolution of affective neuroscience, highlighting the historical path, emotional models and theories, and methodological advancements. A thorough qualitative look at the sharp increase in affective studies provides a beter understanding of where emotion research has focused. An extensive literature review across PubMed was conducted to gather relevant studies focusing on affective topics, neuroimaging techniques, and emotional categories. The findings demonstrated the trends in the publications of the affective neuroscience field over time. The analysis revealed a significant shift in research focus over the years, a specific focus on neuroimaging techniques and certain emotion categories The second part presents an innovative approach to understanding emotions through language. This section delves into the semantic structures underlying affective terms and explores how linguistic and cultural nuances could shape emotional experiences. It examines the challenges in achieving a scientific consensus on the nature of emotions due to their conceptual complexity. This complexity is further compounded by the variety of models proposed to categorize emotions, stemming from basic emotion theories, which suggest a limited number of universal emotions, to dimensional and constructionist theories, which argue for a more fluid and context-dependent understanding of emotional experiences. Through the experimental procedure, participants were instructed to define emotional terms based on the subjective experience. The results demonstrate that emotions are intricately linked within a network-based hierarchical taxonomy based on language, offering a more detailed and systematic classification of emotions than traditional emotion models. This network-based approach elucidates the relationships between various affective terms and their semantic structures, highlighting the complex interplay between language and emotion.
| Item Type: | IMT PhD Thesis |
|---|---|
| Subjects: | R Medicine > RC Internal medicine |
| PhD Course: | Cognitive, Computational and Social Neurosciences |
| Identification Number: | https://doi.org/10.13118/imtlucca/e-theses/437 |
| Date Deposited: | 07 Nov 2024 10:54 |
| URI: | http://e-theses.imtlucca.it/id/eprint/437 |
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