Sands, Emily P; (2024) Vocal Expression and Trait Inference: Resolving Validity Concerns by Considering Expressed Prosody. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Extensive research has highlighted the significant impact of non-verbal behavioural cues on character trait impressions. However, the vocal aspects of these cues have been relatively underexplored in psychological research due to their complex interactions with psychological and contextual factors, often resulting in indeterminate and confounding findings. Recent advancements over the past decade have begun to address these complexities, yet a significant lack of consensus remains on how to operationalise and control these variabilities without neglecting their interdependent nature. A critical area of ambiguity involves the inappropriate application of psychometrics designed for static personality traits to dynamic social signalling issues. This thesis proposes a functional framework to understand the interaction between an expressor and a perceiver, focusing on the prosodic features of voice as they communicate trait-based information. It posits that effective communication hinges more on the congruence of encoding and decoding shared vocal and social cues rather than on identifying stable personality patterns. This dissertation advances the field through six exploratory studies that investigate both the perception and vocal production of traits. Initial studies reveal how listeners deduce traits from vocal cues and contextual influences, presenting significant findings on how acoustic variables interact with factors such as gender, accent, occupation, and status in shaping trait perceptions. Subsequent research aims to elucidate the processes of vocal trait expression and the contextual generalisability of these expressions, grounded in shared social and display rules. The findings are rigorously analysed through a multimodal approach, including visual spectrogram analysis, inter-speaker reliability assessments, perceptual recognition tests, and the application of the Brunswik Lens Model. The discussion extends to potential applications for AI integration, underscoring the broader implications of this research

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