Thesis

Narrative : the temporal architecture of adult-infant interaction

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Awarding institution
  • University of Strathclyde
Date of award
  • 2024
Thesis identifier
  • T17131
Person Identifier (Local)
  • 201966177
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • This thesis examines the temporal architecture of adult-infant interactions (i.e. themanner in which an interaction unfolds over time). In particular, we explore the existence andnature of a common narrative temporal framework underpinning adult-infant interaction,consisting of phases of arousal and intensity split into four distinct states: introduction,development, climax and resolution. This framework is considered a fundamental structure ofhuman cognition, and to be central in human communication and learning. We began byexploring the current state adult-infant interaction research, and identified that recent worklargely fails to consider an underlying narrative element. We then sought to address this‘narrative gap’ through theoretical, empirical and methodological contributions. We firstapplied narrative theory to the neonatal imitation paradigm, viewing the imitative exchangebetween experimenter and infant as being inherently dialogical in nature. On this basis weproposed that underlying successful displays of imitation by neonates was a narrativeframework. We then explored the development of narrative through infancy by conducting alongitudinal examination of mother-infant interactions when infants were aged 4 months, 7months and 10 months. We hypothesised that the duration of infant positive affect would be afunction of the narrative phase reached in an interaction, with older infants engaging inlonger interactions that reached more advanced narrative phases. Our results supported thesepredictions (except for interaction durations that decreased with infant age). Finally, weoutlined a methodological pipeline to automate the identification of narrative phases in adultinfant engagements. We first described the training and evaluation of a deep learning basedmarkerless motion tracking model specifically tailored for the tracking of adult and infantmovement during dyadic engagements. We then proposed a machine learning analysispipeline for the clustering of this movement data according to narrative phases, thusremoving the potential for human bias in the identification of narrative.
Advisor / supervisor
  • Delafield-Butt, Jonathan
Resource Type
DOI

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