Thesis
Narrative : the temporal architecture of adult-infant interaction
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2024
- Thesis identifier
- T17043
- 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. the manner in which an interaction unfolds over time). In particular, we explore the existence and nature 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 of human cognition, and to be central in human communication and learning. We began by exploring the current state adult-infant interaction research, and identified that recent work largely fails to consider an underlying narrative element. We then sought to address this ‘narrative gap’ through theoretical, empirical and methodological contributions. We first applied narrative theory to the neonatal imitation paradigm, viewing the imitative exchange between experimenter and infant as being inherently dialogical in nature. On this basis we proposed that underlying successful displays of imitation by neonates was a narrative framework. We then explored the development of narrative through infancy by conducting a longitudinal examination of mother-infant interactions when infants were aged 4 months, 7 months and 10 months. We hypothesised that the duration of infant positive affect would be a function of the narrative phase reached in an interaction, with older infants engaging in longer interactions that reached more advanced narrative phases. Our results supported these predictions (except for interaction durations that decreased with infant age). Finally, we outlined a methodological pipeline to automate the identification of narrative phases in adultinfant engagements. We first described the training and evaluation of a deep learning based markerless motion tracking model specifically tailored for the tracking of adult and infant movement during dyadic engagements. We then proposed a machine learning analysis pipeline for the clustering of this movement data according to narrative phases, thus removing the potential for human bias in the identification of narrative.
- Advisor / supervisor
- Delafield-Butt, Jonathan
- Resource Type
- DOI
Las relaciones
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PDF of thesis T17043 | 2024-09-25 | Público | Descargar |