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Title: Computational models of attachment and self-attachment
Author: Cittern, David
ISNI:       0000 0004 6348 4137
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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We explore, using a variety of models grounded in computational neuroscience, the dynamics of attachment formation and change. In the first part of the thesis we consider the formation of the traditional organised forms of attachment (as defined by Mary Ainsworth) within the context of the free energy principle, showing how each type of attachment might arise in infant agents who minimise free energy over interoceptive states while interacting with caregivers with varying responsiveness. We show how exteroceptive cues (in the form of disrupted affective communication from the caregiver) can result in disorganised forms of attachment (as first uncovered by Mary Main) in infants of caregivers who consistently increase stress on approach, but can have an organising (towards ambivalence) effect in infants of inconsistent caregivers. The second part of the thesis concerns Self-Attachment: a new self-administrable attachment-based psychotherapy recently introduced by Abbas Edalat, which aims to induce neural plasticity in order to retrain an individual's suboptimal attachment schema. We begin with a model of the hypothesised neurobiological underpinnings of the Self-Attachment bonding protocols, which are concerned with the formation of an abstract, self-directed bond. Finally, using neuroscientific findings related to empathy and the self-other distinction within the context of pain, we propose a simple spiking neural model for how empathic states might serve to motivate application of the aforementioned bonding protocols.
Supervisor: Edalat, Abbas Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral