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Self-attachment: A Holistic Approach to Computational Psychiatry

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Computational Neurology and Psychiatry

Part of the book series: Springer Series in Bio-/Neuroinformatics ((SSBN,volume 6))

Abstract

There has been increasing evidence to suggest that the root cause of much mental illness lies in a sub-optimal capacity for affect regulation. Cognition and emotion are intricately linked and cognitive deficits, which are characteristic of many psychiatric conditions, are often driven by affect dysregulation, which itself can usually be traced back to sub-optimal childhood development. This view is supported by Attachment Theory, a scientific paradigm in developmental psychology, that classifies the type of relationship a child has with a primary care-giver to one of four types of insecure or secure attachments. Individuals with insecure attachment in their childhoods are prone to a variety of mental illness, whereas a secure attachment in childhood provides a secure base in life. We therefore propose, based on previous work, a holistic approach to Computational Psychiatry, which is informed by the development of the brain during infancy in social interaction with its primary care-givers. We identify the protocols governing the interaction of a securely attached child with its primary care-givers that produce the capacity for affect regulation in the child. We contend that these protocols can be self-administered to construct, by neuroplasticity and long term potentiation, new “optimal” neural pathways in the brains of adults with insecure attachment history. This procedure is called Self-attachment and aims to help individuals create their own attachment objects which has many parallels with Winnicott’s notion of transitional object, Bowlby’s comfort objects, Kohut’s empathetic self-object as well as religion as an attachment object. We describe some mathematical models for Self-attachment: a game-theoretic model, a model based on the notion of a strong pattern in an energy based associative neural network and several neural models of the human brain.

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Acknowledgements

I would like to thank David Cittern for reading the article and providing me with various suggestions.

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Authors and Affiliations

  1. Algorithmic Human Development Group, Department of Computing, Imperial College London, London, UK

    Abbas Edalat

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  1. Abbas Edalat
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Correspondence to Abbas Edalat .

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  1. Department of Physics, Kalamazoo College, Kalamazoo, Michigan, USA

    Péter Érdi

  2. School of Engineering, University of Lincoln, Lincoln, United Kingdom

    Basabdatta Sen Bhattacharya

  3. Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA

    Amy L. Cochran

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Edalat, A. (2017). Self-attachment: A Holistic Approach to Computational Psychiatry. In: Érdi, P., Sen Bhattacharya, B., Cochran, A. (eds) Computational Neurology and Psychiatry. Springer Series in Bio-/Neuroinformatics, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-49959-8_10

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