Strong Attractors of Hopfield Neural Networks to Model Attachment Types and Behavioural Patterns

Abstract — We study the notion of a strong attractor of a Hopfield neural model as a pattern that has been stored multiple times in the network, and examine its properties using basic mathematical techniques as well as a variety of simulations. It is proposed that strong attractors can be used to model attachment types in developmental psychology as well as behavioural patterns in psychology and psychotherapy. We study the stability and basins of attraction of strong attractors in the presence of other simple attractors and show that they are indeed more stable with a larger basin of attraction compared with simple attractors. We also show that the perturbation of a strong attractor by random noise results in a cluster of attractors near the original strong attractor measured by the Hamming distance. We investigate the stability and basins of attraction of such clusters as the noise increases and establish that the unfolding of the strong attractor, leading to its breakup, goes through three different stages. Finally the relation between strong attractors of different multiplicity and their influence on each other are studied and we show how the impact of a strong attractor can be replaced with that of a new strong attractor. This retraining of the network is proposed as a model of how attachment types and behavioural patterns can undergo change. I

A pilot study to evaluate the efficacy of self-attachment to treat chronic anxiety and/or depression in Iranian women

The aim of this pilot study was to evaluate the efficacy of the new Self-Attachment Technique (SAT) in treating resistant anxiety and depression, lasting at least three years, among Iranian women from different social backgrounds. In this intervention, the participant, using their childhood photos, imaginatively creates an affectional bond with their childhood self, vows to consistently support and lovingly re-raise this child to emotional well-being. We conducted a longitudinal study with repeated measurement to evaluate the efficacy of SAT using ANOVA. Thirty-eight women (N=30) satisfying the inclusion and exclusion criteria were recruited from different parts of Tehran. To describe the SAT protocols, a total of eight one-to-one sessions were offered to the recruits, the first four were weekly while the last four were fortnightly. The participants were expected to practice the protocols for twenty minutes twice a day. Two questionnaires, GAD-7 and PHQ-9, were used to measure anxiety and depression levels before and after the intervention and in a three-month follow-up. Thirty women completed the course. The change in the anxiety level between the pre-test and the post-test was significant at p<0.001 with effect size 2.6. The change in anxiety between pre-test and follow-up test was also significant at p<0.001 with effect size 3.0 respectively. The change in anxiety between the post-test and the follow-up was significant at p<0.05 with effect size 0.6. For depression, the change between the pre-test and the post-test or the follow-up was significant at p<0.001 with effect size 2.5 for each

The attachment control system and computational modeling: Origins and prospects

From his first attempts to explain attachment phenomena in the 1940s through his Attachment and Loss trilogy (Bowlby, 1969/1982, 1973, 1980), John Bowlby reformulated the theoretical underpinnings of attachment theory several times. He initially attempted to explain attachment phenomena in psychoanalytic terms. Then he invoked ethological theory in the explanation of how and why people behave as they do in close personal relationships. The mature theoretical framework that he presented between 1969 and 1982 in the attachment and loss trilogy retained strengths and insights, ultimately situating them within an overarching control systems framework. This article describes key stages in Bowlby's theoretical development, with particular emphasis placed on the emergence of control systems theory as a cornerstone of the mature theory. It also compares Bowlby's control systems approach to contemporary cognitive science approaches. It concludes by suggesting how Bowlby's control systems formulation could evolve along the path opened up by contemporary work in computational modeling and how it could benefit by doing so