Computational models of attachment and self-attachment

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.Open Acces

Reinforcement Learning for Nash Equilibrium Generation

ABSTRACT We propose a new conceptual multi-agent framework which, given a game with an undesirable Nash equilibrium, will almost surely generate a new Nash equilibrium at some predetermined, more desirable pure action profile. The agent(s) targeted for reinforcement learn independently according to a standard model-free algorithm, using internally-generated states corresponding to high-level preference rankings over outcomes. We focus in particular on the case in which the additional reward can be considered as resulting from an internal (re-)appraisal, such that the new equilibrium is stable independent of the continued application of the procedure

Disorganised attachment and misleading exteroceptive cues.

<p>Mean (over repetitions) proportion each action was chosen by the infant during the last 10 iterations (y-axis), when they were paired with a highly unresponsive caregiver (q = 0.05) exhibiting varying rates of misleading affective communication errors (values of <i>b</i> ∈ {0, 0.25, 0.5, 0.75, 1} along the x-axis, for fixed <i>a</i> = 1 and <i>c</i> = 0).</p

Avoidant attachment as free energy minimisation over interoceptive observations.

<p>The figure illustrates the emergence of avoidant attachment for an infant paired with a low-q (unresponsive) caregiver. A: Mean (over repetitions) expected negative free-energies on the final step of each iteration. B: Mean (over repetitions) final-step expected precision. C: Mean (over repetitions) proportion each action was chosen over all iterations.</p

Expected negative free energies and action selection probabilities for perfect generative models.

<p>The charts show expected negative free energies and action selection probabilities for an agent that has a perfect generative model of their environment (which is defined by parameters <i>g</i> = 2, <i>h</i> = 0.75, <i>m</i> = 2 and <i>n</i> = 0.9, and varying responsiveness <i>q</i>). A: Mean (over repetitions) expected negative free energies for Seek, Guarded Seek and Avoid on final step of free energy minimisation (y-axis), for different values of q (x-axis). B: Mean (over repetitions) action selection probabilities (z-axis) on each of the 4 steps of free energy minimisation (x-axis) for different values of q (y-axis) for Seek (C: Guarded Seek, D: Avoid).</p

Ambivalent attachment for combinations of misleading and ambiguous exteroceptive cues.

<p>Mean (over repetitions) proportion of actions chosen by the infant during the last 10 iterations, when they were paired with an inconsistent caregiver (q = 0.4) exhibiting varying rates and types of affective communication errors. A: Proportions for a = b = 1-c and c decreasing from 1 to 0. B: Proportions for a = b = 0.75 with c decreasing from 0.25 to 0. C: Proportions for a = b = 0.5 and c decreasing from 0.5 to 0. D: Proportions for a = b = 0.25 and c decreasing from 0.75 to 0.</p

Ambivalent attachment as free energy minimisation over interoceptive observations.

<p>The figure illustrates the emergence of ambivalent attachment for an infant paired with a mid-q (inconsistent) caregiver. A: Mean (over repetitions) expected negative free-energies on the final step of each iteration. B: Mean (over repetitions) final-step expected precision. C: Mean (over repetitions) proportion each action was chosen over all iterations.</p

Avoidant and disorganised attachment with exteroceptive cues.

<p>Mean (over repetitions) proportion each action was chosen over all iterations. A: Proportions for an infant paired with a highly unresponsive caregiver (q = 0.05) with no ACEs (a = b = 1, c = 0). B: Proportions for an infant paired with a highly unresponsive caregiver (q = 0.05) with misleading ACEs about subsequent Ignore behaviour (a = 1, b = 0.6, c = 0).</p

Secure, avoidant and ambivalent attachment with additional exteroceptive cues.

<p>This figure illustrates the mean (over repetitions) proportion of actions selected. A: Proportions for an infant paired with a highly responsive caregiver (q = 0.9) with no ACEs (a = b = 1, c = 0). B: Proportions for an infant paired with a highly unresponsive caregiver (q = 0.05) with no ACEs (a = b = 1, c = 0). C: Proportions for an infant paired with an inconsistent caregiver (q = 0.4) with no ACEs (a = b = 1, c = 0). D: Proportions for an infant paired with an inconsistent caregiver (q = 0.4) with both ambiguous and misleading ACEs (a = b = 0.25, c = 0.5).</p

Heatmap of action selection, for various parameter configurations and a perfect generative model.

<p>Action selection proportions (black = 0, white = 1) per 4-step iteration of free energy minimisation (averaged over repetitions) for <i>g</i> = 2 and varying <i>h</i> (rows), <i>m</i> and <i>n</i> (x-axis) and <i>q</i> (y-axis). A: Proportions for Seek with <i>h</i> = 0.05. B: Proportions for Guarded Seek with <i>h</i> = 0.05. C: Proportions for Avoid with <i>h</i> = 0.05. D: Proportions for Seek with <i>h</i> = 0.5. E: Proportions for Guarded Seek with <i>h</i> = 0.5. F: Proportions for Avoid with <i>h</i> = 0.5. G: Proportions for Seek with <i>h</i> = 1. H: Proportions for Guarded Seek with <i>h</i> = 1. I: Proportions for Avoid with <i>h</i> = 1. J: Proportions for Seek with <i>h</i> = 1.5. K: Proportions for Guarded Seek with <i>h</i> = 1.5. L: Proportions for Avoid with <i>h</i> = 1.5. M: Proportions for Seek with <i>h</i> = 1.95. N: Proportions for Guarded Seek with <i>h</i> = 1.95. O: Proportions for Avoid with <i>h</i> = 1.95.</p