Enabling self-identification in intelligent agent: insights from computational psychoanalysis
Lingyu Li, Chunbo Li

TL;DR
This paper integrates computational psychoanalysis with active inference to explore self-identification in intelligent agents, proposing models that unify imaginary and symbolic identification with implications for AI and mental health.
Contribution
It introduces a novel framework combining Lacanian psychoanalysis with active inference, including models like FreeAgent, to advance understanding of self-identification in AI.
Findings
Imaginary identification linked to body schema with minimal free energy
Symbolic identification modeled through neural networks optimizing free energy
Potential applications in mental health and AI development
Abstract
Building upon prior framework of computational Lacanian psychoanalysis with the theory of active inference, this paper aims to further explore the concept of self-identification and its potential applications. Beginning with two classic paradigms in psychology, mirror self-recognition and rubber hand illusion, we suggest that imaginary identification is characterized by an integrated body schema with minimal free energy. Next, we briefly survey three dimensions of symbolic identification (sociological, psychoanalytic, and linguistical) and corresponding active inference accounts. To provide intuition, we respectively employ a convolutional neural network (CNN) and a multi-layer perceptron (MLP) supervised by ChatGPT to showcase optimization of free energy during motor skill and language mastery underlying identification formation. We then introduce Lacan's Graph II of desire, unifying…
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Taxonomy
Topicsadvanced mathematical theories · Opinion Dynamics and Social Influence · Psychotherapy Techniques and Applications
