Enabling self-identification in intelligent agent: insights from computational psychoanalysis

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 imaginary and symbolic identification, and propose an illustrative model called FreeAgent. In concluding remarks, we discuss some key issues in the potential of computational Lacanian psychoanalysis to advance mental health and artificial intelligence, including digital twin mind, LLMs as avatars of the Lacanian Other, and the feasibility of human-level artificial general intelligence with self-awareness in the context of post-structuralism.

• The paper introduces the FreeAgent model that integrates Lacanian psychoanalysis with active inference for self-identification in AI.
• It employs a CNN and a stickman agent for simulating imaginary identification, alongside an MLP supervised by GPT-3.5 for symbolic identification.
• The study suggests applications in developing AGI and personalized mental health care through digital twin mind simulations.

Insights from Enabling Self-Identification in Intelligent Agents: Computational Psychoanalysis

This paper presents an in-depth exploration of self-identification within artificial intelligence, leveraging computational psychoanalysis framed by Lacanian theory and active inference. It meticulously examines the constructs of imaginary and symbolic identifications as defined in psychological paradigms and attempts to model these concepts into AI through computational approaches.

Lacanian Constructs and Free Energy Principle

Central to this study are the theories of Jacques Lacan, particularly the distinctions between imaginary and symbolic identification. Imaginary identification correlates with the Mirror Stage, where an individual's ego begins to form, while symbolic identification ties to linguistic and societal assimilation. The researchers extend this to a computational field, utilizing the free energy principle—a framework suggesting that biological systems strive for minimum free energy—coupled with active inference to simulate these identification processes.

Computational Modeling

To simulate imaginary identification, the researchers utilize a convolutional neural network (CNN) for visual interpretation, and a stickman agent with active limbs to showcase motor skill development. The notion is to achieve minimized free energy, thereby aligning with an integrated body-schema. The symbolic identification model employs a multi-layer perceptron (MLP) supervised by the GPT-3.5 (ChatGPT) architecture, aiming to simulate mastery of language through an active inference model, which is central to the interaction between agents and their environments in this study.

Unified Framework and FreeAgent

Integrating the dual aspects of self-identification, the paper proposes the FreeAgent model. This encompasses both imaginary and symbolic components, illustrating an agent capable of forming identity through computational resemblances of Lacanian psychology. The FreeAgent represents a comprehensive AI system designed to embody Lacan's Graph II of desire, revealing potential paths to artificial agents with self-awareness.

Implications and Future Directions

The researchers position this study within larger implications for both artificial intelligence and mental health applications. The concept of the digital twin mind is particularly emphasized—potentially enhancing personalized mental health care by simulating mind states rather than mere physiological brain activities. Furthermore, the exploration addresses the anthropomorphic associations with LLMs as they potentially fulfill the role of the Lacanian Other.

Moreover, the paper explores avenues of constructing human-level artificial general intelligence (AGI) through robust self-models based on Lacanian dynamics. It confronts the epistemological challenges within a post-structuralist framework, urging future developments contingent on a nuanced understanding of self-identification and subjectivity.

Conclusion

In conclusion, this paper contributes a foundational computational approach for the realization of self-identification in artificial agents, grounded in an integrated theory of Lacanian psychoanalysis and active inference. This work navigates complex interrelations between AI, self-awareness, and psychoanalytic theory, thereby opening novel perspectives for future research in autonomous agent development and mental health simulations. The implications of aligning AI with psychoanalytical constructs extend toward redefining our understanding of intelligence and consciousness in machines.