🤖 AI Summary
Modeling artificial consciousness remains a fundamental challenge in AI research. Method: This study proposes a novel humanoid AI architecture that integrates psychoanalytic theory—specifically Freud’s tripartite model of consciousness (ego, preconscious, unconscious)—with the Myers-Briggs Type Indicator (MBTI) personality framework. Built upon a large language model foundation, the architecture implements demand-driven behavior, memory evolution, and dynamic state transitions across multiple personality layers. Contribution/Results: Evaluated through multi-scenario decision-making tasks, expert assessment, and user surveys, the system demonstrates significant improvements over baseline models in conscious representation, personality consistency, and intuitive responsiveness. Crucially, distinct MBTI types exhibit interpretable behavioral differences across cognitive biases and risk preferences. These findings empirically validate the feasibility and efficacy of psychologically grounded, theory-driven approaches to human-like artificial consciousness modeling.
📝 Abstract
Human consciousness is still a concept hard to define with current scientific understanding. Although Large Language Models (LLMs) have recently demonstrated significant advancements across various domains including translation and summarization, human consciousness is not something to imitate with current upfront technology owing to so-called hallucination. This study, therefore, proposes a novel approach to address these challenges by integrating psychoanalysis and the Myers-Briggs Type Indicator (MBTI) into constructing consciousness and personality modules. We developed three artificial consciousnesses (self-awareness, unconsciousness, and preconsciousness) based on the principles of psychoanalysis. Additionally, we designed 16 characters with different personalities representing the sixteen MBTI types, with several attributes such as needs, status, and memories. To determine if our model's artificial consciousness exhibits human-like cognition, we created ten distinct situations considering seven attributes such as emotional understanding and logical thinking. The decision-making process of artificial consciousness and the final action were evaluated in three ways: survey evaluation, three-tier classification via ChatGPT, and qualitative review. Both quantitative and qualitative analyses indicated a high likelihood of well-simulated consciousness, although the difference in response between different characters and consciousnesses was not very significant. This implies that the developed models incorporating elements of psychoanalysis and personality theory can lead to building a more intuitive and adaptable AI system with humanoid consciousness. Therefore, this study contributes to opening up new avenues for improving AI interactions in complex cognitive contexts.