MACHINE PSYCHOLOGY: BRIDGING HUMAN LEARNING PRINCIPLES AND ARTIFICIAL GENERAL INTELLIGENCE DEVELOPMENT
DOI:
https://doi.org/10.5281/zenodo.17804308Ключевые слова:
Machine Psychology, Artificial General Intelligence, Operant Conditioning, Relational Frame Theory, Cognitive Architecture, NARS, Adaptive SystemsАннотация
The pursuit of Artificial General Intelligence (AGI) represents one of the most ambitious goals in artificial
intelligence research, yet current approaches often overlook the foundational principles that govern biological intelligence.
This paper introduces and examines Machine Psychology as an interdisciplinary framework that systematically integrates
principles from learning psychology-particularly operant conditioning and Relational Frame Theory-with adaptive reasoning
systems to advance AGI development. We propose a bidirectional learning model wherein psychological principles
inform AI architecture while AI systems provide novel insights into cognitive mechanisms. Through analysis of recent
implementations using the Non-Axiomatic Reasoning System (NARS), we demonstrate how core psychological constructs
such as reinforcement learning, derived relational responding, and functional equivalence can be computationally realized
to produce flexible, context-sensitive artificial cognition. This framework addresses critical limitations in contemporary
AI systems, including brittleness in novel contexts, inability to generalize across domains, and lack of metacognitive
capabilities. The paper further explores implementation challenges specific to developing economies, using Uzbekistan’s
accelerated digital transformation as a case study for culturally-adapted AGI development strategies. We conclude that
Machine Psychology offers a principled pathway toward human-level artificial intelligence while simultaneously enriching
our understanding of natural cognition.
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