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This paper reviews the evolution of psychological theories in human-computer interaction (HCI), proposing a four-layer integrative framework that encompasses Micro-cognitive, Meso-affective, Macro-social, and Self-constructive dimensions. By establishing a progressive logic from cognitive foundations to the goal of human-machine symbiosis, the framework highlights how psychology has transitioned from a supplementary role to a central pillar in shaping interaction paradigms. Key findings underscore the implications for emerging technologies like generative AI and embodied agents, while also addressing challenges such as agency erosion and ethical risks.
The shift from viewing psychology as a mere evaluative tool to a proactive design paradigm could redefine the future of human-machine partnerships.
Human-computer interaction (HCI) is undergoing a paradigm shift from"tool use"toward"partnership"and even"mind symbiosis,"with psychology evolving from a supplementary explanatory tool to a core pillar shaping interaction paradigms and long-term relationships. This paper systematically reviews relevant research and proposes a four-layer integrative framework comprising the Micro-cognitive, Meso-affective, Macro-social, and Self-constructive layers. The framework reveals that: the cognitive layer constitutes the foundation of interaction, the affective layer drives relational engagement, the social layer regulates trust and behavior through norms, and the self-constructive layer points to the ultimate goal of human-machine symbiosis. These four layers follow a progressive logic of"foundation--mediation--context--goal."The review further argues that psychology has shifted from"post-hoc evaluation"to a"proactive design paradigm,"with direct implications for emerging scenarios such as generative AI and embodied agents. Concurrently, this paper identifies core challenges including the nature of affect, agency erosion, and ethical risks, and proposes future directions such as bidirectional theory of mind and personalized co-evolution. This framework offers a systematic theoretical perspective for understanding complex human-machine relationships.
