AI systems are increasingly used as scientific tools to study human cognition, brain function, and cultural dynamics. Models originally developed for artificial tasks—such as neural networks, language models, and generative systems—now provide powerful frameworks for probing perception, learning, reasoning, memory, and decision making.

Research in this axis uses AI to analyze naturalistic data such as speech, text, behavior, and neural activity. Computational models are treated as explicit hypotheses about cognitive or neural mechanisms, allowing researchers to test how learning, representation, and dynamics give rise to observable behavior. In many cases, this work leads to the development of new analytical and conceptual tools that refine theoretical constructs, such as internal representations, cognitive states, or dimensions of subjective experience.

Beyond individual cognition, AI models trained on large-scale cultural or historical data are used to study long-term patterns of behavior, communication, and social change. In clinical contexts, machine-learning techniques help infer cognitive or neurological markers from complex multimodal data. Across these domains, AI serves as a bridge between empirical observation, theory, and explanation.

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