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"Artificial Grammar Learning" as a book category covers works about how structured patterns and rule-based sequences are acquired by humans, animals, or machines through exposure to exemplars generated from formal grammars. Books in this category include theoretical overviews of the AGL paradigm, empirical studies of implicit and explicit sequence learning, experimental and methodological guides, and computational treatments such as grammar induction, statistical learning, and neural-network models. They typically address topics like finite-state and context-free grammars, generalization and transfer, cognitive and neural mechanisms of pattern learning, and applications to language acquisition, developmental psychology, and machine learning. Intended for researchers, advanced students, and practitioners in cognitive science, linguistics, neuroscience, and AI, the category bridges experimental design, data analysis, and computational theory.

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