Mind, Body, and Language: Bridging Theoretical and Empirical Perspectives in Cognitive Science and Education
Keywords:
Embodied cognition, First language acquisition, Second language acquisition, AI learning models, Cognitive linguistics, Sensorimotor interaction, Language and the brainAbstract
Purpose: This study aims to explore the interrelationship between the mind, body, and language and to assess how linguistic theories and cognitive science inform a holistic model of language acquisition.
Methods and Materials: The research employs a qualitative content analysis methodology, examining scholarly texts across linguistics, cognitive science, SLA, and AI. Texts were selected from peer-reviewed journals and seminal books, and analyzed using a coding scheme to identify key concepts such as embodied cognition, sensorimotor interaction, and social context. The analysis is structured around five linguistic frameworks—Generative Grammar, Cognitive Linguistics, Functionalism, Post-Structuralism, and Sociocultural Theory—and how each relates to language, mind, and body.
Findings: The study reveals that traditional cognitive models like Generative Grammar view language acquisition as a mental process, minimizing the body’s role. In contrast, Cognitive Linguistics, Functionalism, and Sociocultural Theory emphasize the embodied and socially mediated nature of language learning. Empirical findings show that gestures, motor actions, and physical engagement are essential for both FLA and SLA. Moreover, embodied AI systems, which integrate sensorimotor feedback, increasingly mimic human-like language learning. Cognitive Linguistics and Sociocultural Theory were found to be most applicable to real-world and AI-based language learning contexts due to their focus on the integration of physical, cognitive, and social dimensions.
Conclusion: This research advocates for a paradigm shift in understanding language acquisition as a holistic process involving the mind, body, and environment. It demonstrates that embodied and socially interactive learning models provide a more comprehensive framework for both human and machine language learning. The findings suggest that future educational and AI systems should prioritize embodiment and contextual interaction to enhance language processing and retention.
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