Artificial Intelligence and Human Language Cognition
An Integrative Comparative Framework
Keywords:
artificial intelligence; human language cognition; large language models; grounding; multimodality; intentionality; conceptual reviewAbstract
This article presents an integrative conceptual review comparing contemporary artificial intelligence language systems with human language cognition. A targeted, non-systematic search of foundational and recent literature published through July 2026 covered linguistics, psycholinguistics, cognitive science, philosophy of mind, natural language processing, multimodal learning, interpretability, and embodied robotics. Sources were selected based on their direct relevance to five recurring comparative dimensions: representation, acquisition and adaptation, processing, grounding and interaction, and intentionality, consciousness, and responsibility. The synthesis distinguishes functional linguistic competence from representational grounding, phenomenal consciousness, and moral responsibility, thereby avoiding the assumption that fluent performance either proves or refutes human-like understanding. The resulting framework treats AI-human differences as continua rather than fixed binaries and differentiates text-only language models from multimodal, tool-using, interactive, and embodied systems. Current evidence shows substantial convergence in formal language performance, prediction, abstraction, and some internally represented, world-relevant structures. However, it also reveals persistent differences in developmental history, sensorimotor coupling, online social learning, reliability profiles, and the evidence available for subjective experience or autonomous responsibility. These differences support a cautious model of cognitive complementarity: task allocation should depend on capability, verifiability, stakes, contextual sensitivity, and accountability rather than on broad claims that either humans or AI are categorically superior. The framework is interpretive and revisable. It would require revision if future systems demonstrate robust causal grounding, durable autonomous goals, cross-context learning from embodied interaction, or empirically credible indicators of consciousness.
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