<![CDATA[The rapid development of Artificial Intelligence (AI), particularly Natural Language Processing (NLP), has brought together two fundamental disciplines: Psycholinguistics and Computer Science. This research seeks to bridge the theoretical gap between how the human brain processes and generates language (psycholinguistics) and how machines model and replicate these processes (AI). This research employed a comparative-analytical literature review. Data was collected from leading academic journals focused on AI language models (such as Transformer and Large Language Models/LLMs) and theories of human language processing (such as Serial Processing and Connectionist Models). The analysis focused on three main dimensions: lexicon acquisition, syntactic processing, and pragmatic understanding. It was found that while modern AI excels at predicting word order and syntactic structure based on probability (like statistical approaches in cognition), it still falls short of fully replicating semantic processing tied to experience, awareness, and social context (a hallmark of human processing). Current AI models demonstrate impressive speeds in lexical inference but often fail at tasks requiring a theory of mind or a multi-layered understanding of pragmatics. Integrating psycholinguistic principles into AI architectures holds great potential for developing systems that are not only efficient but also more natural and human-like in their interactions. Further research is needed to build AI models that reflect the complex bottom-up and top-down processes in the human brain.]]>
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