Large-Language-Model Tools and the Theory of Lexical Priming: where technology and human cognition meet and diverge

This paper revisits Michael Hoey’s Lexical Priming Theory (2005) in the light of recent discussions of Large Language Models as forms of machine learning (commonly referred to as AI), which have been the centre of a lot of publicity in the wake of tools like OpenAI’s ChatGPT or Google’s BARD/Gemini. Historically, theories of language have faced inherent difficulties, given language's exclusive use by humans and the complexities involved in studying language acquisition and processing. The intersection between Hoey's theory and Machine Learning tools, particularly those employing Large Language Models (LLMs), has been highlighted by several researchers. Hoey's theory relies on the psychological concept of priming, aligning with approaches dating back to Ross M. Quillian's 1960s proposal for a "Teachable Language Comprehender." The theory posits that every word is primed for discourse based on cumulative effects, a concept mirrored in how LLMs are trained on vast corpora of text data. This paper tests LLM-produced samples against naturally (human-)produced material in the light of a number of language usage situations, investigates results from A.I. research and compares the results with how Hoey describes his theory. While LLMs can display a high degree of structural integrity and coherence, they still appear to fall short of meeting human-language criteria which include grounding and the objective to meet a communicative need.