Humans vs. machines: can enaction theory help?
Authors
Luca Pallanti, Rawad ChakerKey Information
Journal
Published in Vol. 4 No. 1 (2026) of AI-Linguistica. Linguistic Studies on AI-Generated Texts and Discourses.
AI-Linguistica. Linguistic Studies on AI-Generated Texts and Discourses is a new scholarly journal aiming at providing a publishing plateform for researchers from all areas of Linguistics (interfacing with neighboring fields: Communication Science, Media and Journalism Studies, Computational Linguistics) to reflect on generated texts from a variety of perspectives: theoretical, descriptive, and applied.
We understand ‘generated texts’ in a broad sense, including formats as diverse as texts generated by Large Language Models, AI-powered smart agents (i.e. chatbots, voice assistants, social bots etc.), writing assistance tools, template-based software, and neural machine translation services.
About the JournalAbstract
This study seeks to contribute to ongoing research on the formal differences between textual corpora produced by humans and those generated by generative artificial intelligences (GenAI). To this end, we draw on enactive theory and apply it to linguistics, conceiving the asymmetrical structural coupling between an organism and its environment as the basis of cognition. The relations between an actor and the environment are non-linear and irregular: within the perception–action loop, we selectively attend to elements of the environment that are meaningful for action. We propose a novel approach by using Sentence Length Variance (SLV) as an indicator of this asymmetry, or internal irregularity. We also experimented with prompting the machine to integrate greater variance into its texts. We found significant differences between human corpora and AI corpora in terms of SLV, as well as notable divergences among certain GenAI systems. When prompted for higher SLV, only one GenAI produced texts comparable to human writing. We advance several explanations for these findings, which we regard as promising avenues for future research. We conclude by outlining the theoretical implications of our findings, arguing that an enactive linguistics offers a fruitful framework for studying textual differences between human and AI-generated discourse.
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