Reading is best understood as a cultural-cognitive performance involving living bodies actively engaging with materials, not as a silent, disembodied neural process. The authors propose cognitive pacemaking—an action-perception phenomenon—as the key mechanism for controlling attention during reading, driven by embodied modulations of lived temporality. Meaning emerges from multimodal engagement with the text, not just linguistic decoding. The framework combines close reading of a classic literary text with a qualitative study of university students reading different short texts. Empirical reading research should examine how embodied reading varies across contexts, genres, media, and personalities to better design reading settings.
Large language models (LLMs) generate complex linguistic patterns that challenge the distinction between machine computation and human understanding. This article analyzes AI from a systems-theoretical perspective, arguing that classical Turing machines are not sense-making systems because they lack self-reference and the ability to make contingent selections. Artificial neural networks exhibit a novel, loosely coupled interaction with social systems by extracting patterns from societal communication. The paper proposes understanding LLMs as producing a new form of artificial meaning—a recursive reflection of socially shaped linguistic patterns—rather than as purely technical tools or genuine cognitive entities. This perspective calls for critical reflection on how AI transforms understanding of communication and cognition.