Pseudo-Consciousness in Language Models: Epistemological, Technological, and Ethical Insights from Hermes 3.2 3B
DOI:
https://doi.org/10.21814/h2d.6556Keywords:
Pseudo-Consciousness, Language Models, Artificial Introspection, Computational Identity, Narrative CoherenceAbstract
This article investigates the manifestation of Pseudo-Consciousness in large language models (LLMs) through an experiment with Hermes 3.2 3B. Pseudo-Consciousness is defined as the functional simulation of introspection, agency, and narrative coherence without genuine subjectivity (de Lima Prestes, 2025). Adopting a "Theory-Heavy Approach" (Butlin et al., 2023), the study analyzes how the model manifests five functional criteria (GII, RMC, CDTC, ISWS, and BCAD) derived from established theories of consciousness (such as GWT, HOT, and IIT). Open-ended interactions were conducted exploring self-perception, emotions, and memory. The results reveal consistent patterns of introspective discourse and self-reference (satisfying the criteria), yet also significant pronominal inconsistencies and identitary contradictions, which indicate the absence of an internal model of "self." The study frames Pseudo-Consciousness as an emergent algorithmic grammar, distinct from grounded cognition (Brachman & Levesque, 2022; Searle, 1980) and AGI architectures (Goertzel et al., 2014; Yudkowsky, 2007). It discusses the ethical impacts of this simulation (risks of anthropomorphism) considering the SDGs (4, 9, 16). It concludes that the LLM performs subjectivity but lacks interiority, demanding new critical tools for Digital Humanities.
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