Abstract
Generative artificial intelligence (AI) systems are statistical models of language, yet users routinely describe them as understanding, knowing, or caring. We treat this as a problem in social cognition rather than machine metaphysics, and make three contributions. First, we specify the explanandum: mental-state attribution, the inference that a system has beliefs, intentions, or experience, of which consciousness attribution is the strong end-point. We distinguish it from perceived agency, social presence, and moral patiency, which are dissociable. Second, we argue that such attribution is normal and nonetheless wrong: it is an inferential system operating outside its calibration range, and normality confers no accuracy. Third, we integrate user-side cognition, system-side design, and motivational function into a dual-route model: an inferential route on which bias engagement mediates exposure effects, moderated by LLM literacy and conditional factors, and a motivated route on which the same vulnerabilities raise attribution directly, because attribution does work for the attributer. We state the model as six falsifiable propositions and adjudicate it against six competing explanations, naming for each an observation on which the accounts diverge. We close by operationalising metacognitive literacy as an evaluable intervention that cannot substitute for system-side accountability.
Cite this work
Carlos Toxtli-Hernández, Manuel Delaflor, and Alejandro Tapia-V.. 2026. AI Consciousness? Attribution and Cognitive Biases. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2026.1899551
@article{Toxtli2026AIConsciousness,
title = {AI Consciousness? Attribution and Cognitive Biases},
author = {Toxtli, Carlos and Delaflor, Manuel and Tapia-V., Alejandro},
journal = {Frontiers in Psychology},
volume = {17},
pages = {1899551},
year = {2026},
doi = {10.3389/fpsyg.2026.1899551},
note = {Perspective; accepted September 15, 2026, forthcoming},
url = {https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1899551/abstract}
}Related
