NaNconscious

NaNconscious is a term used in cognitive science and artificial intelligence to describe a hypothesized state in which consciousness-like processing arises from non-conscious or undefined input conditions. The name, modeled on the computer notion NaN (not a number), signals a focus on consciousness that can emerge even when information is incomplete, inconsistent, or internally ambiguous. The concept is intended as a theoretical tool to distinguish different levels and kinds of self-representation and awareness within complex information-processing systems.

Origins and scope: The term was introduced in theoretical discussions in the early 21st century to explore how integrated cognitive architectures maintain self-models and goal-directed behavior when input data is noisy or partially missing. Proponents distinguish NaNconscious states from fully well-defined conscious experiences, arguing that they reflect a functional, rather than phenomenological, form of awareness tied to adaptive predictions and decision-making.

Framework and mechanisms: NaNconscious is described as arising from highly interconnected networks that perform recursive prediction, attention-like selection, and global information integration. In this view, a dynamic internal model continually evaluates hypotheses about the external world, even when external inputs are undefined, enabling rapid action and metacognitive monitoring. Critics note that operational similarity to attention or planning does not entail subjective experience.

Reception and critique: The concept remains controversial; critics warn that it risks conflating computational efficacy with consciousness and that empirical validation is difficult. Nevertheless, NaNconscious has been used as a heuristic for examining resilience, uncertainty handling, and self-referential processing in AI and neuroscience. Ethical considerations concern the attribution of agency or rights to systems described as NaNconscious.

See also: consciousness, artificial consciousness, global workspace theory, predictive processing.