Attribution of consciousness to non-human animals: insights from AI and multidimensional frameworks.
Front Psychol May 12, 2026 Salvatore G. Chiarella, Matteo Laurenzi, Marianna D’Onofrio et al.
People often show a double bias when attributing consciousness to non-human systems. Non-human animals receive low attributions of consciousness despite behavioral and neurobiological evidence suggesting subjective experience, while disembodied AI systems like large language models receive elevated attributions of consciousness despite lacking sensory or bodily substrates. This asymmetry indicates that folk judgments are shaped more by observers' cue-weighting heuristics than by the intrinsic properties of the systems. The authors propose that multidimensional, non-hierarchical frameworks, such as Birch's model and the Pattern Theory of Self, can serve as diagnostic tools to study how evidence dimensions are weighted in attributional contexts, replacing a ladder of human-like capacities with a landscape of profiles across taxa and system types.