The self in non-human animals is often studied in a limited, dichotomous way that separates low-level bodily and affective aspects from high-level cognitive ones. A proposed framework based on the Pattern Theory of Self (PTS) treats the self as a dynamic, multidimensional construct with graded, non-hierarchical dimensions—ranging from bodily and affective to intersubjective and normative. This approach accommodates variability within and across species, allowing researchers to investigate how the self emerges in different degrees and forms shaped by ecological niches and adaptive demands, without relying on anthropocentric biases.
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.