  Evaluating the cognitive capacities of large language models (LLMs) requires
overcoming not only anthropomorphic but also anthropocentric biases. This
article identifies two types of anthropocentric bias that have been neglected:
overlooking how auxiliary factors can impede LLM performance despite competence
(Type-I), and dismissing LLM mechanistic strategies that differ from those of
humans as not genuinely competent (Type-II). Mitigating these biases
necessitates an empirically-driven, iterative approach to mapping cognitive
tasks to LLM-specific capacities and mechanisms, which can be done by
supplementing carefully designed behavioral experiments with mechanistic
studies.
