  Building on research arguing for the possibility of conceptual and
categorical knowledge acquisition through statistics contained in language, we
evaluate predictive language models (LMs) -- informed solely by textual input
-- on a prevalent phenomenon in cognitive science: typicality. Inspired by
experiments that involve language processing and show robust typicality effects
in humans, we propose two tests for LMs. Our first test targets whether
typicality modulates LM probabilities in assigning taxonomic category
memberships to items. The second test investigates sensitivities to typicality
in LMs' probabilities when extending new information about items to their
categories. Both tests show modest -- but not completely absent --
correspondence between LMs and humans, suggesting that text-based exposure
alone is insufficient to acquire typicality knowledge.
