Language models like chatGPT motivate an approach to meaning known as distributional semantics, that words mean what they do because of how they’re distributed in language. In this talk I will describe some evidence from my lab that suggests metrics from large language models do a good job of predicting behavioral and neural responses to some aspects of human language. I go on to describe some research that highlights important differences in meaning processing in humans and the ‘understanding’ displayed by language models. Discrepancies are particularly noteworthy in studies of joke comprehension.