People can infer the magnitude of other people's knowledge, even when they cannot infer its contents

Inferences about other people's knowledge and beliefs are central to social interaction. However, it is often not possible to tell what exactly other people know, because their behavior is consistent with a range of potential epistemic states. Nonetheless, in many of these situations we often have coarse intuitions about how much someone knows, despite being unable to pinpoint the exact content of their knowledge. Here we sought to explore this capacity in humans, by comparing their performance to a normative model capturing this kind of broad epistemic-state inference, centered on the expectation that agents maximize epistemic utilities. We evaluate our model in a graded inference task where people had to infer how much an agent knew based on the actions they chose (Experiment 1), and joint inferences about how much someone knew and how much they believed they could learn (Experiment 2). Critically, the agent's knowledge was always under-determined by their behavior, but the behavior nonetheless contained information about how much knowledge they possessed or believed they could gain. Our model captures nuanced patterns in participant judgments, revealing that people have a quantitative capacity to infer amorphous knowledge from minimal behavioral evidence.