  While humans naturally develop theory of mind (ToM), the capability to
understand other people's mental states and beliefs, state-of-the-art large
language models (LLMs) underperform on simple ToM benchmarks. We posit that we
can extend our understanding of LLMs' ToM abilities by evaluating key human ToM
precursors -- perception inference and perception-to-belief inference -- in
LLMs. We introduce two datasets, Percept-ToMi and Percept-FANToM, to evaluate
these precursory inferences for ToM in LLMs by annotating characters'
perceptions on ToMi and FANToM, respectively. Our evaluation of eight
state-of-the-art LLMs reveals that the models generally perform well in
perception inference while exhibiting limited capability in
perception-to-belief inference (e.g., lack of inhibitory control). Based on
these results, we present PercepToM, a novel ToM method leveraging LLMs' strong
perception inference capability while supplementing their limited
perception-to-belief inference. Experimental results demonstrate that PercepToM
significantly enhances LLM's performance, especially in false belief scenarios.
