This academic paper explores how people attribute beliefs to others as a way of explaining their actions, focusing on the explanatory strength of a belief rather than just its probability. The authors developed a computational model that assesses this strength using three factors: accuracy, informativity, and causal relevance. Through an experiment where participants ranked belief statements describing a player's actions in a puzzle game, the research suggests that causal relevance is the strongest predictor of human belief attribution, although a combination of accuracy and informativity also plays a role. This indicates that people tend to favor beliefs that are perceived as causes for observed behavior.