The expression of motion verbs differs between languages. The path of motion, such as crossing or entering, is more prominently featured in path-based languages such as Spanish than in manner-based languages such as English. Here, we revisit the data from a study on manner and path biases in verb lexicalization, and create a hierarchical Baysian computational model to further explore, verify, and define these biases. With this model, we can discover the large differences in subjects' pre-existing manner and path biases that depend on the syntactic frame in which new verbs appear, as well as a difference in the learning rate between English speakers taking the experiment in English and bilingual Spanish speakers taking the experiment in Spanish. We can also use the model to predict the responses of subjects in the experiment with more accuracy than before.