Bayesian modeling of manner and path psychological data

How people and computers can learn the meaning of words has long been a key ques-tion for both AI and cognitive science. It is hypothesized that a person acquires a bias to favor the characteristics of their native language, in order to aid word learning. Other hypothesized aids are syntactic bootstrapping, in which the learner assumes that the meaning of a novel word is similar to that of other words used in a similar syntax, and its complement, semantic bootstrapping, in which the learner assumes that the syntax of a novel word is similar to that of other words used in similar situ-ations. How these components work together is key to understanding word learning. Using cognitive psychology and computer science as a platform, this thesis attempts to tackle these questions using the classic example of manner and path verb bias. A series of cognitive psychology experiments was designed to gather information o