Bayesian modeling of manner and path psychological data

Abstract

How people and computers can learn the meaning of words has long been a key question 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 situations. 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 on this bias. Considerable flexibility of the subject's bias was demonstrated during these experiments. Another separate series of experiments was conducted using different syntactic frames for the novel verbs to address the question of bootstrapping. The resulting information was used to design a Bayesian model which successfully predicts the human behavior in the psychological experiments that were conducted. Dynamic parameters were required to account for subjects revising their expected manner and path verb distributions during the course of an experiment. Bayesian model parameters that were optimized for rich syntactic frame data performed equally well in predicting poor syntactic frame data.