When push comes to shove: A computational model of the role of motor control in the acquisition of action verb

Children learn a variety of verbs for hand actions starting in their second year of life. The semantic distinctions can be subtle, and they vary across languages, yet they are learned quickly. How is this possible? This dissertation explores the hypothesis that to explain the acquisition and use of action verbs, motor control must be taken into account. It presents a model of embodied semantics--based on the principles of neural computation in general and on the human motor system in particular--which takes a set of labelled actions and learns both to label novel actions and to obey verbal commands. A key feature of the model is the executing schema, an active controller mechanism which, by actually driving behavior, allows the model to carry out verbal commands. A hard-wired mechanism links the activity of executing schemas to a set of linguistically important features including hand posture, joint motions, force, aspect and goals. The feature set is relatively small and is fixed, helping to make learning tractable. Moreover, the use of traditional feature structures facilitates the use of model merging, a Bayesian probabilistic learning algorithm which rapidly learns plausible word meanings, automatically determines an appropriate number of senses for each verb, and can plausibly be mapped to a connectionist recruitment learning architecture. The learning algorithm is demonstrated on a handful of English verbs, and also proves capable of making some interesting distinctions found crosslinguistically.