This project will extend and test a theory and computer model of human reasoning. People routinely reason using schemas, knowledge structures that describe general situations, events, rules, or relationships. For example, a Restaurant schema might specify the kinds of events that take place in restaurants, and the relationships between the actors in those events (e.g., chefs, waiters, customers); a Family Relation schema might specify the kinds of relationships that define various family members, such as sisters, brothers, parents, and uncles. A schema is both general, in that it refers to many specific situations, and highly structured, in that it specifies the relationships between objects, events, or even between other relationships. Psychologists and computer scientists have long recognized the utility of schemas as a basis for reasoning: If one can recognize an object or situation as an instance of a general schema, then one can use the schema to reason about that object or situation. Although the generality and structured nature of schemas make them extremely useful, these features also make schemas very difficult to model. Traditional symbolic approaches to computer modeling are good at representing structured information, but they have difficulty in flexibly matching specific instances to general facts; by contrast, connectionist models are good at flexibly matching instances to general categories, but they have great difficulty representing structured information (such as rules and relationships). We have recently developed a theory of human schema-based learning and reasoning, and implemented this theory as a working computer model. The model, called LISA (Learning and Inference with Schemas and Analogies), combines the flexibility of a connectionist architecture with the capacity to represent and learn relational structures. The resulting system can (a) learn general schemas from specific examples, (b) match new instances to learned schemas, and (c) use the schemas to make inductive inferences about the new examples. LISA makes several novel predictions about human learning and reasoning. Part of the NSF-supported research will be a series of experiments to test these predictions. Another part of the research will extend the LISA model to account for aspects of human story and event comprehension (e.g., If someone tells us `There are four plates per tray,` how do we know that this means each tray will hold, or contain, four plates?), and aspects of spatial reasoning (e.g., If we are told that Bill is taller than Charles and Abe is taller than Bill, how do we figure out that Abe is taller than Charles?). Importantly, the extended LISA model will account for these seemingly different capacities (as well as other capacities, such as reasoning by analogy) in terms of the same basic mechanisms as it uses to account for our ability to reason using schemas. The knowledge gained from the project will contribute to the development of methods for enhancing human learning and reasoning abilities, and also to the development of artificially intelligent systems.
