Coalescing the Vapors of Human Experience into a Viable and Meaningful Comprehension.

Abstract

Models of learning concepts or theories often invoke a stochastic search process, in which learners generate hypotheses through some structured random process and then evaluate them on some data measuring their quality or value. To be successful within a reasonable time-frame, these models need ways of generating good candidate hypotheses before the data are considered. Schulz (2012a) has proposed that studying the origins of new ideas in more everyday contexts, such as how we think up new names for things, can provide insight into the cognitive processes that generate good hypotheses for learning. We propose a simple generative model for how people might draw on their experience to propose new names in everyday domains such as pub names or action movies, and show that it captures surprisingly well the names that people actually imagine. We discuss the role for an analogous hypothesis-generation mechanism in enabling and constraining causal theory learning.

Tomer Ullman

Primary Investigator

My research focuses on the structure and origin of knowledge, guided by perspectives and methods from cognitive science, cognitive development, and computational modeling. By combining these, I hope to better understand the form and development of the basic commonsense reasoning that guides our interaction with the world and the people in it.