Joe Pater writes:
Tom Griffiths of UC Berkeley will be visiting on Friday, April 5th, an event jointly sponsored by ICESL and the Institute for Computational Social Science. He will be giving a talk from 12:30 - 2 on "Identifying human inductive biases", details below.
From 2:30 - 3:15 on that same day, we will hold a meeting with Griffiths in Dickinson 206 in which we discuss a paper of his:
Hsu, A., & Griffiths, T. L. (2009). Differential use of implicit negative evidence in generative and discriminative language learning. Advances in Neural Information Processing Systems 22. http://cocosci.berkeley.edu/tom/papers/gendisclang.pdf
Identifying human inductive biases
Friday, April 5, 2013 • 12:30PM–2PM • Lunch provided
Campus Center, Room 917
Abstract: People are remarkably good at acquiring complex knowledge from limited data, as is required in learning causal relationships, categories, or aspects of language. Successfully solving inductive problems of this kind requires having good "inductive biases" - constraints that guide inductive inference. Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can facilitate this project, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models are traditionally used to solve this problem, and then present a new approach that uses Markov chain Monte Carlo algorithms as the basis for an experimental method that magnifies the effects of inductive biases. This approach provides some surprising insights into how information changes through cultural transmission (relevant to understanding processes like language evolution) and shows how ideas from computer science and statistics can lead to new empirical paradigms for cognitive science research.
