@conference {3639, title = {Human Learning in Atari}, booktitle = {AAAI Spring Symposium Series}, year = {2017}, abstract = {

Atari games are an excellent testbed for studying intelligent behavior, as they offer a range of tasks that differ widely in their visual representation, game dynamics, and goals presented to an agent. The last two years have seen a spate of research into artificial agents that use a single algorithm to learn to play these games. The best of these artificial agents perform at better-than-human levels on most games, but require hundreds of hours of game-play experience to produce such behavior. Humans, on the other hand, can learn to perform well on these tasks in a matter of minutes. In this paper we present data on human learning trajectories for several Atari games, and test several hypotheses about the mechanisms that lead to such rapid learning.\ 

}, author = {Pedro Tsividis and Thomas Pouncy and Jacqueline L. Xu and Joshua B. Tenenbaum and Samuel J Gershman} } @proceedings {1205, title = {Hypothesis-Space Constraints in Causal Learning}, year = {2015}, month = {07/2015}, address = {Pasadena, CA}, url = {https://mindmodeling.org/cogsci2015/papers/0418/index.html}, author = {Pedro Tsividis and Joshua B. Tenenbaum and Laura Schulz} } @article {1811, title = {Information Selection in Noisy Environments with Large Action Spaces}, volume = {Columbus, OH}, year = {2015}, author = {Pedro Tsividis and Samuel J Gershman and Joshua B. Tenenbaum and Laura Schulz} }