Timezone: »

 
Invited Talk #6: Features or Bugs: Synergistic Idiosyncrasies in Human Learning and Decision-Making
Angela Yu

Fri Dec 13 03:00 PM -- 03:30 PM (PST) @
in Biological and Artificial Reinforcement Learning »

Combining a multi-armed bandit task and Bayesian computational modeling, we find that humans systematically under-estimate reward availability in the environment. This apparent pessimism turns out to be an optimism bias in disguise, and one that compensates for other idiosyncrasies in human learning and decision-making under uncertainty, such as a default tendency to assume non-stationarity in environmental statistics as well as the adoption of a simplistic decision policy. In particular, reward rate underestimation discourages the decision-maker from switching away from a “good” option, thus achieving near-optimal behavior (which never switches away after a win). Furthermore, we demonstrate that the Bayesian model that best predicts human behavior is equivalent to a particular form of Q-learning often used in the brain sciences, thus providing statistical, normative grounding to phenomenological models of human and animal behavior.

Author Information

Angela Yu (UC San Diego)

More from the Same Authors