Abstract
This paper studies a new measure for the cost of learning that allows the different attributes of the options faced by an agent to differ in their associated learning costs. The new measure maintains the tractability of Shannon's classic measure but produces richer choice predictions and identifies a new form of informational bias significant for welfare and counterfactual analysis that is conducted with the multinomial logit model. Necessary and sufficient conditions are provided for optimal agent behavior under the new measure for the cost of learning.