: Chess has long been a model system to study complex thought processes. In particular, a consensus has emerged in that chess expertise comes in two forms: the ability to calculate variations (search) and the ability to recognize and remember meaningful patterns on the board (pattern recognition). Given the intricacies of the game, a robust statistical answer to these queries requires a solid experimental framework designed to provide large datasets. Among the various game formats, rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. The most relevant aspect of this cognitive experiment is the amount of data it produces: using web-based conduits (http://www.freechess.org/(link is external)), thousands of players play simultaneously, making millions of decisions per day that can be easily recorded. We use rapid chess as a laboratory to explore decision making in a natural setup. We have studied the structure of the time players take to make a move during a game, and analyzed millions of instances. This approach allowed us to identify a number of statistical fingerprints that uniquely characterize the emergent structure of the game.