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Massive-scale neuroscience

MateMarote: Many 1-to-1 educational models are spreading all over the world: the One Laptop Per Child program (OLPC), directed by Nicholas Negroponte and Conectar Igualdad program from Argentina are two of the biggest ones deployed. This initiatives open up a new era with massive-scale educational resources. The fact that all learning and teaching tasks are represented in the same digital environment is a formidable tool for educational applications, as well as for research and experimentation. The main objective of this project is the development and use of techniques of machine learning to study massive-scale corpus associated to cognitive processes, taken from the mobile computers of an entire country, which may help identify the mental operations which are chained in the design of programs or routines. The goal is twofold: first, decoding of regularities in the corpus to infer the generative rules of human thought, and their incorporation into artificial intelligence models. Conversely, it is expected to promote the use of synergistic human-scale computing to solve intractable computational architectures in-silico.



Decision making: 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 (, 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 \textsl{millions} of instances. This approach allowed us to identify a number of statistical fingerprints that uniquely characterize the emergent structure of the game. See our Publications section.