![]() ![]() The consideration of time, therefore, introduces a wealth of dimensions to evaluate the complexity of the game. Time constraints and playing ability therefore interact in a highly non-trivial manner. Thus, time pressure provokes a selective enhancement of rapid object recognition, favoring the best players, but also increases the likelihood of errors and blunders, which in turn tends to equalize the game. Not surprisingly, even grandmasters make more errors and blunders under conditions in which they have less time than usual to select their moves (Chabris and Hearst, 2003). The reduction of chess expertise to speed is, however, overly simplistic: firstly, there is substantial evidence that chess experts do not search “wider”, they do search “deeper” than weaker players (Holding and Reynolds, 1982 Saariluoma, 1990) secondly, as players are forced to play faster, their ability during regular play under normal time controls becomes less predictive of their performance (Van Der Maas and Wagenmakers, 2005). On the contrary, I have always thought that fast playing is a measure of the ability to play chess” (Bronstein and Fürstenberg, 1995). David Bronstein, arguably one of the most inventive chess players, was an adept of this view: “ I have always defended playing under time pressure, and I do not think a shortage of time is a bad thing. Indeed, an idea very dear to the folklore of chess is that good players do not calculate more, just calculate better. ![]() Naively, one would expect that the temporal pressure represented by time budgets in different formats of the game should further amplify these differences. The prevalent view is that expert players, as opposed to weaker ones, excel specifically at rapid object recognition abilities (Gobet and Simon, 1996a, b Burns, 2004). 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). Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation.Ĭhess has long been a model system to study complex thought processes (Groot, 1965 Charness, 1981 Holding and Reynolds, 1982 Gobet and Simon, 1996a Schultetus and Charness, 1999 Reingold et al., 2001a, b). First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Our results also have practical implications. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. We generated a database of response times (RTs) and position value in rapid chess games. ![]() Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. In a chess game, players choose consecutively around 40 moves in a finite time budget. Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. ![]()
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