de Lafuente Figure 1 | A decision mechanism in chess. After an opponent’s move (d5), a player evaluates his choices by assigning a goodness value (subjective score) to each possible response. He must then calculate how the score will change after a sequence of moves involving the most likely opponent’s responses. When accumulated score, that is, the summed value of each move within a sequence, reaches an acceptance criterion, the move initiating the sequence is played on the board. Deeper calculations, at the expense of longer response times, could be achieved by elevating the acceptance criterion. How the human brain assigns values to chess moves is still largely unknown, but in addition to raw piece value, its evaluation function likely incorporates variables such as elapsed time (Churchland et al., 2008), long term plans, and the opponent’s estimated strength. evaluation of the choices. Ideally, the deci- sion bound should be adjusted according to board complexity and remaining time. It is known, in fact, that the criterion value must be raised in relation to the number of choices in order to maintain a constant level of per- formance (Usher et al., 2002). This would explain why players make slower decision as the entropy of board increases. Several simplifying assumptions of this decision algorithm need rigorous testing. To spare an extra mechanism for determining evalu- ation order, move sequences are assumed to be calculated in parallel. Additionally, only the height of the decision criterion deter- mines RT, so the model assumes no bound on the number of evaluated moves. As it is, openings would be analyzed the same way as the middle games, an assumption that might not hold for those players able to play “by the book,” i.e., relying on proven sequences of moves. In its simplest form, however, this decision model could potentially account for some of the uncovered statistical trends, namely, the linear relation of RT mean and standard deviation, as well as the long tails of RT distributions. Chess expertise likely depends on a number of cognitive skills such as fast pat- tern recognition to quickly grasp the geist of a position, or the capacity to hold long sequences of moves in working memory (Simon and Chase, 1973). The results of Sigman and colleagues now indicate that a hallmark of strong chess players, and suc- cessful decision making, is the ability to Decision making in chess constantly adjust the decision criteria to meet the changing demands of accuracy, complexity, and remaining time. RRfescne Blasius, B., and Tönjes, R. (2009). Zipf’s law in the popu- larity distribution of chess openings. Phys. Rev. Lett. 103, 218701. Charness, N., Reingold, E. M., Pomplun, M., and Stampe, D. M. (2001). The perceptual aspect of skilled per- formance in chess: evidence from eye movements. Mem. Cogn. 29, 1146–1152. Chase, W. G., and Simon, H. A. (1973). Perception in chess. Cogn. Psychol. 4, 55–81. Churchland, A. K., Kiani, R., and Shadlen, M. N. (2008). Decision making with multiple alternatives. Nat. Neurosci. 6, 693–702. Hyatt, R. (1984). Using time wisely. ICCA J. 7, 4–9. Luce, D. R. (1986). Response Times. New York: Oxford University Press. Palmer, J., Huk, A. C., and Shadlen, M. N. (2005). The effect of stimulus strength on the speed and accuracy of a perceptual decision. J. Vis. 5, 376–404. Ratcliff, R. (1978).A theory of memory retrieval. Psychol. Rev. 85, 59–108. Shannon, C. E. (1950).A chess-playing machine. Sci. Am. 182, 48–51. Sigman, M., Etchemendy, P., Slezak, D. F., and Cecchi, G. A. (2010). Response time distributions in rapid chess: a large-scale decision making experiment. Front. Neurosci. 4:60. doi: 10.3389/fnins.2010.00060 Simon, H.A., and Chase,W. G. (1973). Skill in chess. Am. Sci. 61, 394–403. Turing, A. M. (1950). Computing machinery and intel- ligence. Mind 59, 433–460. Usher, M., Olami, Z., and McClelland, J. L. (2002). Hick’s law in a stochastic race model with speed-accuracy tradeoff. J. Math. Psychol. 46, 704–715. Received: 07 December 2010; accepted: 05 January 2011; published online: 25 January 2011. Citation: de Lafuente V (2011) Flexible decisions and chess expertise. Front. Neurosci. 5:4. doi: 10.3389/ fnins.2011.00004 This article was submitted to Frontiers in Decision Neuroscience, a specialty of Frontiers in Neuroscience. Copyright © 2011 de Lafuente. This is an open-access article subject to an exclusive license agreement between the authors and Frontiers Media SA, which permits unre- stricted use, distribution, and reproduction in any medium, provided the original authors and source are credited. Frontiers in Neuroscience | Decision Neuroscience January 2011 | Volume 5 | Article 4 | 2