Goalkeeper Game
The Goalkeeper Game is a tool used to investigate the conjecture that the brain performs statistical model selection.[1] It was developed in 2015 by researchers from the Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat), a research center at the University of São Paulo (USP).
The Goalkeeper Game is a video game in which the player assumes the role of a goalkeeper during a football penalty shoot-out and aims to predict the position in the goal where the ball will be kicked: the left side, the right side, or the center. The game consists of a sequence of penalty kicks in which the positions of the ball may be generated deterministically or randomly according to a strategy described by a context tree and unknown to the player. The strategy is fixed for each level and, as the player, acting as the goalkeeper, achieves a sufficient number of correct predictions, depending on the strategy tree, the level ends and a new one begins with a more complex tree. As the game progresses, the expectation is that, after a large number of attempts in each level, the player, in the role of goalkeeper, will be able to make sense of the strategy and achieve a high-scoring performance.[2] Versions of the Goalkeeper Game are available for the web, desktop computers, and mobile devices.
History
The game is based on ideas proposed by researcher and RIDC NeuroMat director Antonio Galves concerning stochastic processes applied to neuronal interaction,[3][4] in which each neuron fires randomly according to a point process with a rate that depends on its membrane potential. At the moment of firing, the membrane potential of the firing neuron is reset to 0 and, simultaneously, the membrane potentials of the other neurons are increased by an amount of potential 1N. Thus, as the size N of the system diverges, the distribution of membrane potentials becomes deterministic and is described by a limiting density that obeys a nonlinear PDE, which is a hyperbolic conservation law.[5]
Researchers have discussed hypotheses according to which the brain retrieves statistical regularities from stimuli. A new statistical approach developed by RIDC NeuroMat makes it possible to address this conjecture. This approach is based on a new class of stochastic processes, namely sequences of random objects driven by chains with memory of variable length.[6][7][8]
Research
The Goalkeeper Game is currently used by RIDC NeuroMat in its research as an assessment tool in basic and applied neuroscience. In Parkinson's disease (PD), both automaticity and gait are related to dopaminergic loss. Because the Goalkeeper Game is an instrument designed to assess automaticity, it is a potential tool for indirectly evaluating dopaminergic loss and gait.
The Goalkeeper Game enables the collection of large amounts of data. Statistical analysis of players' correct-response rates has therefore proved sensitive to cognitive decline associated with the players' decision-making models. Considering that gait performance under complex conditions depends on the decision-making process underlying obstacle negotiation, speed selection, and other factors, the collected results suggest that performance in the Goalkeeper Game is associated with gait performance under complex conditions.[2]
References
- ↑ de Castro, B. M. (2016). Processos estocásticos conduzidos por cadeias com memória de alcance variável e o jogo do goleiro. (Ph.D. thesis). Universidade de São Paulo, São Paulo, Brazil
- ↑ 2.0 2.1 Stern, Rafael B.; d'Alencar, Matheus Silva; Uscapi, Yanina L.; Gubitoso, Marco D.; Roque, Antonio C.; Helene, André F.; Piemonte, Maria Elisa Pimentel (2020). "Goalkeeper Game: A New Assessment Tool for Prediction of Gait Performance Under Complex Condition in People With Parkinson's Disease". Frontiers in Aging Neuroscience. 12. doi:10.3389/fnagi.2020.00050. ISSN 1663-4365. PMC 7064547 Check
|pmc=value (help). PMID 32194393 Check|pmid=value (help). Unknown parameter|article-number=ignored (help) - ↑ Galves, A.; Löcherbach, E. (2013-06-01). "Infinite Systems of Interacting Chains with Memory of Variable Length—A Stochastic Model for Biological Neural Nets". Journal of Statistical Physics. 151 (5): 896–921. arXiv:1212.5505. Bibcode:2013JSP...151..896G. doi:10.1007/s10955-013-0733-9. ISSN 1572-9613. Retrieved 2025-03-20.
- ↑ "FAPESP Na Mídia". namidia.fapesp.br. Retrieved 2025-03-20.
- ↑ De Masi, A.; Galves, A.; Löcherbach, E.; Presutti, E. (2015-02-01). "Hydrodynamic Limit for Interacting Neurons". Journal of Statistical Physics. 158 (4): 866–902. arXiv:1401.4264. Bibcode:2015JSP...158..866D. doi:10.1007/s10955-014-1145-1. ISSN 1572-9613. Retrieved 2025-03-20.
- ↑ Duarte, A.; Fraiman, R.; Galves, A.; Ost, G.; Vargas, C. (2018-01-11). "Context tree selection for functional data". arXiv:1602.00579 [q-bio.NC].
- ↑ "Em Paris, a matemática do cérebro vai entrar em campo com o Cepid NeuroMat". Jornal da USP (in português). 2022-10-19. Archived from the original on 2024-12-07. Retrieved 2025-03-20. Unknown parameter
|url-status=ignored (help) - ↑ Hernández, Noslen; Galves, Antonio; Garcia, Jesus; Gubitoso, Marcos Dimas; Vargas, Claudia D. (2023-03-19), Probabilistic prediction and context tree identification in the Goalkeeper Game, arXiv:2303.00102
External links
This article "Goalkeeper Game" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Goalkeeper Game. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.
