# Show Reference: "Neural Mechanism for Stochastic Behavior During a Competitive Game"

Neural Mechanism for Stochastic Behavior During a Competitive Game Neural networks : the official journal of the International Neural Network Society, Vol. 19, No. 8. (October 2006), pp. 1075-1090, doi:10.1016/j.neunet.2006.05.044 by Alireza Soltani, Daeyeol Lee, Xiao-Jing J. Wang
@article{soltani-et-al-2006,
abstract = {Previous studies have shown that non-human primates can generate highly stochastic choice behaviour, especially when this is required during a competitive interaction with another agent. To understand the neural mechanism of such dynamic choice behaviour, we propose a biologically plausible model of decision making endowed with synaptic plasticity that follows a reward-dependent stochastic Hebbian learning rule. This model constitutes a biophysical implementation of reinforcement learning, and it reproduces salient features of behavioural data from an experiment with monkeys playing a matching pennies game. Due to interaction with an opponent and learning dynamics, the model generates quasi-random behaviour robustly in spite of intrinsic biases. Furthermore, non-random choice behaviour can also emerge when the model plays against a non-interactive opponent, as observed in the monkey experiment. Finally, when combined with a meta-learning algorithm, our model accounts for the slow drift in the animal's strategy based on a process of reward maximization.},
author = {Soltani, Alireza and Lee, Daeyeol and Wang, Xiao-Jing J.},
doi = {10.1016/j.neunet.2006.05.044},
issn = {0893-6080},
journal = {Neural networks : the official journal of the International Neural Network Society},
keywords = {ann, games, learning, model, probabilities},
month = oct,
number = {8},
pages = {1075--1090},
pmcid = {PMC1752206},
pmid = {17015181},
posted-at = {2013-08-27 03:03:06},
priority = {2},
title = {Neural Mechanism for Stochastic Behavior During a Competitive
Game},
url = {http://dx.doi.org/10.1016/j.neunet.2006.05.044},
volume = {19},
year = {2006}
}


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