title: Neural mechanism for stochastic behaviour during a competitive game.
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.
authors.affiliation: Department of Physics and Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA. alireza.soltani@yale.edu
authors.forename: Alireza
authors.initials: A
authors.lastname: Soltani
authors.forename: Daeyeol
authors.initials: D
authors.lastname: Lee
authors.forename: Xiao-Jing
authors.initials: XJ
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal; D002755:Choice Behavior / Q000502:physiology*; D003163:Competitive Behavior / Q000502:physiology*; D000882:Haplorhini; D006801:Humans; D016013:Likelihood Functions; D008959:Models, Neurological; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D011337:Probability Learning; D011930:Reaction Time / Q000502:physiology; D012054:Reinforcement, Psychology; D013269:Stochastic Processes