Download "Neural mechanism for stochastic behaviour during a competitive game"

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 noninteractive
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.

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