Neural mechanism for stochastic behaviour during a competitive game

Alireza Soltani, Daeyeol Lee, Xiao Jing Wang

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1075-1090
Number of pages16
JournalNeural Networks
Volume19
Issue number8
DOIs
StatePublished - Oct 1 2006

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Keywords

  • Decision making
  • Game theory
  • Meta-learning
  • Reinforcement learning
  • Reward-dependent stochastic Hebbian learning rule
  • Synaptic plasticity

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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