Neural mechanism for stochastic behaviour during a competitive game

Neural Netw. 2006 Oct;19(8):1075-90. doi: 10.1016/j.neunet.2006.05.044.

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.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Animals
  • Behavior, Animal
  • Choice Behavior / physiology*
  • Competitive Behavior / physiology*
  • Haplorhini
  • Humans
  • Likelihood Functions
  • Models, Neurological*
  • Neural Inhibition / physiology
  • Neural Networks, Computer
  • Neurons / physiology*
  • Probability Learning
  • Reaction Time / physiology
  • Reinforcement, Psychology
  • Stochastic Processes*