Differential effects of reward and punishment in decision making under uncertainty: a computational study - Duffin, Elaine; Bland, Amy R.; Schaefer, Alexandre; De Kamps, Marc (Frontiers in Neuroscience, 2014)
Computational models of learning have proved largely successful in characterising potential mechanisms which allow humans to make decisions in uncertain and volatile contexts. We report here findings that extend existing knowledge and show that a modified reinforcement learning model which differentiates between prior reward and punishment can provide the best fit to human behaviour in decision making under uncertainty. More specifically, we examined the fit of our modified reinforcement learning model to human behavioural data in a probabilistic two-alternative decision making task with rule reversals. Our results demonstrate that this model predicted human behaviour better than a series of other models based on reinforcement learning or Bayesian reasoning. Unlike the Bayesian models, our modified reinforcement learning model does not include any representation of rule switches. When our task is considered purely as a machine learning task, to gain as many rewards as possible without trying to describe human behaviour, the performance of modified reinforcement learning and Bayesian methods is similar. Others have used various computational models to describe human behaviour in similar tasks, however, we are not aware of any who have compared Bayesian reasoning with reinforcement learning modified to differentiate rewards and punishments.