Differential effects of reward and punishment in decision making under uncertainty: a computational study.

Frontiers in Neuroscience

Alexandre eSchaefer¹  Marc eDe Kamps²  Elaine eDuffin²  Amy Rachel Bland³ 

[1] Monash University, Sunway Campus;University of Leeds;University of Manchester;

关键词: Decision Making;    reinforcement learning;    uncertainty;    volatility;    Bayesian learning;    Reward and punishment;   

DOI  :  10.3389/fnins.2014.00030

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown