Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis.

@article{Chase2015ReinforcementLM,
  title={Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis.},
  author={Henry W. Chase and Poornima Kumar and Simon B. Eickhoff and Alexandre Y Dombrovski},
  journal={Cognitive, affective & behavioral neuroscience},
  year={2015},
  volume={15 2},
  pages={435-59}
}
Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments-prediction error-is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies have suggested that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and… CONTINUE READING
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