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. 2009 Aug;30(3):515-26.
doi: 10.1111/j.1460-9568.2009.06872.x. Epub 2009 Jul 28.

Tonically active neurons in the striatum differentiate between delivery and omission of expected reward in a probabilistic task context

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Tonically active neurons in the striatum differentiate between delivery and omission of expected reward in a probabilistic task context

Paul Apicella et al. Eur J Neurosci. 2009 Aug.

Abstract

Tonically active neurons (TANs) in the primate striatum are responsive to rewarding stimuli and they are thought to be involved in the storage of stimulus-reward associations or habits. However, it is unclear whether these neurons may signal the difference between the prediction of reward and its actual outcome as a possible neuronal correlate of reward prediction errors at the striatal level. To address this question, we studied the activity of TANs from three monkeys trained in a classical conditioning task in which a liquid reward was preceded by a visual stimulus and reward probability was systematically varied between blocks of trials. The monkeys' ability to discriminate the conditions according to probability was assessed by monitoring their mouth movements during the stimulus-reward interval. We found that the typical TAN pause responses to the delivery of reward were markedly enhanced as the probability of reward decreased, whereas responses to the predictive stimulus were somewhat stronger for high reward probability. In addition, TAN responses to the omission of reward consisted of either decreases or increases in activity that became stronger with increasing reward probability. It therefore appears that one group of neurons differentially responded to reward delivery and reward omission with changes in activity into opposite directions, while another group responded in the same direction. These data indicate that only a subset of TANs could detect the extent to which reward occurs differently than predicted, thus contributing to the encoding of positive and negative reward prediction errors that is relevant to reinforcement learning.

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