Tonically active neurons in the striatum differentiate between delivery and omission of expected reward in a probabilistic task context
European Journal of Neuroscience. 2009-08-01; 30(3): 515-526
DOI: 10.1111/j.1460-9568.2009.06872.x
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Apicella P(1), Deffains M, Ravel S, Legallet E.
Author information:
(1)Laboratoire de Neurobiologie de la Cognition, Université de Provence CNRS,
Marseille Cedex 3, France.
Comment in
Eur J Neurosci. 2009 Aug;30(3):514.
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.