Condition interference in rats performing a choice task with switched variable- and fixed-reward conditions.
Because humans and animals encounter various situations, the ability to adaptively decide upon responses to any situation is essential. To date, however, decision processes and the underlying neural substrates have been investigated under specific conditions; thus, little is known about how various conditions influence one another in these processes. In this study, we designed a binary choice task with variable- and fixed-reward conditions and investigated neural activities of the prelimbic cortex and dorsomedial striatum in rats. Variable- and fixed-reward conditions induced flexible and inflexible behaviors, respectively; one of the two conditions was randomly assigned in each trial for testing the possibility of condition interference. Rats were successfully conditioned such that they could find the better reward holes of variable-reward-condition and fixed-reward-condition trials. A learning interference model, which updated expected rewards (i.e., values) used in variable-reward-condition trials on the basis of combined experiences of both conditions, better fit choice behaviors than conventional models which updated values in each condition independently. Thus, although rats distinguished the trial condition, they updated values in a condition-interference manner. Our electrophysiological study suggests that this interfering value-updating is mediated by the prelimbic cortex and dorsomedial striatum. First, some prelimbic cortical and striatal neurons represented the action-reward associations irrespective of trial conditions. Second, the striatal neurons kept tracking the values of variable-reward condition even in fixed-reward-condition trials, such that values were possibly interferingly updated even in the fixed-reward condition.
Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University Okinawa, Japan ; Graduate School of Information Science and Technology, The University of Tokyo Tokyo, Japan.
Akihiro
A
Funamizu
Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University Okinawa, Japan.
Makoto
M
Ito
Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University Okinawa, Japan.
Kenji
K
Doya
Graduate School of Information Science and Technology, The University of Tokyo Tokyo, Japan ; Research Center for Advanced Science and Technology, The University of Tokyo Tokyo, Japan.
Ryohei
R
Kanzaki
Graduate School of Information Science and Technology, The University of Tokyo Tokyo, Japan ; Research Center for Advanced Science and Technology, The University of Tokyo Tokyo, Japan.
Hirokazu
H
Takahashi
Q-learning; goal-directed; habit; prefrontal cortex; reinforcement learning; striatum; task switching