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• W2167821068 abstract "Learning theory suggests that animals attend to pertinent environmental cues when reward contingencies unexpectedly change so that learning can occur. We have previously shown that activity in basolateral nucleus of amygdala (ABL) responds to unexpected changes in reward value, consistent with unsigned prediction error signals theorized by Pearce and Hall. However, changes in activity were present only at the time of unexpected reward delivery, not during the time when the animal needed to attend to conditioned stimuli that would come to predict the reward. This suggested that a different brain area must be signaling the need for attention necessary for learning. One likely candidate to fulfill this role is the anterior cingulate cortex (ACC). To test this hypothesis, we recorded from single neurons in ACC as rats performed the same behavioral task that we have used to dissociate signed from unsigned prediction errors in dopamine and ABL neurons. In this task, rats chose between two fluid wells that produced varying magnitudes of and delays to reward. Consistent with previous work, we found that ACC detected errors of commission and reward prediction errors. We also found that activity during cue sampling encoded reward size, but not expected delay to reward. Finally, activity in ACC was elevated during trials in which attention was increased following unexpected upshifts and downshifts in value. We conclude that ACC not only signals errors in reward prediction, as previously reported, but also signals the need for enhanced neural resources during learning on trials subsequent to those errors." @default.
• W2167821068 created "2016-06-24" @default.
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• W2167821068 date "2011-12-14" @default.
• W2167821068 modified "2023-10-15" @default.
• W2167821068 title "Attention for Learning Signals in Anterior Cingulate Cortex" @default.
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• W2167821068 doi "https://doi.org/10.1523/jneurosci.4715-11.2011" @default.
• W2167821068 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3285822" @default.
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