How instructed knowledge shapes aversive learning

In humans, expectations reflect prior experience and instructed knowledge. Most models of aver- sive learning make predictions about brain responses as a function of reinforcement alone. Recent studies of reward learning indicate that striatal learning is modulated when participants are instructed about stimulus contingencies. The aim of this study was to test whether instructed knowledge modulates associative fear learning. Participants performed a Pavlovian aversive learning paradigm. Two cues were presented: One (the CS+) was paired with shock on 30 % of trials, whereas the second (the CS-) was never paired with shock. Fol- lowing 20 trials, contingencies reversed. There were three reversals across the session. Participants were assigned to two groups: The Instructed Group was informed about contingencies prior to learning and upon each reversal, whereas the Feedback Group received no information. We analyzed skin conductance responses (SCRs) and brain responses to cues. Fear expression tracked con- tingency reversals (i. e. larger SCRs for current CS+ than CS-), and the Instructed Group showed stronger differential responses. The Instructed Group showed greater activation in right DLPFC, while the Feedback Group showed greater activation in bilateral striatum. We fit a quantitative model with a dynamic learning rate to SCRs to isolate the timecourse of learning in the Feedback Group, focusing on prediction error and associability. We then tested whether Instructions modulated the neural correlates of feedback-driven sig- nals. We observed group differences in bilateral ventral striatum, such that only the Feedback Group showed striatal prediction errors. These results reveal that instructed knowledge influences aversive learning. Instructions enhance fear acqui- sition and expression, and prediction errors are not observed when instructions are veridical. The DLPFC is likely to play a key role in maintaining instructions, which in turn modulate fear expression.

Authors

• Lauren Atlas
• Bradley Doll
• Nathaniel Daw
• Jian Li

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