Impaired Punishment Learning in Conduct Disorder

Abstract

Objective: Conduct disorder (CD) has been associated with deficits in the use of punishment to guide reinforcement learning (RL) and decision making. This may explain the poorly planned and often impulsive antisocial and aggressive behavior in affected youths. Here, we used a computational modeling approach to examine differences in RL abilities between CD youths and typically developing controls (TDCs). Specifically, we tested 2 competing hypotheses that RL deficits in CD reflect either reward dominance (also known as reward hypersensitivity) or punishment insensitivity (also known as punishment hyposensitivity).

Method: The study included 92 CD youths and 130 TDCs (aged 9-18 years, 48% girls) who completed a probabilistic RL task with reward, punishment, and neutral contingencies. Using computational modeling, we investigated the extent to which the 2 groups differed in their learning abilities to obtain reward and/or to avoid punishment.

Results: RL model comparisons showed that a model with separate learning rates per contingency explained behavioral performance best. Importantly, CD youths showed lower learning rates than TDCs specifically for punishment, whereas learning rates for reward and neutral contingencies did not differ. Moreover, callous-unemotional (CU) traits did not correlate with learning rates in CD.

Conclusion: CD youths have a highly selective impairment in probabilistic punishment learning, regardless of their CU traits, whereas reward learning appears to be intact. In summary, our data suggest punishment insensitivity rather than reward dominance in CD. Clinically, the use of punishment-based intervention techniques to achieve effective discipline in patients with CD may be a less helpful strategy than reward-based techniques.

Keywords: computational modeling; conduct disorder; decision making; punishment; reinforcement.