Punishment

Punishment involves learning about the relationship between behavior and its adverse consequences. Punishment is fundamental to reinforcement learning, decision-making and choice, and is disrupted in psychiatric disorders such as addiction, depression, and psychopathy. However, little is known about the brain mechanisms of punishment and much of what is known is derived from study of superficially similar, but fundamentally distinct, forms of aversive learning such as fear conditioning and avoidance learning. Here we outline the unique conditions that support punishment, the contents of its learning, and its behavioral consequences. We consider evidence implicating GABA and monoamine neurotransmitter systems, as well as corticostriatal, amygdala, and dopamine circuits in punishment. We show how maladaptive punishment processes are implicated in addictions, impulse control disorders, psychopathy, anxiety, and depression and argue that a better understanding of the cellular, circuit, and cognitive mechanisms of punishment will make important contributions to next generation therapeutic approaches.

Results from clinical studies suggest that drug relapse and craving are often provoked by exposure to drug-associated contexts. Since 2002, this phenomenon has been modeled in laboratory animals using the ABA renewal model. In the classical version of this model, rats with a history of drug self-administration in one context (A) undergo extinction in a different context (B) and reinstate (or relapse to) drug seeking after exposure to the original drug-associated context (A). In a more recent version of the model introduced in 2013, the experimental conditions in context A are identical to those used in the classical model, but drug-reinforced responding in context B is suppressed by probabilistic punishment. The punishment-based ABA renewal model is proposed to resemble abstinence in humans, which is often initiated by the desire to avoid the negative consequences of drug use. The goal of our review is to discuss similarities and differences in mechanisms that play a role in suppression of drug seeking in context B and context-induced relapse to drug seeking in context A in the two models. We first describe psychological mechanisms that mediate extinction and punishment of drug-reinforced responding in context B. We then summarize recent findings on brain mechanisms of context-induced relapse of drug seeking after extinction, or punishment-imposed abstinence. These findings demonstrate both similarities and differences in brain mechanisms underlying relapse in the two variations of the ABA renewal model. We conclude by briefly discussing clinical implications of the preclinical studies.

Differences in the prevalence and presentation of psychiatric illnesses in men and women suggest that neurobiological sex differences confer vulnerability or resilience in these disorders. Rodent behavioral models are critical for understanding the mechanisms of these differences. Reward processing and punishment avoidance are fundamental dimensions of the symptoms of psychiatric disorders. Here we explored sex differences along these dimensions using multiple and distinct behavioral paradigms. We found no sex difference in reward-guided associative learning but a faster punishment-avoidance learning in females. After learning, females were more sensitive than males to probabilistic punishment but less sensitive when punishment could be avoided with certainty. No sex differences were found in reward-guided cognitive flexibility. Thus, sex differences in goal-directed behaviors emerged selectively when there was an aversive context. These differences were critically sensitive to whether the punishment was certain or unpredictable. Our findings with these new paradigms provide conceptual and practical tools for investigating brain mechanisms that account for sex differences in susceptibility to anxiety and impulsivity. They may also provide insight for understanding the evolution of sex-specific optimal behavioral strategies in dynamic environments.

ImportanceMechanistic biomarkers for guiding treatment selection require selective sensitivity to specific pharmacological interventions. Reinforcement learning processes show potential, but there have been conflicting and sometimes inconsistent reports on how dopamine and serotonin-2 key targets in treating common mental illnesses-affect reinforcement learning in humans.ObjectiveTo perform a meta-analysis of pharmacological manipulations of dopamine and serotonin and examine whether they show distinct associations with reinforcement learning components in humans.Data sourcesOvid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies published between January 1, 1946, and January 19, 2023 (repeated April 9, 2024, and October 15, 2024), investigating dopaminergic or serotonergic effects on reward and punishment processes in humans according to PRISMA guidelines.Study selectionStudies reporting randomized, placebo-controlled, dopaminergic or serotonergic manipulations on a behavioral outcome from a reward or punishment processing task in healthy humans were included.Data extraction and synthesisStandardized mean difference (SMD) scores were calculated for the comparison between each drug (dopamine or serotonin) and placebo on a behavioral reward or punishment outcome and quantified in random-effects models for overall reward or punishment processes and 4 main subcategories. Study quality (Cochrane Collaboration tool), moderators, heterogeneity, and publication bias were also assessed.Main outcomes and measuresPerformance on reward or punishment processing tasks.ResultsIn total, 102 studies conducted among healthy volunteers were included (2291 participants receiving dopamine vs 2284 receiving placebo and 1491 receiving serotonin vs 1523 receiving placebo). Dopamine was associated with an increase in overall reward (SMD, 0.18; 95% CI, 0.09 to 0.28) but not punishment function (SMD, -0.06; 95% CI, -0.26 to 0.13). Serotonin was not meaningfully associated with overall punishment (SMD, 0.22; 95% CI, -0.04 to 0.49) or reward (SMD, 0.02; 95% CI, -0.33 to 0.36). Dopaminergic and serotonergic manipulations had distinct associations with subcomponents. Dopamine was associated with reward learning or sensitivity (SMD, 0.26; 95% CI, 0.11 to 0.40), reward discounting (SMD, -0.08; 95% CI, -0.14 to -0.01), and reward vigor (SMD, 0.32; 95% CI, 0.11 to 0.54). By contrast, serotonin was associated with punishment learning or sensitivity (SMD, 0.32; 95% CI, 0.05 to 0.59), reward discounting (SMD, -0.35; 95% CI, -0.67 to -0.02), and aversive pavlovian processes (within-participant studies only; SMD, 0.36; 95% CI, 0.20 to 0.53).Conclusions and relevanceIn this study, pharmacological manipulations of both dopamine and serotonin had measurable associations with reinforcement learning in humans. The selective associations with different components suggest that reinforcement learning tasks could form the basis of selective, mechanistically interpretable biomarkers to support treatment assignment.