Punishment insensitivity emerges from impaired contingency detection, not aversion insensitivity or reward dominance

© 2019, eLife Sciences Publications Ltd. All rights reserved. Our behaviour is shaped by its consequences – we seek rewards and avoid harm. It has been reported that individuals vary markedly in their avoidance of detrimental consequences, i.e. in their sensitivity to punishment. The underpinnings of this variability are poorly understood; they may be driven by differences in aversion sensitivity, motivation for reward, and/or instrumental control. We examined these hypotheses by applying several analysis strategies to the behaviour of rats (n = 48; 18 female) trained in a conditioned punishment task that permitted concurrent assessment of punishment, reward-seeking, and Pavlovian fear. We show that punishment insensitivity is a unique phenotype, unrelated to differences in reward-seeking and Pavlovian fear, and due to a failure of instrumental control. Subjects insensitive to punishment are afraid of aversive events, they are simply unable to change their behaviour to avoid them

A cognitive pathway to punishment insensitivity

Individuals differ in their sensitivity to the adverse consequences of their actions, leading some to persist in maladaptive behaviors. Two pathways have been identified for this insensitivity: a motivational pathway based on excessive reward valuation and a behavioral pathway based on autonomous stimulus–response mechanisms. Here, we identify a third, cognitive pathway based on differences in punishment knowledge and use of that knowledge to suppress behavior. We show that distinct phenotypes of punishment sensitivity emerge from differences in what people learn about their actions. Exposed to identical punishment contingencies, some people (sensitive phenotype) form correct causal beliefs that they use to guide their behavior, successfully obtaining rewards and avoiding punishment, whereas others form incorrect but internally coherent causal beliefs that lead them to earn punishment they do not like. Incorrect causal beliefs were not inherently problematic because we show that many individuals benefit from information about why they are being punished, revaluing their actions and changing their behavior to avoid further punishment (unaware phenotype). However, one condition where incorrect causal beliefs were problematic was when punishment is infrequent. Under this condition, more individuals show punishment insensitivity and detrimental patterns of behavior that resist experience and information-driven updating, even when punishment is severe (compulsive phenotype). For these individuals, rare punishment acted as a “trap,” inoculating maladaptive behavioral preferences against cognitive and behavioral updating

Medial Orbitofrontal Cortex Regulates Instrumental Conditioned Punishment, but not Pavlovian Conditioned Fear

Abstract Bidirectionally aberrant medial orbitofrontal cortical (mOFC) activity has been consistently linked with compulsive disorders and related behaviors. Although rodent studies have established a causal link between mOFC excitation and compulsive-like actions, no such link has been made with mOFC inhibition. Here we use excitotoxic lesions of mOFC to investigate its role in sensitivity to punishment; a core characteristic of many compulsive disorders. In our first experiment, we demonstrated that mOFC lesions prevented rats from learning to avoid a lever that was punished with a stimulus that co-terminated with footshock. Our second experiment demonstrated that retrieval of punishment learning is also somewhat mOFC-dependent, as lesions prevented the extended retrieval of punishment contingencies relative to shams. In contrast, mOFC lesions did not prevent rats from re-acquiring the ability to avoid a punished lever when it was learned prior to lesions being administered. In both experiments, Pavlovian fear conditioning to the stimulus was intact for all animals. Together, these results reveal that the mOFC regulates punishment learning and retrieval in a manner that is separate from any role in Pavlovian fear conditioning. These results imply that aberrant mOFC activity may contribute to the punishment insensitivity that is observed across multiple compulsive disorders.</jats:p

Role of anterior insula cortex in context-induced relapse of nicotine-seeking

Tobacco use is the leading cause of preventable death worldwide, and relapse during abstinence remains the critical barrier to successful treatment of tobacco addiction. During abstinence, environmental contexts associated with nicotine use can induce craving and contribute to relapse. The insular cortex (IC) is thought to be a critical substrate of nicotine addiction and relapse. However, its specific role in context-induced relapse of nicotine-seeking is not fully known. In this study, we report a novel rodent model of context-induced relapse to nicotine-seeking after punishment-imposed abstinence, which models self-imposed abstinence through increasing negative consequences of excessive drug use. Using the neuronal activity marker Fos we find that the anterior (aIC), but not the middle or posterior IC, shows increased activity during context-induced relapse. Combining Fos with retrograde labeling of aIC inputs, we show projections to aIC from contralateral aIC and basolateral amygdala exhibit increased activity during context-induced relapse. Next, we used fiber photometry in aIC and observed phasic increases in aIC activity around nicotine-seeking responses during self-administration, punishment, and the context-induced relapse tests. Next, we used chemogenetic inhibition in both male and female rats to determine whether activity in aIC is necessary for context-induced relapse. We found that chemogenetic inhibition of aIC decreased context-induced nicotine-seeking after either punishment-or extinction-imposed abstinence. These findings highlight the critical role nicotine-associated contexts play in promoting relapse, and they show that aIC activity is critical for this context-induced relapse following both punishment and extinction-imposed abstinence

Visualizing Infralimbic Control over Incubation of Cocaine Craving

© 2019 Elsevier Inc. The infralimbic prefrontal cortex serves complex roles in controlling drug-seeking behavior. In this issue of Neuron, Cameron et al. (2019) provide new insights into engagement and function of the infralimbic cortex → nucleus accumbens corticostriatal pathway in the incubation of cocaine craving

The role of the lateral habenula in punishment.

The lateral habenula (LHb) is a small epithalamic structure that projects via the fasciculus retroflexus to the midbrain. The LHb is known to modulate midbrain dopamine (DA) neurons, including inhibition of ventral tegmental area (VTA) neurons via glutamatergic excitation of the GABAergic rostromedial tegmental nucleus (RMTg). A variety of lines of evidence show activity in LHb and the LHb-RMTg pathway is correlated with, and is sufficient to support, punishment learning. However, it is not immediately clear whether LHb is necessary for punishment. Here we used a within-subjects punishment task to assess the role of LHb in the acquisition and expression of punishment as well as in aversive choice. Rats that pressed two individually presented levers for pellet rewards rapidly suppressed responding to one lever if it also caused footshock deliveries (punished lever) but continued pressing a second lever that did not cause footshock (unpunished lever). Infusions of an AMPA receptor antagonist (NBQX) into LHb had no effect on the acquisition or expression of this punishment, or on aversive choice, but did increase locomotion. Infusion of the sodium channel blocker bupivacaine likewise had no effect on expression of punishment. However, infusion of the calcium channel blocker mibefradil did affect expression of punishment by significantly decreasing the latency with which rats responded on the punished lever and significantly increasing unpunished lever-pressing. Taken together, these findings indicate that the LHb plays a limited role in punishment, influencing only latency to respond. This role is linked to calcium channel permeability and not AMPA receptor or sodium channel permeability

Behavioral and neurobiological mechanisms of punishment: Implications for psychiatric disorders

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

Analyzing Event-Related Transients: Confidence Intervals, Permutation Tests, and Consecutive Thresholds

Fiber photometry has enabled neuroscientists to easily measure targeted brain activity patterns in awake, freely behaving animal. A focus of this technique is to identify functionally-relevant changes in activity around particular environmental and/or behavioral events, i.e., event-related activity transients (ERT). A simple and popular approach to identifying ERT is to summarize peri-event signal [e.g., area under the curve (AUC), peak activity, etc.,] and perform standard analyses on this summary statistic. We highlight the various issues with this approach and overview straightforward alternatives: waveform confidence intervals (CIs) and permutation tests. We introduce the rationale behind these approaches, describe the results of Monte Carlo simulations evaluating their effectiveness at controlling Type I and Type II error rates, and offer some recommendations for selecting appropriate analysis strategies for fiber photometry experiments