Anhedonic Traits Do Not Impair Performance in a 3-Arm Bandit Task.

Reward and punishment sensitivity are known to be altered in anorexia nervosa (AN). Most research has examined these constructs separately although motivated behavior is influenced by considering both the potential for reward and risk of punishment. The present study sought to compare the relative balance of reward and punishment sensitivity in AN versus healthy controls (HCs) and examine whether motivational bias is associated with AN symptoms and treatment outcomes. Adolescents and adults with AN (n = 262) in a partial hospitalization program completed the Eating Disorders Examination Questionnaire (EDE-Q), Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scales, and Sensitivity to Punishment/Sensitivity to Reward Questionnaire (SPSRQ) at admission and discharge. HCs (HC; n = 90) completed the BIS/BAS and SPSRQ. Motivational Bias Scores were calculated to reflect the dominance of reward versus punishment sensitivity. Individuals with AN demonstrated significantly greater bias toward punishment sensitivity than HC. In AN, a bias toward punishment was associated with higher EDE-Q Global score at admission. Change in motivational bias during treatment predicted EDE-Q Global scores, but not BMI, at discharge, with greater increases in reward sensitivity or greater decreases in punishment sensitivity during treatment predicting lower eating pathology. Similar findings were observed using the BIS/BAS and SPSRQ. Change in motivational bias during treatment is associated with improved outcomes in AN. However, it appears that much of the change in motivational bias can be attributed to changes in punishment sensitivity, rather than reward sensitivity. Future research should examine the mechanisms underlying punishment sensitivity decreases during treatment. Sensitivity to reward and punishment may be important treatment targets for individuals with anorexia nervosa (AN). To date, most research has considered reward and punishment sensitivity separately, rather than examining their relationship to each other. We found that the balance of reward and punishment sensitivity (i.e., motivational bias) differs between healthy controlsand those with AN and that this bias is associated with eating disorder symptoms and treatment outcome.

Acute subjective response to alcohol as a function of reward and punishment sensitivity

Using the resting state functional network as a stable neural trait indicator, this study employed a panel cross-lag model to construct a dynamic, causal ‘brain-behavior-time’ framework. It identified key risk pathways from stable neural traits to addictive behaviors, mediated by psychological traits, thereby providing methodological support for targeted intervention. A recent study by He and colleagues has made a pivotal methodological contribution to addiction research by elucidating the dynamic, causal pathways through which stable neural traits, mediated by psychological vulnerabilities, predispose individuals to addictive behaviors [1]. The study's innovative integration of resting-state functional connectivity with panel cross-lagged modeling provides a breakthrough idea for addiction research and intervention, and its methodological value and practical significance are worth exploring. The study's strength lies in its rigorous methodology. It used a cross-sectional sample of 1142 college students for behavioral-brain analysis and a longitudinal follow-up cohort of 303 students for behavioral tracking. The functional connectivity strength between the limbic network (LN) and six other functional networks served as the core neural indicator. Resting-state functional networks, which reflect the brain's intrinsic activity patterns, demonstrate temporal stability [2] and function as biological markers. Specifically, the functional state of LN is directly associated with an individual's perception and response to reward and punishment signals [3, 4]. By correlating this stable neural characteristic with long-term psychological trait data—including reward sensitivity and punishment sensitivity—the study revealed significant findings that LN strength showed a positive correlation with reward sensitivity and a negative correlation with punishment sensitivity. Furthermore, the functional connectivity strength between LN and other networks also demonstrated relationships with reward and punishment sensitivity. These findings establish a foundational ‘brain-behavior’ link that exhibits considerable stability. The study further revealed the dynamic causal relationship between variables through a panel cross-lag model: reward sensitivity at baseline measurement (T1) positively predicted online game addiction tendencies at follow-up measurement (T2); the Iowa Gambling Task net score at T1 negatively predicted reward sensitivity at T2; and punishment sensitivity exerted an indirect effect on addictive tendencies by positively influencing reward sensitivity at T2. The punishment sensitivity also acts indirectly on addictive tendencies by positively affecting reward sensitivity in T2. These results break through the static limitations of cross-sectional studies [5] and provide strong evidence that ‘high reward sensitivity is a core risk factor for addiction’ with both chronological and causal orientation and upgrade the ‘brain-behavior’ model to a dynamic developmental model. He and colleagues integrated LN strength into the panel cross-lag model. Their analysis revealed that T1 LN strength not only directly shapes reward sensitivity at T1 and negatively influences punishment sensitivity at T1, but also predicts reward sensitivity at T2. This establishes the complete pathway from neural traits to psychological traits and finally to behavioral outcomes. This study provides a more coherent theoretical framework for understanding the underlying mechanisms of addiction development by defining how neural features predict behavior through psychological mediating factors. This research paradigm offers insights for clinical intervention and public health. Clinically, the ‘neural indicators → psychological traits → behavioral outcomes’ model can be used to predict individual addictive tendencies by evaluating the functional status of core brain regions like the LN [6], thereby enabling personalized intervention plans. In the field of public health, it facilitates the screening of at-risk adolescents based on characteristics such as abnormal LN function and high reward sensitivity, achieving early identification and prevention of addictive behaviors and promoting a shift from ‘passive treatment’ to ‘active early warning’. In summary, this study successfully translates fundamental discoveries into clinical and public health impacts through its innovative methodology. By mapping key risk pathways and enabling precisely targeted interventions, it establishes a new paradigm for neurobehavioral research. This approach not only provides a framework for understanding addiction mechanisms, but also paves the way for innovative strategies in preventing and treating a broad spectrum of mental health disorders, thereby offering substantial scientific and societal benefits. Zihan Lv: Writing—original draft. Xiaoming Li: Project administration; resources; supervision; validation; writing—review and editing. Xiaochu Zhang: Project administration; resources; supervision; validation; writing—review and editing. This work was supported by grants from National Key R&D Program of China (2024YFF0507600), the Chinese National Programs for Brain Science and Brain-like Intelligence Technology (2021ZD0202101), the National Natural Science Foundation of China (32571266, 32171080, 32400919 and 32200914), the Project of Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing QianKeHe Platform [ZSYS(2024)001], Natural Science Foundation of Anhui Province (2408085QC081), the Humanities and Social Science Fund of the Ministry of Education of China (24YJCZH014) and Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior. None.

Reward and punishment sensitivity and alcohol use: The moderating role of executive control

Accumulating evidence suggests that individual differences in punishment and reward sensitivity are associated with functional alterations in neural systems underlying error and feedback processing. In particular, individuals highly sensitive to punishment have been found to be characterized by larger mediofrontal error signals as reflected in the error negativity/error-related negativity (Ne/ERN) and the feedback-related negativity (FRN). By contrast, reward sensitivity has been shown to relate to the error positivity (Pe). Given that Ne/ERN, FRN, and Pe have been functionally linked to flexible behavioral adaptation, the aim of the present research was to examine how these electrophysiological reflections of error and feedback processing vary as a function of punishment and reward sensitivity during reinforcement learning. We applied a probabilistic learning task that involved three different conditions of feedback validity (100%, 80%, and 50%). In contrast to prior studies using response competition tasks, we did not find reliable correlations between punishment sensitivity and the Ne/ERN. Instead, higher punishment sensitivity predicted larger FRN amplitudes, irrespective of feedback validity. Moreover, higher reward sensitivity was associated with a larger Pe. However, only reward sensitivity was related to better overall learning performance and higher post-error accuracy, whereas highly punishment sensitive participants showed impaired learning performance, suggesting that larger negative feedback-related error signals were not beneficial for learning or even reflected maladaptive information processing in these individuals. Thus, although our findings indicate that individual differences in reward and punishment sensitivity are related to electrophysiological correlates of error and feedback processing, we found less evidence for influences of these personality characteristics on the relation between performance monitoring and feedback-based learning.

Resting-state EEG theta activity and risk learning: sensitivity to reward or punishment?

Gender differences in punishment and reward sensitivity in a sample of Taiwanese college students

Reinforcement sensitivity theory and occupational health: BAS and BIS on the job

Reward and punishment sensitivity in dysfunctional eating and hazardous drinking women: Associations with family risk

Childhood callous-unemotional (CU) traits are associated with a neurocognitive response style of high reward and low punishment sensitivity, which may make these children particularly vulnerable to substance misuse. However, the mechanisms explaining the link between CU traits and substance use are poorly understood. This study investigated the mediating influences of reward and punishment sensitivity on the association between childhood CU traits and adolescent substance use. Using data from the UK Millennium Cohort Study, mediation analyses were conducted to investigate the potential indirect effects of age 14 reward and punishment sensitivity in risk-taking on the relationships between age 11 CU traits and alcohol, cannabis, and other illicit drug use at age 17. No direct effects of CU traits on substance use were found when accounting for gender, baseline alcohol use, poverty, emotional symptoms, conduct problems, hyperactivity, and verbal ability at age 11. Indirect effects of increased reward sensitivity on the relationship between CU traits and increased use were seen for alcohol, cannabis, and other drugs. There was a significant indirect effect of reduced punishment sensitivity on the relationship between CU traits and increased alcohol use, but not cannabis or other substance use. Findings suggest that reward and punishment sensitivity may have independent effects on decision-making processes contributing to adolescent substance use. Prevention and early intervention for substance use should consider modifying intervention strategies to fit the needs of adolescents with a callous interpersonal style and a neurocognitive profile characterized by a high drive for rewards and low risk aversion.

Associations between sensitivity to punishment, sensitivity to reward, and gambling

BackgroundThe current study set out to improve our understanding of the characteristics of individuals who are motivated to restrict their food intake yet who nevertheless fail to do so. We examined whether punishment sensitivity (PS) was related to restrained eating, and reward sensitivity (RS) to perceived dieting success. Additionally, it was examined whether executive control (EC) moderates the association between RS and perceived dieting success.MethodsFemale student participants (N = 290, aged 17–29, BMI between 18.5 and 38.0) completed questionnaires on restrained eating, perceived dieting success, RS and PS, and carried out a behavioral task to index EC.ResultsPS was indeed positively related to restrained eating. RS was positively related to perceived dieting success, yet, EC did not moderate this association.ConclusionThe current study adds to the evidence that PS is related to individuals’ motivation to restrict their food intake. Furthermore, it shows support for the suggestion that RS may facilitate food restriction.

How do dysfunctional eating and hazardous drinking women perform on behavioural measures of reward and punishment sensitivity?

Externalizing behavior, the UPPS-P Impulsive Behavior scale and Reward and Punishment Sensitivity

Reward and punishment sensitivity seem important traits in understanding behavior in general and psychopathology in particular. Though the definitions used for reward and punishment sensitivity differentiate between responsivity and motivation, the measures thus far used to assess these constructs do not. Further, specificity of the type of reward (e.g., drugs) and punishment (e.g., spiders) in questionnaires might result in measurement bias especially when examining the relationship with psychopathology. Therefore, we developed a stimulus-independent multidimensional questionnaire of reward and punishment sensitivity that differentiates between responsivity and motivation. This study addresses the psychometric qualities of this newly developed reward and punishment responsivity and motivation questionnaire (RPRM-Q). On the basis of exploratory ordinal factor analysis (N = 273) that was used to examine the quality of the initial pool of 39 items, the number of items was reduced to 18. Confirmatory ordinal factor analysis on the remaining items in an independent sample (N = 328) supported a 18-item four-factor model, and showed acceptable to good internal reliability. The relationship between the subscales of the RPRM-Q and often used questionnaires was examined in the combined sample (N = 601), which showed some first support for the ability of the new questionnaire to differentiate between responsivity and motivation to approach/avoid. The findings indicate that the RPRM-Q might be a helpful instrument to further test the relevance of punishment and reward sensitivity in psychopathology.