A meta-analysis on shared and distinct neural correlates of the decision-making underlying altruistic and retaliatory punishment

Using Machine Learning to Determine a Functional Classifier of Retaliation and Its Association With Aggression

Objective

Methods to determine integrity of integrated neural systems engaged in functional processes have proven elusive. This study sought to determine the extent to which a machine learning retaliation classifier (retaliation vs unfair offer) developed from a sample of typically developing (TD) adolescents could be applied to an independent sample of clinically concerning youth and the classifier-determined functional integrity for retaliation was associated with antisocial behavior and proactive and reactive aggression.

Method

Blood oxygen level-dependent response data were collected from 82 TD and 120 clinically concerning adolescents while they performed a retaliation task. The support vector machine algorithm was applied to the TD sample and tested on the clinically concerning sample (adolescents with externalizing and internalizing diagnoses).

Results

The support vector machine algorithm was able to distinguish the offer from the retaliation phase after training in the TD sample (accuracy = 92.48%, sensitivity = 89.47%, and specificity = 93.18%) that was comparably successful in distinguishing function in the test sample. Increasing retaliation distance from the hyperplane was associated with decreasing conduct problems and proactive aggression.

Conclusion

The current study provides preliminary data of the importance of a retaliation endophenotype whose functional integrity is associated with reported levels of conduct problems and proactive aggression.

Diversity & Inclusion Statement

We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex balance in the selection of non-human subjects. We worked to ensure diversity in experimental samples through the selection of the cell lines. We worked to ensure diversity in experimental samples through the selection of the genomic datasets. Diverse cell lines and/or genomic datasets were not available. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

The science of justice: The neuropsychology of social punishment

The social punishment (SP) of norm violations has received much attention across multiple disciplines. However, current models of SP fail to consider the role of motivational processes, and none can explain the observed behavioral and neuropsychological differences between the two recognized forms of SP: second-party punishment (2PP) and third-party punishment (3PP). After reviewing the literature giving rise to the current models of SP, we propose a unified model of SP which integrates general psychological descriptions of decision-making as a confluence of affect, cognition, and motivation, with evidence that SP is driven by two main factors: the amount of harm (assessed primarily in the salience network) and the norm violator's intention (assessed primarily in the default-mode and central-executive networks). We posit that motivational differences between 2PP and 3PP, articulated in mesocorticolimbic pathways, impact final SP by differentially impacting the assessments of harm and intention done in these domain-general large-scale networks. This new model will lead to a better understanding of SP, which might even improve forensic, procedural, and substantive legal practices.

Anterior insula and dorsal anterior cingulate cortex as a hub of self-regulation: combining activation likelihood estimation meta-analysis and meta-analytic connectivity modeling analysis

Self-regulation, which is an individual's ability to control their emotions and behaviors in pursuit of goals, is a complex cognitive function that relies on distributed brain networks. Here, we used activation likelihood estimation (ALE) to conduct two large-scale meta-analyses of brain imaging studies of emotional regulation and behavioral regulation. We used single analysis of ALE to identify brain activation regions associated with behavioral regulation and emotion regulation. The conjunction results of the contrast analysis of the two domains showed that the crucial brain regions of dorsal anterior cingulate cortex (dACC), bilateral anterior insula (AI), and right inferior parietal lobule (IPL) are nested within the brain areas of the two regulation domains at the spatial and functional level. In addition, we assessed the coactivation pattern of the four common regions using meta-analytic connectivity modeling (MACM). The coactivation brain patterns based on the dACC and bilateral AI overlapped with the two regulation brain maps in a high proportion. Furthermore, the functional characters of the identified common regions were reverse-inferenced using the BrainMap database. Collectively, these results indicate that the brain regions of dACC and bilateral AI, playing a crucial role as a hub to other brain regions and networks by effective connectivity in self-regulation, are spatially nested in the brain network of behavioral regulation and emotion regulation.

A meta-analysis on shared and distinct neural correlates of the decision-making underlying altruistic and retaliatory punishment

Individuals who violate social norms will most likely face social punishment sanctions. Those sanctions are based on different motivation aspects, depending on the context. Altruistic punishment occurs if punishment aims to re‐establish the social norms even at cost for the punisher. Retaliatory punishment is driven by anger or spite and aims to harm the other. While neuroimaging research highlighted the neural networks supporting decision‐making in both types of punishment in isolation, it remains unclear whether they rely on the same or distinct neural systems. We ran an activation likelihood estimation meta‐analysis on functional magnetic resonance imaging data on 24 altruistic and 19 retaliatory punishment studies to investigate the neural correlates of decision‐making underlying social punishment and whether altruistic and retaliatory punishments share similar brain networks. Social punishment reliably activated the bilateral insula, inferior frontal gyrus, midcingulate cortex (MCC), and superior and medial frontal gyri. This network largely overlapped with activation clusters found for altruistic punishment. However, retaliatory punishment revealed only one cluster in a posterior part of the MCC, which was not recruited in altruistic punishment. Our results support previous models on social punishment and highlight differential involvement of the MCC in altruistic and retaliatory punishments, reflecting the underlying different motivations.