Harm to others outweighs harm to self in moral decision making.

Concern for the suffering of others is central to moral decision making. How humans evaluate others' suffering, relative to their own suffering, is unknown. We investigated this question by inviting subjects to trade off profits for themselves against pain experienced either by themselves or an anonymous other person. Subjects made choices between different amounts of money and different numbers of painful electric shocks. We independently varied the recipient of the shocks (self vs. other) and whether the choice involved paying to decrease pain or profiting by increasing pain. We built computational models to quantify the relative values subjects ascribed to pain for themselves and others in this setting. In two studies we show that most people valued others' pain more than their own pain. This was evident in a willingness to pay more to reduce others' pain than their own and a requirement for more compensation to increase others' pain relative to their own. This "hyperaltruistic" valuation of others' pain was linked to slower responding when making decisions that affected others, consistent with an engagement of deliberative processes in moral decision making. Subclinical psychopathic traits correlated negatively with aversion to pain for both self and others, in line with reports of aversive processing deficits in psychopathy. Our results provide evidence for a circumstance in which people care more for others than themselves. Determining the precise boundaries of this surprisingly prosocial disposition has implications for understanding human moral decision making and its disturbance in antisocial behavior.

A social inference model of idealization and devaluation.

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A computational account of how individuals resolve the dilemma of dirty money.

Money can be tainted when it is associated with direct or indirect harm to others. Deciding whether to accept "dirty money" poses a dilemma because money can be used to help others, but accepting dirty money has moral costs. How people resolve the dilemma of dirty money remains unknown. One theory casts the dilemma as a valuation conflict that can be resolved by integrating the costs and benefits of accepting dirty money. Here, we use behavioral experiments and computational modeling to test the valuation conflict account and unveil the cognitive computations employed when deciding whether to accept or reject morally tainted cash. In Study 1, British participants decided whether to accept "dirty" money obtained by inflicting electric shocks on another person (versus "clean" money obtained by shocking oneself). Computational models showed that the source of the money (dirty versus clean) impacted decisions by shifting the relative valuation of the money's positive and negative attributes, rather than imposing a uniform bias on decision-making. Studies 2 and 3 replicate this finding and show that participants were more willing to accept dirty money when the money was directed towards a good cause, and observers judged such decisions to be more praiseworthy than accepting dirty money for one's own profit. Our findings suggest that dirty money can be psychologically "laundered" through charitable activities and have implications for understanding and preventing the social norms that can justify corrupt behavior.

How peer influence shapes value computation in moral decision-making.

Moral behavior is susceptible to peer influence. How does information from peers influence moral preferences? We used drift-diffusion modeling to show that peer influence changes the value of moral behavior by prioritizing the choice attributes that align with peers' goals. Study 1 (N = 100; preregistered) showed that participants accurately inferred the goals of prosocial and antisocial peers when observing their moral decisions. In Study 2 (N = 68), participants made moral decisions before and after observing the decisions of a prosocial or antisocial peer. Peer observation caused participants' own preferences to resemble those of their peers. This peer influence effect on value computation manifested as an increased weight on choice attributes promoting the peers' goals that occurred independently from peer influence on initial choice bias. Participants' self-reported awareness of influence tracked more closely with computational measures of prosocial than antisocial influence. Our findings have implications for bolstering and blocking the effects of prosocial and antisocial influence on moral behavior.

A Computational Phenotype of Disrupted Moral Inference in Borderline Personality Disorder.

BACKGROUND: Borderline personality disorder (BPD) is a serious mental disorder characterized by marked interpersonal disturbances, including difficulties trusting others and volatile impressions of others' moral character, often resulting in premature relationship termination. We tested a hypothesis that moral character inference is disrupted in BPD and sensitive to democratic therapeutic community (DTC) treatment. METHODS: Participants with BPD (n = 43; 20 untreated and 23 DTC-treated) and control participants without BPD (n = 106) completed a moral inference task where they predicted the decisions of 2 agents with distinct moral preferences: the "bad" agent was more willing than the "good" agent to harm others for money. Periodically, participants rated their subjective impressions of the agent's moral character and the certainty of those impressions. We fit a hierarchical Bayesian learning model to participants' trialwise predictions to describe how beliefs about the morality of the agents were updated by new information. RESULTS: The computational mechanisms of moral inference differed for patients with untreated BPD relative to matched control participants and patients with DTC-treated BPD. In patients with BPD, beliefs about harmful agents were more certain and less amenable to updating relative to both control participants and participants who were treated with DTC. CONCLUSIONS: The findings suggest that DTC may help the maintenance of social relationships in BPD by increasing patients' openness to learning about adverse interaction partners. The results provide mechanistic insights into social deficits in BPD and demonstrate the potential for combining objective behavioral paradigms with computational modeling as a tool for assessing BPD pathology and treatment outcomes.

Modeling Morality in 3-D: Decision-Making, Judgment, and Inference.

Humans face a fundamental challenge of how to balance selfish interests against moral considerations. Such trade-offs are implicit in moral decisions about what to do; judgments of whether an action is morally right or wrong; and inferences about the moral character of others. To date, these three dimensions of moral cognition-decision-making, judgment, and inference-have been studied largely independently, using very different experimental paradigms. However, important aspects of moral cognition occur at the intersection of multiple dimensions; for instance, moral hypocrisy can be conceived as a disconnect between moral decisions and moral judgments. Here we describe the advantages of investigating these three dimensions of moral cognition within a single computational framework. A core component of this framework is harm aversion, a moral sentiment defined as a distaste for harming others. The framework integrates economic utility models of harm aversion with Bayesian reinforcement learning models describing beliefs about others' harm aversion. We show how this framework can provide novel insights into the mechanisms of moral decision-making, judgment, and inference.

Exposure to violence affects the development of moral impressions and trust behavior in incarcerated males.

Individuals exposed to community violence are more likely to engage in antisocial behavior, resulting in a dramatic increase in contact with justice and social service systems. Theoretical accounts suggest that disruptions in learning underlie the link between exposure to violence and maladaptive behaviors. However, empirical evidence specifying these processes is sparse. Here, in a sample of incarcerated males, we investigated how exposure to violence affects the ability to learn about the harmfulness of others and use this information to adaptively modulate trust behavior. Exposure to violence does not impact the ability to accurately develop beliefs about agents' harm preferences and predict their choices. However, exposure to violence disrupts the ability to form moral impressions that dissociate between agents with distinguishable harm preferences, and subsequently, the ability to adjust trust behavior towards different agents. These findings reveal a process that may explain the association between exposure to violence and maladaptive behavior.

Beliefs about bad people are volatile.

People form moral impressions rapidly, effortlessly and from a remarkably young age1-5. Putatively 'bad' agents command more attention and are identified more quickly and accurately than benign or friendly agents5-12. Such vigilance is adaptive, but can also be costly in environments where people sometimes make mistakes, because incorrectly attributing bad character to good people damages existing relationships and discourages forming new relationships13-16. The ability to accurately infer the moral character of others is critical for healthy social functioning, but the computational processes that support this ability are not well understood. Here, we show that moral inference is explained by an asymmetric Bayesian updating mechanism in which beliefs about the morality of bad agents are more uncertain (and therefore more volatile) than beliefs about the morality of good agents. This asymmetry seems to be a property of learning about immoral agents in general, as we also find greater uncertainty for beliefs about the non-moral traits of bad agents. Our model and data reveal a cognitive mechanism that permits flexible updating of beliefs about potentially threatening others, a mechanism that could facilitate forgiveness when initial bad impressions turn out to be inaccurate. Our findings suggest that negative moral impressions destabilize beliefs about others, promoting cognitive flexibility in the service of cooperative but cautious behaviour.

Inferences about moral character moderate the impact of consequences on blame and praise.

Moral psychology research has highlighted several factors critical for evaluating the morality of another's choice, including the detection of norm-violating outcomes, the extent to which an agent caused an outcome, and the extent to which the agent intended good or bad consequences, as inferred from observing their decisions. However, person-centered accounts of moral judgment suggest that a motivation to infer the moral character of others can itself impact on an evaluation of their choices. Building on this person-centered account, we examine whether inferences about agents' moral character shape the sensitivity of moral judgments to the consequences of agents' choices, and agents' role in the causation of those consequences. Participants observed and judged sequences of decisions made by agents who were either bad or good, where each decision entailed a trade-off between personal profit and pain for an anonymous victim. Across trials we manipulated the magnitude of profit and pain resulting from the agent's decision (consequences), and whether the outcome was caused via action or inaction (causation). Consistent with previous findings, we found that moral judgments were sensitive to consequences and causation. Furthermore, we show that the inferred character of an agent moderated the extent to which people were sensitive to consequences in their moral judgments. Specifically, participants were more sensitive to the magnitude of consequences in judgments of bad agents' choices relative to good agents' choices. We discuss and interpret these findings within a theoretical framework that views moral judgment as a dynamic process at the intersection of attention and social cognition.

Moral transgressions corrupt neural representations of value.

Moral systems universally prohibit harming others for personal gain. However, we know little about how such principles guide moral behavior. Using a task that assesses the financial cost participants ascribe to harming others versus themselves, we probed the relationship between moral behavior and neural representations of profit and pain. Most participants displayed moral preferences, placing a higher cost on harming others than themselves. Moral preferences correlated with neural responses to profit, where participants with stronger moral preferences had lower dorsal striatal responses to profit gained from harming others. Lateral prefrontal cortex encoded profit gained from harming others, but not self, and tracked the blameworthiness of harmful choices. Moral decisions also modulated functional connectivity between lateral prefrontal cortex and the profit-sensitive region of dorsal striatum. The findings suggest moral behavior in our task is linked to a neural devaluation of reward realized by a prefrontal modulation of striatal value representations.

Association of Neural and Emotional Impacts of Reward Prediction Errors With Major Depression.

Importance: Major depressive disorder (MDD) is associated with deficits in representing reward prediction errors (RPEs), which are the difference between experienced and predicted reward. Reward prediction errors underlie learning of values in reinforcement learning models, are represented by phasic dopamine release, and are known to affect momentary mood. Objective: To combine functional neuroimaging, computational modeling, and smartphone-based large-scale data collection to test, in the absence of learning-related concerns, the hypothesis that depression attenuates the impact of RPEs. Design, Setting, and Participants: Functional magnetic resonance imaging (fMRI) data were collected on 32 individuals with moderate MDD and 20 control participants who performed a probabilistic reward task. A risky decision task with repeated happiness ratings as a measure of momentary mood was also tested in the laboratory in 74 participants and with a smartphone-based platform in 1833 participants. The study was conducted from November 20, 2012, to February 17, 2015. Main Outcomes and Measures: Blood oxygen level-dependent activity was measured in ventral striatum, a dopamine target area known to represent RPEs. Momentary mood was measured during risky decision making. Results: Of the 52 fMRI participants (mean [SD] age, 34.0 [9.1] years), 30 (58%) were women and 32 had MDD. Of the 74 participants in the laboratory risky decision task (mean age, 34.2 [10.3] years), 44 (59%) were women and 54 had MDD. Of the smartphone group, 543 (30%) had a depression history and 1290 (70%) had no depression history; 918 (50%) were women, and 593 (32%) were younger than 30 years. Contrary to previous results in reinforcement learning tasks, individuals with moderate depression showed intact RPE signals in ventral striatum (z = 3.16; P = .002) that did not differ significantly from controls (z = 0.91; P = .36). Symptom severity correlated with baseline mood parameters in laboratory (ρ = -0.54; P < 1 × 10-6) and smartphone (ρ = -0.30; P < 1 × 10-39) data. However, participants with depression showed an intact association between RPEs and happiness in a computational model of momentary mood dynamics (z = 4.55; P < .001) that was not attenuated compared with controls (z = -0.42; P = .67). Conclusions and Relevance: The neural and emotional impact of RPEs is intact in major depression. These results suggest that depression does not affect the expression of dopaminergic RPEs and that attenuated RPEs in previous reports may reflect downstream effects more closely related to aberrant behavior. The correlation between symptom severity and baseline mood parameters supports an association between depression and momentary mood fluctuations during cognitive tasks. These results demonstrate a potential for smartphones in large-scale computational phenotyping, which is a goal for computational psychiatry.

Dissociable Effects of Serotonin and Dopamine on the Valuation of Harm in Moral Decision Making.

An aversion to harming others is a core component of human morality and is disturbed in antisocial behavior. Deficient harm aversion may underlie instrumental and reactive aggression, which both feature in psychopathy. Past work has highlighted monoaminergic influences on aggression, but a mechanistic account of how monoamines regulate antisocial motives remains elusive. We previously observed that most people show a greater aversion to inflicting pain on others than themselves. Here, we investigated whether this hyperaltruistic disposition is susceptible to monoaminergic control. We observed dissociable effects of the serotonin reuptake inhibitor citalopram and the dopamine precursor levodopa on decisions to inflict pain on oneself and others for financial gain. Computational models of choice behavior showed that citalopram increased harm aversion for both self and others, while levodopa reduced hyperaltruism. The effects of citalopram were stronger than those of levodopa. Crucially, neither drug influenced the physical perception of pain or other components of choice such as motor impulsivity or loss aversion, suggesting a direct and specific influence of serotonin and dopamine on the valuation of harm. We also found evidence for dose dependency of these effects. Finally, the drugs had dissociable effects on response times, with citalopram enhancing behavioral inhibition and levodopa reducing slowing related to being responsible for another's fate. These distinct roles of serotonin and dopamine in modulating moral behavior have implications for potential treatments of social dysfunction that is a common feature as well as a risk factor for many psychiatric disorders.

How serotonin shapes moral judgment and behavior.

Neuroscientists are now discovering how hormones and brain chemicals shape social behavior, opening potential avenues for pharmacological manipulation of ethical values. Here, we review recent studies showing how altering brain chemistry can alter moral judgment and behavior, focusing in particular on the neuromodulator serotonin and its role in shaping values related to harm and fairness. We synthesize previous findings and consider the potential mechanisms through which serotonin could increase the aversion to harming others. We present a process model whereby serotonin influences social behavior by shifting social preferences in the positive direction, enhancing the value people place on others' outcomes. This model may explain previous findings relating serotonin function to prosocial behavior, and makes new predictions regarding how serotonin may influence the neural computation of value in social contexts.