Neurobiological Mechanisms of Responding to Injustice

Abstract

People are particularly sensitive to injustice. Accordingly, deeper knowledge regarding the processes that underlie the perception of injustice, and the subsequent decisions to either punish transgressors or compensate victims, is of important social value. By combining a novel decision-making paradigm with functional neuroimaging, we identified specific brain networks that are involved with both the perception of, and response to, social injustice, with reward-related regions preferentially involved in punishment compared with compensation. Developing a computational model of punishment allowed for disentangling the neural mechanisms and psychological motives underlying decisions of whether to punish and, subsequently, of how severely to punish. Results show that the neural mechanisms underlying punishment differ depending on whether one is directly affected by the injustice, or whether one is a third-party observer of a violation occurring to another. Specifically, the anterior insula was involved in decisions to punish following harm, whereas, in third-party scenarios, we found amygdala activity associated with punishment severity. Additionally, we used a pharmacological intervention using oxytocin, and found that oxytocin influenced participants' fairness expectations, and in particular enhanced the frequency of low punishments. Together, these results not only provide more insight into the fundamental brain mechanisms underlying punishment and compensation, but also illustrate the importance of taking an explorative, multimethod approach when unraveling the complex components of everyday decision-making.SIGNIFICANCE STATEMENT The perception of injustice is a fundamental precursor to many disagreements, from small struggles at the dinner table to wasteful conflict between cultures and countries. Despite its clear importance, relatively little is known about how the brain processes these violations. Taking an interdisciplinary approach, we combine methods from neuroscience, psychology, and economics to explore the neurobiological mechanisms involved in both the perception of injustice as well as the punishment and compensation decisions that follow. Using a novel behavioral paradigm, we identified specific brain networks, developed a computational model of punishment, and found that administrating the neuropeptide oxytocin increases the administration of low punishments of norm violations in particular. Results provide valuable insights into the fundamental neurobiological mechanisms underlying social injustice.

Keywords: compensation; computational modeling; neuroimaging; oxytocin; punishment; social norms.

Figure 1.

Trial outline of a second-party punishment game in the Justice Game. In this second-party punishment sample trial, a Taker takes 100 chips from the participant and the participant can decide how much, if any, chips he wants to spend on punishment. Fixation screen: 2–5 s; Start screen: 2 s; Taker decision: 4 s; Response screen: 6.5 s. The 4 s window indicating the number of chips taken (Taker decision) was entered into the general linear models used for our fMRI data analysis.

Figure 2.

Mean amount of chips spent as a function of game type and number of chips taken by the Taker. Error bars are SEM. n = 54.

Figure 3.

Frequency of punishment in the second-party punishment games. Participants in the oxytocin group administered smaller punishments (10–50 chips) more often than participants in the placebo group. Error bars are SEM. n = 54.

Figure 4.

Frequency of punishment in third-party punishment game. y-axis, Percentage of trials; x-axis, number of chips spent per punishment. A, Taker takes 25 chips; (B) Taker takes 50 chips; (C) Taker takes 75 chips; and (D) Taker takes 100 chips. Error bars indicate SEM. The more chips the Taker takes, the stronger the effect of oxytocin on the administration of smaller punishments. n = 54.

Figure 5.

Neural correlates of fairness: trials in which Taker took no chips versus trials in which chips were taken. MNI slice: left, x = −8; right, z = 4. Displayed at p < 0.05 FWE whole-brain corrected. n = 53.

Figure 6.

Neural correlates of unfairness: trials in which Taker took chips versus trials in which no chips were taken, with activity correlating with the number of chips taken. MNI slice: left, x = 46; right, x = −4. Displayed at p < 0.001 corrected. n = 53.

Figure 7.

Brain contrast maps displayed at p < 0.001 uncorrected. A, Punishment versus compensation: trials in which participants invested in second- and third-party punishment contrasted with trials in which participants invested in compensation; y = 14 (MNI). n = 42. B, Not punishing in response to unfair treatment: trials in which the Taker took chips and participants chose to not punish versus trials in which the Taker took chips and participants punished; x = −49 (MNI). n = 20.

Figure 8.

Mean parameter estimates per game type. A, Means of parameter θ reflecting the decision to punish. B, Means of parameter α reflecting the decision to punish hard. Error bars indicate SEM. n = 53.