Determining the appropriate punishment for a norm violation requires consideration of both the perpetrator's state of mind (for example, purposeful or blameless) and the strong emotions elicited by the harm caused by their actions. It has been hypothesized that such affective responses serve as a heuristic that determines appropriate punishment. However, an actor's mental state often trumps the effect of emotions, as unintended harms may go unpunished, regardless of their magnitude. Using fMRI, we found that emotionally graphic descriptions of harmful acts amplify punishment severity, boost amygdala activity and strengthen amygdala connectivity with lateral prefrontal regions involved in punishment decision-making. However, this was only observed when the actor's harm was intentional; when harm was unintended, a temporoparietal-medial-prefrontal circuit suppressed amygdala activity and the effect of graphic descriptions on punishment was abolished. These results reveal the brain mechanisms by which evaluation of a transgressor's mental state gates our emotional urges to punish.
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The authors would also like to acknowledge helpful comments made by N.A. Farahany on the development of the scenario stimuli. Preparation of this article was supported by an award to the Vanderbilt University Central Discovery Grant Program to R.M. and O.D.J., as well as contributions from the John D. and Catherine T. MacArthur Foundation. Its contents reflect the views of the authors and do not necessarily represent the official views of either the John D. and Catherine T. MacArthur Foundation or The MacArthur Foundation Research Network on Law and Neuroscience. The authors also gratefully acknowledge support from the Center for Integrative and Cognitive Neuroscience at Vanderbilt University as well as support by the National Center for Research Resources, Grant UL1 RR024975-01, which is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06.
The authors declare no competing financial interests.
Integrated supplementary information
A separate sample of participants was recruited to provide ratings of affective responses to GL and PL scenarios (GL group: n = 21; PL group: n = 20). The design of this study was identical to our imaging study, except that subjects performed the study in a laboratory at Vanderbilt rather than in an MRI scanner, and instead of punishment decisions they were asked to report on how strong their emotional experience was for six discrete emotions using a 0-9 scale, where 0 indicated “not at all” and 9 indicated “extreme”. The emotions rated were anger, contempt, disgust, fear, and sadness. Across all emotion categories we found a trend-level main effect of graphic language, such that individuals in the GL group gave higher ratings than those in the PL group (F(1,39) = 2.98, p < 0.092), as well as a significant Group X Emotion interaction (F(1,36) = 3.13, p = 0.027). This interaction was primarily driven by low ratings for fear across both groups; when fear was dropped from the ANOVA, a significant main effect of graphic language was present across the remaining 4 emotions, such that individuals in the GL group gave higher ratings than those in the PL group (F(1,39) = 4.54, p = 0.040). Reported p-values indicate results of post-hoc between-groups t-tests (*p > 0.05, **p > 0.01, one-tailed). Error-bars represent standard error of the mean.
All subjects were given up to 60 s to read and respond to each scenario, though average trial RTs were only 30 s. This temporal window was determined based on pilot data suggesting that >99% trials would be completed within a 60 s time span. Timeouts occurred for a total of 1.57% of scenarios, and they did not differ between the GL group (M = 1.40, SD = 2.29) and the PL group (M = 0.73, SD =1.33) (t28 = 0.97, p = 0.40). Because condition-specific differences in “time-on-task” can have significant effects on BOLD signal, we assessed reaction times (RT) in each of the scenario conditions. There were no significant differences in RT between Intentional (M = 30.00 s, SD = 5.46) and Unintentional (M = 30.84 s, SD = 5.30) scenarios when averaged across both GL and PL groups (t29 = -1.15, p = 0.26). There were also no differences in RT between the GL and PL groups for either the Intentional (GL: M = 28.87 s, SD = 4.6; PL: M = 31.10 s, SD = 6.15; t28 = -1.13, p = 0.27) or Unintentional scenarios (GL M = 30.5, SD = 5.25; PL: M = 31.15, SD = 5.52; t28 = -0.31, p = 0.76), and there was no significant blameworthiness X language interaction for RT (F(1,28) = 1.21, p = 0.28).
To examine the spatial overlap between areas identified in our BOLD amplitude and GCM results, conjunction analyses of the corresponding SPMs were performed. Specifically, for each conjunction analysis, a binary mask was generated for each SPM such that voxels that were included in a whole-brain, cluster-corrected area were coded as ‘1’, while all other voxels were coded as ‘0’. These masks were then added together, and all voxels with a resulting value of ‘2’ were interpreted as representing areas of conjunction between the two maps.A. SPM showing conjunction of independently whole-brain corrected SPMs for the BOLD amplitude Intentional > Unintentional contrast and the blameworthiness-by-language interaction contrast of GCM analysis with amygdala seed. B. SPM showing conjunction of independently whole-brain corrected SPMs for the BOLD amplitude left TPJ between Unintentional > Intentional contrast and the Unintentional > Intentional GCM analysis with left dACC seed..
Supplementary Figure 4 SPM displaying left amygdala activation identified by the interaction contrast of blameworthiness and harm.
To test whether the findings of our blameworthiness-by-language interaction generalized to other sources of affect, a whole-brain blameworthiness-by-harm interaction contrast was examined. The four levels of harm (Murder, Maim, Assault, and Property Damage) were dummy-coded from 4 to 1, with 4 representing Murder. The interaction contrast specified was as: (Intentional [+2, +1, -1, -2])> (Unintentional [+2, +1, - 1, -2]), with the numbers in brackets referring to the weights applied to each harm-level within each blameworthiness condition. This analysis identified a significant cluster in the left amygdala, that showed overlap with the peak of the left amygdala cluster identified by our blameworthiness X language interaction (Left Amygdala peak x = -28, y = -8, z = -22, Z = 4.01, p < 0.05 (cluster-corrected). Importantly, this effect was present equally for the both the GL and PL groups, suggesting that the amygdala is generally involved in the integration of harm information in a blameworthiness-dependent manner. Map is displayed at a whole-brain corrected threshold of p < 0.05 (cluster-corrected), rendered on a single-subject T1-weighted image. In addition to the amygdala, there are bilateral activation foci in lateral temporal cortex.
Supplementary Figures 1–4 and Supplementary Tables 1–4 (PDF 1536 kb)
All scenarios used in the current study are presented in a separate PDF file included with this submission. Scenarios in this PDF file are organized by harm-type (murder/death, maim, assault, property damage), with each scenario “stem” presented in its four variations: Intentional/GL, Intentional/PL, Unintentional/GL and Unintentional/PL. (PDF 25169 kb)
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Treadway, M., Buckholtz, J., Martin, J. et al. Corticolimbic gating of emotion-driven punishment. Nat Neurosci 17, 1270–1275 (2014). https://doi.org/10.1038/nn.3781
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