Computational protocol: Power to Punish Norm Violations Affects the Neural Processes of Fairness-Related Decision Making

Similar protocols

Protocol publication

[…] Participants were scanned using a 3T Siemens scanner at the Shanghai Key Laboratory of Magnetic Resonance of East China Normal University. Anatomical images were acquired using a T1-weighted, multiplanar reconstruction (MPR) sequence (TR = 1900 ms, TE = 3.42 ms, 192 slices, slice thickness = 1 mm, FOV = 256 mm, matrix size = 256 ∗ 256) (). After that, functional images with 35 slices were acquired using a gradient-echo echo-planar imaging (EPI) sequence (TR = 2200 ms, TE = 30 ms, FOV = 220 mm, matrix size = 64 ∗ 64, slice thickness = 3 mm, gap = 0.3 mm) ().Data preprocessing and statistical analyses were performed with the SPM8 software package (Wellcome Department of Cognitive Neurology, London). The first five functional images were discarded from each subject to allow scanner equilibrium effects. Then, all functional images were slice timing corrected, realigned, normalized into the MNI space (resampled at 2 mm ∗ 2 mm ∗ 2 mm voxels), and smoothed with an 8-mm full-width half maximum isotropic Gaussian kernel.First-level analyses were then performed for each subject using an event-related design. We modeled the onset of the proposal for six types of events AcceptFair UG (accepted fair offers in UG, mean = 11.9 trials, maximum = 12 trials, minimum = 10 trials), AcceptUnfair UG (accepted unfair offers in UG, mean = 8.7 trials, maximum = 14 trials, minimum = 6 trials), RejectUnfair UG (rejected unfair offers in UG, mean = 15.1 trials, maximum = 18 trials, minimum = 10 trials), AcceptFair IG (accepted fair offers in IG, mean = 11.8 trials, maximum = 12 trials, minimum = 10 trials), AcceptUnfair IG (accepted unfair offers in IG, mean = 13.5 trials, maximum = 18 trials, minimum = 8 trials) and RejectUnfair IG (rejected unfair offers in IG, mean = 10.3 trials, maximum = 16 trials, minimum = 6 trials). Additional regressors of no interest were created for partner presentation, decision phase and trials with no responses. For partner presentation, we modeled the onset of the presentation of the proposer’s picture. For the decision phase, the onset of the decision cue was modeled for six types of events. For trials with no responses (i.e., trials where participants did not respond), we modeled the onset of the proposal and the onset of the decision cue. All the regressors were modeled with zero duration and convolved with a canonical hemodynamic response function (HRF). Additional regressors included in the design matrix comprised six realignment parameters and one overall mean during the whole phase. Low-frequency noise was filtered by applying a cutoff of 128 s in the models. Six contrast images (AcceptFair UG, AcceptUnfair UG, RejectUnfair UG, AcceptFair IG, AcceptUnfair IG, and RejectUnfair IG) for proposal presentation were acquired for each subject at the first-level analysis. These images were then analyzed in a flexible factorial design at the second group level employing a random effects model.A conjunction analysis using the conjunction null hypothesis () was conducted first with the (Unfair – Fair)UG and (Unfair – Fair)IG contrasts to explore common brain regions activated by unfairness in both UG and IG. The (Unfair – Fair) contrast was calculated as (AcceptUnfair + RejectUnfair – 2∗AcceptFair). Then, the Unfairness (Unfair vs. Fair) ∗ Context (UG vs. IG) interaction defined by the (Unfair – Fair)UG – (Unfair – Fair)IG and reverse contrasts were computed to assess the influence of the power to punish norm violations on unfairness perception. Next, a second conjunction analysis was conducted using the (RejectUnfair – AcceptUnfair)UG and (RejectUnfair – AcceptUnfair)IG contrasts to determine activated areas common to rejection of unfair offers in both UG and IG. The Response (Accept vs. Reject) ∗ Context (UG vs. IG) interaction defined by the (RejectUnfair – AcceptUnfair)UG – (RejectUnfair – AcceptUnfair)IG and reverse contrasts were computed to identify brain areas showing modulation of the responder’s responses to unfair offers by the power to punish norm violations. Brain activations modulated by the power to punish norm violations were identified by the (IG – UG) and reverse contrasts. Additionally, correlation analyses between rejection rates and corresponding contrasts were performed to test for brain-behavior relations. We first correlated rejection rates of unfair offers in UG with the (Unfair – Fair)UG contrast. Then, a similar correlation was conducted between rejection rates in IG and the (Unfair – Fair)IG contrast. We also calculated the absolute difference in rejection rates between UG and IG for each subject and correlated them with the (Unfair – Fair)IG – (Unfair – Fair)UG and (RejectUnfair – AcceptUnfair)IG – (RejectUnfair – AcceptUnfair)UG contrasts respectively.For all analyses, an initial voxel-level threshold of uncorrected p < 0.001 was used. Then for regions with a priori hypotheses, small volume correction (SVC) was applied for multiple comparisons. These regions include bilateral AI, dACC, DLPFC, and VMPFC. The MRIcro software was used to create masks required in the SVC procedure. The masks of insula and ACC were defined based on the automated anatomical labeling atlas (AAL) (). For DLPFC and VMPFC, the masks were made using a two stage process. First, we defined a sphere with the radius of 15mm and the center at the coordinates (left DLPFC, MNI -34 46 20; right DLPFC, MNI 39 37 26; VMPFC, MNI 2 41 -6) from previous studies (; ). Then these spheres were intersected with the corresponding Brodmann areas (DLPFC BA9, BA46; VMPFC BA10, BA11, BA24, BA25, BA32). Only activations surviving the voxel-level threshold of family-wise error (FWE) corrected p < 0.05 after SVC were reported in the results. For regions without a priori hypotheses, a cluster-level threshold of p < 0.05 after FWE correction for multiple comparisons across the whole brain was used. The MarsBaR toolbox was used to extract beta values when significant activations were observed. […]

Pipeline specifications

Software tools SPM, MRIcro, AAL
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens