The experiments were carried out under the assumption that HP and HD stimuli were both perceived as more unpleasant than their corresponding neutral counterparts. To ensure this, we focused on the reference trials and excluded all subjects/sessions in which the HP/HD stimuli were rated as neutral (median unpleasantness: HP ≤ −5 or HD ≤ −5), or considered as equally (or less) unpleasant than the corresponding controls (HP ≤ LP or HD ≤ LD). In addition, as in the remaining sample, thermal pain was rated as slightly more unpleasant than disgusting odors; we removed subjects/blocks where such divergence was too extreme (HP-HD ≥ 18). As a result, 8 participants of 33 were excluded from the analysis of experiment 1 (final sample N = 25). Likewise, 26 of 108 blocks (27 subjects × 4 blocks per subject) were excluded from experiment 2 (final sample N = 27). These exclusion criteria ensured the best trade-off between sample size and matched unpleasantness between modalities. A test comparing directly HP versus HD differences provided strong support in favor of the null hypothesis (Exp. 1: BF = 3.16; Exp. 2: BF = 5.31). Hence, our selection procedure ensured matched unpleasantness between thermal and olfactory stimuli in the reference trials and was applied to all subsequent analytical steps.

Subjective ratings. The analysis of the behavioral responses in the postdilemma trials was carried out as follows. For each subject, for each condition, single-trial ratings of interest (unpleasantness ratings from the stimuli epochs; appropriateness ratings from dilemma epochs) were fed into a linear mixed model with modality (thermal, olfactory), unpleasantness (neutral, unpleasant), and dilemma (moral, nonmoral) as fixed factors and subject identity as a random factor (with random intercept and slope for the fixed factors). Furthermore, we exploited the data from the validation pilot (fig. S1) by replacing the factor dilemma with continuous predictors describing the appropriateness/emotional engagement associated with each dilemma. This was achieved by using as predictors the median ratings from the validation experiment (subjects who read the French version of the scenarios were modeled as a function of the French validation data, whereas subjects who read the English version of the modeled as function of the English validation data). In case of model misconvergence, the random structure of the model was simplified until convergence was reached. The analysis was carried out with R 4.0.2 software (, with the aid of the lmerTest package. P values associated with the estimated parameters and t test were calculated through approximation of the degrees of freedom, as implemented in lmerTest. This analysis was complemented with formal model comparison through the estimation of the BF for linear mixed models (with subjects’ identity specified as a random factor), as implemented in the BayesFactor package for R (

Galvanic skin response. In experiment 1, GSR was recorded through Beckman Ag-AgCl electrodes (8-mm-diameter active area) filled with an isotonic, 0.05 M NaCl, electrode paste, attached to the participant’s left hand on the palmar side of the middle phalanges of the second and third fingers. The electrodes were connected to the MP150 Biopac System (Santa Barbara, CA) for GSR recording at a 1000-Hz sampling rate. For each subject, single-trial estimates of GSR were calculated using the MATLAB package Ledalab ( (21). More specifically, the raw time course was down sampled to 50 Hz, preprocessed through adaptive Gaussian smoothing, and visually inspected for potential movement artifacts, which were corrected through spline interpolation. The resulting signal was then deconvolved using continuous decomposition analysis, which separates traces into tonic and physic signal of galvanic activity. For the purpose of the present study, we considered a galvanic phasic response as reliable if exceeding 0.02 μS. Hence, single-trial event-related responses were calculated as the sum amplitude of all suprathreshold phasic responses occurring between 1 and 7 s from the stimulus onset (in olfactory stimulations) or from the time in which temperature reached plateau (in thermal stimulations). These values were analyzed with a mixed model framework similar to that of behavioral ratings. Notably, however, given the high amount of zero responses in GSRs (see fig. S2B), we implemented a generalized linear mixed model with Tweedie compound Poisson distribution (link-log), which allows us to account for an inflated amount of zero values in the dataset (22). The analysis was carried out with the cplm package of R. P values associated with the estimated parameters and t test were calculated through approximation of the degrees of freedom, as implemented in the parameter package. This analysis was complemented with formal model comparison through the estimation of the BF. However, as the BayesFactor package does not allow modeling generalized linear mixed model with Tweedie distribution, the BF of GSR was estimated through BIC approximation (37).

Imaging data: Acquisition. In experiment 2, functional images were acquired using a 3T whole-body MRI scanner (Trio TIM, Siemens) with a 32-channel head coil. We used an echo planar imaging (EPI) sequence with repetition time (TR) = 2100 ms, echo time (TE) = 30 ms, flip angle = 50°, 36 interleaved slices, 64 × 64 pixels, 3 mm × 3 mm × 3 mm voxel size, and 3.9-mm slice spacing. A field map was also estimated through the acquisition of two functional images with different echo times (short TE = 5.19 ms; long TE = 7.65 ms). Last, structural images were acquired with a T1 weighted three-dimensional sequence (MPRAGE, TR/TI/TE = 1900/900/2.27 ms, flip angle = 9°, parallel accelleration (PAT) factor = 2, 192 sagittal slices, 1 mm × 1 mm × 1 mm voxel sizes, 256 × 256 pixels).

Imaging data: Preprocessing. Preprocessing of functional images was carried out with the software SPM12 ( For each subject, the volumes were realigned, unwrapped using a field map image, coregistered to the structural image, normalized to a template based on 152 brains from the Montreal Neurological Institute with a resolution of 2 mm × 2 mm × 2 mm, and smoothed by convolution with an 8-mm full-width at half-maximum isotropic Gaussian kernel.

Imaging data: First-level analysis. Data were then fed into a first-level analysis using the general linear model framework implemented in SPM12. For each experimental block, we modeled each kind of stimulus event as follows: Olfactory stimuli were modeled as events of 3 s whose onset corresponded to the estimated time in which odorants reached participants’ noses; thermal stimuli were modeled as events of 2 s whose onset corresponded to the time of the plateau temperature. As for dilemma epochs, we fitted each dilemma reading period and each dilemma rating period, with a boxcar function with a duration corresponding to the dilemma reading/rating time. This led to 29 regressors on each block [eight dilemma reading epochs, eight dilemma rating epochs, eight stimuli events following the dilemmas, five “reference trials” (LP, HP, LP, HD, and positive odor)], which were convolved with a canonical hemodynamic response function and associated with regressors describing their first-order time derivative. We also included nine covariates of no interest: These were the six differential realignment parameters, an estimate of inspiration-based changes in the signal (based on a response function from the PhysIO toolbox:, and the average time courses extracted from anatomical masks of white matter and cerebrospinal fluid. Low-frequency signal drifts were filtered using a cutoff period of 128 s.

Imaging data: Second-level analysis. The average parameter estimates from the first-level model were fed into separate second-level group analyses testing the effects associated with thermal stimuli, olfactory stimuli, dilemma reading epochs, and dilemma rating epochs. For the analysis of thermal/olfactory epochs, the parameters associated with both reference and postdilemma trials were fed into a second-level flexible factorial analyses with a within-subject factor with six levels (2 unpleasantness × 3 dilemma) and subjects as a random factor. For the analysis of dilemma epochs, the parameters were fed into a second-level flexible factorial analyses with within-subject factor with eight levels (2 modality × 2 unpleasantness × 2 dilemma) and subjects as a random factor. In modeling the variance components, we allowed the factor condition to have unequal variance between its levels, whereas the factor subjects was modeled with equal variance. Activations were considered significant if exceeding an extent threshold allowing P < 0.05 correction for multiple comparison for the whole brain, with an underlying height threshold corresponding to P < 0.001 uncorrected.

Imaging data: Mediation analysis. We investigated the interplay between neural processes underlying moral and disgust processing, through mediation analysis combined with robust iteratively reweighted least squares (38). This was achieved by assessing whether the moral content of the dilemma (coded as a dichotomic variable 0: nonmoral, 1: moral) influenced the subsequent disgust-evoked activity HD > LD in predefined regions (left vAI, see Results), and whether such relationship was mediated by the neural response evoked by the dilemmas preceding the olfactory stimulations. We ran a voxelwise mediation, modeling moral processing activity for each brain coordinate of interest, to estimate three parameters: brain regions that show increased activity for moral dilemmas (path a); brain regions that predict changes in subsequent disgust-related response, when controlling for path a (path b); and brain regions that formally mediate the relationship between dilemmas and disgust signals (path a × b). Given that we modeled dilemma reading and rating epochs separately, we repeated the mediation analysis twice, once for each epoch. Each analysis thus led to three whole-brain parameter maps (one for each path), whose deviance from zero was assessed with bootstrap techniques (10,000 resamples). As this analysis was conceived to map regions sensitive to moral processing, we constrained our hypothesis by focusing only on the structures that were implicated in the main effect of moral > nonmoral dilemmas in the flexible factorial analysis (see the “Dilemma events” section). Within this mask (5748 voxels for reading, 1262 for rating epochs), we report as significant those effects surviving q < 0.05 under false discovery rate correction. The analysis was run with the Mediation Toolbox (

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