Behavior and skin conductance

Higher stimulus temperatures (50 °C > 49 °C > 48 °C) significantly increased pain ratings (β = 7.55 [95% CI 5.88–9.22], t(35) = 8.85, p < 0.001, Cohen’s d = 1.50). Consistent with our predictions, both social information and CS indicative of high (vs. low) pain increased ratings of pain expectations (Fig. 1c; Social HIGH –Social LOW : β = 11.75 [9.59, 13.92], t(35) = 10.65, p < 0.001, Cohen’s d = 1.80; CS HIGH –CS LOW : β = 1.45 [0.51, 2.38], t(35) = 3.02, p = 0.005, Cohen’s d = 0.51). The effects of social information on expectations were much stronger than those of the CS (t(35) = 7.66, p < 0.001, Cohen’s d = 1.28). The Social effect on expectations decreased slightly over time (cue x time interaction, β = −0.05 [−0.08, −0.03], t(35) = −4.57, p < 0.001, Cohen’s d = 0.77), but was present throughout the experiment (Fig. 1d). In contrast, CS effects on expectations only emerged over time, reflecting learning (cue x time interaction, β = 0.04 [0.01, 0.06], t(35) = 3.02, p = 0.004, Cohen’s d = 0.51).

Both types of information strongly influenced pain as well (Fig. 1c): heat at the same, medium-level intensity (49 °C) was rated as more painful in the Social HIGH compared with the Social LOW condition (β = 4.78 [3.27, 6.29], t(35) = 6.19, p < 0.001, Cohen’s d = 1.05), and in the CS HIGH compared with the CS LOW condition (β = 0.74 [0.28, 1.20], t(35) = 3.13, p = 0.003, Cohen’s d = 0.53). In parallel to expectations, pain ratings were more strongly influenced by social information than CS cues (t(35) = 4.86, p < 0.001, Cohen’s d = 0.81). While CS effects on pain became stronger with time (reflecting learned pain modulation, β = 0.02 [0.0007, 0.038], t(35) = 2.03, p = 0.049, Cohens’ d = 0.34), the social influence on pain ratings remained stable over time (i.e., cue x time interaction: p > 0.20, Fig. 1e).

Expectations formally mediated both Social and CS effects on pain (path ab effect for Social, β = 5.56 [4.31, 6.81], t(35) = 8.56, p < 0.001, Cohen’s d = 1.47; for CS, β = 0.47 [0.18, 0.76], t(35) = 3.27, p < 0.001, Cohen’s d = 0.53, Fig. 1f; see also Supplementary Fig. 1), replicating our previous findings42.

Paralleling the behavioral findings, skin conductance responses (SCR) to pain (Fig. 1c; Supplementary Fig. 2) were higher in Social HIGH than Social LOW trials (β = 0.08 [0.003, 0.16], t(35) = 2.04, p = 0.049, Cohens’ d = 0.34). SCR were only numerically higher for CS HIGH compared to CS LOW trials (t(35) = 1.47, p = 0.15). In contrast to our earlier findings, expectation ratings did not significantly mediate social information and CS effects on SCR (t test, both p’s > 0.20), potentially due to noisier physiological data in the MRI environment.

Effects on established multivariate brain measures related to pain

We next tested for effects on two previously described multivariate brain markers of pain. The neurological pain signature (NPS43) was optimized and validated to predict pain ratings with high accuracy during evoked pain related to multiple types of noxious stimuli. It is thought to reflect primarily pain of nociceptive origin43, as it does not respond to several forms of psychological pain modulation44,45. The stimulus-intensity independent pain signature (SIIPS1)46 was developed to predict pain independent of nociceptive input and mediates effects on pain of several psychological manipulations, including cued expectancy and perceived control46. Here, while single-trial fMRI responses in both NPS and SIIPS were significantly associated with trial-by-trial pain ratings (NPS: β = 0.032 [0.015, 0.049], t(35) = 3.69, p < 0.001, Cohens’ d = 0.62; SIIPS: β = 0.0002 [0.00008, 0.0004], t(35) = 3.06, p = 0.004, Cohens’ d = 0.52), neither of the two patterns was significantly modulated by either the social information or the CS (t tests, all p’s > 0.1, see Fig. 1c). This suggests that social information and learning effects on pain may not influence primary nociceptive processing, but exert their effects via other mechanisms.

Whole brain mediation analysis

We estimated two multilevel-mediation models17,41,47 to assess the mechanisms of social information and associative learning on pain-related brain activity modeled on a single-trial level (see Fig. 2a). In brief, Social HIGH versus Social LOW and CS HIGH versus CS LOW served as predictors (coded 1 and −1 for high and low, respectively), and single-trial pain ratings served as the outcome. Trial-by-trial beta images for medium-temperature pain events (matched on stimulus intensity) were included as mediators. CS cues served as first-level covariates in the Social→Brain→Pain model, and vice versa for the CS→Brain→Pain model, so that both models controlled for both cue effects. Path a effects describe changes in activity based on the manipulation (similar to a classic 2nd level contrast), i.e., Social HIGH > Social LOW and CS HIGH > CS LOW . Path b characterized brain activity that predicts pain ratings independent of the two experimental manipulations. Path ab (the a*b product or “mediation effect”) characterized brain activity that significantly mediates the effect of the manipulations (social information and CS) on pain ratings. An extended overview of mediation is provided in Supplementary Fig. 1.

Fig. 2 Whole brain mediation analysis. a Overview. A mass-univariate mediation analysis was performed to identify: (1) activity increases for high > low social information and CS (Path a), (2) activity associated with increased pain ratings when controlling for path a effects (path b), and (3) activity formally mediating the effects of social information and CS on pain ratings (dashed arrows, path ab). b Path b effects. Significant pain-related activity independent of the experimental manipulations was found in mid cingulate, posterior and mid insula, thalamus, cerebellum, and other regions. The wedge plot (with the radius of each wedge proportional to the correlation strength) indicates a high spatial correlation of this effect with the Somatomotor network (Pearson correlation coefficient r = 0.28, see Supplementary Table 6). The ten most strongly associated Neurosynth terms are shown on the right (decreasing brightness indicates order of associations, see Supplementary Table 7). c Path a effects for Social (purple), CS effects (green), and their conjunction (blue). Social information effects (increased activity for Social HIGH > Social LOW ) were found in ACC, anterior insula, dlPFC, and parietal areas. Those effects most strongly mapped on the frontoparietal (r = 0.13) and dorsal attention networks (r = 0.12, see wedge plot and Supplementary Table 6) and were associated with terms reflecting cognitive tasks (see Supplementary Table 7). CS effects (CS HIGH > CS LOW ) were seen in limbic areas and cerebellum, and showed a more diffuse mapping on large-scale networks and meta-analytic terms. d Path ab effects for Social (purple), CS effects (green), and their conjunction (blue). Social influence effects on pain mapped on the frontoparietal and dorsal attention networks (both r’s = 0.06) and on terms associated with cognitive control. CS effects mapped on the default mode network (r = 0.06) and were associated with terms related to semantic processing. All maps were thresholded at FDR q < 0.05 corrected for multiple comparisons across the whole brain (gray matter masked) with adjacent areas thresholded at p < 0.01 and p < 0.05 (uncorrected) for display Full size image

Cue-independent contributions to pain

We first identified brain areas associated with higher pain ratings independent of social information and CS cues (Path b, q < 0.05 FDR-corrected, yellow in Fig. 2b). These included areas typically involved in pain processing, such as the dorsal anterior cingulate cortex (ACC) and premotor cortex, mid and posterior insulae, somatosensory cortex (leg area, corresponding to the stimulation site), ventrolateral thalamus, and cerebellum, as well as occipital (visual) cortex.

In order to assess how these regions map onto large-scale networks, we calculated the spatial pattern similarity (Pearson correlation coefficients) of the mediation maps to seven cortical resting-state networks48. The wedge plot in Fig. 2b shows these similarity values, with the radius of each wedge proportional to the correlation strength and area proportional to variance explained. Pain-predictive activity independent of manipulations was concentrated in somatomotor, ventral attention, and visual networks (see Supplementary Tables 1, 6).

We further calculated the similarity between the Path b map and association test z-score brain maps of 525 terms from the Neurosynth meta-analytic database49. While relationships with terms should be interpreted with caution and are only suggestive, they can be useful for contextualizing findings and provide a more fine-grained comparison with existing large-scale databases than the canonical seven-network parcellation. The top ten unique terms associated with pain-related (path b) effects were themed around pain processing (e.g., “noxious”’, “heat”, “pain”, “painrelated”) and somatomotor function (“foot”, “limb”, “sensation”, “somatosensory”, “muscle”, “voluntary”, see Supplementary Table 7). Thus, meta-analysis-based decoding here seemed related to the stimulation to the right calf muscle.

Social information effects on pain

Increased activity for the Social HIGH compared to the Social LOW condition was found in several cortical and subcortical areas (path a effects, purple in Fig. 2c, FDR q < 0.05 corrected, Supplementary Table 2). First, they comprised areas associated with cognitive control and top-down attention, including dlPFC, inferior parietal lobule (IPL), and intraparietal sulcus (IPS). Second, they included brain areas associated with salience, affect, and pain processing, i.e., ACC, anterior insula and operculum, and ventrolateral thalamus. Third, increased activity was found in brain areas involved in somatosensory integration, such as mid insula and parietal operculum. In addition, inferior temporal and visual cortex showed increased activation for Social HIGH > Social LOW .

These social information effects on pain were spatially correlated mostly with the dorsal attention and frontoparietal networks (see Supplementary Table 6). Paralleling the network findings, the top ten decoding results for Social HIGH > Social LOW using Neurosynth meta-analytic maps included terms associated with attention and cognitive control (e.g., “topdown”, “distractor”) and with word and number processing (e.g., “counting”, “orthographic”, “number”, “lexical”, Supplementary Table 7). Thus, though some regions identified are reliably involved in social cognition, we do not have strong reasons to believe that the processes engaged are uniquely social; they may reflect effects of control and attention on pain more generally.

Mediators of social information effects on pain ratings (path ab, purple in Fig. 2d; Supplementary Table 3) included a similar set of regions, especially vlPFC, dorsomedial prefrontal cortex (dmPFC), dlPFC, IPL/IPS, visual cortex, inferior temporal sulcus, orbitofrontal cortex, and visual cortex. In close correspondence to path a effects, the networks most prominently involved in mediation were the dorsal attention and frontoparietal networks, with a smaller contribution of the default mode network (see Supplementary Table 6). The top ten decoding terms based on Neurosynth included words associated with cognitive control and attention (e.g., “memory”, “maintenance”, “switch”, “executive”, “attentional”, see Supplementary Table 7).

We next used a conjunction analysis to identify spatial overlap in brain areas responding to the Social HIGH > Social LOW manipulation (path a) and those mediating the effects of this manipulation on pain outcomes (path ab). The mediation effect is driven by a combination of responses to the experimental manipulation, correlations with pain, and correlations in individual differences between these two effects (i.e., stronger responses to the Social HIGH > Social LOW manipulation correlated with stronger effects of the brain region on pain). Thus, the ab effect encompasses all the elements required to link the manipulation, brain, and behavior, but does not guarantee that all significant regions show significant cue effects. The conjunction analysis therefore shows regions that show both Social HIGH > Social LOW effects and mediation effects (purple in Fig. 3a). It confirmed the important contribution of the dlPFC, IPS, dmPFC, vlPFC, and visual cortex to social information effects on pain. Higher single-trial activity averaged over this set of regions was correlated with higher single-trial expectation ratings (β = 0.86 [0.33, 1.40], t(35) = 3.15, p = 0.003, Cohen’s d = 0.53), suggesting that these areas were involved in the generation of explicit expectations based on the social information. Individual differences in how much social information influenced expectation ratings did not moderate this relationship between brain activity and expectations (t test, p > 0.10).

Fig. 3 Conjunction of path a and ab effects. a Social information effects were consistent across paths a and ab in dmPFC, vlPFC, dlPFC, IPS, and visual cortex. Trial-wise average activity (betas) in these regions correlated significantly with trial-wise expectation ratings (individual slopes in purple). Average activity did not significantly predict individual differences in the social influence effects on expectations (scatter plot). b CS effects were consistent across paths a and ab in the hippocampus, cerebellum, and fusiform gyrus. Trial-wise average activity in these areas correlated significantly with trial-wise expectation ratings (individual slopes in green). Individual differences in the strength of the activation were correlated with individual differences in how much expectations ratings were driven by the CS. Conjunction effects are displayed as the intersection of activation for paths a and ab, each of them thresholded at P < 0.05 FDR-corrected and adjacent voxels at p < 0.05 uncorrected. Asterisk reflects significant Pearson correlation coefficient (p < 0.05). Shaded error bands reflect bootstrapped 95% confidence intervals. Source data are provided as a Source Data file Full size image

Learning effects on pain

Increased activity for CS HIGH compared with CS LOW was found in a largely different set of brain regions (path a, green in Fig. 2c), including “limbic” areas such, as hippocampus, caudate, and retrosplenial cortex, as well as cerebellum, precentral gyrus, and left IPS. These activations mapped weakly onto a combination of limbic, dorsal attention and visual networks (Fig. 2c; Supplementary Tables 4, 6). Accordingly, the top ten Neurosynth associations for the CS HIGH > CS LOW map spanned over various topics including language (“pseudowords”, “phonetic”) and mentalizing (“persons”, “mentalizing”) (see Supplementary Table 7).

Significant mediation of learning effects on pain (path ab) was seen in many areas, including dmPFC, medial and lateral orbitofrontal cortex, left anterior insula/operculum, caudate, hippocampus, retrosplenial cortex, fusiform gyrus, dlPFC, IPL, and cerebellum (green in Fig. 2d; Supplementary Table 5). Some of those effects partially overlapped or were neighboring with mediation effects of social influence. Yet, learning effects were mostly correlated with default and limbic networks and with meta-analytic maps associated with semantic processing (e.g., “lexical”, “word”, “semantically”, “gestures”, see Supplementary Tables 6, 7). As noted above, this does not strongly imply that these processes were involved, but suggests similarity to regions engaged in associative learning, including semantic associations.

We again used a conjunction analysis to illustrate the overlap in brain regions that showed increased activation to pain for CS HIGH > CS LOW (path a effects), and regions that statistically mediated the effects on pain ratings (path ab effects). This conjunction revealed three main regions of bilateral effects: hippocampus, fusiform gyrus, and cerebellum (green in Fig. 3b). In parallel to the social mediator regions, higher activity in these areas again correlated with higher trial-by-trial expectation ratings (β = 0.58 [0.09, 1.06], t(35) = 2.34, p = 0.025, Cohen’s d = 0.39; Fig. 3b, middle). This association was modulated by individual differences in how much expectations were driven by the learning cues (β = 0.21 [0.07, 0.35], t(34) = 2.84, p = 0.008, Cohen’s d = 0.49; Fig. 3b, right)—however, this relationship with individual differences was largely driven by a single participant (~3 STD above the mean on both brain and behavior). Larger sample sizes are needed to investigate individual differences in more detail. In sum, these results show the contribution of brain regions involved in memory, learning, and object recognition in learned pain modulation and (as with unconditioned social information) suggests a role for conscious (reportable) expectation in this process.

Cue-related and interaction effects

For completeness, we tested how social information and CS influenced brain activity at the time of cue presentation, again demonstrating largely different systems responding to each type of cue and mediating their effects on expectations (see Supplementary Fig. 3). We further explored interaction effects between social and CS conditions, shown in Supplementary Fig. 4. Broadly, these analyses confirmed the importance of frontoparietal, orbitofrontal, and temporal systems in pain expectancy, with ventral and dorsal systems preferentially involved in associative learning and social information effects, respectively.

Similarity versus separability of social influence and learning effects

To test for commonalities between social information and learning effects, we performed a conjunction analysis (using a conjunction null50). At a lenient threshold (p < 0.05 uncorrected voxels adjacent to FDR-significant voxels), CS and social influence showed a few small clusters of common path a effects in visual cortex and bilateral superior parietal lobule (see Fig. 2c). To quantify the degree of overlap vs. dissociation, we computed the Dice coefficient for voxels activated at 0.05 uncorrected. The Dice coefficient—which can theoretically range between 0 and 1, where 0 reflects complete separation and 1 reflects perfect overlap—for path a was 0.024, thus suggesting relatively little overlap between path a activations for Social and CS effects. For path ab, shared mediation effects of social influence and CS effects on pain were observed at p < 0.01 and p < 0.05 uncorrected, notably in dmPFC, dlPFC, vlPFC, and IPL (see Fig. 2d), with a Dice coefficient of 0.081. Thus, while these clusters of conjunction effects were relatively small in size, this suggests that parts of the frontoparietal network may be involved in top-down modulation of pain based on expectations irrespective of their sources. In sum, mediation maps suggest a dissociation in the regions mediating both learned and instructed cue effects on pain, along with some potential similarities in frontal and parietal regions.

While the peak areas involved in each type of pain modulation are distinct, it is conceivable that they reflect similar underlying activity patterns with distinct peaks surviving significance thresholds. In order to test this possibility, we performed several additional analyses.

First, we assessed whether social influence and CS-related activity could be separated based on distributed multivariate activity. We trained a support vector machine (SVM) classifier using leave-one-subject-out cross-validation to separate individual beta images for Social HIGH > Social LOW from beta images for CS HIGH > CS LOW (path a effects) in all brain areas associated with mediation (in the a and ab conjunction images) for either social or learning effects (i.e., union of regions displayed in Fig. 3a, b). This SVM yielded significant and moderately accurate predictions (forced-choice, mean ± SE = 72% ± 7.5%, binomial test, P = 0.011), indicating separable, reliably distinct patterns of activity for social and learning effects during pain in these brain regions (though it does not rule out potential similarities as well).

Second, since conjunction effects were found in frontal and parietal regions, we tested how individual (unthresholded and normalized) beta images for Social and CS path a effects engaged more fine-grained parcellations of the frontoparietal network (Fig. 4), based on an established 16-network cortical parcellation48,51 (Supplementary Fig. 5). Overall, the frontoparietal network (but no other network) was significantly more activated for Social compared to CS path a effects (t(35) = 3.1, p = 0.0042, Cohen’s d = 0.51). Further, the frontoparietal subnetworks “Control A” and “Control B" in both left and right hemisphere were more activated for the Social compared with the CS path a effect (see Fig. 4a). In contrast, both left and right “Control C” subnetworks did not differ between Social and CS effects. This is consistent with the observation that strong Social effects were observed in the prefrontal, lateral parietal, and temporal parts of the frontoparietal network, but not in the medial parietal cortex or posterior cingulate cortex (which constitute the “Control C” subnetwork, see Fig. 4b).

Fig. 4 Difference between Social and CS effects in frontoparietal control subnetworks. a Difference in mean path a beta weight (Social–CS) in Control A (dark and bright red), Control B (dark and bright purple), and Control C (dark and bright blue) subnetworks in the left and right hemisphere (dark and bright colors, respectively). Each dot reflects the difference in mean beta estimates of one participant. Significantly greater activation for social information was found in the Control A (left: t(35) = 2.3, p = 0.027, 95% CI = [0.03, 0.39], Cohen’s d = 0.38; right: t(35) = 2.9, p = 0.006, 95% CI = [0.08, 0.43], Cohen’s d = 0.48) and the Control B (left: t(35) = 2.6, p = 0.012, 95% CI = [0.08, 0.59], Cohen’s d = 0.44; right: t(35) = 2.6, p = 0.013, 95% CI = [0.07, 0.51], Cohen’s d = 0.44), but not in the Control C network. Asterisks denote networks with significant differences between social information and CS effects (using t-tests, p < 0.05). Source data are provided as a Source Data file. b Display of frontoparietal control subnetworks A (dark and bright red), B (dark and bright purple), and C (dark and bright blue) on sagittal and transversal brain slices Full size image

Third, we analyzed the spatial covariation between the unthresholded weight maps for Social and CS mediation effects (path ab), and summarized the voxel-level covariation within each network. Here, voxels are the unit of analysis, and voxels within a network may have diverse functional relationships with CS and social information effects. In Fig. 5 and Supplementary Fig. 6, we plotted the joint distribution of both type of effects—i.e., the weight (effect magnitude) of each voxel for the Social mediation effect on the x- and for the CS mediation effects on the y-axis, separately for each network52,53. Effects in any given voxel could be positive, negative, or near-zero for each of the Social and CS mediation effects. This lends itself to classifying voxels within each network into eight equally-sized octants depending on the relative Social and CS effects. Voxels in Octants 1 and 3 were selectively related to positive mediation of CS and social influence respectively. Octants 5 and 7 showed selective negative effects of CS or Social cues, respectively. Voxels in Octants 2 and 6 show positive and negative mediation effects that are common and in the same direction for both cue types. Voxels in these octants drive positive spatial correlations across voxels within the network as a whole, indicating overlap. Finally, voxels in Octants 8 and 4 are those with positive CS effects, but negative Social cue effects, or vice versa. Voxels in these octants drive negative spatial correlations, indicating dissimilarity. Furthermore, to provide an overall measure for voxels in each octant, we computed the sum of squared distances (SSD) from the origin, thus combining a measure of both absolute numbers of voxels in each octant and their (squared) distance from the origin.

Fig. 5 Voxel-level spatial covariation in Social and CS mediation (path ab) effects. Scatter plots display unthresholded single-voxel beta weights for CS (y-axis) and Social (x-axis) mediation effects (path ab). Each dot represents a voxel. Different colors are assigned to eight octants that reflect positive mediation for CS but not Social effects (Octant 1), positive mediation effects for both (Octant 2), positive mediation of Social, but not CS effects (Octant 3), and so on. Radial grids display distance from the origin (0,0) in 0.02 unit steps. These maps are descriptive, unthresholded illustrations of mediation beta weights. Units are arbitrary. a Voxel-level similarity in the frontoparietal network, showing the highest sum of squared distances (SSD, displayed in bar plots) from the origin in Octants 2 and 3, reflecting shared (CS and Social) positive and uniquely positive mediation of Social effects, respectively. Voxels in Octants 1–3 (green, blue, and purple, respectively) within the frontoparietal network (light gray area) are displayed on a lateral brain surface plot (far right). b Voxel-level similarity in the limbic network, showing highest SSD in Octants 1 and 8 (see bar plot), reflecting selective mediation of CS effects and suppression of Social effects. Suppression effects are in the opposite direction from, and thus “work against”, the overall effects of cues on pain ratings. Voxels in Octants 1–3 (green, blue, and purple, respectively) in the limbic network (light gray area) are plotted on a medial brain surface (far right). See Supplementary Fig. 6 for the results in all seven large-scale networks. Source data are provided as a Source Data file Full size image

This analysis of the spatial covariation of Social and CS effects shows qualitatively different patterns across networks. Visual, ventral attention, and default mode networks (Supplementary Fig. 6) have peak SSDs in Octant 2, reflecting a disproportionately large number of voxels that show positive (if not necessarily significant) effects for both Social and CS mediation, indicating some overlap in these networks. However, these networks also contained many voxels with positive weights only for CS (Octant 1) or only for Social mediation effects (Octant 3). Frontoparietal (Fig. 5) and dorsal attention networks show large effects in the shared positive (Octant 2) and in uniquely Social mediation effects (Octant 3). The limbic network (Fig. 5) shows a peak in voxels mediating CS, but not (or even suppressing) Social effects (Octant 1 and 8). Finally, the Somatomotor network was associated with many voxels showing suppressor effects for CS (negative slopes in mediation) and shared suppressor effects for CS and Social effects, in line with its role in primary nociceptive, but less in contextual pain modulation effects. Overall, this analysis provides more detailed evidence for shared and non-shared elements within each network. The networks with the strongest evidence for some shared processing include the dorsal and ventral attention, frontoparietal, and default mode networks, but these similarities are offset by the differential responses in the vast majority of voxels in these networks (the overall spatial covariance across voxels is relatively weak). Those with the strongest evidence for dissimilar effects of Social and CS include the Limbic (CS preference) and Somatomotor networks.