Contributions of insula and superior temporal sulcus to interpersonal guilt and responsibility in social decisions
- Masters in Neuroscience Program, University of Bonn, Germany
- Institute of Psychology, University of Bern, Switzerland
- Center for Economics and Neuroscience, University of Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, Medical Faculty, University of Bonn, Germany
- Department of Psychiatry, University of Oldenburg, Germany
Figures
Figure 1
Experimental design.
In every trial, participants were presented with pairs of monetary options (a safe and a risky option; the risky option was a lottery with equally probable high and low outcomes). There were three conditions: a non-social ‘Solo’ condition, in which the participant’s choice led to an outcome just for themselves (left panel); and two conditions in which choices were made by the participant (‘Social’ condition) or by their partner (‘Partner’ condition) and led to outcomes affecting both players (right panel). Importantly, selecting the risky option in the social or partner conditions led to the lottery being played out independently for both players; that is, participant and partner could receive the high or low outcome independently from each other (coloured boxes). Selecting the safe option led to both players receiving an equal outcome.
Figure 2
Participant choices in Studies 1 (outside fMRI, N = 40) and 2 (inside fMRI, N = 44).
(A, D) The probability of choosing the risky option (lottery) in both Solo and Social conditions is well explained by the difference in expected value of the risky and safe choice options (EVrisky – Vsafe). Participants chose the risky option slightly more often in the Solo condition than in the Social condition in Study 1 (A) but not in Study 2 (D). Lines are predicted values of a logit linear mixed regression model fitted to the choice data (see Results). Error areas indicate 95% pointwise confidence intervals for the predicted values. Triangles indicate individual average choice proportions binned by EVrisky – Vsafe value; size of triangle reflects the number of participants contributing to a datapoint. Blue upward-pointing and red downward-pointing triangles are data from the Solo and Social conditions, respectively. (B, E) Risk premiums did not differ between Solo and Social conditions. (C, F) Values of the risk aversion parameter ρ in the Solo and Social conditions were broadly consistent with Risk premium values, but showed that participants were slightly more risk averse in the Social than in the Solo condition in Study 1 only (see Results). In panels B, C, E and F, grey lines and markers show individual data, red lines show means with 95% confidence intervals about the means, and grey areas are kernel density plots representing the distribution of the data.
Figure 3 with 1 supplement
Participant momentary happiness in Studies 1 (A–D) and 2 (E–H).
Happiness varied with rewards received by the participant (A, E) and by the partner (B, F). Each dot is one trial; data are pooled across participants. Lines are fitted regression lines. A computational model taking into account expected, previous and current rewards, reward prediction errors for both participant and partner, and decision-maker (Responsibility Redux model, see Results) predicted the variations in participants’ momentary happiness well (C, G, and Table 1). Changes in momentary happiness after lottery choices in Social and Partner conditions varied with lottery outcome and decision-maker (D, H). Data were binned according to outcome for each participant and decision-maker (Social = participant chose the lottery, Partner = partner chose the lottery). Crucially, responsibility for low lottery outcomes for the partner decreased participant happiness more than the same outcomes following partner choices (see Results), which fits the definition of interpersonal guilt. In D and H, dots are individual datapoints, the bar indicates the mean, the error bars are 95% confidence intervals about the mean, and the stars indicate the significance of the ‘guilt effect’ (see text): ***p < 0.001; **p < 0.01.
Figure 3—figure supplement 1
Parameter recovery for Responsibility Redux model.
Stability of the estimated parameters of the temporal difference models was evaluated by attempting to recover parameters from synthetic data created using each participant’s real estimated parameters. After fitting each participant’s momentary happiness data (see above), we synthesized new momentary happiness data based on each participant’s estimated parameters, added 1SD of noise to the happiness data, fitted the model to these synthetic data, and repeated this procedure 10 times, for both studies. We then compared these new estimated parameters to the actual parameters from which the synthetic data were generated. For each parameter, we calculated the mean of each participant’s recovered parameters and regressed these means on the participants’ actual parameters (black dots and grey regression line, with 95% confidence interval). Each coloured dot is one recovered parameter value; different colours represent different participants.
Figure 4
BOLD responses.
(A) Regions showing a greater response when participants chose the risky (lottery) rather than the safe option, irrespective of Social or Solo condition. (B) Regions showing a greater response when participants chose for both themselves and their partner rather than just for themselves (Social > Solo). (C) Coefficients of linear mixed models (LMMs) indicate that two of these regions, precuneus and TPJ, were most active when participants chose the lottery in the Social condition. R–S indicates results of LMMs based on the Risky–Safe response difference. All coefficients and differences reported are significantly different from 0 (see Appendix 1—table 4). (D) Brain regions more active during receipt of the outcomes of lotteries than safe choices (all conditions). (E) Coefficients of LMMs indicated that insula ROIs (see D) mirrored the guilt effect observed in our behavioural data: voxels here responded more to low lottery outcomes (L) for the partner when these resulted from participant’s rather than the partner’s choices, even when responses to high outcomes were subtracted (L–H). All coefficients and differences reported are significantly different from 0 (see Appendix 1—table 6). (F) A mass-univariate, voxel-wise analysis showed a compatible result: A cluster of voxels within the left insula ROI showed higher responses to low lottery outcomes for the partner if these resulted from participant rather than partner choices. (G) Activation in bilateral ventral striatum explained by a computational model-based regressor coding participant rewards. (H) Within brain regions sensitive to outcomes of risky choices, one cluster in the left superior temporal sulcus region showed a higher response to partner reward prediction errors resulting from participant rather than partner choices. (I) Response in this cluster to the computational-model-based regressors coding participant reward prediction resulting from participant and partner choices, for both sessions of the experiment. All results shown survive thresholding at p < 0.05 corrected for multiple comparisons at the cluster level, based on a voxel-wise uncorrected threshold of p < 0.001. Colours in panels A–I indicate T values. In C and E, bars indicate the estimated coefficients. In C, E, and I, error bars are 95% confidence intervals.
Figure 5 with 1 supplement
Changes in functional connectivity between the left anterior insula (seed) and a cluster in the right inferior frontal gyrus at the time of the choice as a function of condition (Social vs. Solo) and choice (Risky or Safe).
In the righthand panel, dots are data of individual participants, the markers represent means, and error bars indicate 95 confidence intervals about the mean.
Figure 5—figure supplement 1
Functional connectivity with the left TD-model-defined superior temporal sulcus (STS; seed) during choices in Solo and Social conditions.
Connectivity between the left STS (seed) and a cluster in the left inferior frontal gyrus that did not survive corrections for multiple tests, where connectivity with the left STS (the seed region) showed the opposite pattern: connectivity was highest when participants made Safe choices for themselves and Risky choices for both players (puncorrected = 0.001, T = 4.44, Z = 4.30, 35 voxels, peak at MNI [–48 14 6]). In the righthand panel, dots are data of individual participants, the markers represent means, and error bars indicate 95 confidence intervals about the mean.
Tables
Table 1
Fits of computational models to momentary happiness data.
| Model | N param | Mean R2 | Mean R2adj | BIC | AIC |
|---|---|---|---|---|---|
| Study 1 | |||||
| Basic | 3 | 0.328 | 0.305 | –1000 | –1399 |
| Inequality | 4 | 0.346 | 0.316 | –916 | –1416 |
| Guilt-envy | 5 | 0.354 | 0.316 | –785 | –1385 |
| Responsibility | 5 | 0.370 | 0.333 | –866 | –1466 |
| Responsibility Redux | 4 | 0.361 | 0.331 | –999 | –1499 |
| Study 2 | |||||
| Basic | 3 | 0.374 | 0.340 | –693 | –1062 |
| Inequality | 4 | 0.394 | 0.350 | –620 | –1080 |
| Guilt-envy | 5 | 0.405 | 0.350 | –491 | –1043 |
| Responsibility | 5 | 0.433 | 0.380 | –616 | –1168 |
| Responsibility Redux | 4 | 0.422 | 0.379 | –735 | –1195 |
-
BIC, Bayesian Information Criterion; AIC, Akaike’s Information Criterion. BIC and AIC values are summed across participants. As in previous work by Rutledge et al., 2014; Rutledge et al., 2016, model fits were performed with individually Z-scored happiness ratings. Best values of each variable for each study are highlighted in bold font. For model details, see Results and Methods (Equations 4–8).
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Software, algorithm | MATLAB | https://www.mathworks.com/products/matlab.html | RRID:SCR_001622 | Version R2016B and R2024A |
| Software, algorithm | Psychtoolbox | http://psychtoolbox.org | RRID:SCR_002881 | |
| Software, algorithm | SPM | http://psychtoolbox.org | RRID:SCR_007037 | SPM12 (7771) |
| Software, algorithm | R | https://www.r-project.org | RRID:SCR_001905 | Version 4.2.1 |
| Software, algorithm | lme4 | https://cran.r-project.org/package=lme4 | RRID:SCR_015654 | |
| Software, algorithm | JASP | https://jasp-stats.org | RRID:SCR_015823 | Version 0.16.1 |
| Software, algorithm | Measures of Effect Size Toolbox for Matlab | https://github.com/hhentschke/measures-of-effect-size-toolbox | RRID:SCR_014703 |
Appendix 1—table 1
Mixed-effects regressions on choices.
For details of the models, see Results and Methods in the main text. ***p < 0.001; **p < 0.01; *p < 0.05.
| Study 1 probit | Study 1 linear | Study 2 probit | Study 2 linear | |
|---|---|---|---|---|
| (Intercept) | 0.10 | 0.53*** | –0.04 | 0.49*** |
| (0.08) | (0.02) | (0.07) | (0.02) | |
| EVrisky − Vsafe | 0.07*** | 0.02*** | 0.09*** | 0.02*** |
| (0.01) | (0.00) | (0.01) | (0.00) | |
| Condition Social | 0.14* | 0.03^ | 0.01 | 0.01 |
| (0.06) | (0.02) | (0.06) | (0.02) | |
| (EVrisky − Vsafe) * Condition | –0.00 | –0.00 | –0.00 | 0.00 |
| (0.00) | (0.00) | (0.01) | (0.00) | |
| R2 (ord) | 1.000 | 0.284 | 1.000 | 0.306 |
| R2 (adj) | 1.000 | 0.283 | 1.000 | 0.305 |
| AIC | 4909 | 5395 | 3864 | 4313 |
| BIC | 4974 | 5466 | 3927 | 4382 |
| LogLikelihood | –2445 | –2687 | –1922 | –2146 |
| N | 4680 | 4680 | 3833 | 3833 |
Appendix 1—table 2
Linear mixed model regressions on happiness data following lottery choices.
We fitted several models to the data in order to assess the stability of the effects. In both studies, Model 5 (Equation 9 in the Results section of the main text), which contained all three two-way interaction terms, explained the data best, so its parameters for the crucial partnerHigh:participantDecided interaction are reported in the main text. All models contained the three main fixed effects and subject as a random effect (random intercepts), Models 2–4 contained one or two interaction terms. ***p < 0.001; **p < 0.01; *p < 0.05.
| Study 1 | |||||
|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
| (Intercept) | –0.58*** | –0.57*** | –0.47*** | –0.47*** | –0.38*** |
| (0.06) | (0.06) | (0.06) | (0.07) | (0.07) | |
| participantHigh [0,1] | 0.96*** | 0.95*** | 0.95*** | 0.94*** | 0.76*** |
| (0.05) | (0.07) | (0.05) | (0.07) | (0.09) | |
| partnerHigh [0,1] | 0.43*** | 0.43*** | 0.23** | 0.23** | 0.05 |
| (0.05) | (0.05) | (0.07) | (0.07) | (0.09) | |
| participantDecided [0,1] | –0.17** | –0.18* | –0.37*** | –0.39*** | –0.38*** |
| (0.05) | (0.08) | (0.08) | (0.09) | (0.09) | |
| participantHigh:participantDecided | - | 0.02 | - | 0.02 | 0.02 |
| (0.11) | (0.11) | (0.1) | |||
| partnerHigh:participantDecided | - | - | 0.40*** | 0.40*** | 0.39*** |
| (0.11) | (0.11) | (0.1) | |||
| participantHigh:partnerHigh | - | - | - | - | 0.35*** |
| (0.1) | |||||
| R2 (ord) | 0.25 | 0.25 | 0.259 | 0.259 | 0.266 |
| R2 (adj) | 0.248 | 0.247 | 0.256 | 0.256 | 0.262 |
| N | 1216 | 1216 | 1216 | 1216 | 1216 |
| Study 2 | |||||
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
| (Intercept) | –0.54*** | –0.50*** | –0.45*** | –0.43*** | –0.26** |
| (0.06) | (0.07) | (0.07) | (0.08) | (0.09) | |
| participantHigh [0,1] | 1.01*** | 0.95*** | 1.02*** | 0.96*** | 0.65*** |
| (0.06) | (0.09) | (0.06) | (0.09) | (0.11) | |
| partnerHigh [0,1] | 0.35*** | 0.35*** | 0.20* | 0.20* | –0.11 |
| (0.06) | (0.06) | (0.09) | (0.09) | (0.11) | |
| participantDecided [0,1] | –0.22*** | –0.28** | –0.38*** | –0.43*** | –0.45*** |
| (0.06) | (0.09) | (0.09) | (0.11) | (0.11) | |
| participantHigh:participantDec. | - | 0.11 | - | 0.1 | 0.12 |
| (0.12) | (0.12) | (0.12) | |||
| partnerHigh:participantDecided | - | - | 0.29* | 0.28* | 0.31** |
| (0.12) | (0.12) | (0.12) | |||
| participantHigh:partnerHigh | - | - | - | - | 0.58*** |
| (0.12) | |||||
| R2 (ord) | 0.28 | 0.281 | 0.285 | 0.285 | 0.304 |
| R2 (adj) | 0.278 | 0.278 | 0.282 | 0.281 | 0.299 |
| N | 876 | 876 | 876 | 876 | 876 |
Appendix 1—table 3
Details of clusters with activation varying as a function of choice and condition, during the decision phase (output of second-level SPM model plus Cohen’s d for each cluster).
| Anatomy | Size (N vox.) | T | d | Z | MNI | ||
|---|---|---|---|---|---|---|---|
| x | y | z | |||||
| Risky > safe | |||||||
| VStriatum R | 385 | 7.14 | 0.79 | 6.78 | 10 | 12 | −4 |
| VStriatum R | - | 5.28 | - | 5.12 | 14 | 22 | 4 |
| VStriatum L | 393 | 6.15 | 0.62 | 5.92 | –10 | 8 | −6 |
| Social > solo | |||||||
| Precuneus R | 737 | 5.72 | 0.63 | 5.53 | 0 | –62 | 38 |
| Precuneus L | - | 5.17 | - | 5.02 | –10 | –56 | 34 |
| Precuneus L | - | 4.91 | - | 4.79 | −2 | –54 | 34 |
| Angular L (TPJ) | 320 | 4.50 | 0.55 | 4.40 | –34 | –58 | 26 |
| Temporal Sup L | - | 3.59 | - | 3.54 | –52 | –56 | 20 |
| Medial PFC R | 302 | 3.97 | 0.54 | 3.90 | 4 | 52 | 22 |
| Medial PFC L | - | 3.97 | - | 3.90 | −6 | 58 | 22 |
| Medial PFC L | - | 3.63 | - | 3.57 | −6 | 54 | 32 |
Appendix 1—table 4
Linear mixed model regressions on BOLD response parameter estimates obtained during the decision phase – response during choice.
The parameter estimates of all voxels of the ROIs identified using the contrasts Risky > Safe and Social > Solo (see above and main text) were fitted with linear mixed models. The parameters of the best-fitting model (lowest BIC) for each ROI are reported below. We note here that the Run factor and interactions with it had significant effects in several ROIs, which shows that some of the effects reported varied across runs. However, for the sake of brevity, we will not discuss these results further. ***p < 0.001; **p < 0.01; *p < 0.05.
| VStria L | VStria R | MPFC | Precun | TPJ L | |
|---|---|---|---|---|---|
| (Intercept) | –0.78*** | –1.52*** | 1.18*** | 1.39*** | 0.94*** |
| (0.13) | (0.13) | (0.18) | (0.18) | (0.14) | |
| choice | –0.26*** | 0.78*** | –0.38*** | 0.97*** | 0.55*** |
| (0.08) | (0.07) | (0.09) | (0.07) | (0.08) | |
| condition | 0.01 | 0.34*** | –0.63*** | –0.40*** | –0.26*** |
| (0.03) | (0.02) | (0.03) | (0.02) | (0.03) | |
| run | 0.32*** | 0.96*** | –0.26*** | 0.30*** | 0.09* |
| (0.04) | (0.03) | (0.04) | (0.03) | (0.04) | |
| choice:condition | 0.47*** | 0.04 | 0.12** | –0.30*** | –0.24*** |
| (0.04) | (0.03) | (0.04) | (0.03) | (0.04) | |
| choice:run | 0.33*** | –0.29*** | 0.09 | –0.56*** | –0.37*** |
| (0.05) | (0.05) | (0.06) | (0.05) | (0.05) | |
| condition:run | –0.01 | –0.22*** | 0.17*** | –0.02 | –0.02 |
| (0.02) | (0.02) | (0.02) | (0.01) | (0.02) | |
| choice:condition:run | –0.18*** | 0.05* | –0.01 | 0.16*** | 0.12*** |
| (0.02) | (0.02) | (0.03) | (0.02) | (0.02) | |
| R2 (ord) | 0.136 | 0.158 | 0.208 | 0.170 | 0.175 |
| R2 (adj) | 0.136 | 0.158 | 0.208 | 0.170 | 0.175 |
Appendix 1—table 5
Linear mixed model regressions on BOLD response parameter estimates obtained during the decision phase – difference between Risky and Safe choices, Social vs. Solo.
To better understand the choice:condition interaction, which was significant in all ROIs except the right striatum, we subtracted the response to safe choices from the response to risky choices for the four remaining ROIs and submitted these differences to additional linear mixed models, as above. The first model contained a factor socialVsSolo, in which data from the social condition were weighted positively, and trials in the solo condition were weighted negatively. As above, we tested these models both with and without the factor Run and associated interaction, and we report the best-fitting model in the table below: a dash (‘-’) in the row displaying parameters for the run and socialVsSolo:run regressors indicates that the model without factor run was better-fitting for this ROI.
| VStria L | MPFC | Precun | TPJ L | |
|---|---|---|---|---|
| (Intercept) | 0.67*** | –0.03 | 0.38*** | 0.07 |
| (0.11) | (0.10) | (0.11) | (0.09) | |
| socialVsSolo | –0.47*** | –0.10*** | 0.30*** | 0.24*** |
| (0.03) | (0.01) | (0.02) | (0.03) | |
| run | –0.03* | - | –0.24*** | –0.14*** |
| (0.02) | (0.01) | (0.01) | ||
| socialVsSolo:run | 0.18*** | - | –0.16*** | –0.12*** |
| (0.02) | (0.01) | (0.02) | ||
| R2 (ord) | 0.085 | 0.075 | 0.068 | 0.091 |
| R2 (adj) | 0.085 | 0.075 | 0.067 | 0.091 |
| N | 94,320 | 72,480 | 176,880 | 76,800 |
Appendix 1—table 6
Linear mixed model regressions on BOLD response parameter estimates obtained during the decision phase – difference between Risky and Safe choices, Social vs. Partner.
Finally, we repeated this analysis with models containing a factor socialVsPartner, in which data from the social condition were weighted positively, and trials in the partner condition were weighted negatively. Here again, we report the best-fitting model from the versions with and without the factor run.
| VStria L | MPFC | Precun | TPJ L | |
|---|---|---|---|---|
| (Intercept) | 0.61*** | –0.03 | 0.38*** | 0.07 |
| (0.11) | (0.10) | (0.11) | (0.09) | |
| socialVsPartner | –0.07*** | 0.23*** | 0.39*** | 0.07** |
| (0.01) | (0.01) | (0.02) | (0.03) | |
| run | - | - | –0.24*** | –0.14*** |
| (0.01) | (0.01) | |||
| socialVsPartner:run | - | - | –0.20*** | 0.01 |
| (0.01) | (0.02) | |||
| R2 (ord) | 0.090 | 0.225 | 0.233 | 0.228 |
| R2 (adj) | 0.090 | 0.225 | 0.233 | 0.228 |
| N | 94,320 | 72,480 | 176,880 | 76,800 |
Appendix 1—table 7
Details of clusters with higher activation during risky vs. safe outcomes (second-level SPM model, with Cohen’s d for each cluster).
Note: A dash (‘-‘) in the Size or d column indicates that the peak reported on that line is part of a cluster whose centre is the next peak without dash listed above it.
| Anatomy | Size (N v.) | T | d | Z | x | y | z |
|---|---|---|---|---|---|---|---|
| Insula R | 1586 | 11.87 | 2.27 | Inf | 30 | 22 | –10 |
| - | 10.51 | - | Inf | 42 | 22 | −8 | |
| - | 7.11 | - | 6.99 | 50 | 22 | 6 | |
| Insula L | 1164 | 11.8 | 1.98 | Inf | –30 | 20 | –10 |
| - | 6.15 | - | 6.07 | –50 | 16 | 8 | |
| Dorsomedial_PFC | 2962 | 9.70 | 2.38 | Inf | 2 | 42 | 36 |
| - | 8.31 | - | Inf | 2 | 20 | 60 | |
| - | 7.99 | - | 7.81 | 4 | 40 | 20 | |
| Temp_Mid_R (STS) | 80 | 6.06 | 1.14 | 5.98 | 48 | –24 | −8 |
| - | 4.93 | - | 4.89 | 50 | –34 | −2 | |
| VStria_L | 30 | 5.73 | 1.23 | 5.66 | –10 | 0 | −6 |
| VStria_R | 40 | 5.68 | 1.18 | 5.62 | 8 | 4 | 2 |
| Parietal_Inf_R | 193 | 5.64 | 1.55 | 5.58 | 40 | –48 | 46 |
| - | 5.12 | - | 5.07 | 36 | –60 | 54 | |
| - | 5.09 | - | 5.05 | 48 | –36 | 48 | |
| DSL_PFC_R | 56 | 5.51 | 1.38 | 5.46 | 42 | 38 | 24 |
| Parietal_Inf_L | 64 | 5.27 | 1.25 | 5.21 | –46 | –44 | 46 |
| - | 5.03 | - | 4.99 | –38 | –42 | 38 |
Appendix 1—table 8
Linear mixed model regressions on BOLD response parameter estimates obtained during the outcome phase – Risky minus Safe outcomes.
The parameter estimates of all voxels of the nine ROIs identified using the contrasts Risky > Safe outcome (see main Text) were fitted with linear mixed models. The parameters of the best-fitting model (lowest BIC) for each ROI are reported below. Social was a dummy variable with the value of 1 for the Social condition and 0 for the Partner condition; LowOutcome was a dummy with the value of 1 for Low lottery outcome and 0 for High lottery outcome; Run was a dummy with the value of 1 for run 1 and 2 for run 2. Subject was the only random factor. We note here that the Run factor and interactions with it were significant in several ROIs, which indicates that some of the effects reported varied across runs. However, for the sake of brevity, we will not discuss these results further. ***p < 0.001; **p < 0.01; *p < 0.05; ^p < 0.1.
| VStriaL | VStriaR | InsulaL | InsulaR | STS_R | |
|---|---|---|---|---|---|
| (Intercept) | 1.79*** | 1.12*** | 0.73*** | 0.52*** | 1.09*** |
| (0.25) | (0.27) | (0.14) | (0.15) | (0.18) | |
| Social | –1.30*** | –0.68*** | 0.42*** | 0.42*** | 0.21^ |
| (0.22) | (0.08) | (0.04) | (0.03) | (0.12) | |
| LowOutcome | –1.14*** | –0.12 | 0.83*** | 0.58*** | 0.51*** |
| (0.22) | (0.08) | (0.04) | (0.03) | (0.12) | |
| Run | –0.82*** | - | 0.18*** | 0.34*** | 0.40*** |
| (0.10) | (0.02) | (0.02) | (0.06) | ||
| Social:LowOutcome | 0.95** | 0.77*** | –0.76*** | –0.25*** | –0.81*** |
| (0.31) | (0.12) | (0.06) | (0.05) | (0.18) | |
| Social:Run | 0.89*** | - | –0.30*** | –0.29*** | –0.67*** |
| (0.14) | (0.03) | (0.02) | (0.08) | ||
| LowOutcome:Run | 0.60*** | - | –0.75*** | –0.43*** | –0.66*** |
| (0.14) | (0.03) | (0.02) | (0.08) | ||
| Social:LowOutcome:Run | –0.30 | - | 0.80*** | 0.30*** | 0.99*** |
| (0.20) | (0.04) | (0.03) | (0.11) | ||
| R2 (ord) | 0.200 | 0.201 | 0.086 | 0.099 | 0.170 |
| R2 (adj) | 0.200 | 0.201 | 0.086 | 0.099 | 0.170 |
| N | 9600 | 12,800 | 372,480 | 507,520 | 25,600 |
| ParL | ParR | dmPFC | FrontR | ||
| (Intercept) | –0.22 | 0.96*** | 0.73*** | –0.22 | |
| (0.21) | (0.22) | (0.10) | (0.21) | ||
| Social | 0.14 | –0.60*** | –0.48*** | –0.07 | |
| (0.13) | (0.09) | (0.02) | (0.15) | ||
| LowOutcome | 0.74*** | –0.81*** | 0.16*** | –0.80*** | |
| (0.13) | (0.09) | (0.02) | (0.15) | ||
| Run | 0.94*** | 0.69*** | 0.24*** | 0.90*** | |
| (0.06) | (0.04) | (0.01) | (0.07) | ||
| Social:LowOutcome | 0.12 | 2.17*** | 0.69*** | 2.05*** | |
| (0.18) | (0.13) | (0.03) | (0.22) | ||
| Social:Run | –0.29*** | 0.00 | 0.18*** | –0.23* | |
| (0.08) | (0.06) | (0.01) | (0.10) | ||
| LowOutcome:Run | –0.54*** | 0.41*** | –0.17*** | 0.53*** | |
| (0.08) | (0.06) | (0.01) | (0.10) | ||
| Social:LowOutcome:Run | 0.26* | –0.88*** | –0.33*** | –1.21*** | |
| (0.12) | (0.08) | (0.02) | (0.14) | ||
| R2 (ord) | 0.255 | 0.220 | 0.063 | 0.218 | |
| R2 (adj) | 0.255 | 0.220 | 0.063 | 0.218 | |
| N | 20,480 | 61,760 | 947,840 | 17,920 | |
Appendix 1—table 9
Linear mixed model regressions on BOLD response parameter estimates obtained during the outcome phase – response during LowOutcomes.
To identify regions likely to be involved in the guilt effect, we focused on the regions engaged when participants rather than their partner made the choice, that is, the regions responding significantly more to the Social than the Partner condition. Of these regions, the insulae and the right middle temporal cortex also showed a significant Social:LowOutcome interaction. To better understand this interaction in these three ROIs, we ran additional models to test the effect of the Social compared to the Partner condition on the responses to Low lottery outcomes only, and on their response difference between Low and High lottery outcomes. The results for the response to low lottery outcomes were:
| InsulaL | InsulaR | MidTempR | |
|---|---|---|---|
| (Intercept) | 0.70*** | 0.95*** | 1.22*** |
| (0.18) | (0.18) | (0.18) | |
| Social | 0.41*** | 0.18*** | –0.12** |
| (0.01) | (0.01) | (0.04) | |
| R2 (ord) | 0.150 | 0.148 | 0.231 |
| R2 (adj) | 0.150 | 0.148 | 0.231 |
| N | 186,240 | 253,760 | 12,800 |
Appendix 1—table 10
Linear mixed model regressions on BOLD response parameter estimates obtained during the outcome phase – difference LowOutcome – HighOutcome.
The results of the models fitted to the response difference (response to Low lottery outcomes minus response to High lottery outcomes) were:
| InsulaL | InsulaR | MidTempR | |
|---|---|---|---|
| (Intercept) | –0.30 | –0.07 | –0.48* |
| (0.19) | (0.16) | (0.23) | |
| Social | 0.44*** | 0.19*** | 0.67*** |
| (0.02) | (0.01) | (0.04) | |
| R2 (ord) | 0.116 | 0.091 | 0.253 |
| R2 (adj) | 0.116 | 0.091 | 0.253 |
| N | 186,240 | 253,760 | 12800 |
Appendix 1—table 11
Participant’s judgments of their partner.
| Study 1 | Study 2 | |
|---|---|---|
| How sympathetic did you find them? | 8.56 (1.7) | 9.18 (1.17) |
| How well could you cooperate with them? | 8.35 (1.29) | 8.68 (1.18) |
| How honest did they seem? | 9.05 (1.11) | 9.34 (1.10) |
| How open were they? | 8.43 (1.89) | 9.05 (1.12) |
| How sociable were they? | 8.63 (1.61) | 9.28 (1.18) |
-
Scale used was 1 (minimum) to 10 (maximum). Mean and standard deviations are reported.
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Contributions of insula and superior temporal sulcus to interpersonal guilt and responsibility in social decisions
eLife 14:RP105391.
https://doi.org/10.7554/eLife.105391.3
