Document at v1.0.0
10.7554/eLife.105391
d4414da8ad2d2ff2a8ada28e4d54c40229c816911900a197eb0886411ca1f3e4 · where else it appears
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"value": "Imagine that you go out to dinner with a friend and it is your turn to choose the restaurant. You can choose between two restaurants: one that you both know well, with very predictable food of good quality, and a new restaurant that neither you nor your friend has eaten in before. You decide to try the new one. Unfortunately, while your dish is nice, your friend’s turns out to be worse than the known restaurant’s dishes. How would you feel? Would you feel differently if it had been your friend’s turn to choose the restaurant? Being responsible for such suboptimal outcomes for others can induce a feeling of interpersonal guilt, formally described as a negative emotional response to harming someone with whom one has a positive social bond "
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"value": ". However, we are interested in the mechanisms involved in a specific, as yet understudied aspect of guilt: the kind that can result from everyday choices that expose others to an uncertain outcome. To study this, we built on findings from experimental social decision-making tasks."
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"value": "Several studies have used games from behavioural economics or perceptual tasks linked to punishment of a partner. For example, participants behaving in accord with an economic definition of guilt aversion during a trust game showed activation in insula, supplementary motor area, dorsolateral prefrontal cortex (dlPFC) and temporal parietal junction "
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"value": ". The guilt context increased advantageous-inequity aversion and decreased disadvantageous-inequity aversion and affected the neural correlates of these inequities (respectively, mentalizing-related and emotion- and conflict-related regions). Finally, another study contrasted guilt and shame: confederates either experienced economic loss due to bad participant advice (participants experienced guilt), or experienced no loss when participants’ bad advice was correctly refused by the confederate (participants experienced shame) "
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"value": ". Guilt relative to shame activated supramarginal gyrus and temporo-parietal junction as well as orbitofrontal cortex, ventrolateral, and dorsolateral prefrontal cortex. Multivariate analyses revealed that guilt could be distinguished from shame based on activation in ventral anterior cingulate cortex and dorsomedial prefrontal cortex."
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"value": ". In this elegant study, participants could delegate their choice between a risky and a safe option to a group or decide themselves; in separate conditions, the choice affected payoff either only for the participant or for all group members. Interestingly, most participants displayed responsibility aversion, and this effect could not be explained by guilt, suggesting people associate a psychological cost with assuming responsibility for others’ outcomes. However, in many situations, one does not have the possibility to delegate a decision to others, and choosing a risky option in such a case may evoke guilt when the outcome for others is negative. The neural mechanisms underlying guilt evoked during such situations of social responsibility are still unknown. These questions are the subject of the present study. Similarly to Edelson et al.’s and more recent studies "
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"value": ". This allowed us to fit computational models to happiness data and search for networks sensitive to reward prediction errors resulting from participant or partner choices. We ran two experiments, Study 1 outside the MRI scanner and Study 2 during fMRI, with separate groups of participants."
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"value": "We analysed our behavioural data using several complementary methods: choices were modelled with mixed-effects regressions serving as manipulation checks; risk preferences expressed in choices were assessed using a comprehensive expected utility model as well as with a simpler, more robust ‘risk premium’ approach; and happiness data were fitted, in addition to the computational models, with several linear mixed models (LMMs) to assess the impact of both the participant’s and their partner’s rewards, the impact of agency and their interactions. Inspired by findings reported in previous neuroimaging of social emotions, we also used several methods to analyse our fMRI data, including conventional methods (both region-of-interest and mass univariate); mixed-effects regression models; computational model-based analyses (inspired by, e.g., "
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"value": ". The behavioural modelling is thus complemented by neuroimaging analyses that offer insight about both the activity in regions associated with guilt as well as their place in a wider network, providing an in-depth comprehensive analysis of the mechanisms behind guilt evoked by social responsibility."
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"value": "). Fitting several models revealed that lottery outcomes influenced happiness, and that the identity of the decision-maker played a role ("
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"value": " = 0.33, 95% CI = [0.10 0.56]). When the partner received the low lottery outcome, participant happiness was lower when they rather than the partner had chosen the lottery ("
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"value": "Figure 3D, H"
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"value": "). When the partner received the low lottery outcome of a participant-chosen lottery, they would presumably feel let down because the participant could have chosen the better safe option. As participants knew this, they were likely to feel ‘simple guilt’ "
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"value": ". This behavioural effect (difference in happiness obtained when the partner received low lottery outcomes after participant rather than partner choices) is thus compatible with ‘simple guilt’, and we will thus refer to it as ‘guilt effect’. The ‘guilt effect’ occurred whether the participant received the high lottery outcome (Study 1: "
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"value": " = 33.5). In Study 2, we will compare individual ‘guilt effect’ values to individual guilt-related brain activation patterns (see end of the section on BOLD signal results). Responsibility for choices did not influence happiness following positive lottery outcomes for the partner (both studies, all |"
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"value": "In sum, in both studies, we found evidence that participants’ happiness was influenced by the outcomes of choices for their partner, especially by outcomes resulting from participant decisions, that is, partner outcomes for which participants were responsible. Within these outcomes, participants felt worse following low lottery outcomes for the partner if those outcomes were consequences of their own choice rather than the partner’s, which we interpret as interpersonal guilt."
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"value": "To assess whether activations in these five regions were sensitive to the interaction between choice and condition using a sensitive method, we analysed the responses in all the voxels in these regions using LMMs. Our model included factors "
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"value": "In our definition, guilt occurs due to responsibility for low lottery outcomes for the partner. To search for regions involved in this situation, we again analysed voxel responses using LMMs. Our first model included factors "
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"value": "). Thus, activation in our insula ROIs increased in situations during which participants experienced guilt for low outcomes impacting their partner, compared to similar outcomes resulting from the partner’s choices."
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"value": "To attempt to confirm these results with a classic fine-grained mass-univariate voxel-wise analysis, we searched, within regions responding more to outcomes of risky compared to safe choices, for higher responses to low lottery outcomes for the partner following participant choices compared to the same outcomes resulting from partner choices. We found a weak response in a small cluster within the left anterior insula (peak "
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"value": "). Given the documented association between anterior insula and guilt (see Introduction), we proceeded to test whether this result survived correction for family-wise errors due to multiple comparisons restricted to the left anterior insula grey matter [defined anatomically and thus independently from our findings, as the anterior short gyrus, middle short gyrus, and anterior inferior cortex in an anatomical maximum probability map "
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"value": "). We thus used this model to search for voxels responding more to partner reward prediction errors resulting from participant rather than partner choices, within the regions sensitive to outcomes of risky choices. We found this effect in one cluster within the left STS (p"
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"value": ". Connectivity between aIns and the right IFG varied depending on whether participants chose the risky or safe option and whether only the participant or both themselves and the partner were affected by the outcome of this choice, suggesting that this part of prefrontal cortex is sensitive to guilt-related information during social choices. Computational models explained trial-by-trial variations in momentary happiness during the task; the best-fitting model differentiated between partner reward prediction errors resulting from participant and partner choices, indicating that the impact of outcomes for the partner on participants’ happiness varied depending on who chose. This confirms the importance of responsibility in determining the emotional consequences of risky social choices. fMRI analyses based on this computational model identified a left STS region responding more to partner reward prediction errors resulting from participant rather than partner choices. This suggests a critical role of the STS in monitoring the consequences of one’s risky decisions on others, an essential social cognitive function. A last analysis showed that connectivity between this STS region and the right IFG varied depending on condition and choice, suggesting that the information processed in the STS is relayed to the IFG during social decisions. Our findings add to current understanding of the neural mechanisms underlying responsibility for, and guilt evoked by, the outcomes on others of social decisions under economic risk."
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"value": ". Given the roles of the left STS/TPJ and aIns in guilt and social decisions, it is not surprising to find information exchanges between these structures. More experiments investigating specific computations in which these regions are involved (as in, e.g., "
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"value": "There are several limitations to our study. The first limitation is that the partner’s decisions were in fact taken by an algorithm. This was not communicated to the participants and was thus a case of deception, which is inadmissible in behavioural economics. To our defence, this study was planned as a social neuroscience study and was executed before we had taken into account the practices of behavioural economists. However, this approach did have the advantage of eliminating potentially complex iterative reciprocal influences of decisions and outcomes between the players, which would have led to much more complicated emotional states and decisions that would have been difficult to understand and model. Thus, from an analytical point of view, our approach might have actually allowed a cleaner comparison between decisions and outcomes than if the partner had really made the decisions. The fact that partner outcomes also influenced participants’ momentary happiness demonstrates that participants were not emotionally detached from the consequences of their actions on their partner. This finding is, of course, essential for the validity of our results and suggests that the effects could get stronger with more direct interactions. Therefore, although we will abstain from using deceptive practices when pursuing this research, we believe our findings to be valid."
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"value": ". The ‘neurometrics’ analysis of our guilt-related activation maps showed a significant similarity to a published guilt-related brain signature "
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"value": "Several open questions remain at the end of this study. As discussed above, asking participants directly about which emotions they have felt during the different stages of this task would allow us to link subjective experience with our analytical measures. Testing more participants would allow us to assess the impact of inter-individual variations in personality traits on the experience as well as the behavioural and neural correlates of guilt and responsibility. Using more trials in the experiment would allow separate modelling of risk preferences in gain and loss trials in each experimental condition using expected utility models, and could allow testing whether changes in momentary happiness affect subsequent choices. Varying partner identities (friends, stranger, and artificial agent) could reveal the impact of social discounting on guilt and responsibility. Functional connectivity analyses could also be performed for the data obtained in the feedback part of the paradigm. In sum, we believe that this experimental approach lends itself very well to the study of several aspects of social emotions."
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"value": " condition; gain, loss and mixed trials) were presented in randomized order, with the constraint that there could not be more than two trials of the same condition in a row. Participants had unlimited time to choose between the safe or risky option. Decisions were displayed for 2 s and lottery outcomes for 2.5 s. Trials were separated in time by an inter-stimulus interval (ISI) of 1–2 s drawn randomly from a gamma distribution."
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"value": "Every two trials, one ISI after the outcome of the previous trial, participants were asked ‘How happy are you right now?’. They could respond by selecting a value on a scale from ‘very happy’ (right) to ‘very unhappy’ (left) by moving a cursor with a button press. The start position of the cursor was the midpoint of the scale, and the scale had 100 selectable options. For analysis, happiness ratings were "
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"value": "In Study 2, participants performed two sessions of the experiment described above inside the fMRI scanner. All parameters were identical except that ISIs varied from 3 to 11 s (drawn randomly from a gamma distribution). In Study 2, instead of playing against another same-sex participant, participants played either against experimenter MG or TW depending on sex (participant and partner were always of same sex) who were outside the scanner."
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"value": " for each participant and condition, defined as the difference between the expected value of a lottery and its certainty equivalent (positive values indicate risk aversion, negative values risk seeking). The certainty equivalent is the smallest amount an individual would be indifferent to spending on a lottery. We estimated the certainty equivalent of the lotteries by fitting to each participant’s decisions in each condition the logistic regression model with residual "
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"value": "Next, we estimated risk attitudes using expected utility theory. In this approach, an agent chooses between two options by comparing their expected utility values. Utility values reflect the agent’s subjective values or preferences for a set of choice options, revealed by the agent’s choices. Expected utility theory uses utility functions to describe the agents’ preferences, including that agent’s attitude towards risk. One simple model of risk aversion termed Constant Absolute Risk Aversion (CARA) assumes an exponential utility function:"
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"code": "\\begin{document}$$\\displaystyle \\begin{array}{ll} Happiness\\left (t\\right) &=w_{1}\\sum \\limits_{j=1}^{t}\\gamma ^{t-j}CR_{j}+w_{2}\\sum \\limits_{j=1}^{t}\\gamma ^{t-j}EV_{j}+w_{3}\\sum \\limits_{j=1}^{t}\\gamma ^{t-j}sRPE_{j} \\\\ &+w_{4}\\sum \\limits_{j=1}^{t}\\gamma ^{t-j}social\\_ pRPE_{j}+ w_{5}\\sum \\limits_{j=1}^{t}\\gamma ^{t-j}partner\\_ pRPE_{j}\\end{array}$$\\end{document}",
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"value": "Imaging data were collected on a 3T Siemens TRIO MRI system (Siemens AG, Erlangen, Germany) with a Siemens 32-channel head coil. Functional data were acquired using a T2* echo-planar imaging (EPI) BOLD sequence, with a repetition time (TR) of 2500 ms, an echo time (TE) of 30 ms, 37 slices with voxel sizes of 2 × 2 × 3 mm, a flip angle of 90°, a field of view of 192 mm and PAT two acceleration. To exclude participants with apparent brain pathologies and facilitate normalization of the functional data, a high-resolution T1-weighted structural image was acquired, with a TR of 1660 ms, a TE of 2540 ms, 208 slices with voxel sizes of 0.8 × 0.8 × 0.8 mm and a field of view of 256 mm. Data were then pre-processed and analysed as in previous studies (e.g., "
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"value": " using standard procedures in SPM12. The first five volumes of each functional time series were discarded to allow for T1 signal equilibration. The structural image of each participant was coregistered with the mean functional image of that participant. Functional images were corrected for head movement between scans by a 6-parameter affine realignment to the first image of the time-series and then re-realigned to the mean of all images. The structural scan of each participant was spatially normalized to the current Montreal Neurological Institute template (MNI305) by segmentation and non-linear warping to reference tissue probability maps in MNI space, and the resulting normalization parameters were applied to all functional images which were then resampled at 2 × 2 × 2 mm voxel size, then smoothed using an 8 mm full width at half maximum Gaussian kernel. Time series were de-trended by the application of a high-pass filter (cut-off period, 128 s)."
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"value": "The pre-processed functional data were analysed using a standard two-stage approach based on the general linear model (GLM) implemented in SPM12 running in MATLAB, with corrections for multiple comparisons. Individual participants’ data were modelled with fixed effects models, and their summary data were entered in random effects models for group statistics and inferences at the population level."
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"value": "The fixed effects models implemented a mass univariate analysis applied to the pre-processed data. The first GLM (GLM1) included the following event types for each session: presentation of choice options, safe choice, risky choice (=lottery), receipt of outcome of safe option, all modelled separately for the "
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"value": " conditions, and receipt of low lottery outcome by the participant; a final regressor modelled the button presses that occurred when reporting momentary happiness. All events were modelled as stick functions at the time of occurrence and convolved by SPM using the canonical haemodynamic function. In addition, we created another GLM (GLM2) with regressors designed to identify brain regions whose activation reflected the variables of the best-fitting computational model (see above). This model contained regressors coding the receipt of certain rewards ("
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"value": "). Those regressors were applied as follows to each experimental session of each participant: (1) fitting the computational model to the momentary happiness data; (2) creation of a series of stick functions representing the time of occurrence of the modelled events during the scan, with stick magnitude determined by the fitted value calculated by the model; and (3) convolution of the series of stick functions with the canonical BOLD function (as implemented in SPM). The 5 computational regressors were entered as covariates into each participant’s model, which also contained one regressor modelling the button presses that occurred when reporting momentary happiness. All GLMs contained six motion-correction parameters included as regressors of no-interest to account for motion-related artefacts. Regression coefficients (parameter estimates) were estimated for each regressor in each voxel of each participant’s brain."
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"value": " for SPM to run seed-to-voxel functional connectivity analyses. We ran two models for each participant, both based on GLM1, with the following seeds: the left insula cluster more sensitive to the outcomes of "
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"string": "As the authors already note, they did not directly ask participants to report their feelings of guilt. The authors clearly describe this limitation, and also note that in addition to guilt, other emotions like empathy could also be at play in interpersonal decisions. Despite this limitation, this study provides insights into the neural and behavioral mechanisms of responsibility and guilt in social decision making, and how they influence behavior."
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{
"content": [
{
"type": "Text",
"value": {
"string": "The authors aimed to characterize neurocomputational signals underlying interpersonal guilt and responsibility. Across two studies, one behavioral and one fMRI, participants made risky economic decisions for themselves or for themselves and a partner; they also experienced a condition in which the partners made decisions for themselves and the participant. The authors also assessed momentary happiness intermittently between choices in the task. Briefly, results demonstrated that participants' self-reported happiness decreased after disadvantageous outcomes for themselves and when both they and their partner were affected; this effect was exacerbated when participants were responsible for their partner's low outcome, rather than the opposite, reflecting experienced guilt. Consistent with previous work, BOLD signals in the insula correlated with experienced guilt, and insula-right IFG connectivity was enhanced when participants made risky choices for themselves and safe choices for themselves and a partner."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Strengths:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "This study implements an interesting approach to investigating guilt and responsibility; the paradigm in particular is well-suited to approach this question, offering participants the chance to make risky v. safe choices that affect both themselves and others. I appreciate the assessment of happiness as a metric for assessing guilt across the different task/outcome conditions, as well as the implementation of both computational models and fMRI."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We thank Reviewer 1 for their positive assessment of our manuscript."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "Weaknesses:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "In spite of the overall strengths of the study, I think there are a few areas in which the paper fell a bit short and could be improved."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We thank Reviewer 1 for their comments, which we have used to improve our manuscript. We hope that these changes address the issues raised by the Reviewer."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(1) While the framing and goal of this study was to investigate guilt and felt responsibility, the task implemented - a risky choice task with social conditions - has been conducted in similar ways in past research that were not addressed here. The novelty of this study would appear to be the additional happiness assessments, but it would be helpful to consider the changes noted in risk-taking behavior in the context of additional studies that have investigated changes in risky economic choice in social contexts (e.g., Arioli et al., 2023 Cerebral Cortex; Fareri et al., 2022 Scientific Reports)."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We certainly agree that several previously published studies have relied on risky choice tasks with social conditions. In this revised version, we now mention these two studies in the substantially revised Introduction."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(2) The authors note they assessed changes in risk preferences between social and solo conditions in two ways - by calculating a 'risk premium' and then by estimating rho from an expected utility model. I am curious why the authors took both approaches (this did not seem clearly justified, though I apologize if I missed it). Relatedly, in the expected utility approach, the authors report that since 'the number of these types of trials varied across participants', they 'only obtained reliable estimates for [gain and loss] trials in some participants' - in study 1, 22 participants had unreliable estimates and in study 2, 28 participants had unreliable estimates. Because of this, and because the task itself only had 20 gains, 20 losses, and 20 mixed gambles per condition, I wonder if the authors can comment on how interpretable these findings are in the Discussion. Other work investigating loss aversion has implemented larger numbers of trials to mitigate the potential for unreliable estimates (e.g., Sokol-Hessner et al., 2009)."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that we have not clearly justified why we have taken two approaches to assess risk preferences. In short, while the expected utility approach is a more comprehensive method to model a participant’s choices, we had not sufficiently considered the need for the large number of trials required to fit such models when designing our experiment. Calculating the risk premium was the less comprehensive, simpler alternative that we could calculate for all participants. We have now mentioned this fact in the Results section. As the only difference in risk aversion across conditions was found in Study 1 using the expected utility method, which could only be successfully applied in a minority of participants, we believe that this difference should not be taken as a strong finding. We have now mentioned this fact in the revised Discussion."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(3) One thing seemingly not addressed in the Discussion is the fact that the behavioral effect did not replicate significantly in study 2."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that we had not sufficiently discussed the fact that there were (slight but significant) differences in risk preferences between the Solo and Social conditions in Study 1 but not in Study 2. We now do so in the revised Discussion, and write the following:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“Participants made slightly more risk-seeking choices when deciding for themselves than for both themselves and the partner in Study 1, but this difference disappeared in Study 2. The "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "ρ"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " parameter on which this finding in Study 1 is based could only be estimated in a minority of participants due to a relatively low number of trials, which suggests that this finding may not be very reliable. The simpler and more robust method (evaluation of a risk premium) showed no difference in risk aversion across conditions in either study. Overall, we believe that we do not have strong evidence of differences in risk preferences across conditions.”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(4) Regarding the computational models, the authors suggest that the Reponsibility and Responsibility Redux models provided the best fit, but they are claiming this based on separate metrics (e.g., in study 1, the redux model had the lowest AIC, but the responsibility only model had the highest R^2; additionally, the basic model had the lowest BIC). I am wondering if the authors considered conducting a direct model comparison to statistically compare model fits."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that we should run formal, direct model comparison tests. We now ran likelihood-ratio tests which showed that the Responsibility model was the best. We now report this in the Results section, just below Table 1:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“A likelihood ratio test (Equation 9) revealed that the Responsibility model fitted better than all the other models, including the "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Responsibility Redux"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " model (Study 1: all "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "LR"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " ≥ 47.36, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " < 0.0001; Study 2: all "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "LR"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " ≥ 77.83, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " < 0.0001).”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(5) In the reporting of imaging results, the authors report in a univariate analysis that a small cluster in the left anterior insula showed a stronger response to low outcomes for the partner as a result of participant choice rather than from partner choice. It then seems as though the authors performed small volume correction on this cluster to see whether it survived. If that is accurate, then I would suggest that this result be removed because it is not recommended to perform SVC where the volume is defined based on a result from the same whole-brain analysis (i.e., it should be done a priori)."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "As indicated in the manuscript, the small insula cluster centered at [-28 24 -4] and shown in Figure 4F survived corrections for multiple tests within the anatomically-defined anterior insula (based on the anatomical maximum probability map described in Faillenot et al., 2017), which is independent of the result of our analysis. Functionally defining the small volume based on the same data would indeed be circular and misleading “double-dipping”. We have most certainly NOT done this. The reason why we selected the anterior insula is because it is one of the regions most frequently associated with guilt (see the explanations in our Introduction, which refers for example to Bastin et al., 2016; Lamm & Singer, 2010; Piretti et al., 2023). Thus we feel that performing small-volume correction within the anatomically-defined anterior insula is a valid analysis. We fully acknowledge that, independently of any correction, the effect and the cluster are small. We now write:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“We found a weak response in a small cluster within the left anterior insula (peak "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "T"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 3.95, d = 0.59, 22 voxels, peak intensity at [-28 24 -4]; Figure 4F). Given the documented association between anterior insula and guilt (see Introduction), we proceeded to test whether this result survived correction for family-wise errors due to multiple comparisons restricted to the left anterior insula gray matter [defined anatomically and thus independently from our findings, as the anterior short gyrus, middle short gyrus, and anterior inferior cortex in an anatomical maximum probability map (Faillenot et al., 2017)]. This correction resulted in a p value of 0.024. This result, although it is only a small effect in a small cluster, is consistent with the mixed model analysis reported earlier.”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "Reviewer #2 (Public review):"
}
}
],
"type": "Strong"
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Summary"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "This manuscript focuses on the role of social responsibility and guilt in social decision-making by integrating neuroimaging and computational modeling methods. Across two studies, participants completed a lottery task in which they made decisions for themselves or for a social partner. By measuring momentary happiness throughout the task, the authors show that being responsible for a partner's bad lottery outcome leads to decreased happiness compared to trials in which the participant was not responsible for their partner's bad outcome. At the neural level, this guilt effect was reflected in increased neural activity in the anterior insula, and altered functional connectivity between the insula and the inferior frontal gyrus. Using computational modeling, the authors show that trial-by-trial fluctuations in happiness were successfully captured by a model including participant and partner rewards and prediction errors (a 'responsibility' model), and model-based neuroimaging analyses suggested that prediction errors for the partner were tracked by the superior temporal sulcus. Taken together, these findings suggest that responsibility and interpersonal guilt influence social decision-making."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Strengths"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "This manuscript investigates the concept of guilt in social decision-making through both statistical and computational modeling. It integrates behavioral and neural data, providing a more comprehensive understanding of the psychological mechanisms. For the behavioral results, data from two different studies is included, and although minor differences are found between the two studies, the main findings remain consistent. The authors share all their code and materials, leading to transparency and reproducibility of their methods."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "The manuscript is well-grounded in prior work. The task design is inspired by a large body of previous work on social decision-making and includes the necessary conditions to support their claims (i.e., Solo, Social, and Partner conditions). The computational models used in this study are inspired by previous work and build on well-established economic theories of decision-making. The research question and hypotheses clearly extend previous findings, and the more traditional univariate results align with prior work."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "The authors conducted extensive analyses, as supported by the inclusion of different linear models and computational models described in the supplemental materials. Psychological concepts like risk preferences are defined and tested in different ways, and different types of analyses (e.g., univariate and multivariate neuroimaging analyses) are used to try to answer the research questions. The inclusion and comparison of different computational models provide compelling support for the claim that partner prediction errors indeed influence task behavior, as illustrated by the multiple model comparison metrics and the good model recovery."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We thank the reviewer very much for their comprehensive description of our study and the positive assessment of our study and approach."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "Weaknesses"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "As the authors already note, they did not directly ask participants to report their feelings of guilt. The decrease in happiness reported after a bad choice for a partner might thus be something else than guilt, for example, empathy or feelings of failure (not necessarily related to guilt towards the other person). Although the patterns of neural activity evoked during the task match with previously found patterns of guilt, there is no direct measure of guilt included in the task. This warrants caution in the interpretation of these findings as guilt per see."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We fully agree that not directly asking participants about feelings of guilt is a clear limitation of our study. While we already mention this in our Discussion, we have expanded our discussion of the consequences on the interpretation of our results along the lines described by the reviewer in the revised manuscript. We would like to thank the reviewer for proposing these lines of thought, and have now made the following changes to the text:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "In the first paragraph of the discussion, we now write: “Being responsible for choosing a lottery that yielded a low outcome for a partner made our participants feel worse than witnessing the same outcome resulting from their partner’s choice, which we interpret as interpersonal guilt; although we note that we have not asked participants specifically about which emotion they felt in these situations."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Later on, in the third paragraph focusing on the anterior insula, we now write: “This replicates a large body of evidence associating aIns with feelings of guilt evoked during social decisions (see Introduction). Because we have neither asked our participants specifically what they felt in these situations, nor specifically whether they experienced guilt, we cannot exclude the possibility that they have instead or in addition felt empathy for their partner, a feeling of failure or bad luck, or some other emotion.”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "As most comparisons contrast the social condition (making the decision for your partner) against either the partner condition (watching your partner make their decision) or the solo condition (making your own decision), an open question remains of how agency influences momentary happiness, independent of potential guilt. Other open questions relate to individual differences in interpersonal guilt, and how those might influence behavior."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "How agency influences momentary happiness or variations thereof during the course of an experiment such as ours is an interesting question in itself. We now ran linear mixed models assessing agency (i.e. we compared happiness in conditions Solo & Social conditions vs. Partner condition), which revealed lower happiness in Solo and Social conditions (i.e. when it was the participant’s turn to decide) in both studies. This is interesting in itself and may reflect the drive behind responsibility aversion reported by Edelson et al.’s 2018 study: being assigned the role of the decider in a social setting may make people slightly unhappy, perhaps due to “weight of the responsibility”. We now report these findings in the Results section, including this proposed explanation; because we were not specifically interested in responsibility aversion, we do not discuss this further in the Discussion. The edited text is under the new subsection entitled ‘Momentary happiness: effects of agency, responsibility and guilt’, on page 12:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“Next, we assessed whether happiness varied depending on the participant’s agency (Social + Solo vs. Partner), and found happiness to be lower when the participant chose, independent of the outcome (Study 1: "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "t"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": "(3600) = -3.92, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 0.00009, β = -0.14, 95% CI = [-0.20 -0.07]; Study 2: "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "t"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": "(2870) = -6.07, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 0.000000001, β = -0.24, 95% CI = [-0.31 -0.16]). . This is interesting in itself and may reflect the drive behind responsibility aversion reported by Edelson et al.’s 2018 study: being assigned the role of the decider in a social setting may make people slightly unhappy, perhaps due to “weight of the responsibility”. To specifically search for a sign of interpersonal guilt, [...]”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Regarding individual differences: this is a very interesting topic that we have not addressed here due to the (relatively) small number of participants in our studies, but we might consider this for future follow-up studies, which we mention in the Discussion paragraph regarding open questions."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "This manuscript is an impressive combination of multiple approaches, but how these different approaches relate to each other and how they can aid in answering slightly different questions is not very clearly described. The authors could improve this by more clearly describing the different methods and their added value in the introduction, and/or by including a paragraph on implications, open questions, and future work in the discussion."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We thank the reviewer for their appreciation of our complementary approach, and agree that we had not sufficiently explained the reasons why we used several methods. We have now added a paragraph explaining this at the end of the Introduction (page 5):"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“We analysed our behavioural data using several complementary methods: choices were modelled with mixed-effects regressions serving as manipulation checks; risk preferences expressed in choices were assessed using a comprehensive expected utility model as well as with a simpler, more robust “risk premium” approach; and happiness data were fitted, in addition to the computational models, with several linear mixed models to assess the impact of both the participant’s and their partner’s rewards, the impact of agency and their interactions. Inspired by findings reported in previous neuroimaging of social emotions, we also used several methods to analyse our fMRI data, including conventional methods (both region-of-interest and mass univariate); mixed-effects regression models; computational model-based analyses (inspired by e.g. Konovalov et al., 2021; Rutledge et al., 2014); and functional connectivity (e.g. Edelson et al., 2018; Konovalov et al., 2021). The behavioural modelling is thus complemented by neuroimaging analyses that offer insight about both the activity in regions associated with guilt as well as their place in a wider network, providing an in-depth comprehensive analysis of the mechanisms behind guilt evoked by social responsibility.”"
}
}
],
"type": "Paragraph"
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{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "In addition, as suggested we added the following paragraph on open questions and future work in the Discussion:"
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“Several open questions remain at the end of this study. As discussed above, asking participants directly about which emotions they have felt during the different stages of this task would allow us to link subjective experience with our analytical measures. Testing more participants would allow us to assess the impact of inter-individual variations in personality traits on the experience as well as the behavioural and neural correlates of guilt and responsibility. Using more trials in the experiment would allow separate modelling of risk preferences in gain and loss trials in each experimental condition using expected utility models, and could allow testing whether changes in momentary happiness affect subsequent choices. Varying partner identities (friends, strangers, artificial agent) could reveal the impact of social discounting on guilt and responsibility. In sum, we believe that this experimental approach lends itself very well to the study of several aspects of social emotions.”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "However, taken together, this study provides useful insights into the neural and behavioral mechanisms of responsibility and guilt in social decision-making and how they influence behavior."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We thank the reviewer again for their appreciation of our work and hope that our revisions improved the manuscript."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "Recommendations for the authors:"
}
}
],
"type": "Strong"
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "Reviewer #1 (Recommendations for the authors):"
}
}
],
"type": "Strong"
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "The majority of my suggestions are in the public review, so I will not repeat them here. But in general, I like the paper, and in addition to my other comments, I think that there should be more discussion of the potential limitations of the study and conclusions that can be drawn. I also thought parts of the results were a little hard to follow, particularly in the 'momentary happiness' section. Perhaps an additional subsection here might help with flow."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that we could have discussed further the limitations of our study and the conclusions that can be drawn from it, which we have now done in the last paragraphs of the Discussion in this revised version."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "To improve the structure of the section on ‘momentary happiness’, we separated this section into two, entitled: ‘Momentary happiness: links to reward‘ and ‘Momentary happiness: effects of agency, responsibility and guilt’, which should facilitate the reading of this long section. We proceeded in a similar manner for the Choices section, which is now subdivided into ‘Choices: manipulation check’ and ‘Choices: risk preferences’. We believe that these changes have indeed improved the readability of our manuscript."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "Reviewer #2 (Recommendations for the authors):"
}
}
],
"type": "Strong"
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Overall, I believe this manuscript was well-designed, consists of extensive analyses, and provides interesting new insights into the mechanisms underlying social decision-making. I mostly have some clarifying questions and minor comments, which are described below."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "(1) Integration of prior findings in the first paragraphs of the Introduction. Although all the previous work described in the 2nd-5th paragraph introduction is interesting, it felt a bit like an enumeration of findings rather than an integrated introduction leading to the current research question. At the end of paragraph 5, it becomes clear how these findings relate to the current research question, but I believe it will improve the flow and readability of the introduction if this becomes clear earlier on."
}
}
],
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],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that we could have integrated the cited previous work into the Introduction so that the text builds up to the research question. We have now extensively reworked several paragraphs in the Introduction (pages 3-5) and hope that these changes have made it easier to follow."
}
}
],
"type": "Paragraph"
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{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(2) For the risk attitudes (Choices), you describe pooling the gains and losses and then comparing the social and solo conditions. I was wondering whether you also looked at potential differences between gains and losses (delta measure) for social versus the solo condition (so a comparison of the delta). Based on prior work, I can imagine that the difference in risk attitudes for gains and losses might differ when making decisions for yourself versus when you're doing it for a partner. In general, I was wondering how you explain these findings, as there is also a lot of work showing differences in risk-taking patterns for gains and losses."
}
}
],
"type": "Paragraph"
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],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that we could have compared delta measures between solo and social conditions. However, as we describe in the Results section and comment on in the Discussion, the relatively low number of trials made separate fitting of gain and loss trials across conditions difficult. While this question could thus be addressed in subsequent versions of our experiment with more trials, such a fine-grained analysis of the decisions was not the focus of our current study."
}
}
],
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{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(3) On page 11, you state: \"in particular the partner's reward prediction errors resulting from the participants' decisions, i.e. those pRPE for which participants were responsible.\" From the results described in the paragraph above, this doesn't become clear (e.g., there's no distinction made between social_pRPE and partner_pRPE in the text), as it only discusses differences in weights between pRPE and sRPE. I would recommend including some more information in the main text on these main modeling findings, so one doesn't have to go to the Supplemental Materials to understand them."
}
}
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],
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{
"content": [
{
"type": "Text",
"value": {
"string": "We did indeed fail to report these findings in the text! We thank the reviewer for pointing this out. We have now edited this passage as follows:"
}
}
],
"type": "Paragraph"
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{
"content": [
{
"type": "Text",
"value": {
"string": "“Crucially, we find here that the partner’s reward prediction errors (social_pRPE and partner_pRPE) contributed to explaining changes in participants’ momentary happiness: the Responsibility and ResponsibilityRedux models explained the data better than the models without these parameters (see Table 1). In particular, the partner’s reward prediction errors resulting from the participants’ decisions (social_pRPE), i.e. those pRPE for which participants were responsible, contributed to explaining our data (weights for social_pRPE were greater than 0: Responsibility model: Study 1: "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Z"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 2.85, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 0.004, Study 2: "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Z"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 3.26, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 0.001; "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Responsibility Redux"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " model: Study 1: "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Z"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 2.93, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 0.003, Study 2: "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Z"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 3.30, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 0.001; weights for social_pRPE tended to be higher than weights for partner_pRPE: Responsibility model: Study 1: "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Z"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 2.14, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 0.033; Study 2: "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Z"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 1.41, "
}
},
{
"content": [
{
"type": "Text",
"value": {
"string": "p"
}
}
],
"type": "Emphasis"
},
{
"type": "Text",
"value": {
"string": " = 0.16).”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(4) The functional connectivity findings seem to come out of nowhere and are not introduced or described anywhere prior in the manuscript. It is therefore not completely clear why you conducted these analyses, or what they add above and beyond previous analyses. Already introducing this method earlier on would fix that."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that we could have introduced functional connectivity analyses earlier in the text, particularly given the many previous studies in our field using this technique. We have now done this at the end of a new last paragraph of the Introduction:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“Inspired by findings reported in previous neuroimaging of social emotions, we also used several methods to analyse our fMRI data, including conventional methods (both region-of-interest and mass univariate); mixed-effects regression models; computational model-based analyses (inspired by e.g. Konovalov et al., 2021; Rutledge et al., 2014); and functional connectivity (e.g. Edelson et al., 2018; Konovalov et al., 2021). The behavioural modelling is thus complemented by neuroimaging analyses that offer insight about both the activity in regions associated with guilt as well as their place in a wider network, providing an in-depth comprehensive analysis of the mechanisms behind guilt evoked by social responsibility.”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(5) For the functional connectivity findings: I was wondering why you only looked at the choice phase, and not at the feedback phase. I understand that previous work focused on the choice phase, but for the purpose of this study (focus on guilt), I can imagine it is also interesting to see what happens with feedback. In the discussion, you also state \"How we feel when we witness our decisions' consequences on others is an important signal to consider when attempting to make good social decisions.\" (p. 19), which is more focused on the feedback rather than choice, and also supports the idea that looking at the feedback moment might be relevant."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that we could also have looked at the functional connectivity during the feedback phase. The main reason why we had originally not done so was time constraints. At the current time we would in addition point out that the manuscript is already very long and contains many analyses of behavioural and fMRI data. Adding this analysis would cost additional time and would further delay the publication of our manuscript, which we would prefer to avoid. However, one could of course look at these effects in subsequent analyses of the same data or in subsequent versions of this experiment. We have now mentioned this in the Discussion, in the paragraphs on open questions."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "Minor comments:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "(1) For some of the Figures, it would be helpful if the subtitles were more informative. For Figure 2 and Figure 3 for example, it would be nice if Study 1 and Study 2 were not only mentioned in the figure description but also in the actual figure. For Figures 3 and 4, it would be helpful to have significance stars for the bar plots as well."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We agree that these changes make the figures more easily understandable and have implemented them all, except for adding stars on Figure 4, because all bar plots in panels C and E would have been labeled with two or more stars, which would have made the figure difficult to read. We have now mentioned the fact that all these coefficients were significant in the figure legend."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(2) For some of the Supplementary Results, it would be very helpful if there was a legend or description. This is already the case for most of the SR, but not for all."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We have now added a legend to all elements of the Supplementary Results."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "Some questions that came to mind while going through them:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "- Supplementary Table 1: which p-values correspond to the significance stars? This information is included for Supplementary Table 2, but not for ST1."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We have now added the missing information in ST1."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "- Supplementary Figure 1: do the colors correspond to different participants?"
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We have now specified that the colors do indeed correspond to different participants."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "- Supplementary Table 5 (final table): what do the - represent? As in, why is there no value for \"run\" for the MPFC? At first, I thought you only included the significant values, but then I noticed a few non-significant values as well, so it wasn't completely clear to me why some of the values were missing. This also applies to Supplementary Table 6."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We have indeed forgotten to explain this. The ‘-’ in Supplementary Tables 4 and 6 indicate that the linear mixed model without the factor ‘run’ was the better-fitting one. We have now added the following explanation in the text accompanying Supplementary Table 4:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“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.”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "(3) I came across a few minor typos or sentences that were not completely clear to me."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "- On page 3: \"Patients with damage to ventromedial prefrontal cortex (vmPFC) seem insensitive to guilt when playing social economic games (Krajbich et al., 2009).\" This sentence felt a bit out of nowhere and doesn't logically follow from the previous sentences."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We have now revised the descriptions of this previous study as well as several others and how they fit into the research question."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "- On page 3: \"In another study, participant errors in a difficult perception task lead to a partner feeling pain and evoked activations in left aIns and dlPFC (Koban et al., 2013).\" This sentence doesn't really flow, and from the wording, it is not completely clear whether it's the errors or the partner pain that led to the aIns and dlPFC activation."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We have now revised the description of this study as well, as follows:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“In another study, partners received painful stimuli when participants made errors during a difficult perception task. These errors evoked activations in the left aIns and dlPFC in the participants (Koban et al., 2013).”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "- Supplementary Figure 1: there is a missing period after the sentence \"We then compared these new estimated parameters to the actual parameters from which the synthetic data were generated\""
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We have now added a missing comma after “generated”."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "- On page 5: \"We ran two experiments, Study 1 outside fMRI and Study 2 during fMRI, with separate groups of participants.\" I would change \"outside fMRI\" to outside the MRI scanner or something like that, as it's not completely correct to say \"outside fMRI\"."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We have changed the sentence to “outside the MRI scanner”."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "- On page 6: for the first result, there are currently two p-values reported (p < 2.5e-20 and p < 2e-16). I believe this is an error?"
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "This was indeed an error! We have re-run this analysis, noticed that also the degrees of freedom were miscalculated, and have updated this result and the effect of condition (solo vs social). Results are almost identical as previously and all conclusions hold. We have also checked the other analyses reported in this paragraph – all results replicate exactly."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "- On page 6: \"Supplemental Table 1\" should be \"Supplementary Table 1\" (for consistency)."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Done."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "On page 8: \"participants in both conditions of both studies\", I would change \"of both studies\" to \"for both studies\"."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Done."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "On page 8: for the \"Momentary Happiness\" paragraph, it would be helpful if you could briefly describe the Rutledge method here, for people who are unfamiliar with the approach."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We now write the following at the beginning of this paragraph:"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "“Following Rutledge and colleagues’ methodology, which considers that changes in momentary happiness in response to outcomes of a probabilistic reward task are explained by the combined influence of recent reward expectations and prediction errors arising from those expectations, we fitted computational models to each participant’s happiness data.”"
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"content": [
{
"type": "Text",
"value": {
"string": "On page 10: \"Wilkoxon sign-rank tests\", should be \"Wilcoxon\"."
}
}
],
"type": "Paragraph"
}
],
"type": "QuoteBlock"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "Done."
}
}
],
"type": "Paragraph"
},
{
"content": [
{
"type": "Text",
"value": {
"string": "We thank the reviewer for their careful reading of our manuscript. We believe that these changes have indeed improved our manuscript."
}
}
],
"type": "Paragraph"
}
],
"doi": "10.7554/eLife.105391.3.sa3",
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"date": "2024-11-22",
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"description": "This manuscript was published as a preprint.",
"url": "https://doi.org/10.1101/2020.05.25.107300"
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{
"date": "2025-03-24",
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"description": "This manuscript was published as a reviewed preprint.",
"url": "https://doi.org/10.7554/eLife.105391.1"
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"date": "2026-02-25",
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"description": "The reviewed preprint was revised.",
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