People’s decisions are influenced by their beliefs, which may be based on advice from other humans or, alternatively, on information from non-human sources such as road signs. But which sources do we find more reliable? Although scientists have studied the importance of social context in the way we process information, it is not fully understood how the brain processes information differently depending on who provides it.

Trudel et al. investigated the differences in the way humans evaluate information from human sources, such as advisors, compared to non-human sources, like inanimate objects, by monitoring brain activity and analyzing the results using a computational approach. In the experiments, 24 participants received a reward for locating a hidden dot on a circle under two different conditions: they either received a clue on the dot’s location from an image of a human face (social condition), representing an advisor, or from an inanimate object (non-social condition). Participants received information from many different advisors and inanimate objects, and the accuracy of the clues given by any of them varied from source to source. Each time, participants reported whether they thought the advice was reliable.

The results of monitoring the participants’ brain activity showed that they used different strategies when assessing the reliability of advice from a human than when the information came from a non-human source. Additionally, participants based their judgments about an advisor more strongly on past experiences with them, that is, if an advisor had given them good advice in the past, they were more likely to rely on their advice. Conversely, judgments about an inanimate object were based more strongly on recent experiences with that object.

Interestingly, participants were more certain when making judgments about the accuracy of cues given by advisors compared to inanimate objects, and they also updated their assessment of human sources less according to new evidence that contradicted their initial belief. This suggests that people may form more stable opinions about the reliability of sources when they receive information in social contexts, possibly because they expect more consistent behavior from humans. This stability in judgments about advisors was also reflected in the signal of brain areas that are often involved when interacting with others.

The work of Trudel et al. shows that even the suggestion that the source of a piece of advice is human can change how we process the information. This is especially important because humans spend increasingly more time in the digital world. Awareness of our biased assessment of human sources will have implications for designing interactive tools to guide human decision-making as well as strategies to develop critical thinking.

To behave adaptively, humans and many other species learn from their conspecifics which actions to take and which ones to avoid. The human ability to learn not only from direct interactions with the environment, but also from other people contributes critically to our behavioural repertoires as individuals and also to collective culture (Boyd and Richerson, 2009; Gweon, 2021). When seeking information about which courses of actions to pursue, we routinely turn to others for advice and adjust our own course of action based on the quality of their advice (Bang et al., 2017; Bang et al., 2020; Behrens et al., 2008; De Martino et al., 2017; Hertz et al., 2017). Clearly, it is important to learn over time whether an advice giver is predicting external events accurately. This process, sampling and learning from other people’s advice, is arguably a process that may be very similar to learning from non-social cues about the occurrence of external events (Akaishi et al., 2016; Trudel et al., 2021). In other words, the cognitive operations underlying this type of information search might occur both in social and in non-social scenarios. Therefore, by comparing information sampling from social versus non-social sources, we address a long-standing question in cognitive neuroscience, the degree to which any neural process is specialized for, or particularly linked to, social as opposed to non-social cognition (Chang and Dal Monte, 2018; Diaconescu et al., 2017; Frith and Frith, 2010; Frith and Frith, 2012; Grabenhorst and Schultz, 2021; Lockwood et al., 2020a; Lockwood et al., 2018; Soutschek et al., 2016; Wittmann et al., 2018; Rushworth et al., 2012).

Given their similarities, it is expected that both types of learning will depend on common neural mechanisms. However, given the importance and ubiquity of social learning, it may also be that the neural mechanisms that support learning from social advice are at least partially specialized and distinct from those concerned with learning that is guided by non-social sources. However, it is less clear on which level information is processed differently when it has a social or non-social origin. It has recently been argued that differences between social and non-social learning can be investigated on different levels of Marr’s information processing theory: differences could emerge at an input level (in terms of the stimuli that might drive social and non-social learning), at an algorithmic level or at a neural implementation level (Lockwood et al., 2020a). It might be that, at the algorithmic level, associative learning mechanisms are similar across social and non-social learning (Heyes, 2012). Other theories have argued that differences might emerge because goal-directed actions are attributed to social agents which allows for very different inferences to be made about hidden traits or beliefs (Shafto et al., 2012). Such inferences might fundamentally alter learning about social agents compared to non-social cues.

There is evidence that at least some parts of the brain are specialized for negotiating social situations (Noonan et al., 2014; Sallet et al., 2011; Sliwa and Freiwald, 2017). For example, neuroimaging studies in macaques show a positive correlation between group size and areas in the temporal lobe (Sallet et al., 2011), in an area that has similarities with the temporoparietal junction (TPJ) in humans (Mars et al., 2013). In turn, human TPJ and the macaque STS region have been associated and causally linked to the ability to infer other people’s beliefs (Hill et al., 2017; Schurz et al., 2017) and, in monkeys, to inferring the intended actions of others (Ong et al., 2021). Dorsomedial prefrontal cortex (dmPFC) is a second important node within this network again both in humans (Wittmann et al., 2018; Hampton et al., 2008; Nicolle et al., 2012; Piva et al., 2019; Suzuki et al., 2012) and in non-human primates (Noritake et al., 2018; Yoshida et al., 2012). For example, a recent study demonstrates dmPFC is causally implicated in social cognition and the maintenance of separate representations of oneself and of other individuals even when people interact (Wittmann et al., 2021). There are however, studies that report many common brain areas linked to reward and motivational processes in the context of both social and non-social behaviour suggesting at the very least that social information processing is not completely separate (Behrens et al., 2008; Boorman et al., 2013; Ruff and Fehr, 2014; Will et al., 2017; Zink et al., 2008).For example, tracking the probability of being correct or successful, often referred to as ‘confidence’ in one’s choice, has been shown to correlate with activation in perigenual anterior cingulate cortex in both social (Wittmann et al., 2016) and non-social (Bang and Fleming, 2018) settings. Here, we seek to understand the behavioural and neural computations that differentiate learning from social and non-social information sources and to further test at which level of information processing these differences occur.

It has been proven difficult to dissociate social as opposed to non-social cognitive functions. One reason is the lack of controlled within-subject experimental designs that have rarely been implemented in previous social neuroscience studies. Another reason might be the type of cognitive process that is studied; only cognitive functions that might plausibly occur in either social and non-social scenarios allow for a fair comparison. Here, we attempted to optimize our experimental design to address both these considerations in order to examine whether there are any fundamental differences in behavioural and neural computations when seeking information from social as opposed to non-social sources. First, we implemented a tightly controlled within-subject design with two experimental sessions that were matched in their statistical properties and only differed in their framing (social vs. non-social) and in their precise visual features. This ensured that differences between conditions were confined to the nature of the information source – social or non-social – and therefore that any differences in behaviour or neural activity could only attributed to the social/non-social difference. Second, we compared learning from other people’s advice with learning from non-social cues about the occurrence of external events. On every trial of the experiment, participants witnessed the precision with which a social cue (i.e., advice) or a non-social cue predicted a target. Over time, participants learned about the quality of these predictions and formed estimates of how confident they were in a cue, that is how precisely they expected the target to be predicted by the cue. We show that humans appear more certain in the performance accuracy of social predictors (advisors) compared to non-social predictors. This was evident in more stable confidence judgements across multiple timescales in the social as opposed to the non-social condition; there was a stronger reliance on representations made in the past and a weaker integration of new contradictory evidence. By using a computational approach, we could associate differences in the stability in confidence about social advisors to differences in the Bayesian estimates of participants’ subjective beliefs. We found that two brain areas, dmPFC and posterior TPJ (pTPJ) showed specificity in their processing of social information, not only in their average activation but also by encoding the identity of social cues in their multivariate pattern activation.

We used a social and non-social version of a previously validated paradigm (Trudel et al., 2021) during which participants learned about the performance of social and non-social cues in predicting a target on the circumference of a circle. On every trial, participants received advice on where to find the target (Figure 1 a, b , Figure 1—figure supplement 1). In the social version, faces represented advisors that predicted a target position (a flower’s position on the circumference of a circle) (Figure 1a). Some advisors predicted the target accurately and others less accurately. Participants were instructed that advisors had previously been players of a similar task during which time they could directly learn about the target location (Figure 1c, Figure 1—figure supplement 2). In other words, the other players had learned directly about the target location, while in the main experiment, participants could only infer the target locations from the predictions made by these previous players who now acted as advisors, but not directly from the target. Performance was defined by the size of an angular error, which represented the distance between the location of the predicted flower position (represented by a black dot) and the true position of the flower (represented by a yellow dot).

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In the non-social version, participants were instructed to collect fruits (the fruit position was now the target position and again it was represented by a yellow dot) that could either fall a small or large distance from the tree (this was now the predictor and again it was represented by a black dot). Importantly, task versions were completely matched in their experimental design and statistical properties inherent to the predictors and only differed in terms of their framing towards the participants. Thus, if there were differences in behaviour or neural activity between the conditions, then these could not have been attributed to any difference in protocol but only to a predisposition to treat the two types of predictors differently. Note that, the critical predictive stimuli and targets were, respectively, simply yellow and black dots appearing on a similar circle’s circumference in both conditions.

On every trial, participants indicated their confidence in the advisor’s performance. A symmetrical confidence interval (CI) with a random initial width appeared around the (social or non-social) prediction. The CI could be changed continuously to make it wider or narrower, by pressing buttons repeatedly (one button press resulted in a change of one step in the CI). In this way, participants provided what we refer to as an ‘interval setting’. Reward was received when the target fell within the chosen interval and the number of points increased when the interval size was small. A narrow interval could be set when participants selected a predictor with a small angular error and when they were certain about the predictor’s angular error. Hence, trial-wise confidence judgements allowed us to inspect participants’ beliefs about the performance of the advisor and their uncertainty in this performance estimate. In both social and non-social conditions, the participants’ beliefs about the predictor’s performance could be updated during the outcome phase when they witnessed the angular error between the predictive black dot and the target yellow dot. Participants were presented with multiple social and non-social predictors across six blocks. In both social and non-social settings, predictors varied in the accuracy with which they predicted the target, that is their average angular error (Figure 1c). For each predictor accuracy in the social condition, there was always a predictor in the non-social condition with a matched accuracy level.

Confidence in the performance of social and non-social advisors

We compared trial-by-trial interval setting in relation to the social and non-social advisors/predictors. When setting the interval, the participant’s aim was to minimize it while ensuring it still encompassed the final target position; points were won when it encompassed the target position but were greater when it was narrower. A given participant’s interval setting should, therefore, change in proportion to the participant’s expectations about the predictor’s angular error and their uncertainty about those expectations. Even though, on average, social and non-social sources did not differ in the precision with which they predicted the target (Figure 2—figure supplement 1), participants gave interval settings that differed in their relationships to the true performances of the social advisors compared to the non-social predictors. The interval setting was closer to the angular error in the social compared to the non-social sessions (Figure 2a, paired t-test: social vs. non-social, t(23) = −2.57, p = 0.017, 95% CI = [−0.36 −0.4]). Differences in interval setting might be due to generally lower performance in the non-social compared to social condition, or potentially due to fundamentally different learning processes utilized in either condition. We compared the mean reward amounts obtained by participants in the social and non-social conditions to determine whether there were overall performance differences. There was, however, no difference in the reward received by participants in the two conditions (mean reward: paired t-test social vs. non-social, t(23) = 0.8, p = 0.4, 95% CI = [−0.007 0.016]), suggesting that interval setting differences might not simply reflect better or worse performance but instead suggest quantitative differences in qualitatively similar learning mechanisms.

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Figure 2—source data 1

Source data include data shown in Figure 2a-c.

Further, it includes regression weights shown in Figure 2d for the effects of accuracy, and the effect of uncertainty. It also includes values for the learning index for the first and remaining updates, Figure 2e. Source data include values for both social and non-social conditions.

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Estimating the angular error of an advisor accurately depends on how judgements are adjusted from trial to trial. We compared changes in interval settings on two timescales. First, we did this on a longer timescale, by comparing the absolute change between the current and next judgement of the same predictor (which might only be presented again several trials later, Figure 2b). Second, we did this on a within-trial timescale, by comparing changes of mind prior to the final judgement: participants adjusted the CI’s size by individual button presses, allowing inspections of changes of mind within a trial according to a specific sequence of button presses prior to the final judgement. Changes of mind within a trial were indicated by first becoming more confident (selecting smaller intervals) and then becoming less confident (selecting a bigger interval) before the final judgement (Figure 2c, inset). Participants made fewer adjustments of their confidence judgements across both timescales for social advisors: participants’ absolute changes in their confidence judgements from trial to trial were lower (Figure 2b, paired t-test: social vs. non-social, t(23) = −2.7, p = 0.0125, 95% CI = [−0.3 −0.04]) and they exhibited fewer changes of mind before committing to a final interval setting (paired t-test: social vs. non-social, t(23) = −2.4, p = 0.025, 95% CI = [−6.3 −0.45]). In sum, these results suggest that participants are more certain in their interval setting for social compared to non-social information sources, as they change estimates about an advisor less drastically across trials and commit to their judgement within each trial with less hesitation.

We tested the hypothesis that participants’ certainty in their own beliefs influenced their interval setting differently in social and non-social settings. We used a Bayesian model that allowed dissociation of two aspects of subjective beliefs: first, belief in how well the predictor will perform on a given trial (a belief in the predictor’s ‘accuracy’) and second, the participant’s uncertainty associated with the predictor’s predicted accuracy (‘uncertainty’ of the belief) (Figure 2d, Figure 2—figure supplement 2; Trudel et al., 2021). Both aspects of a belief might impact on the adjustment of the interval setting. Accuracy is the more likely belief parameter of the two to affect interval setting because accuracy reflects a point-estimate of the most likely angular error that an advisor has shown in the past. However, narrow interval settings, which have the potential to yield more reward if they encompass the target (Figure 2a) can only be employed when participants believe the advice given to be a true reflection of the target location (the advice is accurate) and when they are certain in their belief that the advice will be accurate (certainty in the advice’s accuracy). We, therefore, tested the impact of both belief parameters on trial-wise interval settings and examined whether their impact differed when making judgements about social or non-social cues. For both social and non-social predictors, participants were more confident and set a smaller interval when they believed the predictor to be accurate in their target prediction (paired t-test: social vs. non-social, t(23) = 0.6, p = 0.558, 95% CI = [−0.13 0.24]; one sample t-test: non-social, t(23) = 3.2, p = 0.003, 95% CI = [0.09 0.41]; one sample t-test: social, t(23) = 4, p < 0.0001, 95% CI = [0.15 0.46], Figure 2e, Figure 1—figure supplement 2). However, when judging non-social predictors, but not social predictors, interval settings were additionally widened as a function of subjective uncertainty: participants set a larger interval when they were more uncertain about the non-social cue’s accuracy to predict the target (paired t-test: social vs. non-social, t(23) = 3.5, p = 0.0018, 95% CI = [0.11 0.42]; one sample t-test: non-social, t(23) = −3.9, p < 0.0001, 95% CI = [−0.35 −0.1]; one sample t-test: social, t(23) = 0.6, p = 0.56, 95% CI = [−0.1 0.19], Figure 2e). The difference between social and non-social conditions was replicated when using a mixed-effects model (Figure 2—figure supplement 1). Uncertainty about the accuracy of social advisors, however, did not have the same impact: participants’ interval settings for the social advisors appeared only to be guided by the peak of the Bayesian probability distribution, as indicated by the accuracy effect.

We have seen that participants have less changeable estimates of the performances of social advisors compared to non-social advisors (Figure 2b), and this relative increase in certainty about the value of social advice as opposed to non-social information is also reflected in the Bayesian estimates of participants’ subjective beliefs (Figure 2d, e). One possibility is that this increased certainty is caused by relying on a longer-term memory of the observed advice in the social compared to the non-social context. Participants might form an opinion about the performance of social advisors more quickly and are therefore less likely to change it in face of new and possibly contradictory evidence. We therefore tested whether the degree of error-driven behavioural adaptation is different between social and non-social conditions. In other words, we tested whether participants changed their interval setting from one trial to the next to a smaller degree as a function of the discrepancy between their interval setting and the angular error (i.e., prediction error). We calculated a trial-by-trial learning index, similar to a learning rate in a reinforcement learning (RL) model (Barto, 1998; Wittmann et al., 2020; Lockwood and Klein-Flügge, 2020b), that measured the change between the current and next interval setting given the prediction error (Figure 2f; Methods, Equation 2 and 3). A smaller learning index shows less error-driven adjustment of one’s interval setting. Learning indices between conditions were similar for the first observation but declined more steeply for social compared to non-social predictors for the remaining updates (repeated-measures analysis of variance [ANOVA]: interaction between group (social, non-social) × time point (first update, other remaining updates), F(1,23) = 5.8, p = 0.024, Figure 2f). Hence, after the first update, interval setting in the social condition was subsequently less influenced by prediction errors that was the case in the non-social case (paired t-test: social vs. non-social for remaining only, t(23) = 2.7, p = 0.01, 95% CI = [−0.95 −0.13], Figure 2f). As a consequence, judgements relied more on observations made further in the past in the social condition. This is consistent with the observation that participants repeatedly set similar intervals across time in the social condition (i.e., fewer changes across trials) (Figure 2b), and that these settings were less impacted by their own subjective uncertainty (Figure 2e).

The impact of noise in belief updating in social and non-social conditions

So far, we have shown that, in comparison to non-social predictors, participants changed their interval settings about social advisors less drastically across time, relied on observations made further in the past, and were less impacted by their subjective uncertainty when they did so (Figure 2). In further exploratory analyses, we used Bayesian simulation analyses to investigate whether a common mechanism might underlie these behavioural differences. We tested whether the integration of new evidence differed between social and non-social conditions; for example, recent observations might be weighted more strongly for non-social cues while past observations might be weighted more strongly for social cues. Similar ideas were tested in previous studies, when comparing the learning rate (i.e., the speed of learning) in environments of different volatilities (Behrens et al., 2007; Meder et al., 2017). We tested these ideas using established ways of changing the speed of learning during Bayesian updates (Allenmark et al., 2018; Yu and Cohen, 2008). We hypothesized that participants reduce their uncertainty quicker when observing social information. Vice versa, we hypothesized a less steep decline of uncertainty when observing non-social information, indicating that new information can be flexibly integrated during the belief update (Figure 3a).

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We manipulated the amount of uncertainty in the Bayesian model by adding a uniform distribution to each belief update (Figure 3b, c; Equations 10 and 11). Consequently, the distribution’s width increases and is more strongly impacted by recent observations (see example in Figure 3—figure supplement 1). We used these modified Bayesian models to simulate trial-wise interval setting for each participant according to the observations they made by selecting a particular advisor in the social condition or other predictor in the non-social condition. We simulated CIs at each trial. We then used these to examine whether an increase in noise led to simulation behaviour that resembled behavioural patterns observed in non-social conditions that were different to behavioural patterns observed in the social condition.

First, we repeated the linear regression analysis and hypothesized that interval settings that were simulated from noisy Bayesian models would also show a greater negative ‘predictor uncertainty’ effect on interval setting resembling the effect we had observed in the non-social condition (Figure 2e). This was indeed the case when using the noisy Bayesian model: irrespective of social or non-social condition, the addition of noise (increasing weight of the uniform distribution to each belief update) led to an increasingly negative ‘predictor uncertainty’ effect on confidence judgement (new Figure 3d). The absence of difference between the social and non-social conditions in the simulations suggests that an increase in the Bayesian noise is sufficient to induce a negative impact of ‘predictor uncertainty’ on interval setting. Hence, we can conclude that different degrees of noise in the updating process are sufficient to cause differences observed between social and non-social conditions. Next, we used these simulated interval settings and repeated the descriptive behavioural analyses (Figure 2b, f). An increase in noise led to greater changes of confidence across time and a higher learning index (Figure 3—figure supplement 1). In summary, the Bayesian simulations offer a conceptual explanation that can account for both the differences in learning and the difference in uncertainty processing that exist between social and non-social conditions. The key insight conveyed by the Bayesian simulations is that a wider, more uncertain belief distribution changes more quickly. Correspondingly, in the non-social condition, participants express more uncertainty in their confidence estimate when they set the interval, and they also change their beliefs more quickly. Therefore, noisy Bayesian updating can account for key differences between social and non-social condition.

dmPFC and pTPJ covary with social confidence and encode social advisor identity

Next, we tested whether there are brain areas that covary with interval setting that are made about social compared to non-social advisors. Behaviourally, we have shown that judgements about social advisors might reflect the presence and influence of relatively constant and unchanging represen tations in memory. Therefore, a combination of the trial-wise interval setting made in the current (time point t) and past trial (time point t − 1) for the same predictor was used to test for differences between social and non-social advisor judgements (contrast: current + past interval setting). We sign-reversed the interval setting measure such that a higher index now reflected greater confidence in the selected predictor. We focussed on two a priori independent regions of interest (ROIs), dmPFC (Wittmann et al., 2016) and pTPJ (Mars et al., 2013) because of their previous association with inference about beliefs held by others (Schurz et al., 2017; Hampton et al., 2008; Nicolle et al., 2012; Piva et al., 2019; Suzuki et al., 2012; Wittmann et al., 2016; Saxe, 2006); a process that is crucial to the current task. Hence, we tested whether blood-oxygen-level-dependent (BOLD) signal in these brain areas changed in tandem with variation in the intervals participants set. This parametric contrast thus captures trial-by-trial variation in the confidence that participants have in the predictor and is independent of the mean confidence levels. Note, however, that the mean confidence level was not significantly different between social advisors and non-social predictors (paired t-test: mean confidence, social vs. non-social, t(23) = −1.7, p = 0.11, 95% CI = [−0.66 0.07]), even though we observed greater stability of interval setting for social advisors (Figure 2b). Activation in both dmPFC and pTPJ covaried with combined confidence judgements significantly more strongly for social compared to non-social advisors (Figure 4a, paired t-test: social vs. non-social for dmPFC, t(23) = −2.5, p = 0.019, 95% CI = [3 31]; Figure 4b, paired t-test: social vs. non-social for pTPJ: t(23) = −2.3, p = 0.03, 95% CI = [1 22.5]). Next, we performed an exploratory whole-brain analysis and found that these activations in dmPFC and pTPJ were also detectable when examining activity across the brain and using standard cluster-correction techniques to adjust for multiple comparisons when making judgements about social advisors (family-wise error (FWE) cluster corrected with z-score >2.3 and p < 0.05, Supplementary file 1). In addition, activity was significantly related to the same measure in the social condition after whole-brain cluster-correction, in some other areas previously linked with social cognition such as the medial frontal pole and posterior cingulate/medial parietal cortex (Figure 4c, d, for a complete list of cluster-corrected brain areas, see Supplementary file 1).

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Source data include data shown in Figure 4a (contrast: current + past interval setting in dorsomedial prefrontal cortex [dmPFC] region of interest [ROI]), Figure 4b (contrast: current + past interval setting in posterior temporoparietal junction [pTPJ] ROI), Figure 4e (separate effects of current and past interval setting in dmPFC ROI for social condition only), Figure 4e (separate effects of current and past interval setting in pTPJ ROI for social condition only), Figure 4h (Exemplar Discriminability Index [EDI] values for dmPFC ROI), and Figure 4h (EDI values for pTPJ ROI).

Source data include values for both social and non-social conditions.

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Our behavioural results showed that social judgements might be especially impacted by beliefs formed over longer timescales: for example, judgements about social advisors changed less drastically across time, even if new information at the time of the trial outcome was contradictory to one’s current belief (Figure 2f). We tested whether such a differentiation between current (t) and past (t − 1) interval setting was also apparent in the neural activation profile of dmPFC and pTPJ. In other words, we tested whether pTPJ and dmPFC displayed an activation profile consistent with a greater influence of interval settings over the longer term, as opposed to on just the current trial, to a greater degree in the social as opposed to the non-social condition. Consistent with the behavioural results, activation within both areas covaried with interval settings made over longer timescales rather than just on the current trial (one sample t-test, social: dmPFC, past vs. current: t(23) = 3.04, p = 0.0058, 95% CI = [7.6 39.8]; pTPJ, past vs. current, t(23) = 2.25, p = 0.034, 95% CI = [1.1 26.6]; Figure 3e, f; Figure 4—figure supplement 1). There was no difference in the strength of neural activity related to interval setting at the two time scales in the non-social condition (Figure 4—figure supplement 2).

So far, we have focussed on how the univariate activity in dmPFC and pTPJ covaried with the participants’ estimates of the accuracy of predictors. Next, we examined whether the same brain regions carried additional information about the predictors that is orthogonal to what we have examined so far; we sought evidence as to whether the two areas encoded the identities of the cues. We applied a representational similarity analysis (RSA) to BOLD activity patterns evoked by each face and each fruit presentation at the time of response during the confidence phase. We compared the pattern dissimilarity across voxels (measured by the Euclidean distance) in the same a priori independent ROIs in dmPFC and pTPJ as examined previously. For each condition, the Exemplar Discriminability Index (EDI) was calculated which reflects the average dissimilarity in activity elicited across presentations of the same cue (Figure 3g, orange area) compared to the average dissimilarity across presentations of different cues (Figure 3g, red area) (Bang et al., 2020). Hence, a negative EDI indicates that an area’s activity changes less when the same advisor/predictor is presented again than when different advisors/predictors are presented suggesting that it encodes representations of the identity of predictors/advisors. The patterns of activation change in both dmPFC and pTPJ suggested that both areas encoded the identities of social cues more strongly than non-social cues (Figure 3h; repeated-measures ANOVA: main effect of condition (social, non-social) in 2 (condition: social, non-social) × 2 (area: pTPJ, dmPFC) ANOVA: F(1,21) = 5, p = 0.036). An exploratory whole-brain searchlight analysis was performed to test the specificity of dmPFC and pTPJ encoding the identity of social compared to non-social cues. No other cluster-corrected effects were found (Figure 4—figure supplement 3). Additional control analyses show that neural differences between social and non-social conditions are not due to the visually different set of stimuli used in the experiment but are instead representing fundamental differences in processing social compared to non-social information (Figure 4—figure supplement 4). In summary, the conjunction, univariate and multivariate analyses demonstrate that dmPFC and pTPJ represent beliefs about social advisors that develop over a longer timescale and encode the identities of the social advisors.

We examined the behavioural and neural computations that differentiate learning from social and non-social information sources. Using a tightly controlled within-subject design in which conditions only differed in their social or non-social framing, we asked participants to make trial-wise confidence judgements, by setting a visual interval, about the advice of social and non-social information sources. Ideally, confidence should increase with the accuracy of one’s choice (Bang and Fleming, 2018; Kiani and Shadlen, 2009; Lak et al., 2020). Here, participants did not match their confidence to the likely accuracy of their own performance, but instead to the performance of another social or non-social advisor. Participants used different strategies when setting intervals to express their confidence in the performances of social advisors as opposed to non-social advisors. A possible explanation might be that participants have a better insight into the abilities of social cues – typically other agents – than non-social cues – typically inanimate objects. We used a computational approach to test the interplay of two belief estimates that impact interval setting: the belief in how accurate the advice/prediction is and the subjective uncertainty in that belief. Intervals were impacted by several factors; however, it was the influence of subjective uncertainty about the performance estimates that distinguished social and non-social interval setting. Participants selected a greater interval size when making their confidence judgements on non-social trials, indicating that they were more uncertain about the performance accuracies of the non-social cues compared to the social cues.

Being less influenced by ones’ uncertainty about the value of social advice might help explain why people sometimes fail to use evidence to update their assessments of social advisors (Fleming et al., 2018; Rollwage et al., 2020). Detecting uncertainty in environmental contingencies or about one’s own beliefs is achieved with fine-tuned metacognitive abilities (Miyamoto et al., 2021) and is essential for behavioural adaptation (Trudel et al., 2021; Badre et al., 2012). Participants were less influenced by their subjective uncertainty when estimating the performances of social advisors. Moreover, they changed and updated their beliefs about social cues less than about non-social cues when they observed the outcomes of their predictions. Repeating a particular interval size instead of adjusting it from trial to trial might be a better strategy in the current experiment because all advisors had, on average, a stable performance level. However, opting for such a strategy only in the context of social advisors might reflect a natural tendency to expect other social agents to behave consistently and exhibit stable performance levels as soon as an initial learning period is passed. It may be because we make just such assumptions that past observations are used to predict performance levels that people are likely to exhibit next (Wittmann et al., 2021; Wittmann et al., 2016). An alternative explanation might be that participants experience a steeper decline of subjective uncertainty in their beliefs about the accuracy of social advice, resulting in a narrower prior distribution, during the next encounter with the same advisor. During a series of exploratory analyses, we used Bayesian simulations to investigate how uncertainty about beliefs changed from trial to trial and showed that belief updates about non-social cues were consistent with a noisier update process that diminished the impact of experiences over the longer term. From a Bayesian perspective, greater certainty about the value of advice means that contradictory evidence will need to be stronger to alter one’s beliefs. In the absence of such evidence, a Bayesian agent is more likely to repeat previous judgements. Just as in a confirmation bias (Kappes et al., 2020), such a perspective suggests that once we are more certain about others’ features, for example, their character traits, we are less likely to change our opinions about them.

One potential explanation for the assumption of stable performance for social but not non-social predictors might be that participants attribute intentions and motivations to social agents. Even if the social and non-social evidence are the same, the belief that a social actor might have a goal may affect the inferences made from the same piece of information (Shafto et al., 2012). Social advisors first learnt about the target’s distribution and accordingly gave advice on where to find the target. If the social agents are credited with goal-directed behaviour then it might be assumed that the goals remain relatively constant; this might lead participants to assume stability in the performances of social advisors. However, such goal-directed intentions might not be attributed to non-social cues, thereby making judgements inherently more uncertain and changeable across time. Such an account, focussing on differences in attribution in social settings aligns with a recent suggestion that any attempt to identify similarities or differences between social and non-social processes can occur at any one of a number of the levels in Marr’s information theory (Lockwood et al., 2020a). Here, we found that the same algorithm was able to explain social and non-social learning (a qualitatively similar computational model could explain both). However, the extent to which the algorithm was recruited when learning about social compared to non-social information differed. We observed a greater impact of uncertainty on judgements about social compared to non-social information. We have shown evidence for a degree of specialization when assessing social advisors as opposed to non-social cues. At the neural level we focussed on two brain areas, dmPFC and pTP. These brain areas carry signals associated with belief inferences about others, for example how confident we think other people are about their own choices (Bang et al., 2022). Further, recent combined fMRI–TMS studies have also demonstrated the causal importance of these activity patterns for social inference (Hill et al., 2017; Wittmann et al., 2021). Similar processes might be at play in the current study during which participants make inferences about the performance of the social advisor in locating the target. We examined dmPFC and pTPJ, using ROIs derived from previous studies, and showed that these areas represent a combination of present and past confidence judgements that are made about social advisors and that they do this more strongly for social than for non-social cues.

A long-standing question in cognitive neuroscience is the degree to which any neural process is specialized for, or particularly linked to, social as opposed to non-social cognition. The current results suggest that dmPFC and pTPJ activity patterns are different during social cognition and that they carry information that is related to the performance differences that are seen at a behavioural level during a social as opposed to non-social task. Interestingly, it seems that activation in dmPFC and pTPJ was particularly prominent when representing judgements from previous encounters compared to the present judgement. This was apparent not only in univariate activity in these areas that covaried with confidence in advice but also in the multivariate activity patterns that encoded the identity of social agents; RSA showed that activation patterns in both areas encoded the identity of social cues better than the identity of non-social cues. The absence of similar differences in visual cortex suggests that the effects cannot be attributed to basic visual differences between the stimuli used in the two tasks. Hence, pTPJ and dmPFC seem particularly linked to the navigation of social interactions.

We have deposited the choice raw data used for the analyses in the OSF repository at https://osf.io/wjuze/. We also provide fMRI beta coefficients for the contrasts shown in Figure 4. Source data is provided with this paper. The above OSF repository includes code that recreates figures depicted in the main text. For the full Bayesian pipeline, which was used to derive regressors for the behavioural GLM depicted in Figure 2e, please refer to the following OSF repository: https://doi.org/10.17605/OSF.IO/D5QZW.

1. 1. Trudel N
   2. Lockwood P
   3. Rushworth M
   4. Wittmann M

   (2023) Open Science Framework

   Neural activity tracking identity and confidence in social information.

   https://doi.org/10.17605/OSF.IO/D5QZW

1. 1. Akaishi R
   2. Kolling N
   3. Brown JW
   4. Rushworth M

   (2016) Neural mechanisms of credit assignment in a multicue environment

   The Journal of Neuroscience 36:1096–1112.

   https://doi.org/10.1523/JNEUROSCI.3159-15.2016

   • PubMed
   • Google Scholar
2. 1. Allenmark F
   2. Müller HJ
   3. Shi Z

   (2018) Inter-Trial effects in visual pop-out search: factorial comparison of Bayesian updating models

   PLOS Computational Biology 14:e1006328.

   https://doi.org/10.1371/journal.pcbi.1006328

   • PubMed
   • Google Scholar
3. 1. Apps MAJ
   2. Rushworth MFS
   3. Chang SWC

   (2016) The anterior cingulate gyrus and social cognition: tracking the motivation of others

   Neuron 90:692–707.

   https://doi.org/10.1016/j.neuron.2016.04.018

   • PubMed
   • Google Scholar
4. 1. Apps MAJ
   2. Sallet J

   (2017) Social learning in the medial prefrontal cortex

   Trends in Cognitive Sciences 21:151–152.

   https://doi.org/10.1016/j.tics.2017.01.008

   • PubMed
   • Google Scholar
5. 1. Badre D
   2. Doll BB
   3. Long NM
   4. Frank MJ

   (2012) Rostrolateral prefrontal cortex and individual differences in uncertainty-driven exploration

   Neuron 73:595–607.

   https://doi.org/10.1016/j.neuron.2011.12.025

   • PubMed
   • Google Scholar
6. 1. Bang D
   2. Aitchison L
   3. Moran R
   4. Herce Castanon S
   5. Rafiee B
   6. Mahmoodi A
   7. Lau JYF
   8. Latham PE
   9. Bahrami B
   10. Summerfield C

   (2017) Confidence matching in group decision-making

   Nature Human Behaviour 1:0117.

   https://doi.org/10.1038/s41562-017-0117

   • Google Scholar
7. 1. Bang D
   2. Fleming SM

   (2018) Distinct encoding of decision confidence in human medial prefrontal cortex

   PNAS 115:6082–6087.

   https://doi.org/10.1073/pnas.1800795115

   • PubMed
   • Google Scholar
8. 1. Bang D
   2. Ershadmanesh S
   3. Nili H
   4. Fleming SM

   (2020) Private-public mappings in human prefrontal cortex

   eLife 9:e56477.

   https://doi.org/10.7554/eLife.56477

   • PubMed
   • Google Scholar
9. 1. Bang D
   2. Moran R
   3. Daw ND
   4. Fleming SM

   (2022) Neurocomputational mechanisms of confidence in self and others

   Nature Communications 13:4238.

   https://doi.org/10.1038/s41467-022-31674-w

   • PubMed
   • Google Scholar
10. 1. Barto AG

    (1998) Reinforcement learning

    IFAC Proceedings Volumes 31:5.

    https://doi.org/10.1016/S1474-6670(17)38315-5

    • Google Scholar
11. 1. Behrens TEJ
    2. Woolrich MW
    3. Walton ME
    4. Rushworth MFS

    (2007) Learning the value of information in an uncertain world

    Nature Neuroscience 10:1214–1221.

    https://doi.org/10.1038/nn1954

    • PubMed
    • Google Scholar
12. 1. Behrens TEJ
    2. Hunt LT
    3. Woolrich MW
    4. Rushworth MFS

    (2008) Associative learning of social value

    Nature 456:245–249.

    https://doi.org/10.1038/nature07538

    • PubMed
    • Google Scholar
13. 1. Boorman ED
    2. Behrens TE
    3. Rushworth MF

    (2011) Counterfactual choice and learning in a neural network centered on human lateral frontopolar cortex

    PLOS Biology 9:e1001093.

    https://doi.org/10.1371/journal.pbio.1001093

    • PubMed
    • Google Scholar
14. 1. Boorman ED
    2. O’Doherty JP
    3. Adolphs R
    4. Rangel A

    (2013) The behavioral and neural mechanisms underlying the tracking of expertise

    Neuron 80:1558–1571.

    https://doi.org/10.1016/j.neuron.2013.10.024

    • PubMed
    • Google Scholar
15. 1. Boyd R
    2. Richerson PJ

    (2009) Culture and the evolution of human cooperation

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 364:3281–3288.

    https://doi.org/10.1098/rstb.2009.0134

    • PubMed
    • Google Scholar
16. 1. Caruana F
    2. Gerbella M
    3. Avanzini P
    4. Gozzo F
    5. Pelliccia V
    6. Mai R
    7. Abdollahi RO
    8. Cardinale F
    9. Sartori I
    10. Lo Russo G
    11. Rizzolatti G

    (2018) Motor and emotional behaviours elicited by electrical stimulation of the human cingulate cortex

    Brain 141:3035–3051.

    https://doi.org/10.1093/brain/awy219

    • PubMed
    • Google Scholar
17. 1. Chang SWC
    2. Dal Monte O

    (2018) Shining light on social learning circuits

    Trends in Cognitive Sciences 22:673–675.

    https://doi.org/10.1016/j.tics.2018.05.002

    • PubMed
    • Google Scholar
18. 1. De Martino B
    2. Bobadilla-Suarez S
    3. Nouguchi T
    4. Sharot T
    5. Love BC

    (2017) Social information is integrated into value and confidence judgments according to its reliability

    The Journal of Neuroscience 37:6066–6074.

    https://doi.org/10.1523/JNEUROSCI.3880-16.2017

    • PubMed
    • Google Scholar
19. 1. Diaconescu AO
    2. Mathys C
    3. Weber LAE
    4. Kasper L
    5. Mauer J
    6. Stephan KE

    (2017) Hierarchical prediction errors in midbrain and septum during social learning

    Social Cognitive and Affective Neuroscience 12:618–634.

    https://doi.org/10.1093/scan/nsw171

    • PubMed
    • Google Scholar
20. 1. Fleming SM
    2. van der Putten EJ
    3. Daw ND

    (2018) Neural mediators of changes of mind about perceptual decisions

    Nature Neuroscience 21:617–624.

    https://doi.org/10.1038/s41593-018-0104-6

    • PubMed
    • Google Scholar
21. 1. Frith C
    2. Frith U

    (2010) Learning from others: introduction to the special review series on social neuroscience

    Neuron 65:739–743.

    https://doi.org/10.1016/j.neuron.2010.03.015

    • PubMed
    • Google Scholar
22. 1. Frith CD
    2. Frith U

    (2012) Mechanisms of social cognition

    Annual Review of Psychology 63:287–313.

    https://doi.org/10.1146/annurev-psych-120710-100449

    • PubMed
    • Google Scholar
23. 1. Grabenhorst F
    2. Schultz W

    (2021) Functions of primate amygdala neurons in economic decisions and social decision simulation

    Behavioural Brain Research 409:113318.

    https://doi.org/10.1016/j.bbr.2021.113318

    • PubMed
    • Google Scholar
24. 1. Gweon H

    (2021) Inferential social learning: cognitive foundations of human social learning and teaching

    Trends in Cognitive Sciences 25:896–910.

    https://doi.org/10.1016/j.tics.2021.07.008

    • PubMed
    • Google Scholar
25. 1. Hampton AN
    2. Bossaerts P
    3. O’Doherty JP

    (2008) Neural correlates of mentalizing-related computations during strategic interactions in humans

    PNAS 105:6741–6746.

    https://doi.org/10.1073/pnas.0711099105

    • PubMed
    • Google Scholar
26. 1. Hertz U
    2. Palminteri S
    3. Brunetti S
    4. Olesen C
    5. Frith CD
    6. Bahrami B

    (2017) Neural computations underpinning the strategic management of influence in advice giving

    Nature Communications 8:2191.

    https://doi.org/10.1038/s41467-017-02314-5

    • PubMed
    • Google Scholar
27. 1. Heyes C

    (2012) What’s social about social learning?

    Journal of Comparative Psychology 126:193–202.

    https://doi.org/10.1037/a0025180

    • PubMed
    • Google Scholar
28. 1. Hill CA
    2. Suzuki S
    3. Polania R
    4. Moisa M
    5. O’Doherty JP
    6. Ruff CC

    (2017) A causal account of the brain network computations underlying strategic social behavior

    Nature Neuroscience 20:1142–1149.

    https://doi.org/10.1038/nn.4602

    • PubMed
    • Google Scholar
29. 1. Kappes A
    2. Harvey AH
    3. Lohrenz T
    4. Montague PR
    5. Sharot T

    (2020) Confirmation bias in the utilization of others’ opinion strength

    Nature Neuroscience 23:130–137.

    https://doi.org/10.1038/s41593-019-0549-2

    • PubMed
    • Google Scholar
30. 1. Kiani R
    2. Shadlen MN

    (2009) Representation of confidence associated with a decision by neurons in the parietal cortex

    Science 324:759–764.

    https://doi.org/10.1126/science.1169405

    • PubMed
    • Google Scholar
31. 1. Lak A
    2. Okun M
    3. Moss MM
    4. Gurnani H
    5. Farrell K
    6. Wells MJ
    7. Reddy CB
    8. Kepecs A
    9. Harris KD
    10. Carandini M

    (2020) Dopaminergic and prefrontal basis of learning from sensory confidence and reward value

    Neuron 105:700–711.

    https://doi.org/10.1016/j.neuron.2019.11.018

    • PubMed
    • Google Scholar
32. 1. Lockwood PL
    2. Wittmann MK
    3. Apps MAJ
    4. Klein-Flügge MC
    5. Crockett MJ
    6. Humphreys GW
    7. Rushworth MFS

    (2018) Neural mechanisms for learning self and other ownership

    Nature Communications 9:4747.

    https://doi.org/10.1038/s41467-018-07231-9

    • PubMed
    • Google Scholar
33. 1. Lockwood PL
    2. Apps MAJ
    3. Chang SWC

    (2020a) Is there a “ social” brain? implementations and algorithms

    Trends in Cognitive Sciences 24:802–813.

    https://doi.org/10.1016/j.tics.2020.06.011

    • PubMed
    • Google Scholar
34. 1. Lockwood PL
    2. Klein-Flügge MC

    (2020b) Computational modelling of social cognition and behaviour-a reinforcement learning primer

    Social Cognitive and Affective Neuroscience 16:761–771.

    https://doi.org/10.1093/scan/nsaa040

    • PubMed
    • Google Scholar
35. 1. Lockwood PL
    2. Wittmann MK
    3. Nili H
    4. Matsumoto-Ryan M
    5. Abdurahman A
    6. Cutler J
    7. Husain M
    8. Apps MAJ

    (2022) Distinct neural representations for prosocial and self-benefiting effort

    Current Biology 32:4172–4185.

    https://doi.org/10.1016/j.cub.2022.08.010

    • PubMed
    • Google Scholar
36. 1. Mars RB
    2. Jbabdi S
    3. Sallet J
    4. O’Reilly JX
    5. Croxson PL
    6. Olivier E
    7. Noonan MP
    8. Bergmann C
    9. Mitchell AS
    10. Baxter MG
    11. Behrens TEJ
    12. Johansen-Berg H
    13. Tomassini V
    14. Miller KL
    15. Rushworth MFS

    (2011) Diffusion-Weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting-state functional connectivity

    The Journal of Neuroscience 31:4087–4100.

    https://doi.org/10.1523/JNEUROSCI.5102-10.2011

    • PubMed
    • Google Scholar
37. 1. Mars RB
    2. Sallet J
    3. Neubert FX
    4. Rushworth MFS

    (2013) Connectivity profiles reveal the relationship between brain areas for social cognition in human and monkey temporoparietal cortex

    PNAS 110:10806–10811.

    https://doi.org/10.1073/pnas.1302956110

    • PubMed
    • Google Scholar
38. 1. Meder D
    2. Kolling N
    3. Verhagen L
    4. Wittmann MK
    5. Scholl J
    6. Madsen KH
    7. Hulme OJ
    8. Behrens TEJ
    9. Rushworth MFS

    (2017) Simultaneous representation of a spectrum of dynamically changing value estimates during decision making

    Nature Communications 8:1942.

    https://doi.org/10.1038/s41467-017-02169-w

    • PubMed
    • Google Scholar
39. 1. Miyamoto K
    2. Trudel N
    3. Kamermans K
    4. Lim MC
    5. Lazari A
    6. Verhagen L
    7. Wittmann MK
    8. Rushworth MFS

    (2021) Identification and disruption of a neural mechanism for accumulating prospective metacognitive information prior to decision-making

    Neuron 109:1396–1408.

    https://doi.org/10.1016/j.neuron.2021.02.024

    • PubMed
    • Google Scholar
40. 1. Nicolle A
    2. Klein-Flügge MC
    3. Hunt LT
    4. Vlaev I
    5. Dolan RJ
    6. Behrens TEJ

    (2012) An agent independent axis for executed and modeled choice in medial prefrontal cortex

    Neuron 75:1114–1121.

    https://doi.org/10.1016/j.neuron.2012.07.023

    • PubMed
    • Google Scholar
41. 1. Nili H
    2. Wingfield C
    3. Walther A
    4. Su L
    5. Marslen-Wilson W
    6. Kriegeskorte N

    (2014) A toolbox for representational similarity analysis

    PLOS Computational Biology 10:e1003553.

    https://doi.org/10.1371/journal.pcbi.1003553

    • PubMed
    • Google Scholar
42. 1. Noonan MP
    2. Sallet J
    3. Mars RB
    4. Neubert FX
    5. O’Reilly JX
    6. Andersson JL
    7. Mitchell AS
    8. Bell AH
    9. Miller KL
    10. Rushworth MFS

    (2014) A neural circuit covarying with social hierarchy in macaques

    PLOS Biology 12:e1001940.

    https://doi.org/10.1371/journal.pbio.1001940

    • PubMed
    • Google Scholar
43. 1. Noritake A
    2. Ninomiya T
    3. Isoda M

    (2018) Social reward monitoring and valuation in the macaque brain

    Nature Neuroscience 21:1452–1462.

    https://doi.org/10.1038/s41593-018-0229-7

    • PubMed
    • Google Scholar
44. 1. Ong WS
    2. Madlon-Kay S
    3. Platt ML

    (2021) Neuronal correlates of strategic cooperation in monkeys

    Nature Neuroscience 24:116–128.

    https://doi.org/10.1038/s41593-020-00746-9

    • PubMed
    • Google Scholar
45. 1. Piva M
    2. Velnoskey K
    3. Jia R
    4. Nair A
    5. Levy I
    6. Chang SWC

    (2019) The dorsomedial prefrontal cortex computes task-invariant relative subjective value for self and other

    eLife 8:e44939.

    https://doi.org/10.7554/eLife.44939

    • PubMed
    • Google Scholar
46. 1. Rollwage M
    2. Loosen A
    3. Hauser TU
    4. Moran R
    5. Dolan RJ
    6. Fleming SM

    (2020) Confidence drives a neural confirmation bias

    Nature Communications 11:2634.

    https://doi.org/10.1038/s41467-020-16278-6

    • PubMed
    • Google Scholar
47. 1. Ruff CC
    2. Fehr E

    (2014) The neurobiology of rewards and values in social decision making

    Nature Reviews. Neuroscience 15:549–562.

    https://doi.org/10.1038/nrn3776

    • PubMed
    • Google Scholar
48. 1. Rushworth MFS
    2. Kolling N
    3. Sallet J
    4. Mars RB

    (2012) Valuation and decision-making in frontal cortex: one or many serial or parallel systems?

    Current Opinion in Neurobiology 22:946–955.

    https://doi.org/10.1016/j.conb.2012.04.011

    • PubMed
    • Google Scholar
49. 1. Sallet J
    2. Mars RB
    3. Noonan MP
    4. Andersson JL
    5. O’Reilly JX
    6. Jbabdi S
    7. Croxson PL
    8. Jenkinson M
    9. Miller KL
    10. Rushworth MFS

    (2011) Social network size affects neural circuits in macaques

    Science 334:697–700.

    https://doi.org/10.1126/science.1210027

    • PubMed
    • Google Scholar
50. 1. Saxe R

    (2006) Uniquely human social cognition

    Current Opinion in Neurobiology 16:235–239.

    https://doi.org/10.1016/j.conb.2006.03.001

    • PubMed
    • Google Scholar
51. 1. Schurz M
    2. Tholen MG
    3. Perner J
    4. Mars RB
    5. Sallet J

    (2017) Specifying the brain anatomy underlying temporo-parietal junction activations for theory of mind: a review using probabilistic atlases from different imaging modalities

    Human Brain Mapping 38:4788–4805.

    https://doi.org/10.1002/hbm.23675

    • PubMed
    • Google Scholar
52. 1. Shafto P
    2. Goodman ND
    3. Frank MC

    (2012) Learning from others: the consequences of psychological Reasoning for human learning

    Perspectives on Psychological Science 7:341–351.

    https://doi.org/10.1177/1745691612448481

    • PubMed
    • Google Scholar
53. 1. Sliwa J
    2. Freiwald WA

    (2017) A dedicated network for social interaction processing in the primate brain

    Science 356:745–749.

    https://doi.org/10.1126/science.aam6383

    • PubMed
    • Google Scholar
54. 1. Smith SM
    2. Jenkinson M
    3. Woolrich MW
    4. Beckmann CF
    5. Behrens TEJ
    6. Johansen-Berg H
    7. Bannister PR
    8. De Luca M
    9. Drobnjak I
    10. Flitney DE
    11. Niazy RK
    12. Saunders J
    13. Vickers J
    14. Zhang Y
    15. De Stefano N
    16. Brady JM
    17. Matthews PM

    (2004) Advances in functional and structural mr image analysis and implementation as fsl

    NeuroImage 23 Suppl 1:S208–S219.

    https://doi.org/10.1016/j.neuroimage.2004.07.051

    • PubMed
    • Google Scholar
55. 1. Soutschek A
    2. Ruff CC
    3. Strombach T
    4. Kalenscher T
    5. Tobler PN

    (2016) Brain stimulation reveals crucial role of overcoming self-centeredness in self-control

    Science Advances 2:e1600992.

    https://doi.org/10.1126/sciadv.1600992

    • PubMed
    • Google Scholar
56. 1. Suzuki S
    2. Harasawa N
    3. Ueno K
    4. Gardner JL
    5. Ichinohe N
    6. Haruno M
    7. Cheng K
    8. Nakahara H

    (2012) Learning to simulate others’ decisions

    Neuron 74:1125–1137.

    https://doi.org/10.1016/j.neuron.2012.04.030

    • PubMed
    • Google Scholar
57. 1. Trudel N
    2. Scholl J
    3. Klein-Flügge MC
    4. Fouragnan E
    5. Tankelevitch L
    6. Wittmann MK
    7. Rushworth MFS

    (2021) Polarity of uncertainty representation during exploration and exploitation in ventromedial prefrontal cortex

    Nature Human Behaviour 5:83–98.

    https://doi.org/10.1038/s41562-020-0929-3

    • PubMed
    • Google Scholar
58. 1. Will GJ
    2. Rutledge RB
    3. Moutoussis M
    4. Dolan RJ

    (2017) Neural and computational processes underlying dynamic changes in self-esteem

    eLife 6:e28098.

    https://doi.org/10.7554/eLife.28098

    • PubMed
    • Google Scholar
59. 1. Wittmann MK
    2. Kolling N
    3. Faber NS
    4. Scholl J
    5. Nelissen N
    6. Rushworth MFS

    (2016) Self-other mergence in the frontal cortex during cooperation and competition

    Neuron 91:482–493.

    https://doi.org/10.1016/j.neuron.2016.06.022

    • PubMed
    • Google Scholar
60. 1. Wittmann MK
    2. Lockwood PL
    3. Rushworth MFS

    (2018) Neural mechanisms of social cognition in primates

    Annual Review of Neuroscience 41:99–118.

    https://doi.org/10.1146/annurev-neuro-080317-061450

    • PubMed
    • Google Scholar
61. 1. Wittmann MK
    2. Fouragnan E
    3. Folloni D
    4. Klein-Flügge MC
    5. Chau BKH
    6. Khamassi M
    7. Rushworth MFS

    (2020) Global reward state affects learning and activity in raphe nucleus and anterior insula in monkeys

    Nature Communications 11:3771.

    https://doi.org/10.1038/s41467-020-17343-w

    • PubMed
    • Google Scholar
62. 1. Wittmann MK
    2. Trudel N
    3. Trier HA
    4. Klein-Flügge MC
    5. Sel A
    6. Verhagen L
    7. Rushworth MFS

    (2021) Causal manipulation of self-other mergence in the dorsomedial prefrontal cortex

    Neuron 109:2353–2361.

    https://doi.org/10.1016/j.neuron.2021.05.027

    • PubMed
    • Google Scholar
63. 1. Yoshida K
    2. Saito N
    3. Iriki A
    4. Isoda M

    (2012) Social error monitoring in macaque frontal cortex

    Nature Neuroscience 15:1307–1312.

    https://doi.org/10.1038/nn.3180

    • PubMed
    • Google Scholar
64. Conference

    1. Yu AJ
    2. Cohen JD

    (2008)

    Sequential effects: superstition or rational behavior?

    Advances in Neural Information Processing Systems. pp. 1873–1880.

    • Google Scholar
65. 1. Zink CF
    2. Tong Y
    3. Chen Q
    4. Bassett DS
    5. Stein JL
    6. Meyer-Lindenberg A

    (2008) Know your place: neural processing of social hierarchy in humans

    Neuron 58:273–283.

    https://doi.org/10.1016/j.neuron.2008.01.025

    • PubMed
    • Google Scholar

Author details

1. Nadescha Trudel

   1. Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
   2. Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
   3. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   nadeschatrudel@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9372-3640

2. Patricia L Lockwood

   1. Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
   2. Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
   3. Centre for Developmental Science, School of Psychology, University of Birmingham, Birmingham, United Kingdom

   Contribution

   Resources, Software, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Matthew FS Rushworth

   1. Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
   2. Wellcome Centre of Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing – review and editing, Investigation, Resources

   Contributed equally with

   Marco K Wittmann

   Competing interests

   No competing interests declared

4. Marco K Wittmann

   1. Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
   2. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
   3. Department of Experimental Psychology, University College London, London, United Kingdom

   Contribution

   Conceptualization, Supervision, Methodology, Writing – review and editing, Resources

   Contributed equally with

   Matthew FS Rushworth

   For correspondence

   m.wittmann@ucl.ac.uk

   Competing interests

   No competing interests declared

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