Electrical brain activity in the expectancy phase. ( A ) Time – frequency charts of differences between HR and LR offers using dB unit (10 × [log 10 (HR) − log 10 (LR)]). The right panel shows the scalp distribution and the estimated source of power differences between conditions. ( B ) Trial-by-trial analysis using mixed linear model. Color represents the t -value of areas with signi fi cant correlation between the amplitude of early alpha band and logit of the probability of acceptances (logit( A )), and signi fi cant correlation between the amplitude of late alpha band and logit of the entropy (logit( E )). In all scalp distributions and source estimates, we show only signi fi cant clusters. See also Supplementary Figure 5 and Tables 6 – 9. 

Contexts in source publication

Context 1

... and p3), we carried out 2 control analyses. In one we computed the MFN amplitude relative to neighboring positive components (( p3 + p2)/2 – MFN, UR = 0.253 μ V/cm 2 , ER = 0.173 μ V/cm 2 , P = 0.0012). In the other analysis, we fi xed the most negative point occurring in the time window 250 – 300 ms per subject to compute the mean across subjects ( P < 0.001). In both analyses, the MFN difference remained signi fi cant. In accordance with previous studies, the signi fi cant differences in the estimated cortical sources of this component were located in the mPFC and in the medial posterior region (Miltner et al. 1997; Gehring and Willoughby 2002; Luu et al. 2003; Polezzi et al. 2010). We then explored the time – frequency dynamics during the feedback phase and found that risk modulated activity in 2 frequency bands (Fig. 3). In frontal regions, between 240 and 400 ms after the feedback, URs presented a greater upper alpha band power (10 – 15 Hz) than that of ERs (main peak in FCz electrode, P = 0.0001 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 3 and see Supplementary Table 4). In a more extended time window (250 – 700 ms), theta band activity (4 – 7 Hz) was characterized by a stronger increase in power after URs (main peak in FCz electrode, P = 0.00004 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 3 and see Supplementary Table 4). We therefore focused on these time – frequency windows of interest for further analysis (see the Materials and Methods section). In agreement with previous reports (Christie and Tata 2009), the differences in the estimated cortical sources of theta activity were located in the mPFC, mainly on the right hemisphere, and in the medial posterior region. The sources of high alpha activity were located in the mPFC and lateral prefrontal cortex (lPFC; see also Supplementary Fig. 3 for comparison between different alpha ranges). Since expectations changed theta activity, we evaluated whether the degree of risk modulated activity in these frequency bands. For this end, we carried out a trial-by-trial analysis using a mixed linear regression. We estimated the degree of risk using both the probability of acceptance (using the logit transform of the probability of acceptance, logit( A )) and the Shannon entropy of the possible responses (logit( E )). In both cases, we used the coef fi cients of the logistic mixed model of the behavioral study (Table 1). Due to the fact that theta activity may correlate with both responses and expectations, we separated activity related to rejections from that related to acceptances and included the response as a factor in the model. In this analysis, logit( A ) signi fi cantly predicted theta power in rejections (main peaks in Fz and FCz electrodes, P < 0.01 corrected by FDR, Fig. 3 and see Supplementary Table 5). Interestingly, when we added the logit ( E ) (as a measure of the variability of the possible outcomes), it did not turn out to be a signi fi cant predictor. The source estimation of the logit( A ) coef fi cient in rejections was located in the right lPFC, mPFC, and medial posterior region and the source of the responses (as a factor) was located in mPFC and lPFC. In the case of acceptances, logit( A ) sources were placed in mPFC, but these did not survive the multiple comparison correction. All of these results were replicated using a wavelets transform, suggesting that our results are not an artifact of the analytical method (see Supplementary Fig. 4). None of these analyses was signi fi cant for upper alpha band activity. Since feedback-related brain activity changed under different risk perceptions, proposers should have had different anticipatory-related brain activity according to the way they estimated how risky the proposal they had just made was. In order to study this, we explored the period after proposers sent their offers but prior to receiving the corresponding answer from the responder. As in the feedback phase, we fi rst made an exploratory analysis of brain oscillatory activity. In right-central electrodes, we found clear differential anticipatory activity in alpha bands. Alpha activity presented a signi fi cant right-lateralized drop in HR offers in the 400 – 800 ms window after the offer was made (mean peaks in TP8 and FC4 electrodes, P < 0001 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 4 and see Supplementary Table 6). Brain sources estimated for this difference were distributed and included temporo-parietal regions, the left lPFC and the right insular cortex. We therefore focused on these time – frequency windows of interest in order to evaluate whether alpha power depended on risk. For this, we used logit( A ) and logit( E ) as measures of risk. Interestingly, trial-by-trial alpha power correlated positively with logit ( A ) (using a mixed linear model, main effect in FT8 and TP8 electrodes, P < 0.01 corrected by FDR, Fig. 4 and see Supplementary Table 7). Notably, unlike feedback theta activity, when we added the logit( E ) to the model, it also resulted in a signi fi cant predictor (see Supplementary Tables 8 and 9). Previous studies have shown speci fi c temporal dynamics of mesolimbic dopaminergic neurons related to the probabilistic reward anticipation. Some neurons present a transient activation that correlates with the probability of the reward, whereas other neurons present a tonic activation (increasing over the time interval), which is related to the variability or uncertainty of the reward (Schultz 1998; Schultz et al. 2008). To evaluate whether alpha activity has a similar temporal dynamics, we examined an additional late time window (late alpha, − 700 to − 200 ms before the feedback). Interestingly, early alpha fi tted best with the probability, while late alpha correlated better with the entropy (see Supplementary Table 9). In the brain source space, the correlation between early alpha and logit( A ) included the temporo-parietal regions and the insular cortex of the right hemisphere. Interestingly, the correlation between late alpha and logit( E ) included sources in the bilateral medial frontal cortex (Fig. 4). Like in the case of brain activity during the feedback phase, these results were also replicated using a wavelets transform (see Supplementary Fig. 5). As discussed previously, the proposer ’ s behavior was in fl u- enced by both the risk of the preceding offer and the corresponding responder ’ s feedback. This became evident in the change of the next offer in both experiments (Fig. 2 E and see Supplementary S1). Thus, each risk – response combination was followed by a particular change of offer (ER: $+8.1, UA: $ +4.3, UR: $ − 2.1 and EA: $ − 4.5; P < 0.001). Due to the fact that theta and alpha bands showed speci fi c risk modulations, they could underlie fi ne behavioral adaptation processes in social interactions. For example, prior reports suggest that frontal theta band signals error prediction in the context of behavioral adaptation (Cavanagh et al. 2010). To look into this possibility, we analyzed whether the power of these frequency bands predicted the next behavior in a trial-by-trial analysis. Importantly, feedback theta power and expectancy late alpha power together predicted signi fi cantly the next change of offer (alpha main peak TP8 electrode, P < 0.001 uncorrected; theta main effect in FCz and FP1 electrodes, P < 0.001 uncorrected; Fig. 5 and see Supplementary Tables 10 and 11). In accordance with an overall negotiating attitude, we found a strong negative correlation between the current change of offer and the next change of offer (Spearman ’ s ρ = − 0.41, P < 0.001). In other words, after an HR offer, the proposers were willing to make an LR offer and vice versa. This prompted us to include the current change of offer in the model. We were thus able to evaluate whether oscillatory activity underlies behavioral regulation necessary for this social negotiation and is not just an epiphe- nomenon of it. Interestingly, in this model, late alpha and feedback theta were signi fi cant predictors for the next change of offer. In the brain source space, late alpha prediction was associated with activity in the right temporo-parietal region and left insular cortex, and theta prediction was related to activity in lPFC, mPFC, and posterior cingulate cortex (PCC) for rejections. In the case of acceptances, the sources in mPFC did not survive the multiple comparison correction. As in previous analyses, results were replicated using wavelets transform (see Supplementary Fig. 6), con fi rming that our results where not biased by the use of the windowed fast-Fourier transform approach. Theta band activity, thereby, seems to re fl ect brain mechanisms that sense the prediction error: After an acceptance, proposers tend to decrease their offers and, indeed, in this case, theta activity correlated negatively with risk and predicted a smaller decrease in the next offer (Fig. 5; see Supplementary Table 11). In contrast, after a rejection, proposers tend to increase the offer. In this case, theta correlated positively with risk and predicted a smaller increase in the next offer. On the other hand, the fall in alpha band seems to re fl ect a prepara- tory activity for a possible rejection. Accordingly, it correlated with risk and predicted, only when a rejection occurred, an increase in the next offer. People participating in social interactions are able to distinguish safe from risky decisions. In making this distinction, people integrate others ’ intentions and the way others perceive their own intentions. In order to investigate how this integration occurs both at the behavioral and the neural levels, the current study examined individuals playing the iterated ultimatum game in a behavioral and an EEG experiment. The behavioral results indicate that responders reject offers that are far from the ...

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... taken in the next round. In the EEG study, subjects played as proposers against simulated partners whom they believed were human. We used the coef fi cients of the logistic mixed model to simulate responders (see the Materials and Methods section). The distributions of acceptances and rejections, and the offering behaviors related to a rejection were similar to the corresponding ones in the behavioral study (Fig. 2 and see Supplementary Fig. 1), suggesting that simulated responders elicited comparable behaviors in proposers. If our risk classi fi cation was consistent with the risk perceived by the proposer, the error-related activity evoked by the unexpected responses should be larger than that evoked by the expected responses. Speci fi cally, when proposers perceive LRs for their offers, rejections should elicit a higher MFN than when they perceive HRs. To test this hypothesis, we analyzed the time window when a proposer received a rejection. As conjectured, we found a signi fi cant difference between the MFN component of URs and that of ERs in fronto-medial electrodes (main peak in FCz electrode, P = 0.00003 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, see Fig. 3 and Supplementary Table 3 and Fig. 3 for the time course of the evoked response over all scalp electrodes). To rule out the possibility that this difference was due to differences arising from neighboring positive components (mainly p2 and p3), we carried out 2 control analyses. In one we computed the MFN amplitude relative to neighboring positive components (( p3 + p2)/2 – MFN, UR = 0.253 μ V/cm 2 , ER = 0.173 μ V/cm 2 , P = 0.0012). In the other analysis, we fi xed the most negative point occurring in the time window 250 – 300 ms per subject to compute the mean across subjects ( P < 0.001). In both analyses, the MFN difference remained signi fi cant. In accordance with previous studies, the signi fi cant differences in the estimated cortical sources of this component were located in the mPFC and in the medial posterior region (Miltner et al. 1997; Gehring and Willoughby 2002; Luu et al. 2003; Polezzi et al. 2010). We then explored the time – frequency dynamics during the feedback phase and found that risk modulated activity in 2 frequency bands (Fig. 3). In frontal regions, between 240 and 400 ms after the feedback, URs presented a greater upper alpha band power (10 – 15 Hz) than that of ERs (main peak in FCz electrode, P = 0.0001 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 3 and see Supplementary Table 4). In a more extended time window (250 – 700 ms), theta band activity (4 – 7 Hz) was characterized by a stronger increase in power after URs (main peak in FCz electrode, P = 0.00004 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 3 and see Supplementary Table 4). We therefore focused on these time – frequency windows of interest for further analysis (see the Materials and Methods section). In agreement with previous reports (Christie and Tata 2009), the differences in the estimated cortical sources of theta activity were located in the mPFC, mainly on the right hemisphere, and in the medial posterior region. The sources of high alpha activity were located in the mPFC and lateral prefrontal cortex (lPFC; see also Supplementary Fig. 3 for comparison between different alpha ranges). Since expectations changed theta activity, we evaluated whether the degree of risk modulated activity in these frequency bands. For this end, we carried out a trial-by-trial analysis using a mixed linear regression. We estimated the degree of risk using both the probability of acceptance (using the logit transform of the probability of acceptance, logit( A )) and the Shannon entropy of the possible responses (logit( E )). In both cases, we used the coef fi cients of the logistic mixed model of the behavioral study (Table 1). Due to the fact that theta activity may correlate with both responses and expectations, we separated activity related to rejections from that related to acceptances and included the response as a factor in the model. In this analysis, logit( A ) signi fi cantly predicted theta power in rejections (main peaks in Fz and FCz electrodes, P < 0.01 corrected by FDR, Fig. 3 and see Supplementary Table 5). Interestingly, when we added the logit ( E ) (as a measure of the variability of the possible outcomes), it did not turn out to be a signi fi cant predictor. The source estimation of the logit( A ) coef fi cient in rejections was located in the right lPFC, mPFC, and medial posterior region and the source of the responses (as a factor) was located in mPFC and lPFC. In the case of acceptances, logit( A ) sources were placed in mPFC, but these did not survive the multiple comparison correction. All of these results were replicated using a wavelets transform, suggesting that our results are not an artifact of the analytical method (see Supplementary Fig. 4). None of these analyses was signi fi cant for upper alpha band activity. Since feedback-related brain activity changed under different risk perceptions, proposers should have had different anticipatory-related brain activity according to the way they estimated how risky the proposal they had just made was. In order to study this, we explored the period after proposers sent their offers but prior to receiving the corresponding answer from the responder. As in the feedback phase, we fi rst made an exploratory analysis of brain oscillatory activity. In right-central electrodes, we found clear differential anticipatory activity in alpha bands. Alpha activity presented a signi fi cant right-lateralized drop in HR offers in the 400 – 800 ms window after the offer was made (mean peaks in TP8 and FC4 electrodes, P < 0001 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 4 and see Supplementary Table 6). Brain sources estimated for this difference were distributed and included temporo-parietal regions, the left lPFC and the right insular cortex. We therefore focused on these time – frequency windows of interest in order to evaluate whether alpha power depended on risk. For this, we used logit( A ) and logit( E ) as measures of risk. Interestingly, trial-by-trial alpha power correlated positively with logit ( A ) (using a mixed linear model, main effect in FT8 and TP8 electrodes, P < 0.01 corrected by FDR, Fig. 4 and see Supplementary Table 7). Notably, unlike feedback theta activity, when we added the logit( E ) to the model, it also resulted in a signi fi cant predictor (see Supplementary Tables 8 and 9). Previous studies have shown speci fi c temporal dynamics of mesolimbic dopaminergic neurons related to the probabilistic reward anticipation. Some neurons present a transient activation that correlates with the probability of the reward, whereas other neurons present a tonic activation (increasing over the time interval), which is related to the variability or uncertainty of the reward (Schultz 1998; Schultz et al. 2008). To evaluate whether alpha activity has a similar temporal dynamics, we examined an additional late time window (late alpha, − 700 to − 200 ms before the feedback). Interestingly, early alpha fi tted best with the probability, while late alpha correlated better with the entropy (see Supplementary Table 9). In the brain source space, the correlation between early alpha and logit( A ) included the temporo-parietal regions and the insular cortex of the right hemisphere. Interestingly, the correlation between late alpha and logit( E ) included sources in the bilateral medial frontal cortex (Fig. 4). Like in the case of brain activity during the feedback phase, these results were also replicated using a wavelets transform (see Supplementary Fig. 5). As discussed previously, the proposer ’ s behavior was in fl u- enced by both the risk of the preceding offer and the corresponding responder ’ s feedback. This became evident in the change of the next offer in both experiments (Fig. 2 E and see Supplementary S1). Thus, each risk – response combination was followed by a particular change of offer (ER: $+8.1, UA: $ +4.3, UR: $ − 2.1 and EA: $ − 4.5; P < 0.001). Due to the fact that theta and alpha bands showed speci fi c risk modulations, they could underlie fi ne behavioral adaptation processes in social interactions. For example, prior reports suggest that frontal theta band signals error prediction in the context of behavioral adaptation (Cavanagh et al. 2010). To look into this possibility, we analyzed whether the power of these frequency bands predicted the next behavior in a trial-by-trial analysis. Importantly, feedback theta power and expectancy late alpha power together predicted signi fi cantly the next change of offer (alpha main peak TP8 electrode, P < 0.001 uncorrected; theta main effect in FCz and FP1 electrodes, P < 0.001 uncorrected; Fig. 5 and see Supplementary Tables 10 and 11). In accordance with an overall negotiating attitude, we found a strong negative correlation between the current change of offer and the next change of offer (Spearman ’ s ρ = − 0.41, P < 0.001). In other words, after an HR offer, the proposers were willing to make an LR offer and vice versa. This prompted us to include the current change of offer in the model. We were thus able to evaluate whether oscillatory activity underlies behavioral regulation necessary for this social negotiation and is not just an epiphe- nomenon of it. Interestingly, in this model, late alpha and feedback theta were signi fi cant predictors for the next change of offer. In the brain source space, late alpha prediction was associated with activity in the right temporo-parietal region and left insular cortex, and theta prediction was related to activity in lPFC, mPFC, and posterior cingulate cortex (PCC) for rejections. In the case of acceptances, the sources in mPFC did not survive the multiple ...

Context 3

... the time window when a proposer received a rejection. As conjectured, we found a signi fi cant difference between the MFN component of URs and that of ERs in fronto-medial electrodes (main peak in FCz electrode, P = 0.00003 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, see Fig. 3 and Supplementary Table 3 and Fig. 3 for the time course of the evoked response over all scalp electrodes). To rule out the possibility that this difference was due to differences arising from neighboring positive components (mainly p2 and p3), we carried out 2 control analyses. In one we computed the MFN amplitude relative to neighboring positive components (( p3 + p2)/2 – MFN, UR = 0.253 μ V/cm 2 , ER = 0.173 μ V/cm 2 , P = 0.0012). In the other analysis, we fi xed the most negative point occurring in the time window 250 – 300 ms per subject to compute the mean across subjects ( P < 0.001). In both analyses, the MFN difference remained signi fi cant. In accordance with previous studies, the signi fi cant differences in the estimated cortical sources of this component were located in the mPFC and in the medial posterior region (Miltner et al. 1997; Gehring and Willoughby 2002; Luu et al. 2003; Polezzi et al. 2010). We then explored the time – frequency dynamics during the feedback phase and found that risk modulated activity in 2 frequency bands (Fig. 3). In frontal regions, between 240 and 400 ms after the feedback, URs presented a greater upper alpha band power (10 – 15 Hz) than that of ERs (main peak in FCz electrode, P = 0.0001 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 3 and see Supplementary Table 4). In a more extended time window (250 – 700 ms), theta band activity (4 – 7 Hz) was characterized by a stronger increase in power after URs (main peak in FCz electrode, P = 0.00004 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 3 and see Supplementary Table 4). We therefore focused on these time – frequency windows of interest for further analysis (see the Materials and Methods section). In agreement with previous reports (Christie and Tata 2009), the differences in the estimated cortical sources of theta activity were located in the mPFC, mainly on the right hemisphere, and in the medial posterior region. The sources of high alpha activity were located in the mPFC and lateral prefrontal cortex (lPFC; see also Supplementary Fig. 3 for comparison between different alpha ranges). Since expectations changed theta activity, we evaluated whether the degree of risk modulated activity in these frequency bands. For this end, we carried out a trial-by-trial analysis using a mixed linear regression. We estimated the degree of risk using both the probability of acceptance (using the logit transform of the probability of acceptance, logit( A )) and the Shannon entropy of the possible responses (logit( E )). In both cases, we used the coef fi cients of the logistic mixed model of the behavioral study (Table 1). Due to the fact that theta activity may correlate with both responses and expectations, we separated activity related to rejections from that related to acceptances and included the response as a factor in the model. In this analysis, logit( A ) signi fi cantly predicted theta power in rejections (main peaks in Fz and FCz electrodes, P < 0.01 corrected by FDR, Fig. 3 and see Supplementary Table 5). Interestingly, when we added the logit ( E ) (as a measure of the variability of the possible outcomes), it did not turn out to be a signi fi cant predictor. The source estimation of the logit( A ) coef fi cient in rejections was located in the right lPFC, mPFC, and medial posterior region and the source of the responses (as a factor) was located in mPFC and lPFC. In the case of acceptances, logit( A ) sources were placed in mPFC, but these did not survive the multiple comparison correction. All of these results were replicated using a wavelets transform, suggesting that our results are not an artifact of the analytical method (see Supplementary Fig. 4). None of these analyses was signi fi cant for upper alpha band activity. Since feedback-related brain activity changed under different risk perceptions, proposers should have had different anticipatory-related brain activity according to the way they estimated how risky the proposal they had just made was. In order to study this, we explored the period after proposers sent their offers but prior to receiving the corresponding answer from the responder. As in the feedback phase, we fi rst made an exploratory analysis of brain oscillatory activity. In right-central electrodes, we found clear differential anticipatory activity in alpha bands. Alpha activity presented a signi fi cant right-lateralized drop in HR offers in the 400 – 800 ms window after the offer was made (mean peaks in TP8 and FC4 electrodes, P < 0001 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 4 and see Supplementary Table 6). Brain sources estimated for this difference were distributed and included temporo-parietal regions, the left lPFC and the right insular cortex. We therefore focused on these time – frequency windows of interest in order to evaluate whether alpha power depended on risk. For this, we used logit( A ) and logit( E ) as measures of risk. Interestingly, trial-by-trial alpha power correlated positively with logit ( A ) (using a mixed linear model, main effect in FT8 and TP8 electrodes, P < 0.01 corrected by FDR, Fig. 4 and see Supplementary Table 7). Notably, unlike feedback theta activity, when we added the logit( E ) to the model, it also resulted in a signi fi cant predictor (see Supplementary Tables 8 and 9). Previous studies have shown speci fi c temporal dynamics of mesolimbic dopaminergic neurons related to the probabilistic reward anticipation. Some neurons present a transient activation that correlates with the probability of the reward, whereas other neurons present a tonic activation (increasing over the time interval), which is related to the variability or uncertainty of the reward (Schultz 1998; Schultz et al. 2008). To evaluate whether alpha activity has a similar temporal dynamics, we examined an additional late time window (late alpha, − 700 to − 200 ms before the feedback). Interestingly, early alpha fi tted best with the probability, while late alpha correlated better with the entropy (see Supplementary Table 9). In the brain source space, the correlation between early alpha and logit( A ) included the temporo-parietal regions and the insular cortex of the right hemisphere. Interestingly, the correlation between late alpha and logit( E ) included sources in the bilateral medial frontal cortex (Fig. 4). Like in the case of brain activity during the feedback phase, these results were also replicated using a wavelets transform (see Supplementary Fig. 5). As discussed previously, the proposer ’ s behavior was in fl u- enced by both the risk of the preceding offer and the corresponding responder ’ s feedback. This became evident in the change of the next offer in both experiments (Fig. 2 E and see Supplementary S1). Thus, each risk – response combination was followed by a particular change of offer (ER: $+8.1, UA: $ +4.3, UR: $ − 2.1 and EA: $ − 4.5; P < 0.001). Due to the fact that theta and alpha bands showed speci fi c risk modulations, they could underlie fi ne behavioral adaptation processes in social interactions. For example, prior reports suggest that frontal theta band signals error prediction in the context of behavioral adaptation (Cavanagh et al. 2010). To look into this possibility, we analyzed whether the power of these frequency bands predicted the next behavior in a trial-by-trial analysis. Importantly, feedback theta power and expectancy late alpha power together predicted signi fi cantly the next change of offer (alpha main peak TP8 electrode, P < 0.001 uncorrected; theta main effect in FCz and FP1 electrodes, P < 0.001 uncorrected; Fig. 5 and see Supplementary Tables 10 and 11). In accordance with an overall negotiating attitude, we found a strong negative correlation between the current change of offer and the next change of offer (Spearman ’ s ρ = − 0.41, P < 0.001). In other words, after an HR offer, the proposers were willing to make an LR offer and vice versa. This prompted us to include the current change of offer in the model. We were thus able to evaluate whether oscillatory activity underlies behavioral regulation necessary for this social negotiation and is not just an epiphe- nomenon of it. Interestingly, in this model, late alpha and feedback theta were signi fi cant predictors for the next change of offer. In the brain source space, late alpha prediction was associated with activity in the right temporo-parietal region and left insular cortex, and theta prediction was related to activity in lPFC, mPFC, and posterior cingulate cortex (PCC) for rejections. In the case of acceptances, the sources in mPFC did not survive the multiple comparison correction. As in previous analyses, results were replicated using wavelets transform (see Supplementary Fig. 6), con fi rming that our results where not biased by the use of the windowed fast-Fourier transform approach. Theta band activity, thereby, seems to re fl ect brain mechanisms that sense the prediction error: After an acceptance, proposers tend to decrease their offers and, indeed, in this case, theta activity correlated negatively with risk and predicted a smaller decrease in the next offer (Fig. 5; see Supplementary Table 11). In contrast, after a rejection, proposers tend to increase the offer. In this case, theta correlated positively with risk and predicted a smaller increase in the next offer. On the other hand, the fall in alpha band seems to re fl ect a prepara- tory activity for a possible rejection. Accordingly, it correlated with risk and predicted, only when a ...

Context 4

... by a stronger increase in power after URs (main peak in FCz electrode, P = 0.00004 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 3 and see Supplementary Table 4). We therefore focused on these time – frequency windows of interest for further analysis (see the Materials and Methods section). In agreement with previous reports (Christie and Tata 2009), the differences in the estimated cortical sources of theta activity were located in the mPFC, mainly on the right hemisphere, and in the medial posterior region. The sources of high alpha activity were located in the mPFC and lateral prefrontal cortex (lPFC; see also Supplementary Fig. 3 for comparison between different alpha ranges). Since expectations changed theta activity, we evaluated whether the degree of risk modulated activity in these frequency bands. For this end, we carried out a trial-by-trial analysis using a mixed linear regression. We estimated the degree of risk using both the probability of acceptance (using the logit transform of the probability of acceptance, logit( A )) and the Shannon entropy of the possible responses (logit( E )). In both cases, we used the coef fi cients of the logistic mixed model of the behavioral study (Table 1). Due to the fact that theta activity may correlate with both responses and expectations, we separated activity related to rejections from that related to acceptances and included the response as a factor in the model. In this analysis, logit( A ) signi fi cantly predicted theta power in rejections (main peaks in Fz and FCz electrodes, P < 0.01 corrected by FDR, Fig. 3 and see Supplementary Table 5). Interestingly, when we added the logit ( E ) (as a measure of the variability of the possible outcomes), it did not turn out to be a signi fi cant predictor. The source estimation of the logit( A ) coef fi cient in rejections was located in the right lPFC, mPFC, and medial posterior region and the source of the responses (as a factor) was located in mPFC and lPFC. In the case of acceptances, logit( A ) sources were placed in mPFC, but these did not survive the multiple comparison correction. All of these results were replicated using a wavelets transform, suggesting that our results are not an artifact of the analytical method (see Supplementary Fig. 4). None of these analyses was signi fi cant for upper alpha band activity. Since feedback-related brain activity changed under different risk perceptions, proposers should have had different anticipatory-related brain activity according to the way they estimated how risky the proposal they had just made was. In order to study this, we explored the period after proposers sent their offers but prior to receiving the corresponding answer from the responder. As in the feedback phase, we fi rst made an exploratory analysis of brain oscillatory activity. In right-central electrodes, we found clear differential anticipatory activity in alpha bands. Alpha activity presented a signi fi cant right-lateralized drop in HR offers in the 400 – 800 ms window after the offer was made (mean peaks in TP8 and FC4 electrodes, P < 0001 uncorrected, and P < 0.001 corrected by the cluster-based permutation test, Fig. 4 and see Supplementary Table 6). Brain sources estimated for this difference were distributed and included temporo-parietal regions, the left lPFC and the right insular cortex. We therefore focused on these time – frequency windows of interest in order to evaluate whether alpha power depended on risk. For this, we used logit( A ) and logit( E ) as measures of risk. Interestingly, trial-by-trial alpha power correlated positively with logit ( A ) (using a mixed linear model, main effect in FT8 and TP8 electrodes, P < 0.01 corrected by FDR, Fig. 4 and see Supplementary Table 7). Notably, unlike feedback theta activity, when we added the logit( E ) to the model, it also resulted in a signi fi cant predictor (see Supplementary Tables 8 and 9). Previous studies have shown speci fi c temporal dynamics of mesolimbic dopaminergic neurons related to the probabilistic reward anticipation. Some neurons present a transient activation that correlates with the probability of the reward, whereas other neurons present a tonic activation (increasing over the time interval), which is related to the variability or uncertainty of the reward (Schultz 1998; Schultz et al. 2008). To evaluate whether alpha activity has a similar temporal dynamics, we examined an additional late time window (late alpha, − 700 to − 200 ms before the feedback). Interestingly, early alpha fi tted best with the probability, while late alpha correlated better with the entropy (see Supplementary Table 9). In the brain source space, the correlation between early alpha and logit( A ) included the temporo-parietal regions and the insular cortex of the right hemisphere. Interestingly, the correlation between late alpha and logit( E ) included sources in the bilateral medial frontal cortex (Fig. 4). Like in the case of brain activity during the feedback phase, these results were also replicated using a wavelets transform (see Supplementary Fig. 5). As discussed previously, the proposer ’ s behavior was in fl u- enced by both the risk of the preceding offer and the corresponding responder ’ s feedback. This became evident in the change of the next offer in both experiments (Fig. 2 E and see Supplementary S1). Thus, each risk – response combination was followed by a particular change of offer (ER: $+8.1, UA: $ +4.3, UR: $ − 2.1 and EA: $ − 4.5; P < 0.001). Due to the fact that theta and alpha bands showed speci fi c risk modulations, they could underlie fi ne behavioral adaptation processes in social interactions. For example, prior reports suggest that frontal theta band signals error prediction in the context of behavioral adaptation (Cavanagh et al. 2010). To look into this possibility, we analyzed whether the power of these frequency bands predicted the next behavior in a trial-by-trial analysis. Importantly, feedback theta power and expectancy late alpha power together predicted signi fi cantly the next change of offer (alpha main peak TP8 electrode, P < 0.001 uncorrected; theta main effect in FCz and FP1 electrodes, P < 0.001 uncorrected; Fig. 5 and see Supplementary Tables 10 and 11). In accordance with an overall negotiating attitude, we found a strong negative correlation between the current change of offer and the next change of offer (Spearman ’ s ρ = − 0.41, P < 0.001). In other words, after an HR offer, the proposers were willing to make an LR offer and vice versa. This prompted us to include the current change of offer in the model. We were thus able to evaluate whether oscillatory activity underlies behavioral regulation necessary for this social negotiation and is not just an epiphe- nomenon of it. Interestingly, in this model, late alpha and feedback theta were signi fi cant predictors for the next change of offer. In the brain source space, late alpha prediction was associated with activity in the right temporo-parietal region and left insular cortex, and theta prediction was related to activity in lPFC, mPFC, and posterior cingulate cortex (PCC) for rejections. In the case of acceptances, the sources in mPFC did not survive the multiple comparison correction. As in previous analyses, results were replicated using wavelets transform (see Supplementary Fig. 6), con fi rming that our results where not biased by the use of the windowed fast-Fourier transform approach. Theta band activity, thereby, seems to re fl ect brain mechanisms that sense the prediction error: After an acceptance, proposers tend to decrease their offers and, indeed, in this case, theta activity correlated negatively with risk and predicted a smaller decrease in the next offer (Fig. 5; see Supplementary Table 11). In contrast, after a rejection, proposers tend to increase the offer. In this case, theta correlated positively with risk and predicted a smaller increase in the next offer. On the other hand, the fall in alpha band seems to re fl ect a prepara- tory activity for a possible rejection. Accordingly, it correlated with risk and predicted, only when a rejection occurred, an increase in the next offer. People participating in social interactions are able to distinguish safe from risky decisions. In making this distinction, people integrate others ’ intentions and the way others perceive their own intentions. In order to investigate how this integration occurs both at the behavioral and the neural levels, the current study examined individuals playing the iterated ultimatum game in a behavioral and an EEG experiment. The behavioral results indicate that responders reject offers that are far from the social norm of 50%, although there are important interindividual variations. In our iterated version of the ultimatum game, prior behaviors of both players are the only way to infer both the other ’ s intentions and how the other perceives his/her partner ’ s intentions. Notably, previous behavior in fl uences signi fi cantly the responders ’ responses, showing less interindividual variations than the in fl uence of the absolute offer made. We conjecture that within the social norm compliance generated whenever punishment is possible (Fehr and Fischbancher 2004; Spitzer et al. 2007), prior behavior exerts a fi ne-tuning regulation on both risk perception and the expectation of subsequent behavior in an iterated interaction. Accordingly, we have proposed a classi fi cation based on preceding behavior in order to separate HR offers from LR offers. This classi fi cation not only distinguishes offers with high rejection probability and high variability (both cri- teria that de fi ne risk), but also particular situations in a social interaction that generate distinct subsequent behaviors. We used the MFN, an ERP component that has been related to error predictions (Holroyd and Coles 2002), as a neuronal marker ...