doi:10.1093/scan/nst006 SCAN (2014) 9, 412^ 420 Differential reward responses during competition against in- and out-of-network others Dominic S. Fareri and Mauricio R. Delgado Department of Psychology, Rutgers University, 101 Warren Street, Newark, NJ 07102, USA Social interactions occur within a variety of different contexts––cooperative/competitive––and often involve members of our social network. Here, we investigated whether social network modulated the value placed on positive outcomes during a competitive context. Eighteen human participants played a simple card-guessing game with three different competitors: a close friend (in-network), a confederate (out-of-network) and a random number generator (non-social condition) while undergoing functional magnetic resonance imaging. Neuroimaging results at the time of outcome receipt demonstrated a significant main effect of competitor across multiple regions of medial prefrontal cortex, with Blood Oxygen Level Dependent (BOLD) responses strongest when competing against ones friend compared with all other conditions. Striatal BOLD responses demonstrated a more general sensitivity to positive compared with negative monetary outcomes, which an exploratory analysis revealed to be stronger when interacting with social, compared with non-social, competitors. Interestingly, a Granger causality analysis indicated directed influences sent from an medial prefrontal cortex (mPFC) region, which shows social network differentiation of outcomes, and the ventral striatum bilaterally. Our results suggest that when competing against others of varying degrees of social network, mPFC differentially values these outcomes, perhaps treating in-network outcomes as more informative, leaving the striatum to more general value computations. Keywords: effective connectivity; medial prefrontal cortex; social network; striatum; valuation INTRODUCTION preferences––e.g. reciprocity, fairness and reputation––underlie social behavior (Berg et al., 1995; Fehr and Fischbacher, 2002; Fehr and Human behavior often occurs within varying social contexts that color Camerer, 2007). For example, achieving outcomes via mutual cooper- our daily experiences and decisions. We often seek out social rewards, ation with another person elicits stronger BOLD responses in corticos- such as looking for acceptance from others (Somerville et al., 2006), triatal reward circuitry compared with when acting selfishly or when which may be valued subjectively in putative neural reward circuitry mutually cooperating with a computer (Rilling et al., 2002) as well as (Izuma et al., 2008) akin to non-social rewards (Delgado et al., 2000; Knutson et al., 2001; O’Doherty et al., 2002; Knutson et al., 2003; compared with when one’s cooperation goes unreciprocated (Rilling Tricomi et al., 2006; Seymour et al., 2007). One interesting idea is et al., 2004). that the value of social rewards and the influence of social context Competitive contexts, on the other hand, require keeping track of may in part be driven by a fundamental need to feel accepted or others’ behavior so as to be able to outperform a competitor. Such belong (Baumeister and Leary, 1995) and a desire to form meaningful processes rely on cortical structures, particularly medial prefrontal relationships (van Winden et al., 2008), both of which can modulate cortex (mPFC), to monitor self and other performance (de Bruijn behavior. The mere chance to receive social approval for our actions et al., 2009; Howard-Jones et al., 2010) and code for outcomes increases pro-social tendencies (e.g. charitable giving) and more earned against another (Bault et al., 2011). A competitive social context strongly recruits reward circuitry than when there is no chance for has also been found to influence striatal BOLD signals, with responses approval (Izuma et al., 2010), and simply being in the presence of to losses in an auction correlating with a tendency to overbid (Delgado peers lends increased value to engaging in risk-taking behaviors et al., 2008). Taken together, these findings suggest diverging motives (Steinberg and Monahan, 2007; Chein et al., 2011). Furthermore, and mechanisms during cooperative and competitive contexts. both vicarious and shared positive experiences can receive differential As many of our interactions occur with members of our social value depending on whether they occur with others that are perceived networks, it is critical to understand how social network might affect as socially similar (vs dissimilar; Mobbs et al., 2009) or with someone the value placed on earned outcomes within these differing social con- from within (vs outside of) one’s social network (Fareri et al., 2012). texts. Sharing positive outcomes with a close, in-network partner more Taken together, these findings lend credence to the notion that social strongly recruits the striatum than sharing the same outcome with an context can influence neural signals involved in motivated behavior, in out-of-network other (Fareri et al., 2012), suggesting a higher value turn playing a significant role in our daily experiences. attached to outcomes shared with close others. However, it is unclear An interesting question arises, however, when considering that social how competing with an in-network other might affect outcome value; interactions can occur in contexts that are sometimes diametrically i.e. will earning a positive outcome against an in-network other carry opposed––e.g. cooperative vs competitive. Within cooperative social higher or lower value than against someone out-of-networks? To in- contexts, humans often act against their own self-interest, forgoing vestigate this, we administered a simple card-guessing task (adapted maximal personal gains for lesser gains that carry greater social value from Fareri et al., 2012) in which we manipulated participants’ com- in the long run; the motivation here is that concerns for social petitors and roles. Participants competed for separate pots of money against three different competitors: an in-network close friend, an Received 30 August 2012; Accepted 03 January 2013 Advance Access publication 12 January 2013 out-of-network other (confederate) and a random number generator The authors would like to thank Michael Niznikiewicz and Meredith Johnson for assistance with data collection (non-social control). Participants alternated roles between making and Dr Anthony Porcelli for helpful discussion. This research was supported by funding from the National Institute guesses in the game (player) and watching their competitors guess of Mental Health (grant R01MH084081 to M.R.D.). (observer). Importantly, outcomes could benefit one or the other Correspondence should be addressed to Dr Mauricio R. Delgado, Department of Psychology, Rutgers University, Smith Hall, Rm. 340, 101 Warren Street, Newark, NJ 07102, USA. E-mail: firstname.lastname@example.org party irrespective of who was responding; i.e. monetary gains could The Author (2013). Published by Oxford University Press. For Permissions, please email: email@example.com Outcome value in a competitive context SCAN (2014) 413 go to a participant if they made a correct guess or if their competitor photo of a matrix of random numbers (see Supplementary Materials made an incorrect guess. Based on previous work from our group for further discussion). MRI participants alternated roles during the (Fareri et al., 2012), we hypothesized that outcome valuation as task between making the guesses in the game (player) and watching reflected by corticostriatal BOLD responses would be modulated by their competitors make the guesses (observer). social network in this competitive context, with enhanced responses Participants’ task (adapted from Fareri et al., 2012), was simply to observed in mPFC and the striatum at the time of outcome when guess whether the value of a card was lower (1, 2, 3, 4) or higher (6, 7, competing against an in- vs out-of-network competitor. 8, 9) than the number 5 (Figure 1 and Supplementary Materials for trial timeline). The task consisted of 96 trials in total, evenly distrib- uted across four functional runs. Each run contained eight trials per MATERIALS AND METHODS partner condition, randomly presented. Participants’ roles alternated Participants across runs––two player runs and two observer runs, with 48 trials Twenty-four gender-matched participant pairs were recruited using total per role condition––the order of which was counterbalanced posted advertisements from Rutgers-Newark and the surrounding across sessions. During player runs, MRI participants responded area. Six participant pairs were excluded from final analysis. One par- using buttons designated ‘high’ and ‘low’ on an MRI-compatible ticipant withdrew after reporting claustrophobia. Three participant fiber optic response box (Current Designs, Inc.); the friend was pairs failed to meet inclusionary criteria: excessive head motion seated in the control room and pressed a designated button on a com- (>3 mm in any plane) across multiple runs of the session and observed puter keyboard to ‘release’ the MRI participants’ responses (i.e. allow artifact in BOLD images. Two final pairs were excluded because of them to be counted) on trials in which they were competing. During reported explicit plans to split the money earned in the task prior to observer runs, MRI participants made a button press to release their coming in for the scanning session, thus compromising the creation of competitors’ responses. This served as both a motor control as well as a competitive social context. Final analysis was conducted on behav- to keep MRI participants and their friends engaged in the task at all ioral and functional magnetic resonance imaging (fMRI) data from the times. Unknown to MRI participants, confederate and computer remaining 18 MRI participants (mean¼ 20.4 years, s.d.¼ 2.15, 8 responses were pre-programmed into the task. female participants). Behavioral analyses were additionally conducted All trials had $2.00 at stake. Correct guesses resulted in þ$2.00 for on questionnaire data from the cohort of behavioral participants the respondent and $0.00 for the other party; incorrect guesses resulted (mean¼ 21 years, s.d.¼ 3.36). All participant pairs provided informed in the opposite distribution. Thus, the MRI participant could experi- consent prior to participation. This study was approved by the ence positive (þ$2.00) and negative outcomes ($0.00) regardless of Institutional Review Boards of Rutgers University and the University whether they (player runs) or their competitors (observer runs) were of Medicine and Dentistry of New Jersey. making the guesses. No monetary losses were incurred in this task unless a trial was missed. If either the MRI participant or their com- Experimental paradigm petitor did not respond within the requisite amount of time (Figure 1 This study took part over the course of 2 days. Recruited MRI partici- and Supplementary Materials), the ‘#’ symbol would appear; partici- pants were asked to bring a same gender close friend to the experi- pants were told this indicated a monetary loss of $1.00 for both parties mental session (not a romantic partner or family member). After involved. This was intended to encourage responding and to protect providing informed consent on Day 1, participants and their friends against participants potentially not responding in order to prevent a separately completed the Inclusion of Other in Self Scale (IOS; Aron competitor from earning money. Importantly, all outcomes were pre- et al., 1992). This served as a manipulation check by which to assess the determined (50% positive, 50% negative) and randomly presented to degree of closeness within the in-network relationship, because social ensure equivalent experiences across all participants. network was a factor of interest here. The IOS consists of a series of sets We assessed MRI participants’ motivation to beat each competitor of circles varying in their degree of overlap, with increased overlap prior to the task as a subjective baseline measure of competitiveness. indicating increased closeness. Participant pairs were separately Post-session ratings were acquired to assess participants’ experience instructed to choose the set that best characterized their relationship. during the task (e.g. how excited/disappointed they were to win/lose Prior to the end of the Day 1 session, a facial photograph was taken of against each competitor). Ratings were made on 7-point Likert scales the same gender friend and programmed as a stimulus into the Day 2 (1¼ not at all, 7¼ a lot). task. The experimental session took place on Day 2 (typical delay between Behavioral analysis sessions was 1–2 days) at the University Heights Advanced Imaging We conducted Pearson’s correlations between MRI participants’ and Center (Newark, NJ, USA). MRI participants were told that they were friends’ responses on the IOS to probe whether they held similar views going to be playing a simple card-guessing game in which they would of their relationship. MRI participants’ ratings of friend and confed- be competing for monetary outcomes. We manipulated two factors of erate on the IOS were tested with paired sample t-tests; the same was interest: MRI participants’ competitors (1) and role (2) during the done for friends’ responses. Pre- and post-session ratings were exam- game. MRI participants played the game against three different com- ined with separate one-way repeated measures analyses of variance petitors: their friend (in-network), a gender-matched confederate from (ANOVAs). A Greenhouse–Geiser correction was applied for viola- the laboratory (out-of-network other) and a random number gener- tions of sphericity. Where appropriate, post hoc comparisons were ator (RNG). MRI participants were told that their goal was to earn conducted and corrected for multiple comparisons using the sequential more money than each competitor. The confederate was portrayed as Bonferonni method (Holm, 1979; Rice, 1989). another participant in the study who had been trained separately on the task and met the MRI participant and their friend at the start of the fMRI acquisition and analysis experimental session on Day 2. Both the MRI participant and their friend rated the confederate on the IOS as a manipulation check. The Images were acquired using a 3T Siemens Allegra head-only scanner. confederate’s true identity was not revealed until the end of the task to Anatomical images were collected with a T1-weighted MPRAGE limit suspicions of an unfair advantage in the task. The RNG served as sequence (256 256 matrix; FOV¼ 256 mm: 176 1 mm sagittal a non-social control condition and was represented in the task by a slices). Functional images were acquired using a single-shot gradient 414 SCAN (2014) D. S. Fareri and M. R. Delgado activation are not false positives in a given SPM (Forman et al., 1995; Goebel et al., 2006; see also Lieberman and Cunningham, 2009). A cluster threshold of three contiguous voxels (equivalent to 81 mm ) as determined by the plugin was applied, unless otherwise noted. We conducted three main types of analyses: Whole-brain analyses. We conducted a 2 (role) 3 (competitor) 2 (outcome valence) whole- brain repeated measures ANOVA to investigate BOLD responses during the outcome phase of the task. We additionally conducted a 2 (role) 3 (competitor) whole-brain repeated measures ANOVA in order to probe BOLD responses during the response phase (see Supplementary Materials for results). Mean parameter estimates were extracted from functional clusters to characterize resulting significant effects based on an average across all voxels in a region of interest centered around the peak voxel. Where appropriate, post hoc comparisons were conducted and corrected using the sequential Bonferonni method (Holm, 1979; Rice, 1989). We probed modulation of outcome-related BOLD responses by social closeness (Fareri et al., 2012) with whole-brain correlations Fig. 1 Task structure. MRI participants played a simple card-guessing task in which they competed for monetary outcomes against one of three competitors on each trial––a random number gener- between self-reported closeness (IOS) with their friends and outcome- ator, a gender matched confederate or a close, same gender friend (adapted from Fareri et al., 2012). related BOLD responses collapsed across all competitor types (e.g. (A) A picture at the top of the screen indicated MRI participants’ competitor on each trial. MRI positive > negative outcomes). participants’ roles alternated between making guesses (player runs) and observing their competitors make the guesses (observer runs). Responses were made during a 2-s response period, which was followed by jittered inter-stimulus interval (4–10 s) and an ensuing outcome phase (2 s). (B) Correct Second-order contrast. guesses indicated by a green check mark resulted in þ$2.00 for the respondent and $0.00 for the We also conducted a second-order contrast to explore differences in other party on a given trial. Incorrect guesses indicated by a red ‘X’ resulted in $0.00 for outcome value signals (e.g. positive greater than negative) as a function the respondent and þ$2.00 for the other party. Trials were separated by a jittered inter-trial interval (12–14 s). of whether a competitor was a social or non-social entity. We per- formed separate contrasts of positive vs negative outcomes for social echo EPI sequence (TR¼ 2000 ms, TE¼ 25 ms, FOV¼ 192, flip (friendþ confederate) and non-social (RNG) trials at the single par- ticipant level. We then conducted a subtraction of social–non-social angle¼ 808, bandwidth¼ 2604 Hz/Px, echo spacing¼ 44) and com- outcome maps for each participant and combined single-subject prised 35 contiguous oblique-axial slices (3 3 3 mm voxels) parallel subtractions to form a group map that was subjected to a t-test against to the anterior commissure–posterior commissure line. Pre-processing zero. This exploratory analysis was set at a more lenient threshold of and analysis of neuroimaging data were performed using BrainVoyager P < 0.005, whole-brain corrected at the cluster level to five contiguous QX (v2.2, Brain Innovation). Pre-processing consisted of 3D motion voxels (135 mm ) of brain tissue as determined by the Cluster Level correction (six parameters) slice scan time correction (cubic spline Statistical Estimator. interpolation), 3D Gaussian spatial smoothing (4-mm Full width at half maximum (FWHM)), voxelwise linear detrending and high-pass filtering of frequencies (three cycles per time course). Individual ana- Granger causality analysis. tomical and functional datasets were warped to standard Talairach As previous investigations have demonstrated connectivity within cor- stereotaxic space (Talairach and Tournoux, 1988). Individual whole- ticostriatal circuitry during competitive and strategic social inter- brain masks were created and additively combined to create a group actions (e.g. Hampton et al., 2008; Bault et al., 2011), we conducted mask excluding the skull. a Granger causality analysis in BrainVoyager. Granger causality assesses We constructed a single random effects General Linear Model interactions between a seed region of interest and all other areas of the (GLM) using role, competitor and outcome as factors. We modeled brain by assuming a linear dependence between two time series x and y the response and outcome phases of the task with separate regressors as when using vector autoregression (Geweke, 1982). Linear dependence a function of competitor and role conditions in order to capture vari- F between two time series can be quantified as a summation of the x,y ance unique to each phase. Thus, we included a total of 18 regressors of extent to which: past values of one time series x can better predict interest in our model. Six regressors were included modeling the re- values of a second time series y (F ) than past values of y and vice x!y sponse phase (2 s in duration; two levels of role and three levels of versa (F ) as well as the undirected instantaneous influence that may y!x competitor); 12 regressors were included modeling the outcome phase occur between time series x and y (F ) (Goebel et al., 2003; x*y (2 s in duration; two levels of role, three levels of competitor and two Roebroeck et al., 2005). Granger causality thus tests for both effective levels of outcome). One missed trial regressor and six motion param- (directed) and functional (instantaneous) connectivity between a seed eters served as regressors of no interest. Regressors of interest and region and all other areas of the brain (Goebel et al., 2003; Roebroeck missed trial regressors were convolved with a 2-gamma hemodynamic et al., 2005, 2011). We computed separate functional and effective response function. All regressors were z-transformed at the single par- connectivity maps demonstrating interactions between this seed ticipant level. Statistical parametric maps (SPMs) were initially set to region and all other voxels in the brain across the entire timecourse an uncorrected height threshold of P < 0.001, unless otherwise noted, of each functional run (290 TRs) for each participant. As we were and were subsequently corrected for multiple comparisons with a cor- primarily interested in directed influences to and from this seed rected threshold of P < 0.05 at the group level, using the Cluster Level region, we focused on effective connectivity results. Connectivity Statistical Threshold Estimator plugin in BrainVoyager. This correc- maps were computed for each participant and were combined to tion method runs a series of Monte Carlo simulations across the whole form a group map which was subjected to a t-test against zero brain to determine the probability that observed significant clusters of (Dickerson et al., 2010). Group comparison maps were thresholded Outcome value in a competitive context SCAN (2014) 415 at P < 0.005 and corrected using a cluster threshold of six contiguous Given previous findings suggesting social closeness with an voxels (equivalent to 162 mm of contiguous brain tissue) as deter- in-network other as a modulator of shared reward value (Fareri mined by the Cluster Level Statistical Estimator. et al., 2012), we probed a potential role for this factor here. We explored whether any regions demonstrating increased BOLD re- sponses to positive vs negative outcomes (collapsed across competi- RESULTS tors) were further modulated by social closeness with an in-network Behavioral results competitor. Whole-brain correlations between a contrast of positive A simple Pearson’s correlation between MRI participants’ and their greater than negative outcomes and IOS ratings of one’s friend re- friends’ responses on the IOS revealed a significant correlation vealed no significant activation, suggesting that social closeness was [r ¼ 0.68, P¼ 0.002], suggesting similar perceptions of the friend- (16) not playing a significant role during outcome valuation within this ship. Supporting these results, both MRI participants [t ¼ 12.83, (17) competitive social context. P < 0.001] and their friends [t ¼ 12.70, P < 0.001] reported feeling (17) closer to each other than to the confederate, suggesting an effective in- Second-order Contrast. vs out-of-network manipulation. Previous results suggest differential striatal responses during social Assessing MRI participants’ pre-task ratings of competitiveness compared with non-social conditions during outcome receipt (e.g. ‘How much do you want to beat this competitor in the game?’) (Rilling et al., 2002; Delgado et al., 2008). We explored whether striatal with a one-way repeated measures ANOVA revealed a marginally BOLD responses might similarly demonstrate a more general social vs significant main effect [F ¼ 3.045, P¼ 0.084]. Participants (1.335, 22.694) non-social distinction in the current paradigm. We conducted an ex- were marginally more motivated to compete against the RNG ploratory analysis using a second-order contrast of positive greater (mean¼ 6.39, s.d.¼ 0.98) compared with their friend [mean¼ 5.55, than negative outcomes for social–non-social competitors. Results s.d.¼ 1.54; t ¼ 1.97, P¼ 0.065]; this effect was weaker between con- (17) from this exploratory analysis (Supplementary Materials and federate (mean¼ 6.11, s.d.¼ 0.96) and friend [t ¼ 1.49, P¼ 0.15]. (17) Supplementary Figure S1 for additional discussion) revealed increased These results suggest that competing against an in-network other may BOLD responses in a number of striatal subregions (Table 3), includ- have differentially affected participants’ motivation in the task as ing bilateral putamen, when experiencing positive compared with expected, though they did not quite reach significance. Probing negative outcomes against a social compared with non-social post-session ratings of excitement and disappointment for winning/ competitor. losing revealed no significant effects. Granger causality analysis. Neuroimaging results Based on a main effect of competitor emerging in mPFC but not the Whole-brain analyses. striatum and evidence showing connectivity between these two areas Our primary interest in this study was whether level of social network during strategic and competitive interactions (e.g. Hampton et al., with a competitor would modulate corticostriatal outcome value sig- 2008; Bault et al., 2011), we examined interactions between mPFC nals. A 2 (role) 3 (competitor) 2 (outcome) whole-brain repeated and the rest of the brain with a Granger causality analysis. We chose measures ANOVA revealed a main effect of competitor (Table 1) that a cluster in BA10 (x, y, z¼7, 49, 6) as the seed region for this was highly robust across many mPFC regions (Figure 2A). analysis, given our results showing its sensitivity to social network. Importantly, a cluster emerged in a dorsal part of BA10 in mPFC (x, This cluster also contains the peak voxel reported in a recent study y, z¼7, 49, 6), encompassing voxels previously implicated as being by Bault and colleagues (2011) as demonstrating increased BOLD re- sensitive to social gains from risky choices compared to social losses sponses when experiencing positive outcomes gained in comparison to and non-social outcomes (Bault et al., 2011). When collapsing across against another person. Figure 4 depicts effective connectivity results; outcome valence (Figure 2B), BOLD responses here were more positive clusters in red are targets of the mPFC seed region. As can be seen, when competing against one’s friend as compared with the random directed influences are sent to bilateral ventral striatum (Table 4 for number generator [t ¼ 6.45, P¼ 0.000006], or confederate (17) complete list of regions identified in this analysis), which demonstrated [t ¼ 2.42, P¼ 0.027]. This region also showed a more positive (17) a main effect of outcome in our whole-brain ANOVA. BOLD response when competing against the confederate than when competing against the RNG [t ¼ 3.74, P¼ 0.002]. Other areas of (17) DISCUSSION mPFC––dorsomedial PFC (BA9), ventromedial PFC (BA10) and a cluster bordering orbitofrontal cortex (BA11)––all showed this same We investigated whether outcomes experienced during a competitive general pattern. A cluster in posterior cingulate cortex bordering the social context would carry differential value as a function of social cuneus (BA31) showed similar effects (Figure 2C): BOLD responses network. Our results demonstrate that competing against an when competing against the confederate [t ¼ 5.19, P¼ 0.000074] in-network other elicits enhanced outcome value signals in corticos- (17) and friend [t ¼ 4.34, P¼ 0.0004] were more positive than when triatal circuitry. BOLD responses across a wide range of mPFC showed (17) competing against the RNG. A marginally significant trend emerged sensitivity to social network, with activation strongest when evaluating when comparing activation in this region during friend vs confederate outcomes experienced against an in-network competitor. In the ventral trials [t ¼ 1.92, P¼ 0.07]. striatum, BOLD responses were characterized by a main effect of out- (17) Interestingly, we observed no modulation of BOLD activation in the comestronger for positive compared with negative outcomesand a striatum as a function of social network in this task. Rather, a main more general social vs non-social distinction, with increased activation effect of outcome was observed in multiple striatal subregions, includ- observed for positive vs negative outcomes on social compared with ing bilateral ventral caudate nucleus and bilateral putamen (Table 2). non-social trials. A Granger causality analysis further revealed corticos- Striatal BOLD responses were significantly greater for positive com- triatal interactions: an mPFC cluster showing social network sensitivity pared with negative outcomes, irrespective of competitor (Figure 3A sent directed influences to bilateral ventral striatum. Together, our and B). No regions emerged showing a stronger response for negative findings suggest that during a competitive social context involving outcomes (Supplementary Materials for additional ANOVA results). competitors of differing levels of social network, mPFC differentiates 416 SCAN (2014) D. S. Fareri and M. R. Delgado Table 1 Outcome phase 2 3 2 ANOVA: main effect of competitor Region of activation Brodmann area Direction Laterality Talairach coordinates No. of voxels (mm ) F-statistic xy z Cerebellum * R 44 53 21 464 14.45 Inferior/middle frontal gyrus BA11/47 * R 29 31 15 278 19.20 Medial frontal gyrus/OFC BA11 * L 431 15 222 14.67 Subgenual anterior cingulate BA25 * L 719 15 152 13.82 Inferior frontal gyrus BA47 * L 37 19 15 239 14.24 Medial frontal gyrus BA10 * L 746 6 2110 28.50 Medial frontal gyrus BA10 * L 7 49 6 2016 19.66 Cingulate gyrus/corpus callosum BA24 ** L 4 25 15 94 11.56 Medial frontal gyrus BA9 * L 1 46 24 2554 27.50 Inferior parietal lobule/angular gyrus BA39 *** L 49 62 24 710 15.30 Posterior cingulate BA31 * R 2 56 27 1810 20.16 *¼ friend > confederate > RNG; **¼ friend > RNG, friend > confederate; *** friend > RNG, confederate > RNG. Fig. 2 Main effect of competitor (outcome phase). (A) A 2 (role) 3 (competitor) 2 (outcome) whole-brain repeated measures ANOVA revealed a significant main effect of competitor in a number of regions. (B) Parameter estimates extracted from a cluster in BA10 (x, y, z¼7, 49, 6) demonstrate this effect to be driven by enhanced value signals to outcomes on friend trials compared with confederate or RNG trials. (C) Similar results emerged in a cluster of posterior cingulate cortex bordering the cuneus (BA31). Activation maps were set to an initial uncorrected height threshold of P < 0.001 and subsequently, whole-brain corrected at the cluster level to a threshold of P < 0.05. outcome value as a function of competitor, leaving the ventral striatum Importantly, areas of mPFC also encode social information pertaining to self and others (for reviews, see Amodio and Frith, 2006; Wagner to process outcome value in a more coarse or general sense. The striatum and mPFC are well-recognized components of a neural et al., 2012), responding to socially dominant others (Rudebeck et al., valuation system (for reviews see Daw and Doya, 2006; Delgado, 2007; 2006) and close friends in comparison with similar others (Krienen et al., Rangel et al., 2008; Haber and Knutson, 2010), which assigns value to 2010). The present results merge these two literatures, showing that expected and experienced outcomes (Delgado et al., 2000; Knutson et al., during a competitive social context, mPFC differentially assigns value 2001, 2003; O’Doherty et al., 2002, 2004; Delgado et al., 2004; Galvan to experienced positive outcomes as a function of whether one’s com- et al., 2005; Hare et al., 2008) to help guide decision-making (Kennerley petitor was from within or outside of one’s social network. A possible et al., 2006; Rushworth, 2008; Rushworth and Behrens, 2008). explanation for this might be that participants attempted to use Outcome value in a competitive context SCAN (2014) 417 Table 2 Outcome phase 2 3 2 ANOVA: main effect of outcome Region of activation Brodmann area Laterality Talairach coordinates No. of voxels (mm ) F-statistic xy z Cerebellum R 20 77 33 214 24.72 Cerebellum R 35 77 30 690 50.29 Middle frontal gyrus BA11 L 22 37 15 262 35.65 Putamen L 19 1 9 179 22.25 Medial frontal/cingulate gyrus BA10/32 L 19 40 6 253 31.97 Middle frontal gyrus BA10/47 L 43 46 3 405 40.69 Putamen R 17 4 0 187 27.48 Caudate nucleus R 8 10 0 155 19.58 Caudate nucleus/ventral striatum L 10 7 0 428 23.87 Middle frontal gyrus BA10 R 23 58 3 81 27.62 Caudate nucleus L 13 16 6 112 23.12 Inferior frontal gyrus BA45/46 R 35 31 9 142 32.37 Medial occipital gyrus BA18 R 20 89 12 99 22.17 Cingulate gyrus BA31 L 16 44 27 103 21.84 Cingulate gyrus BA33 L 4 32 30 399 25.64 Superior parietal lobule BA7 R 35 65 48 249 27.79 Middle frontal gyrus BA8 R 29 10 48 408 29.95 Middle frontal/superior frontal gyrus BA8 L 28 19 51 82 24.83 interpretation would be consistent with a role for mPFC in outcome monitoring and strategic thinking during competitive contexts in humans (Hampton et al., 2008; de Bruijn et al., 2009; Bault et al., 2011) as well as in rats and non-human primates (Hillman and Bilkey, 2012; Yoshida et al., 2012). Future investigations could more fruitfully explore effects of social network on outcome processing in competitive situations involving dynamic learning scenarios. Our whole-brain analyses demonstrated a main effect of outcome in bilateral ventral striatum, with increased BOLD responses observed for positive vs negative outcomes, consistent with previous iterations of this paradigm (e.g. Delgado et al., 2000, 2003, 2004). This also supports recent investigations of competitive interactions in which the striatum generally comes online during outcome processing (de Bruijn et al., 2009; Hampton et al., 2008) sometimes coding for social compared to non-social outcomes (Bault et al., 2011), but more putative cortical and prefrontal cortical regions support behavioral updating as a func- tion of more complex social information (Hampton et al., 2008). In conjunction with a second-order contrast showing striatal sensitivity to positive social outcomes in the striatum, but not social network, these findings implicate the striatum as performing a more general role in outcome valuation during a competitive social context. We observed effective connectivity within corticostriatal circuitry as a result of a Granger causality analysis: bilateral ventral striatum was a target of directed influence from a cluster of mPFC demonstrated to value positive outcomes earned after risky choices in comparison with another person (Bault et al., 2011). It is possible that the directed influences sent from this cluster of mPFC, which demonstrated an in- vs out-of-network distinction in the present study, led to a more general representation of outcome value in the striatum as opposed to Fig. 3 Main effect of outcome. (A) A significant main effect of outcome emerged during a 2 one that was specifically sensitive to social network in a more motiv- (role) 3 (competitor) 2 (outcome) whole-brain repeated measures ANOVA in bilateral ventral striatum. (B) BOLD responses in the left ventral striatum (x, y, z,¼10, 7, 0) demonstrated ationally salient, cooperative context (Fareri et al., 2012). This may enhanced value signals to positive compared with negative outcomes across all competitor condi- additionally help explain why participants did not differentially rate tions. Activation map was set to an initial uncorrected height threshold of P < 0.001 and subse- excitement for winning or losing against each competitor. We thus quently, whole-brain corrected at the cluster level to a threshold of P < 0.05. suggest that the striatum in part may have processed outcome value more coarsely here due to directed modulation from mPFC, which was outcomes on in-network trials to inform behavior more so than out- coding a finer sensitivity to social network. comes with other competitors, thus lending heavier weight to them. It is important to consider potential caveats regarding this connect- Although we could not directly test this, given the random nature of ivity analysis. Granger causality is an exploratory analysis requiring no outcome distribution and no opportunity for learning, this specific predictions about directionality or an a priori specified 418 SCAN (2014) D. S. Fareri and M. R. Delgado Table 3 Second-order contrast: social–non-social, win > loss Region of activation Brodmann area Laterality Talairach coordinates No. of voxels (mm ) t-statistic xy z Inferior frontal gyrus BA47 R 26 22 21 182 5.51 Putamen/globus pallidus L 19 1 9 418 4.62 Inferior frontal gyrus BA45 L 43 25 0 463 6.28 Putamen L 25 7 3 203 4.28 Putamen R 17 13 6 233 3.92 Putamen L 28 14 6 264 4.77 Middle frontal gyrus BA10 L 40 52 12 193 4.69 Middle temporal gyrus/inferior parietal lobule BA19 L 52 62 18 154 4.01 2008; for review see Vrticka and Vuilleumier, 2012), this result is not necessarily surprising. Competing against a close, in-network other may in fact be orthogonal to the notion of a merged representation of a social relationship inherent in the construct of social closeness (Aron et al., 1992). Rather than sharing a positive experience with another and perhaps reaffirming a friendship with said shared reward (Fareri et al., 2012), positive outcomes in the present task ne- cessarily came at the expense of an in-network friend, which would not be a mutually positive and reaffirming experience. The striatum demonstrated a general sensitivity to positive com- pared with negative outcomes, which was greater when competing against a social entity. We did not observe any significant competitor effects in response to negative outcomes as previously observed (Delgado et al., 2008). This could have been in part due to the com- petitive social context here not being salient enough. Although positive outcomes for the MRI participant in this task resulted in monetary gains, negative outcomes only led to a gain for the competitor and no gain (or cost) for the MRI participant. Previous work in which striatal BOLD responses to social losses correlated with overbidding in an auction (Delgado et al., 2008) necessitated more meaningful decisions. Fig. 4 Effective connectivity results. A Granger causality analysis using a seed region in mPFC that It is plausible that because participants in the present investigation demonstrated a main effect of competitor (x, y, z¼7, 49, 6) revealed directed influences sent simply made guesses, with no true opportunity to maximize earnings, from this region to bilateral ventral striatum (right: x, y, z¼ 11, 13, 3; left: x, y, z¼7, 7, 0). the social manipulation may not have been as motivationally salient as The clusters depicted in red denote directed influences received from the mPFC seed. Activation map was set to an initial uncorrected height threshold of P < 0.005 and subsequently, whole-brain intended. Future work could probe the effects of social network in a corrected at the cluster level to a threshold of P < 0.05. competition through the creation of a more salient and meaningful competitive context, one in which learning an optimal behavioral strat- egy is necessary to beat in- vs out-of-network competitors. Such an network of neural regions involved (Roebroeck et al., 2005). alternative design might better parse contributions of mPFC and stri- This analysis searches for correlations and predictive relationships atum when competing against in-/out-of-network others. This may between the timecourse of activation in a specified seed region and also further delineate behavioral correlates of competitiveness. the rest of the brain. One study (David et al., 2008) contends that Although it is conceivable that competing against one’s friend may Granger causality may not be optimal for fMRI data, because the tem- elicit a stronger competitive desire, it seems equally likely that partici- poral dynamics of the hemodynamic response may be heterogeneous pants might be less motivated to beat their friend and a more salient across the brain. However, other evidence suggests that considering design may further elucidate these divergent predictions. temporal dynamics of fMRI data, and particularly temporal prece- The value placed on experienced outcomes is subject to a great deal dence, is necessary when attempting to model or detect causal influ- of influence across varying social contexts. A common and important ences (Roebroeck et al., 2011; for further discussion also see modulator of experienced outcomes is with whom they occur––some- Valdes-Sosa et al., 2011). one from within or outside our social network. Our findings demon- Social closeness did not modulate outcome valuation as a function strate that when competing against in-network other, increased value of social network in the present study. Closeness ratings in the present signals emerge in mPFC upon outcome receipt, as compared with cohort of participants may have lacked sufficient range or variability to receiving the same outcome in competition with an out-of-network serve as an adequate predictor variable. All potential values of the IOS other or non-social entity. This supports an integrating role for the scale were not represented as selected responses in this sample of mPFC, combining social information with value signals in a competi- participants. Perhaps, with a sample exhibiting more diversity in tive social context. their IOS responses, an effect may have emerged. However, given previous evidence from our group (Fareri et al., 2012) as well as com- SUPPLEMENTARY DATA plementary evidence suggesting general social reward sensitivity may be related to other measures of interpersonal closeness (Vrticka et al., Supplementary data are available at SCAN online. Outcome value in a competitive context SCAN (2014) 419 Table 4 Granger causality analysis: Effective connectivity. Region of activation Brodmann area Laterality Talairach coordinates No. of voxels (mm ) t-statistic xyz Medial frontal gyrus BA11 L 440 12 228 4.93 Medial temporal lobe BA35 L 22 20 12 367 4.49 Ventral striatum R 11 13 3 431 6.62 Ventral striatum L 7 7 0 428 4.30 PCC/corpus callosum BA29 R 2 41 6 1441 4.77 Medial frontal gyrus/cingulate gyrus BA10/32 L 4 49 6 5616 4.69 Thalamus L 4 14 12 364 4.54 Superior frontal gyrus BA10 L 22 49 24 176 4.35 Cingulate gyrus BA32 L 1 28 27 172 4.98 Precuneus/PCC BA31 L 4 44 36 2234 5.43 Medial frontal gyrus BA8 L 7 49 42 216 4.02 Regions receiving directed influence from mPFC seed region (x, y, z¼7, 49, 6). resonance imaging (fMRI): use of a cluster-size threshold. Magnetic Resonance FUNDING Medicine, 33, 636–47. This research was funded by the National Institute of Mental Health Galvan, A., Hare, T.A., Davidson, M., Spicer, J., Glover, G., Casey, B.J. (2005). The role of (R01 MH084081 to M.R.D.). ventral frontostriatal circuitry in reward-based learning in humans. The Journal of Neuroscience, 25, 8650–6. Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time Conflict of Interest series. Journal of the American Statistical Association, 77,304–13. None declared. Goebel, R., Esposito, F., Formisano, E. (2006). Analysis of functional image analysis contest (FIAC) data with BrainVoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component ana- REFERENCES lysis. Human Brain Mapping, 27, 392–401. Amodio, D.M., Frith, C.D. (2006). Meeting of minds: the medial frontal cortex and social Goebel, R., Roebroeck, A., Kim, D.S., Formisano, E. (2003). Investigating directed cortical cognition. Nature Reviews Neuroscience, 7, 268–77. interactions in time-resolved fMRI data using vector autoregressive modeling and Aron, A., Aron, E.N., Smollan, D. (1992). Inclusion of the Other in the Self Scale and the Granger causality mapping. Magnetic Resonance Imaging, 21, 1251–61. structure of interpersonal closeness. Journal of Personality and Social Psychology, 63, Haber, S.N., Knutson, B. (2010). The reward circuit: linking primate anatomy and human 596–612. imaging. Neuropsychopharmacology, 35,4–26. Bault, N., Joffily, M., Rustichini, A., Coricelli, G. (2011). Medial prefrontal cortex and Hampton, A.N., Bossaerts, P., O’Doherty, J.P. (2008). Neural correlates of striatum mediate the influence of social comparison on the decision process. mentalizing-related computations during strategic interactions in humans. Proceedings Proceedings of the National Academy of Sciences of the United States of America, 108, of the National Academy of Sciences of the United States of America, 105, 6741–6. 16044–9. Hare, T.A., O’Doherty, J., Camerer, C.F., Schultz, W., Rangel, A. (2008). Dissociating the Baumeister, R.F., Leary, M.R. (1995). The need to belong: desire for interpersonal attach- role of the orbitofrontal cortex and the striatum in the computation of goal values and ments as a fundamental human motivation. Psychological Bulletin, 117, 497–529. prediction errors. The Journal of Neuroscience, 28, 5623–30. Berg, J., Dickhaut, J., McCabe, K. (1995). Trust, reciprocity, and social history. Games and Hillman, K.L., Bilkey, D.K. (2012). Neural encoding of competitive effort in the anterior Economic Behavior, 10, 122–42. cingulate cortex. Nature Neuroscience, 15, 1290–7. Chein, J., Albert, D., O’Brien, L., Uckert, K., Steinberg, L. (2011). Peers increase adolescent Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian risk taking by enhancing activity in the brain’s reward circuitry. Developmental Science, Journal of Statistics, 6,65–70. 14,F1–10. Howard-Jones, P.A., Bogacz, R., Yoo, J.H., Leonards, U., Demetriou, S. (2010). The neural David, O., Guillemain, I., Saillet, S., et al. (2008). Identifying neural drivers with functional mechanisms of learning from competitors. NeuroImage, 53,790–9. MRI: an electrophysiological validation. PLoS Biology, 6, 2683–97. Izuma, K., Saito, D.N., Sadato, N. (2008). Processing of social and monetary rewards in the Daw, N.D., Doya, K. (2006). The computational neurobiology of learning and reward. human striatum. Neuron, 58, 284–94. Current Opinion in Neurobiology, 16, 199–204. Izuma, K., Saito, D.N., Sadato, N. (2010). Processing of the incentive for social approval in de Bruijn, E.R., de Lange, F.P., von Cramon, D.Y., Ullsperger, M. (2009). When errors are the ventral striatum during charitable donation. Journal of Cognitive Neuroscience, 22, rewarding. The Journal of Neuroscience, 29, 12183–6. 621–31. Delgado, M.R. (2007). Reward-related responses in the human striatum. Annals of the New Kennerley, S.W., Walton, M.E., Behrens, T.E., Buckley, M.J., Rushworth, M.F. (2006). York Academy of Sciences, 1104,70–88. Optimal decision making and the anterior cingulate cortex. Nature Neuroscience, 9, Delgado, M.R., Locke, H.M., Stenger, V.A., Fiez, J.A. (2003). Dorsal striatum responses to 940–7. reward and punishment: effects of valence and magnitude manipulations. Cognitive, Knutson, B., Adams, C.M., Fong, G.W., Hommer, D. (2001). Anticipation of increasing Affective & Behavioral Neuroscience, 3,27–38. monetary reward selectively recruits nucleus accumbens. The Journal of Neuroscience, 21, Delgado, M.R., Nystrom, L.E., Fissell, C., Noll, D.C., Fiez, J.A. (2000). Tracking the hemo- RC159. dynamic responses to reward and punishment in the striatum. Journal of Knutson, B., Fong, G.W., Bennett, S.M., Adams, C.M., Hommer, D. (2003). A region of Neurophysiology, 84, 3072–7. mesial prefrontal cortex tracks monetarily rewarding outcomes: characterization with Delgado, M.R., Schotter, A., Ozbay, E.Y., Phelps, E.A. (2008). Understanding overbidding: rapid event-related fMRI. NeuroImage, 18, 263–72. using the neural circuitry of reward to design economic auctions. Science, 321, 1849–52. Krienen, F.M., Tu, P.C., Buckner, R.L. (2010). Clan mentality: evidence that the Delgado, M.R., Stenger, V.A., Fiez, J.A. (2004). Motivation-dependent responses in the medial prefrontal cortex responds to close others. The Journal of Neuroscience, 30, human caudate nucleus. Cerebral Cortex, 14, 1022–30. 13906–15. Dickerson, K.C., Li, J., Delgado, M.R. (2010). Parallel contributions of distinct human Lieberman, M.D., Cunningham, W.A. (2009). Type I and Type II error concerns in fMRI memory systems during probabilistic learning. NeuroImage, 55, 266–76. research: re-balancing the scale. Social Cognitive and Affective Neuroscience, 4,423–8. Fareri, D.S., Niznikiewicz, M.A., Lee, V.K., Delgado, M.R. (2012). Social network modu- Mobbs, D., Yu, R., Meyer, M., et al. (2009). A key role for similarity in vicarious reward. lation of reward-related signals. The Journal of Neuroscience, 32, 9045–52. Science, 324, 900. Fehr, E., Camerer, C.F. (2007). Social neuroeconomics: the neural circuitry of social pref- O’Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., Dolan, R.J. (2004). erences. Trends in Cognitive Sciences, 11, 419–27. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, Fehr, E., Fischbacher, U. (2002). Why social preferences matter: The impact of non-selfish motices on competition, cooperation and incentives. The Economic Journal, 112,C1–33. 304, 452–4. Forman, S.D., Cohen, J.D., Fitzgerald, M., Eddy, W.F., Mintun, M.A., Noll, D.C. O’Doherty, J.P., Deichmann, R., Critchley, H.D., Dolan, R.J. (2002). Neural responses (1995). Improved assessment of significant activation in functional magnetic during anticipation of a primary taste reward. Neuron, 33,815–26. 420 SCAN (2014) D. S. Fareri and M. R. Delgado Rangel, A., Camerer, C., Montague, P.R. (2008). A framework for studying the neurobiol- Steinberg, L., Monahan, K.C. (2007). Age differences in resistance to peer influence. ogy of value-based decision making. Nature Reviews Neuroscience, 9, 545–56. Developmental Psychology, 43, 1531–43. Rice, W.R. (1989). Analyzing tables of statistical tests. Evolution, 6, 223–5. Talairach, J., Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain: An Rilling, J., Gutman, D., Zeh, T., Pagnoni, G., Berns, G., Kilts, C. (2002). A neural basis for approach to medical cerebral imaging. New York: Theime Medical Publisher. social cooperation. Neuron, 35, 395–405. Tricomi, E., Delgado, M.R., McCandliss, B.D., McClelland, J.L., Fiez, J.A. (2006). Rilling, J.K., Sanfey, A.G., Aronson, J.A., Nystrom, L.E., Cohen, J.D. (2004). Opposing Performance feedback drives caudate activation in a phonological learning task. BOLD responses to reciprocated and unreciprocated altruism in putative reward path- Journal of Cognitive Neuroscience, 18, 1029–43. ways. Neuroreport, 15, 2539–43. Valdes-Sosa, P.A., Roebroeck, A., Daunizeau, J., Friston, K. (2011). Effective connectivity: Roebroeck, A., Formisano, E., Goebel, R. (2005). Mapping directed influence over the brain influence, causality and biophysical modeling. NeuroImage, 58,339–61. using Granger causality and fMRI. NeuroImage, 25, 230–42. van Winden, F., Stallen, M., Ridderinkhof, R. (2008). On the nature, modeling, andn neural Roebroeck, A., Formisano, E., Goebel, R. (2011). The identification of interacting networks bases of social ties. In: Houser, D., Mccabe, K., editors. Neuroeconomics. Bingley, UK: in the brain using fMRI: Model selection, causality and deconvolution. NeuroImage, 58, Emerald Group. 296–302. Vrticka, P., Andersson, F., Grandjean, D., Sander, D., Vuilleumier, P. (2008). Individual Rudebeck, P.H., Buckley, M.J., Walton, M.E., Rushworth, M.F. (2006). A role for the attachment style modulates human amygdala and striatum activation during social ap- macaque anterior cingulate gyrus in social valuation. Science, 313, 1310–2. praisal. PloS One, 3, e2868. Rushworth, M.F. (2008). Intention, choice, and the medial frontal cortex. Annals of the Vrticka, P., Vuilleumier, P. (2012). Neuroscience of human social interactions and adult New York Academy of Sciences, 1124, 181–207. attachment style. Frontiers in Human Neuroscience, 6, 212. Rushworth, M.F., Behrens, T.E. (2008). Choice, uncertainty and value in prefrontal and Wagner, D.D., Haxby, J.V., Heatherton, T.F. (2012). The representation of self and person cingulate cortex. Nature Neuroscience, 11, 389–97. knowledge in the medial prefrontal cortex. Wiley Interdisciplinary Reviews. Cognitive Seymour, B., Daw, N., Dayan, P., Singer, T., Dolan, R. (2007). Differential encoding of Science, 3, 451–70. losses and gains in the human striatum. The Journal of Neuroscience, 27, 4826–31. Yoshida, K., Saito, N., Iriki, A., Isoda, M. (2012). Social error monitoring in macaque Somerville, L.H., Heatherton, T.F., Kelley, W.M. (2006). Anterior cingulate cortex responds frontal cortex. Nature Neuroscience, 15,1307–12. differentially to expectancy violation and social rejection. Nature Neuroscience, 9, 1007–8.
Social Cognitive and Affective Neuroscience – Oxford University Press
Published: Apr 21, 2014
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera