Social status determines how we monitor and evaluate our performance
Maarten A.S.Bokm,1,2,3Evelien Kostermans,3,4Branka Milivojevic,5,6and David De Cremer 1,3
1
Rotterdam School of Management,Erasmus University,PO Box 1738,3000DR,Rotterdam,The Netherlands,2Donders Institute for Brain,Cognition and Behaviour,Radboud University,PO Box 9101,6500HB,Nijmegen,The Netherlands,3Department of Social Psychology,Tilburg University,PO Box 90153,5000LE,Tilburg,The Netherlands,4Behavioural Science Institute,Radboud University,PO Box 9104,6500HE,Nijmegen,The Netherlands,5Rudolf Magnus Institute,UMC Utrecht,Universiteitsweg 100,3584CG Utrecht,The Netherlands,and 6Departments of Developmental and Experimental Psychology,Utrecht University,Heidelberglaan 2,3584CS,Utrecht,The Netherlands
Since people with low status are more likely to experience social evaluative threat and are therefore more inclined to monitor for the threats and inhibit approach behaviour,we expected that low-status subjects would be more engaged in evaluating their own performance,compared with high-status subjects.We created a highly salient social hierarchy bad on the performance of a simple time estimation task.Subjects could achieve high,middle or low status while performing this task sim
ultaneously with other two players who were either higher or lower in status.Subjects received feedback on their own performance,as well as on the performance of the other two players simultaneously.Electroencephalography (EEG)was recorded from all three participants.The results showed that medial frontal negativity (an event-related potential reflecting performance evaluation)was significantly enhanced for low-status subjects.Implications for status-related differences in goal-directed behaviour are discusd.Keywords:status;power;ERP;MFN;FRN;PCA
INTRODUCTION
Social hierarchies feature prominently in a large variety of animal species (Boehm,1999),including humans,and are found to be an important organizing principle in most cul-tures (Sidanius and Pratto,2001).In animals,but also in humans,social status strongly predicts well-being,morbidity and even survival (Sapolsky,2004).Be it in domestic,pro-fessional or recreational ttings,status looms large and de-fines implicit expectations and action predispositions that drive appropriate (social)behaviour (Cummins,2000).Recent work by social scientists has begun to tackle this topic,elucidating behavioural differences between low-and high-status individuals.More specifically,this line of re-arch has focud on the effects of social power.Although power and status are conceptually different,they almost always go hand in hand,which is why we will u the
terms interchangeably here.
One of the more prominent theories in this field holds that high status and power are associated with approach behav-iour,while low power is related to inhibitory behaviour (Keltner et al.,2003).Perspectives on approach and inhibition behaviour have been shaped to a large extent by the theory postulated by Gray (1987)that propos two interacting mo-tivational systems:the behavioural approach system (BAS)
and the behavioural inhibition system (BIS).While the BAS regulates behaviour associated with rewards,such as the ac-quisition of food,x and money,the BIS acts as an alarm system that is triggered by signals of potential punishment and inhibits behaviour that may lead to aversive or harmful outcomes.Although rearch has largely focud on individ-ual (trait)differences in approach and inhibition (e.g.Carver and White,1994),Keltner and colleagues (2003)propod that social status can also influence the relative balance be-tween approach and inhibition.Their theory states that high power activates approach-related process,while low power activates inhibitory process.The reason for this,they pro-po,is that power is associated with optimal access to re-wards.Powerful people more often than not find themlves in environments offering many potential rewards,making it easier for them to approach the rewards.In addition,the powerful are less dependent on others to
acquire the re-wards,making it easier for tho with high status to act in ways that enable them to obtain rewards.For complementary reasons,tho with low status are more inclined to inhibit approach behaviour.The low-status individuals lack access to material and social resources and experience more social threat and punishments,especially the threat of being evaluated unfavourably by tho having higher status.Becau the environment of people with low status is characterized by a high degree of threat and limited access to rewards,they are more inclined to inhibit reward-eking approach behaviour.In support of this theory,we have previously shown that the experience of power directly activates the motivational
Received 6July 2010;Accepted 8February 2011
Correspondence should be addresd to Dr Maarten A.S.Bokm,Rotterdam School of Management,Erasmus University,Burgemeester Oudlaan 50,3062PA Rotterdam,The Netherlands.E-mail:maarten@bokm.nl.
doi:10.1093/scan/nsr010红鼻子鲁道夫
SCAN (2011)1of10
flirtßThe Author (2011).Published by Oxford University Press.For Permissions,plea email:journals.
accomplishedSocial Cognitive and Affective Neuroscience Advance Access published March 18, 2011 at South China Normal Unviversity on September 16, 2011美国大选日期
< Downloaded from
systems in the brain that regulate approach behaviour (Bokm et al.,2011c).High-status subjects showed a greater relative activation of left frontal–cortical areas,which have been specifically related to approach Harmon-Jones,2003),and have been associated with a stron-ger bias to respond to reward related cues(Pizzagalli et al., 2005).
Together,the findings suggest that social status has a profound effect on how people monitor their environment and evaluate their own performance:while high-status sub-jects are more focud on(rewarding)outcomes,low-status subjects are more inclined to evaluate outcomes in terms of potential(social)threat and loss.
This evaluation of performance is reflected by a family of negative-going event-related potentials(ER泡泡少儿英语官网
Ps)that are elicited both when subjects commit errors[error-related negativity(ERN);Falkenstein et al.,1990],as well as when subjects receive negative performance feedback[feedback-related negativity(FRN);Miltner et al.,1997].The ERP components have been suggested to be associated with common underlying neural process(Nieuwenhuis et al., 2004),and for convenience,we will refer to the compo-nents as medial frontal negativity(MFN;Gehring and Willoughby,2002).We have previously found(Bokm et al.,2006,2008)that,while subjects with high BAS-scores displayed a large MFN in the context of potential rewards that could be earned,subjects with high BIS-scores displayed a large MFN in the context of potential loss. Therefore,we propod that the MFN reflects a motiv-ational/affective evaluation of performance outcomes:the MFN may reflect the subjective importance of action out-comes for an individual(e also Gehring and Willoughby, 2002;Pailing and Segalowitz,2004;Hajcak et al.,2005; Yu et al.,2007;Tops and Bokm,2010).
More specifically,we have argued that MFN amplitudes are most dependent on how concerned subjects are over making mistakes,especially in a social context.Indeed,both measures of negative anxiety,neuroticism)and positive agreeableness;Deneve and Cooper,1998)have been shown to affect MFN amplitude while they are also related to concerns over social Tops et al., 2006).The most salient feedback signals are of a social nature,and
negative social evaluation is probably one of the most potent ones,leading to strong physiological respons (Dickerson and Kemeny,2004).Indeed,the MFN,BIS and cortisol levels have all been related to social evaluative threat (Hajcak et al.,2005;Cavanagh and Allen,2008;Tops and Bokm,2011).Importantly,the Anterior Cingulate Cortex (ACC;the putative source of the MFN)has been shown to be involved in processing‘error’signals from the social environ-ment such as potential loss of social resources:exclusion,re-jection and the experience of shame and guilt(Shin et al., 2000;Einberger et al.,2003;Kross et al.,2007).
Since people with low status are more likely to experience social evaluative Fiske,1993;Anderson and Berdahl,2002)and are conquently more inclined to monitor for the threats and inhibit approach behaviour (Keltner et al.,2003),we would expect that,particularly in a hierarchical social context,low-status individuals will be more engaged in evaluating their own dis-play a larger MFN),compared with high-status individuals. This is what we t out to investigate in the prent study. METHOD
be动词的用法Thirty-six healthy participants(ven males),between17 and32(M¼20.9,s.d.¼4.2)years of age,were recruited from the university population.Subjects were invited to the lab three at a time.Upon arrival,they were informed that they were to play an interactive game with the other participants pres
ent in the lab and that we would be record-ing Electroencephalography(EEG)from all of them.To make sure that differences in status were not confounded with baline differences in approach motivation[which has been shown to be related to both MFN amplitudes and social power(Bokm et al.,2006,2011)],we administered the BIS/BAS-scale developed by Carver and White(1994). The social ranking we created was bad on the perform-ance in a simple time estimation reaction time task that subjects performed individually(we will refer to this part of the experiment as the‘rank-inducing ssion’;e Zink et al.,2008).At the start of each trial,a blue circle was pre-nted that changed colour to green after2–2.5s.It was the participants’job to press the respon button exactly1s after the circle had turned green.Respons were considered cor-rect when they were within a certain allowable time interval. Subquently,subjects received feedback on their perform-ance:a smiley face when they responded within the allowable time interval or a sad face when they responded too fast or too slow.Over the320trials that subjects performed in this rank-inducing ssion,we covertly adjusted the duration of allowable-respon interval bad on a subject’s perform-ance.If a respon fell outside the allowable-respon inter-val,the interval was lengthened,while if a respon fell within the allowable-respon interval,the interval was shortened.For the subjects who were to become the ‘top-ranking’players,the interval was lengthened by30ms when respons fell outside the critical interval,and short-ened by only5ms when respons
fell within the interval. For the subjects who were to become the‘middle-ranking’players,the interval was lengthened by5ms when respons fell outside the critical interval,and shortened by5ms when respons fell within the interval.Finally,for subjects who were to become the‘lowest-ranking’players,the interval was lengthened by5ms when respons fell outside the critical interval,and shortened by30ms when respons fell within the interval.After every20trials,summary feedback was displayed(for5s),showing the cumulative percentage cor-rect of all three players.The ranking of the participants was bad on this percentage correct.During the entire experi-ment,the name of the player with the best score was
2of10SCAN(2011)M.A.S.Bokm et al.
at South China Normal Unviversity on September 16,
Downloaded from
prented at the top of the screen,the name of the player with the worst score at the bottom of the screen and the third player in the middle,creating a hierarchical ranking. The names prented were the actual names of the three participants.To make this ranking even more salient,the top player received three stars behind his name,the middle player two and the bottom-ranking player received j
ust one star.Importantly,when the status of the subject changed, both the position of their name on the screen,as well as the number of stars behind their name changed.We made sure that subjects achieved their final ranking at least100 trials before the end of the rank-inducing ssion and that they maintained this status until the end of the first ssion. Twelve subjects acquired the low(two males),middle(two males)and high(three males)rank.
After a short break,subjects continued with the cond ssion that we will refer to as the experimental ssion.In this ssion,the three subjects performed the time estimation task together and were informed that every correct respon from the three participants would be rewarded with five euro-cents,so the maximal earnings per trail would be15euro-cents.Every cent won would be added to an account that was displayed on screen throughout the experiment.Subjects were informed that at the end of the experiment,the money in the account would be distributed equally among the three sub-jects.Subjects received feedback on their own and also on the
others’performance simultaneously,and were informed that the other two subjects also received feedback on performance of all three participants at the same time(Figure1).Subjects performed320trials in this ssion,lasting for$35min.In this cond,experimental ssion the length of the critical interval was manipulated in such a way that all subjects received positive feedback on5
0%of the trials.The hierarchy established in the rank-inducing ssion was maintained so the status of subjects did not change during the experimental ssion,and was visible to the subjects during the entire by the number of stars behind their names and the position of their names on the screen(Figure1).It is important to note that,although the percentage of positive and negative feedback was manipulated,this feedback was actually still contingent upon the participants’performance. What differed between subjects was the time interval within which respons were considered correct.
EEG was recorded from128locations using active Ag–AgCl electrodes(Biomi ActiveTwo,Amsterdam, Netherlands)mounted in an elastic cap.Horizontal EOGs were recorded from two electrodes placed at the outer canthi of both eyes.Vertical EOGs were recorded from electrodes on the infraorbital and supraorbital regions of the right eye placed in line with the pupil.The EEG and EOG signals were sampled at a rate of256Hz,digitally low-pass filtered with a 52Hz cut-off(3dB)and offline rereferenced to an averaged mastoid reference.
All ERP analys were performed using the Brain Vision Analyr software(Brain Products).The data were resampled at100Hz and further filtered with a0.53Hz high-pass filter and a slope of48dB/oct and a40Hz low-pass filter also with a slope of48dB/oct.Artefacts were rejected and eye movement ar
tefacts were corrected,using the Gratton et al.(1983)method.ERPs from each individual subject were averaged parately and a baline voltage aver-aged over the200ms interval preceding feedback was sub-tracted from the averages.
Visual inspection of grand-averaged waveforms and their scalp distributions(Figures2–5)indicated an MFN that reached its maximum between220and320ms after pren-tation of the feedback on midline frontal electrode sites, centred around FCz and Cz.Therefore,the average ampli-tudes in this time window and the electrode positions were entered in a general linear model(GLM)for statistical ana-lys To minimize the effects of differences between groups resulting from non-neural caus and also to minimize the effects of overlap between MFN and other ERP components (most notably the P3),we followed up on the analys by creating difference waves by subtracting ERPs elicited by wins from ERPs associated with loss(e Holroyd and Krigolson,2007)and submitted mean amplitudes of the difference waves recorded from FCz,Cz and Pz in a time window of220–320ms post-feedback to t-tests.To further rule out potential contamination of the MFN by other po-tentially overlapping components,we also analyd the ERP data by conducting a spatial principal component analysis (PCA;Spencer et al.,2001),using the PCA module
provided Fig.1Screenshot of the time estimation reaction time task.At the start of the trial, a blue circle was prented,that changed colour to green after2–2.5s.It was the participant’s job to hit the spacebar exactly1s after the circle had turned green. Subjects were rewarded with five eurocents every time they responded correctly. Respons were considered correct when they were within a certain critical time interval.Two conds after the circle changed colour,subjects were given feedback on their performance:a smiley face accompanied by‘þ5ct’when they responded within the c
ritical time interval or a sad face and‘þ0ct’when they responded too fast or too slow.Note that the name of the player with the best score was prented at the top of the screen,the name of the player with the worst score at the bottom of the screen and the third player in the middle,creating a hierarchical ranking.The names prented were the actual names of the three participants.To make this ranking even more salient,the top player received three stars behind his name,the middle player two and the bottom-ranking player received just one star.
1163Social status and performance evaluation SCAN(2011)3of10
at South China Normal Unviversity on September 16,
Downloaded from
with the Brain Vision Analyzer software.Spatial factor load-ings were obtained by submitting to a PCA the datapoints for each participant and condition,using varimax rotation and taking an eigenvalue of 1as the limit for the number of components extracted.Next,we identi E ed the factor show-ing loadings that were maximal at frontal –central areas (e Holroyd and Coles,2008;Holroyd et al.,2008),enabling us to isolate activity that can be attributed to the MFN.We further analyd the extracted PCA components in the same way as the ERPs,by submitting the average of a
220–320ms time window to statistical analys.Finally,we correlated ERP and PCA data with BIS/BAS scores and per-formance data.
RESULTS BIS/BAS
In order to determine whether there were baline differ-ences in approach motivation between groups that could potentially confound our results,subjects filled out
the
Fig.2Feedback-locked ERPs,averaged over the three status groups (n ¼36),showing that negative feedback elicited a more negative-going ERP in the latency range typically associated with the MFN (here:220–320ms),compared with the ERP elicited by positive feedback.The dotted line reprents the difference wave created by subtracting the ERP associated with positive feedback from the ERP associated with negative
合肥中考查分
groceryfeedback.
Fig.3Feedback-locked ERPs from subjects in the high (n ¼12)and low (n ¼12)status groups,showing a more pronounced difference in amplitude (220–320ms)between positive and negative feedback for low-status subjects,compared with high-status subjects.
4of10SCAN (2011)M.A.S.Bokm et al.
at South China Normal Unviversity on September 16, 2011
< Downloaded from
BIS/BAS questionnaire.Mean BIS scores for the low,middle and high-status groups were 19.0(s.d.¼4.3),22.0(s.d.¼3.9)and 19.8(s.d.¼4.4),respectively,F (2,35)¼1.75,NS.Mean BAS scores were 43.3(s.d.¼2.9),40.8(s.d.¼3.5)and 40.8(s.d.¼5.6),respectively,F (2,35)¼1.36,NS.The results show that there were no significant baline differences in approach motivation between our three status groups.
ERPs
While amplitudes were of comparable magnitude at Cz and FCz,for reasons of clarity and consistency,we will report results from Cz here.However,the reported effects were also significant at FCz.As expected,feedback indicating loss elicited a larger negativity (M ¼2.5m V)in the MFN latency range compared to feedback indicating gains [M ¼5.3m V,F (1,33)¼72.93,P <0.001;e Figure 2].One sample t -tests confirmed that the amplitude of the difference wave (M ¼À2.7m V),created by subtracting the ERPs associated with gains from the ERPs associated with loss,was indeed significantly different from zero,t (35)¼À8.64,P <0.001(Figure 2).This was shown to be true for the MFN in the low-status group [M ¼À3.3m V,t (11)¼À6.77,P <0.001],as well as in the middle-status group [M ¼À2.6m V,t (11)¼À6.24,P <0.001],and also in the high-status group [M ¼À2.6m V,t (11)¼À3.85,P <0.005].Figure 5shows that,in accordance with previous studies reporting MFN,this negativity reached its maximum over frontocentral scalp positions in all three status
groups.
Fig.4Difference waves,created by subtracting the feedback-evoked ERP associated with positive feedback from the ERP associated with negative feedback,for subjects with low (n ¼12),middle (n ¼12)and high (n ¼12)status.MFN was significantly larger for low-status subjects compared with subjects of both middle and high
status.
Fig.5Topographical distributions of the MFN for the three status groups.
Social status and performance evaluation SCAN (2011)5of10
评价英文at South China Normal Unviversity on September 16, 2011
< Downloaded from
