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How do humans learn, through social interaction, whom to depend on in different situations? We compared the extent to which inferred trait attributes—as opposed to learned reward associations previously examined in feedback-based learning contexts—could adaptively inform cross-context social decision making. In four experiments, participants completed a novel task in which they chose to “hire” other players to solve math and verbal questions for money. These players varied in their trait-level competence across these contexts, and, independently, in the monetary rewards they offered to participants across contexts. Results revealed that participants chose partners primarily based on context-specific traits, as opposed to either global (e.g., cross-context) trait impressions or material rewards. When making choices in novel contexts—including determining who to choose for social and emotional support—participants generalized trait knowledge from past contexts that required similar traits. Reward-based learning, by contrast, demonstrated significantly weaker context-sensitivity and generalization. These findings suggest that people form context-dependent trait impressions from interactive feedback and use this knowledge to make flexible social decisions. These results support a novel theoretical account of how interaction-based social learning can support context-specific impression formation and adaptive decision making.

Background Little is known about how conspiracy beliefs and health responses are interrelated over time during the course of the coronavirus disease 2019 (Covid-19) pandemic. This longitudinal study tested two contrasting, but not mutually exclusive, hypotheses through cross-lagged modeling. First, based on the consequential nature of conspiracy beliefs, we hypothesize that conspiracy beliefs predict an increase in detrimental health responses over time. Second, as people may rationalize their behavior through conspiracy beliefs, we hypothesize that detrimental health responses predict increased conspiracy beliefs over time. Methods We measured conspiracy beliefs and several health-related responses (i.e. physical distancing, support for lockdown policy, and the perception of the coronavirus as dangerous) at three phases of the pandemic in the Netherlands (N = 4913): During the first lockdown (Wave 1: April 2020), after the first lockdown (Wave 2: June 2020), and during the second lockdown (Wave 3: December 2020). Results For physical distancing and perceived danger, the overall cross-lagged effects supported both hypotheses, although the standardized effects were larger for the effects of conspiracy beliefs on these health responses than vice versa. The within-person change results only supported an effect of conspiracy beliefs on these health responses, depending on the phase of the pandemic. Furthermore, an overall cross-lagged effect of conspiracy beliefs on reduced support for lockdown policy emerged from Wave 2 to 3. Conclusions The results provide stronger support for the hypothesis that conspiracy beliefs predict health responses over time than for the hypothesis that health responses predict conspiracy beliefs over time.

Humans increasingly rely on artificial intelligence (AI) for efficient and objective decision-making, yet there is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded. As a consequence, their use by human decision makers may result in the propagation, rather than reduction, of existing disparities. To assess this hypothesis empirically, we tested the relation between societal gender inequality and algorithmic search output and then examined the effect of this output on human decision-making. First, in two multinational samples ( n = 37, 52 countries), we found that greater nation-level gender inequality was associated with more male-dominated Google image search results for the gender-neutral keyword “person” (in a nation’s dominant language), revealing a link between societal-level disparities and algorithmic output. Next, in a series of experiments with human participants ( n = 395), we demonstrated that the gender disparity associated with high- vs. low-inequality algorithmic outputs guided the formation of gender-biased prototypes and influenced hiring decisions in novel scenarios. These findings support the hypothesis that societal-level gender inequality is recapitulated in internet search algorithms, which in turn can influence human decision makers to act in ways that reinforce these disparities.

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic., PNAS Nexus, 1 (3), ISSN:2752-6542

Social media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior., Despite the popularity of social media, the psychological processes that drive people to engage in it remain poorly understood. The authors applied a computational modeling approach to data from multiple social media platforms to show that engagement can be explained by mechanisms of reward learning.

The social neuroscience approach to prejudice investigates the psychology of intergroup bias by integrating models and methods of neuroscience with the social psychology of prejudice, stereotyping, and discrimination. Here, we review major contemporary lines of inquiry, including current accounts of group-based categorization; formation and updating of prejudice and stereotypes; effects of prejudice on perception, emotion, and decision making; and the self-regulation of prejudice. In each section, we discuss key social neuroscience findings, consider interpretational challenges and connections with the behavioral literature, and highlight how they advance psychological theories of prejudice. We conclude by discussing the next-generation questions that will continue to guide the social neuroscience approach toward addressing major societal issues of prejudice and discrimination.

The experience of power is typically associated with social disengagement, yet power has also been shown to facilitate configural visual encoding – a process that supports the initial perception of a human face. To investigate this apparent contradiction, we directly tested whether power influences the visual encoding of faces. Two experiments, using neural and psychophysical assessments, revealed that low power impeded both first-order configural processing (the encoding of a stimulus as a face, assessed by the N170 event-related potential) and second-order configural processing (the encoding of feature distances within configuration, assessed using the face inversion paradigm), relative to high-power and control conditions. Power did not significantly affect facial feature encoding. Results reveal an early and automatic effect of low power on face perception, characterized primarily by diminished face processing. These findings suggest a novel interplay between visual and cognitive processes in power’s influence on social behavior. ISSN:1747-0919 ISSN:1747-0927

Social prejudices, based on race, ethnicity, gender, or other identities, pervade how we perceive, think about, and act toward others. Research on the neural basis of prejudice seeks to illuminate its effects by investigating the neurocognitive processes through which prejudice is formed, represented in the mind, expressed in behavior, and potentially reduced. In this article, we review current knowledge about the social neuroscience of prejudice regarding its influence on rapid social perception, representation in memory, emotional expression and relation to empathy, and regulation, and we discuss implications of this work for prejudice reduction interventions.

We agree with Cesario's premise but reject his conclusion: Although experimental studies of racial stereotyping, weapons perception, and shoot decisions typically exclude real-world contextual factors and thus have limited relevance to race disparities (e.g., in policing), these excluded factors comprise systemic, institutional, and individual-level biases that are more likely to amplify racial disparities than negate them.

When economic resources are scarce, racial minorities are often devalued and disenfranchised. We proposed that this pattern extends to visual processing, such that the encoding of minority group faces is disrupted under scarcity-an effect that may facilitate discrimination and contribute to racial disparities. Specifically, we used EEG and fMRI to test whether scarce economic conditions induce deficits in neural encoding of Black faces, and we examined whether this effect is associated with discriminatory resource allocation in behavior. In Study 1, framing resources as scarce (vs. neutral) selectively impaired the neural encoding of Black (vs. White) faces, as indexed by a delayed face-related N170 ERP response, and the degree of this encoding deficit predicted anti-Black allocation decisions. In Study 2, we replicated and extended this effect using fMRI: Resources framed as scarce (vs. neutral) reduced face-sensitive fusiform activity to Black (vs. White) faces. Furthermore, scarcity-decreased fusiform activity to Black faces corresponded with decreased valuation-related striatum activity to predict anti-Black allocation decisions. These findings suggest that impaired visual processing and devaluation occur selectively for minorities under scarcity-an implicit effect that may promote discrimination and contribute to rising disparities observed during economic stress. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

It is widely believed that race divides the world into biologically distinct kinds of people-an essentialist belief inconsistent with reality. Essentialist views of race have been described as early emerging, but this study found that young children (n = 203, Mage = 5.45) hold only the more limited belief that the physical feature of skin color is inherited and stable. Overall, children rejected the causal essentialist view that behavioral and psychological characteristics are constrained by an inherited racial essence. Although average levels of children's causal essentialist beliefs about race were low, variation in these beliefs was related to children's own group membership, exposure to diversity, as well as children's own social attitudes.

For 40 years, research on impression formation and attitudes has relied on dual-process theories that represent knowledge in a single associative network. Although such models explain priming effects and some implicit responses, they are generally silent on other forms of learning and on the interface of social cognition with perception and action. Meanwhile, advances in cognitive neuroscience reveal multiple, interacting forms of learning and memory (e.g., semantic associative memory, Pavlovian conditioning, and instrumental learning), with detailed models of their operations, neural bases, and connections with perceptual and behavioral systems. This memory systems perspective offers a more refined, neurally plausible model of social cognition and attitudes that, I argue, provides a useful and generative account of human social behavior.

Social media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

How do societal stereotypes about social groups become internalized as an individual’s own group-based preferences? Although stereotypes are known to create social expectancies, we propose that stereotypes also shape how one experiences and learns from interactions with group members—a process that may induce internalized group-based preferences. In a series of experiments, participants interacted with players from two groups, described with either positive or negative stereotypes, in a reinforcement learning task presented as a money sharing game. Although players’ sharing behaviors were equated between groups, participants formed more positive reward associations with players from positively-stereotyped groups. Computational modeling revealed that stereotypes set participants’ expectancies about group members’ behavior and biased how they learned from player feedback. We then show that these preferences, once formed, spread unwittingly to others who observe these interactions. Finally, we show that this pattern of biased learning and propagation occurs even when the stereotypic message comes from an opposed politician, suggesting that this transmission—from societal stereotypes to personal preferences—may occur despite one’s explicit goals. These findings provide a mechanistic account of how exposure to social stereotypes can transform into personal group-based preferences and spread to others.

How do group-based interaction tendencies form through encounters with individual group members? In three experiments, in which participants interacted with group members in a reinforcement learning task presented as a money sharing game, participants formed instrumental reward associations with individual group members through direct interaction and feedback. Results revealed that individual-level reward learning generalized to a group-based representation, as indicated in self-reported group attitudes, trait impressions, and the tendency to choose subsequent interactions with novel members of the group. Moreover, group-based reward values continued to predict interactions with novel members after controlling for explicit attitudes and impressions, suggesting that instrumental learning contributes to an implicit form of group-based choice. Experiment 3 further demonstrated that group-based reward effects on interaction choices persisted even when group reward value was no longer predicted of positive outcomes, consistent with a habit-like expression of group bias. These results demonstrate a novel process of prejudice formation based on instrumental reward learning from direct interactions with individual group members. We discuss implications for existing theories of prejudice, the role of habit in intergroup bias, and intervention strategies to reduce prejudice.

Wastewater treatment plants (WWTPs) are known sources of contaminants of emerging concern (CECs) spreading into the environment, as well as, of unpleasant odors. CECs represent a potential hazard for human health and the environment being pharmaceutical or biologically active compounds and they are acquiring relevance in European directives. Similarly, the public concern about odour emissions from WWTPs is also increasing due to the decreasing distance between WWTP and residential areas. This study focuses on the effectiveness of the recently developed MULESL technology (MUch LEss SLudge; WO2019097463) in removing CECs and limiting odour emissions from WWTPs. MULESL technology has been developed for its ability to reduce up to 80% the sludge production from WWTPs. However, it is ought to evaluate if the benefits coming from sludge production reduction do not invalidate CECs removal or negatively affect odour emissions. Thus, the performances of a MULESL and a conventional WWTP (flow rate of 375 m3/d and 3600 m3/d, respectively) were compared while treating the same municipal sewage. Whereas both plants succeeded in removing the traditional gross parameters characterizing wastewaters (e.g. chemical oxygen demand, nitrogen), the MULESL was much more effective than the conventional one in terms of CECs removal for about 60% of the identified compounds showing, however, the same or lower effectiveness for about 30% and 10% of them, respectively. This result was attributed to the high sludge retention time and biomass concentration in the MULESL (enabling enrichment of slow growing microorganisms and forcing biomass to use unusual substrates, respectively), and to the biomass feature to grow in the form of biofilm and granules (favoring micropollutants absorption on biomass). Furthermore, odour impact analysis has shown that the MULESL was characterized by a much lower impact, i.e. 45% lower than that of primary and secondary treatments of the conventional WWTP.

How do we form impressions of people and groups and use these representations to guide our actions? From its inception, social neuroscience has sought to illuminate such complex forms of social cognition, and recently these efforts have been invigorated by the use of computational modeling. Computational modeling provides a framework for delineating specific processes underlying social cognition and relating them to neural activity and behavior. We provide a primer on the computational modeling approach and describe how it has been used to elucidate psychological and neural mechanisms of impression formation, social learning, moral decision making, and intergroup bias.

© 2017 Elsevier Ltd Successful social relationships require a consideration of a partner's thoughts and intentions. This aspect of social life is captured in the social mindfulness paradigm (SoMi task), in which participants make decisions that either limit or preserve options for their interaction partner's subsequent choice. Here we investigated the neural correlates of spontaneous socially mindful and unmindful behaviours. Functional magnetic resonance data were acquired from 47 healthy adolescents and young adults (age 16–27) as they completed the SoMi task. Being faced with socially relevant choices was associated with activity in the medial prefrontal cortex, anterior cingulate, caudate, and insula, which is consistent with prior neuroeconomical research. Importantly, socially mindful choices were associated with activity in the right parietal cortex and the caudate, whereas unmindful choices were associated with activity in the left prefrontal cortex. These neural findings were consistent with the behavioural preference for mindful choices, suggesting that socially mindful decisions are the basic inclination, whereas socially unmindful responses may require greater effort and control. Together, these results begin to uncover the neural correlates of socially mindful and unmindful choices, and illuminate the psychological processes involved in cooperative social behaviour.

March, Gaertner, and Olson (this issue) highlight a critical weakness of most contemporary models of attitudes, whereby a valence-based account of attitude associations often fails to map onto patt...

When the economy declines, existing racial disparities typically expand, suggesting that economic scarcity may promote racial discrimination. To understand this pattern, we examined the effect of perceived scarcity on resource allocations to Black and White American recipients, and tested whether this effect depends on a decision maker's motivation to respond without prejudice. We proposed that scarcity would lead to increased discrimination among those with relatively low internal motivation but not those high in internal motivation. Indeed, we found that when resources were framed as scarce (vs. abundant or a control condition), low-motivation participants allocated less to Black than White recipients, whereas high-motivation participants allocated more to Black than White recipients (Studies 1 and 2). This pattern was strongest when decisions could be made deliberatively (Study 3), and anti-Black allocation bias emerged even in a non-zero-sum context (Studies 4 and 5), suggesting a strategic bias directed against Black recipients rather than in favor of White recipients. These findings indicate that the psychological perception of scarcity can produce racial bias in the distribution of economic resources, depending on the motivations of the decision maker-an effect that may contribute to the increase in racial disparities observed during economic stress. (PsycINFO Database Record

Why do essentialist beliefs promote prejudice? We proposed that essentialist beliefs increase prejudice toward Black people because they imply that existing social hierarchies reflect a naturally occurring structure. We tested this hypothesis in three studies ( N = 621). Study 1 revealed that racial essentialism was associated with increased prejudice toward Blacks among both White and Black adult participants, suggesting that essentialism relates to prejudice according to social hierarchy rather than only to group membership. Studies 2 and 3 experimentally demonstrated that increasing essentialist beliefs induced stronger endorsement of social hierarchies in both Black and White participants, which in turn mediated the effect of essentialism on negative attitudes toward Black people. Together, these findings suggest that essentialism increases prejudice toward low-status groups by increasing endorsement of social hierarchies and existing inequality.

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions – computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each type of learning was expressed in both advisor choices and post-task self-reported liking of advisors. Specifically, participants preferred advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Although participants relied more heavily on model-based learning overall, they varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

The field of social robotics offers an unprecedented opportunity to probe the process of impression formation and the effects of identity-based stereotypes (e.g. about gender or race) on social judgements and interactions. We present the concept of fair proxy communication—a form of robot-mediated communication that proceeds in the absence of potentially biasing identity cues—and describe how this application of social robotics may be used to illuminate implicit bias in social cognition and inform novel interventions to reduce bias. We discuss key questions and challenges for the use of robots in research on the social cognition of bias and offer some practical recommendations. We conclude by discussing boundary conditions of this new form of interaction and by raising some ethical concerns about the inclusion of social robots in psychological research and interventions.This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

People learn about the world by making choices and experiencing feedback—a process characterized by models of reinforcement learning in which people learn to associate their actions with rewarding outcomes. Although reinforcement models provide compelling accounts of feedback-based learning in nonsocial contexts, social interactions typically involve inferences of others' trait characteristics, which may be independent of their reward value. As a result, people may learn differently about humans and nonhumans through reinforcement. In two experiments (and a pilot study), participants interacted with human partners or slot machines that shared money. Computational modeling of behavior revealed different patterns of learning for humans and non-humans: participants relied more on feedback indicating trait generosity (relative to monetary reward) when learning about humans but relied more on monetary reward (relative to generosity) when learning about slots. Furthermore, this pattern of learning had implications for attitudes: whereas participants preferred generous humans, they preferred rewarding slot machines, relative to their respective counterparts. These findings reveal a distinct role for reinforcement learning in social cognition, showing that humans preferentially form abstract trait inferences about other people through feedback in addition to reward associations.

Social neuroscience is an interdisciplinary approach to studying the social mind and behavior by considering their neural and biological underpinnings. This chapter describes the methodological approach of social neuroscience and research that has applied this approach to address the interplay of the person and situation in the domains of social cognition, attitudes, emotion and motivation, intergroup relations, and personality. This chapter illustrates the contribution of neuroscience to classic questions about personality and social psychology, with an emphasis on how the social neuroscience approach promotes the integration of dispositional and situational accounts of the mind and behavior.

Four experiments examined the effect of proactive control on expressions of implicit racial bias. Whereas reactive control is engaged in response to a biasing influence (e.g., a stereotype, temptation, or distraction), proactive control is engaged in advance of such biases, functioning to strengthen task focus and, by consequence, limiting the affordance for a bias to be expressed in behavior. Using manipulations of response interference to modulate proactive control, proactive control was found to eliminate expressions of weapons bias, prejudice, and stereotyping on commonly used implicit assessments. Process dissociation analysis indicated that this pattern reflected changes in controlled processing but not automatic associations, as theorized, and assessments of neural activity, using event-related potentials, revealed that proactive control reduces early attention to task-irrelevant racial cues while increasing focus on task-relevant responses. Together, these results provide initial evidence for proactive control in social cognition and demonstrate its effectiveness at reducing expressions of implicit racial bias. Based on these findings and past research, we present a model of proactive and reactive control that offers a novel and generative perspective on self-regulation and prejudice reduction. (PsycINFO Database Record (c) 2018 APA, all rights reserved)

Perceptual and judgment creep Do we think that a problem persists even when it has become less frequent? Levari et al. show experimentally that when the “signal” a person is searching for becomes rare, the person naturally responds by broadening his or her definition of the signal—and therefore continues to find it even when it is not there. From low-level perception of color to higher-level judgments of ethics, there is a robust tendency for perceptual and judgmental standards to “creep” when they ought not to. For example, when blue dots become rare, participants start calling purple dots blue, and when threatening faces become rare, participants start calling neutral faces threatening. This phenomenon has broad implications that may help explain why people whose job is to find and eliminate problems in the world often cannot tell when their work is done. Science , this issue p. 1465

We suggest three additional improvements to replication practices. First, original research should include concrete checks on validity, encouraged by editorial standards. Second, the reasons for replicating a particular study should be more transparent and balance systematic positive reasons with selective negative ones. Third, methodological validity should also be factored into evaluating replications, with methodologically inconclusive replications not counted as non-replications.

Social anxiety—the fear of social embarrassment and negative evaluation by others—ranks among people's worst fears, and it is often thought to impair task performance. We investigated the neurocognitive processes through which trait social anxiety relates to task performance, proposing a model of the joint contributions of reactive control, theoretically associated with conflict monitoring and activity of the dorsal anterior cingulate cortex (dACC), and proactive control, theoretically associated with top-down regulation and activity of the dorsolateral prefrontal cortex (dlPFC). Participants varying in their degree of trait social anxiety completed the Eriksen flanker task while electroencephalography (EEG) was recorded. Task-related left dlPFC activity was indexed by relative left prefrontal EEG (inverse alpha), and conflict-related dACC activity was indexed by the N2r component of the event-related potential. Stronger activity in both regions predicted better response control, and greater social anxiety was associated with worse response control. Furthermore, for all participants, greater left prefrontal EEG activity predicted better behavioral control, but for high social anxiety participants only, greater N2r responses also predicted behavioral control. This pattern suggests that low social anxiety individuals engaged a proactive control process, driven by dlPFC activity, whereas high social anxiety individuals relied additionally on a reactive control process, driven by conflict-related dACC activity. These findings support a model of control that involves different patterns of interplay between proactive and reactive strategies and may help to explain self-regulatory impairments in social anxiety.

Power is known to promote effective goal pursuit, especially when it requires one to overcome distractions or bias. We proposed that this effect involves the ability to engage and implement cognitive control. In Study 1, we demonstrated that power enhances behavioral performance on a response conflict task and that it does so by enhancing controlled processing rather than by reducing automatic processing. In Study 2, we used an event-related potential index of anterior cingulate activity to test whether power effects on control were due to enhanced conflict sensitivity or action implementation. Power did not significantly affect neural sensitivity to conflict; rather, high power was associated with a stronger link between conflict processing and intended action, relative to low power. These findings suggest a new perspective on how social factors can affect controlled processing and offer new evidence regarding the transition between conflict detection and the implementation of action control.