Current theories suggest that physical pain and social rejection share common neural mechanisms, largely by virtue of overlapping functional magnetic resonance imaging (fMRI) activity. Here we challenge this notion by identifying distinct multivariate fMRI patterns unique to pain and rejection. Sixty participants experience painful heat and warmth and view photos of ex-partners and friends on separate trials. FMRI pattern classifiers discriminate pain and rejection from their respective control conditions in out-of-sample individuals with 92% and 80% accuracy. The rejection classifier performs at chance on pain, and vice versa. Pain-and rejection-related representations are uncorrelated within regions thought to encode pain affect (for example, dorsal anterior cingulate) and show distinct functional connectivity with other regions in a separate resting-state data set (N = 91). These findings demonstrate that separate representations underlie pain and rejection despite common fMRI activity at the gross anatomical level. Rather than co-opting pain circuitry, rejection involves distinct affective representations in humans.

Human neuroimaging research has transitioned from mapping local effects to developing predictive models of mental events that integrate information distributed across multiple brain systems. Here we review work demonstrating how multivariate predictive models have been utilized to provide quantitative, falsifiable predictions; establish mappings between brain and mind with larger effects than traditional approaches; and help explain how the brain represents mental constructs and processes. Although there is increasing progress toward the first two of these goals, models are only beginning to address the latter objective. By explicitly identifying gaps in knowledge, research programs can move deliberately and programmatically toward the goal of identifying brain representations underlying mental states and processes.

(max 170 words)Action monitoring allows the swift detection of conflicts, errors, and the rapid evaluation of outcomes. These processes are crucial for learning, adaptive behavior, and for the regulation of cognitive control. Our review discusses neuroimaging and electrophysiological studies that have explored the contribution of emotional and social factors during action monitoring. Metaanalytic brain activation maps demonstrate reliable overlap of error monitoring, emotional, and social processes in the dorsal mediofrontal cortex (dMFC), lateral prefrontal areas, and anterior insula (AI). Cumulating evidence suggests that action monitoring is modulated by trait anxiety and negative affect, and that activity of the dMFC and the amygdala during action monitoring might contribute to the 'affective tagging' of actions along a valence dimension. The role of AI in action monitoring may be the integration of outcome information with self-agency and social context factors, thereby generating more complex situation-specific and conscious emotional feeling states. Our review suggests that action-monitoring processes operate at multiple levels in the human brain, and are shaped by dynamic interactions with affective and social processes.Keywords: Anterior insula; dorsal cingulate cortex; mediofrontal cortex; amygdala; error monitoring; ERN; feedback processing; social cognition; emotions; cognitive control; emotioncognition interactions; meta-analysis 3 Overview and motivationIn order to adapt their behavior, to detect and learn from errors, and ultimately to increase their chances of survival, humans and other animals have to monitor their actions (Rabbitt, 1966). Flexible regulation of behavior requires its constant evaluation in terms of performance and outcomes, as well as in terms of costs and future consequences. Action and error monitoring have been studied for several decades in psychology and neuroscience (for previous reviews see (Etkin et al., 2011;Moser et al., 2013;Pessoa, 2008;Proudfit et al., 2013;Shackman et al., 2011;Shenhav et al., 2013), we propose that error and action monitoring is an intrinsically affective and social process. However, we show that meta-analytic activation maps support overlapping brain responses to error processing, emotional, and social information processing not only in dMFC, but also in several other brain regions, including anterior insula and lateral prefrontal cortex.Further, recent intracranial electrophysiological recordings showed error-related activity in the amygdala, suggesting this limbic region may contribute to affective responses to errors and negative action outcomes. In the closing part, we outline an integrative framework for 4 understanding the brain systems underlying affective and social interactions with action monitoring, which may be crucial to foster behavioral control in real life.

Spontaneous interpersonal synchronization of rhythmic behavior such as gait or hand clapping is a ubiquitous phenomenon in human interactions, and is potentially important for social relationships and action understanding. Although several authors have suggested a role of the mirror neuron system in interpersonal coupling, the underlying brain mechanisms are not well understood. Here we argue that more general theories of neural computations, namely predictive coding and the Free Energy Principle, could explain interpersonal coordination dynamics. Each brain minimizes coding costs by reducing the mismatch between the representations of observed and own motor behavior. Continuous mutual prediction and alignment result in an overall minimization of free energy, thus forming a stable attractor state.

Beliefs and expectations often persist despite disconfirming evidence. We examine two potential mechanisms underlying such ‘self-reinforcing’ expectancy effects in the pain domain: Modulation of perception and biased learning. In two experiments, cues previously associated with symbolic representations of high or low temperatures preceded painful heat. We examined trial-to-trial dynamics in participants’ expected pain, reported pain, and brain activity. Subjective and neural pain responses assimilated towards cue-based expectations, and pain responses in turn predicted subsequent expectations, creating a positive dynamic feedback loop. Furthermore, we found evidence for a confirmation bias in learning: Higher- and lower-than-expected pain triggered greater expectation updating for high- and low-pain cues, respectively. Individual differences in this bias were reflected in the updating of pain-anticipatory brain activity. Computational modeling provided converging evidence that expectations influence both perception and learning. Together, these effects promote self-reinforcing expectations, helping to explain why beliefs can be resistant to change.

Placebos have been used ubiquitously throughout the history of medicine. Expectations and associative learning processes are important psychological determinants of placebo effects, but their underlying brain mechanisms are only beginning to be understood. We examine the brain systems underlying placebo effects on pain, autonomic, and immune responses. The ventromedial prefrontal cortex (vmPFC), insula, amygdala, hypothalamus, and periaqueductal gray emerge as central brain structures underlying placebo effects. We argue that the vmPFC is a core element of a network that represents structured relationships among concepts, providing a substrate for expectations and a conception of the situation-the self in context-that is crucial for placebo effects. Such situational representations enable multidimensional predictions, or priors, that are combined with incoming sensory information to construct percepts and shape motivated behavior. They influence experience and physiology via descending pathways to physiological effector systems, including the spinal cord and other peripheral organs.

Monitoring one"s own errors is a fundamental ability to guide and improve behavior, with specific neural substrates in the anterior cingulate cortex (ACC).Similarly, we can monitor others" actions and learn by observing their errors. The mirror neuron system may subserve the formation of shared representations for selfgenerated and observed actions, and recent research suggests that monitoring mechanisms also react to errors made by others. However, it remains unknown how these responses are modified when interpersonal context implies different goals for the actor and the observer. To investigate whether differences in social context can influence brain response to observed action errors, we manipulated competition versus cooperation between two participants taking turns in a Go/No-Go task. ERPs simultaneously recorded from both participants showed a typical negativity over frontocentral regions to self-generated errors, irrespective of interpersonal context; but early differential responses to other-generated errors only during cooperation, with sources in precuneus and medial premotor areas. Competition produced a distinct error-related negativity in ACC at later latencies. We conclude that error monitoring for others" actions depends on their congruence with personal goals, and recruits brain systems involved in self-referential processing specifically during cooperation.

People learn about their self from social information, and recent work suggests that healthy adults show a positive bias for learning self-related information. In contrast, social anxiety disorder (SAD) is characterized by a negative view of the self, yet what causes and maintains this negative self-view is not well understood. Here we employ a novel experimental paradigm and computational model to test the hypothesis that biased social learning regarding self-evaluation and self-feelings represents a core feature that distinguishes adults with SAD from healthy controls. Twenty-one adults with SAD and 35 healthy controls (HC) performed a speech in front of three judges. They subsequently evaluated themselves and received performance feedback from the judges, and then rated how they felt about themselves and the judges. Affective updating (i.e., change in feelings about the self over time, in response to feedback from the judges) was modeled using an adapted Rescorla-Wagner learning model. HC demonstrated a positivity bias in affective updating, which was absent in SAD. Further, self-performance ratings revealed group differences in learning from positive feedback—a difference that endured at an average of 1 year follow up. These findings demonstrate the presence and long-term endurance of positively biased social learning about the self among healthy adults, a bias that is absent or reversed among socially anxious adults.