Neural mechanisms of modulations of empathy and altruism by beliefs of others’ pain

Aesop’s fable ‘The boy who cried wolf’ tells a story that villagers run or do not run to help a shepherd boy who cries wolf depending on whether or not they believe that the boy’s crying indicates his actual emotion and need. This story illustrates an important character of human altruistic behavior, that is, perceived cues signaling others’ suffering drives us to do them a favor only when we believe that their suffering is true. Although this character of human altruism was documented over 2000 years ago in Aesop’s fable and is widely observed in current human societies, its psychological and neural underpinnings have not been fully understood. The present study investigated how beliefs of others’ pain (BOP) modulate human altruistic behavior independently of perceived cues signaling others’ suffering and whether the modulation effect, if any, is mediated by changes in empathy for others’ pain and relevant brain underpinnings.

Empathy refers to understanding and sharing of others’ emotional states (Decety and Jackson, 2004) and has been proposed to provide a key motivation for altruistic behavior in both humans and animals (Batson et al., 2015; de Waal, 2008; Decety et al., 2016). Empathy can be induced by perceived cues signaling others’ pain that activate neural responses in brain regions underlying sensorimotor resonance (e.g., the sensorimotor cortex), affective sharing (e.g., the anterior insula [AI] and anterior cingulate cortex [ACC]), and mental state inference/perspective-taking (e.g., the medial prefrontal cortex [mPFC] and temporoparietal junction [TPJ]) (Singer et al., 2004; Jackson et al., 2005; Avenanti et al., 2005; Saarela et al., 2007; Fan and Han, 2008; Shamay-Tsoory et al., 2009; Han et al., 2009; Sheng and Han, 2012; Fan et al., 2011; Lamm et al., 2011; Zhou and Han, 2021). Neural responses to others’ pain in the empathy network and functional connectivity between its key hubs can predict motives for subsequent altruistic actions (e.g., Hein et al., 2010; Hein et al., 2016; Mathur et al., 2010; Luo et al., 2015). These brain imaging findings revealed neural mechanisms underlying the perception-emotion-behavior reactivity (e.g., perceived pain-empathy-help) that occurs often in everyday lives (Eisenberg et al., 2010; Hofman, 2008; Penner et al., 2005). However, empathic neural responses are influenced by multiple factors such as perceptual features depicting others’ pain (Gu and Han, 2007; Li and Han, 2019), observers’ perspectives and attention (Gu and Han, 2007; Li and Han, 2010; Jauniaux et al., 2019), and perceived social relationships between observers and empathy targets (Xu et al., 2009; Avenanti et al., 2010; Hein et al., 2010; Mathur et al., 2010; Sheng and Han, 2012; Azevedo et al., 2013; Sheng et al., 2014; Sheng et al., 2016; Han, 2018; Zhou and Han, 2021). What remains unclear is whether and how BOP modulates empathic brain activity through which to further influence altruistic behavior. To address these issues is crucial for understanding variations of empathy and altruism during complicated social interactions as illustrated in Aesop’s fable.

Beliefs refer to mental representations of something that are not immediately present to the scenes but allow people to think beyond what is here and now (Fuentes, 2019). Beliefs reflect an organism’s endorsement of a particular state of affairs as actual (McKay and Dennett, 2009). Beliefs that best approximate reality enable the believers to act effectively and maximize their survival (Fodor, 1985; Millikan, 1995). Previous research has shown that beliefs affect multiple mental processes such as visual awareness (Sterzer et al., 2008) and processing of emotions (Petrovic et al., 2005) including experiences of pain (Wager et al., 2004; Colloca and Benedetti, 2005). The function of beliefs is also manifested in increasing the efficiency of neural processes involved in decision-making and goal setting (Garcés and Finkel, 2019; Régner et al., 2019). Potential effects of beliefs on empathic neural responses were tested by presenting participants with photographs showing pain inflicted by needle injections into a hand that was believed to be or not to be anesthetized (Lamm et al., 2007). Functional magnetic resonance imaging (fMRI) of brain activity suggested modulations of insular responses to perceived pain by beliefs of anesthetization. However, the results cannot be interpreted exclusively by BOP because the stimuli (i.e., needles) used to induce beliefs of numbed and non-numbed hands were different. An ideal paradigm for testing modulations of empathy by BOP independently of perceived cues signaling others’ pain should compare brain activities in response to identical stimuli under different beliefs and enable researchers to test how BOP influences altruistic behavior.

In six behavioral, electroencephalography (EEG), and fMRI experiments, the current study tested the hypothesis that BOP affects empathy and altruistic behavior by modulating brain activity in response to others’ pain. Specifically, we predicted that lack of BOP may result in the inhibition of altruistic behavior by decreasing empathy and its underlying brain activity. Our behavioral, EEG, and fMRI experiments were designed based on the common beliefs that patients show pain expressions to manifest their actual feelings of pain whereas pain expressions performed by actors/actresses do not indicate their actual emotional states. To examine BOP effects on empathy, we experimentally manipulated BOP by asking participants to learn and remember different identities (i.e., patient or actor/actress) of a set of neutral faces during a learning procedure. Thereafter, we measured self-reports of others’ pain and own unpleasantness from the participants when they viewed learned faces with pain or neutral expressions. During EEG/fMRI recording, the participants were asked to discriminate patient or actor/actress identities of faces with pain or neutral expressions. We compared self-reports of others’ feelings and brain activities related to pain (vs. neutral) expressions of patients’ faces with those related to actors/actresses’ faces. If the perception of patients’ pain expressions implicitly activates BOP whereas perception of actors/actresses’ pain expressions does not activate BOP, we expected that lack of BOP (i.e., to compare actors/actresses vs. patients) would reduce self-report of empathy, empathic brain activity, and altruistic behavior. We further predicted that BOP effects on altruistic behavior might be mediated by decreased empathy and empathic brain activity due to lack of BOP.

Similar to previous research (Jackson et al., 2005; Fan and Han, 2008; Hein et al., 2010; Mathur et al., 2010; Sheng and Han, 2012), we adopted both subjective and objective estimations of empathy for others’ pain. Subjective estimation of empathy for pain depends on the collection of self-reports of others’ painful feelings and one’s own unpleasantness when viewing others’ suffering (e.g., Bieri et al., 1990; Jackson et al., 2005; Lamm et al., 2007; Fan and Han, 2008; Sheng and Han, 2012). Objective estimation of empathy for pain relies on recording of brain activities, using fMRI or EEG, that differentially respond to painful versus non-painful stimuli applied to others (e.g., Singer et al., 2004; Jackson et al., 2005; Gu and Han, 2007; Fan and Han, 2008; Hein et al., 2010) or to others’ faces with pain versus neutral expressions (Botvinick et al., 2005; Saarela et al., 2007; Han et al., 2009; Sheng and Han, 2012). Brain responses to perceived non-painful stimuli applied to others or neutral expressions were also collected to control empathy-unrelated perceptual or motor processes. fMRI studies revealed greater activations in the ACC, AI, and sensorimotor cortices in response to painful compared to non-painful stimuli applied to others (e.g., Singer et al., 2004; Jackson et al., 2005; Gu and Han, 2007; Hein et al., 2010; see Lamm et al., 2011; Fan et al., 2011 for review). EEG studies showed that event-related potentials (ERPs) in response to perceived painful stimulations applied to others’ body parts elicited neural responses that differentiated between painful and neutral stimuli over the frontal region as early as 140 ms after stimulus onset (Fan and Han, 2008; see Coll, 2018 for review). Moreover, the mean ERP amplitudes at 140–180 ms predicted self-report of others’ pain and one’s own unpleasantness (Fan and Han, 2008).

Particularly related to the current work are neuroimaging findings that compared brain responses to pain versus neutral expressions. fMRI studies found that viewing video clips (Botvinick et al., 2005) or pictures (Sheng et al., 2014) showing faces with pain versus neutral expressions or viewing photos of faces of patients who were suffering from provoked pain versus chronic pain (Saarela et al., 2007) induced activations in the ACC, AI, and inferior parietal cortex. Moreover, the cortical areas activated by facial expressions of pain were also engaged by the first-hand experience of pain evoked by thermal stimulation (Botvinick et al., 2005). Moreover, the strengths of AI activations during observation of others’ pain were correlated with subjective feelings of others’ pain (Saarela et al., 2007). ERP studies found that neural responses to pain expressions occurred as early as 130 ms after face onset over the frontal/central regions as indexed by the increased amplitude of a positive component at 128–188 ms (P2) in response to pain compared neutral expressions (Sheng and Han, 2012; Sheng et al., 2013; Sheng et al., 2016; Han et al., 2016; Li and Han, 2019). In addition, the P2 amplitudes in response to others’ pain expressions positively predicted subjective feelings of own unpleasantness induced by others’ pain and self-reports of one’s own empathy traits (Sheng and Han, 2012). In addition, source estimation of the P2 component in response to others’ pain expressions suggested a possible origin in the ACC. Taken together, these brain imaging findings suggest effective subjective and objective measures of empathy (i.e., understanding and sharing of others’ pain) that are suitable for the investigation of neural mechanisms underlying modulations of empathy and altruism by BOP.

In Experiment 1, we randomly assigned patient or actor/actress identities to faces to test how experimentally manipulated BOP associated with face identities caused changes in empathy (i.e., subjective evaluation of others’ pain) and altruistic behavior (i.e., monetary donations). We predicted that lack of BOP related to actors/actresses (vs. patients) would result in reduced empathy and altruistic behavior. In Experiment 2, based on the common belief that an effective medical treatment reduces a patient’s pain, we tested whether decreasing BOP due to knowledge of effective medical treatments of patients also reduced empathy and altruistic behavior.

In Experiments 3 and 4, we investigated whether BOP modulates empathic brain activity by recording EEG signals in response to pain or neutral expressions of faces with patient or actor/actress identities. Brain activities related to empathy were quantified by comparing neural responses to pain versus neutral expressions to exclude neural processes of facial structures, social attributes (e.g., gender), and other empathy-unrelated information. Given previous findings that the P2 amplitude increased to pain compared to neutral expressions and was associated with self-report of sharing of others’ pain (Sheng and Han, 2012; Sheng et al., 2013; Sheng et al., 2016; Han et al., 2016; Li and Han, 2019), we focused on how the P2 amplitude in response to pain (vs. neutral) expressions was modulated by facial identities (i.e., patient or actor/actress) that link to different beliefs (i.e., patients’ pain expressions manifest their actual feelings whereas actors/actresses’ pain expressions do not). Our ERP results showed evidence that actor/actress compared to patient identities of faces decreased the empathic neural responses (i.e., P2 amplitudes in response to pain [vs. neutral] expressions) within 200 ms post-stimulus. In Experiment 5, we further revealed behavioral and EEG evidence that neural responses to pain expressions of faces mediate BOP effects on empathy and monetary donations.

In Experiment 6, we employed fMRI to examine brain regions in which blood oxygen level-dependent (BOLD) signals are modulated by BOP. We examined BOLD responses to faces that had either patient or actor/actress identities, received painful/non-painful stimulations, and showed pain or neutral expressions. fMRI results allowed us to test whether empathic neural responses in the cognitive (i.e., the dorsal mPFC and TPJ; Völlm et al., 2006; Schnell et al., 2011; also see Lamm et al., 2011; Fan et al., 2011; Shamay-Tsoory, 2011), sensorimotor/affective (i.e., the ACC, insula, and sensorimotor cortex; Jackson et al., 2005; Singer et al., 2004; Avenanti et al., 2005), or both nodes of the empathic neural network would be modulated by BOP that was manipulated by assigning different identities (i.e., patient or actor/actress) to empathy targets. In addition, we examined whether neural responses in the empathic network would be able to predict variations of subjective feelings of others’ pain due to lack of BOP.

Taken together, our behavioral and brain imaging results showed consistent evidence that lack of BOP or decreasing BOP resulted in reduced empathy and altruistic behavior. Our findings suggest that BOP may provide a cognitive basis for human empathy and altruism and uncover intermediate brain mechanisms by which BOP influences empathy and altruistic behavior.

We conducted six experiments to investigate psychological and neural mechanisms underlying BOP impacts on empathy and altruistic behavior in humans. We manipulated individuals’ BOP by randomly assigning patient or actor/actress identities to faces as there was a lack of BOP for actors/actresses’ faces but not for patients’ faces. We also estimated individuals’ intrinsic BOP by asking the participants to estimate the effectiveness of medical treatments of patients to trigger BOP as an effective medical treatment reduces a patient’s pain. We further measured brain activity using EEG and fMRI to examine BOP effects on empathic neural responses with high temporal and spatial resolutions, respectively. Our behavioral and neuroimaging findings showed evidence for a functional role of BOP in modulations of the perception-emotion-behavior reactivity by illustrating how BOP predicted and affected self-reports of empathy, empathic brain activities, and monetary donations. Our findings suggest that BOP may provide a cognitive basis for empathy and altruistic behavior in humans.

Experiments 1 and 2 showed behavioral evidence that manipulated changes in BOP caused subsequent variations of self-report of empathy and altruistic behavior along the directions as predicted. Specifically, decreasing BOP concomitant with changes in face identities (from patient to actor/actress) or changes in effective medical treatments (from suffering due to a disease to recovery due to medical treatment) significantly reduced self-report of both cognitive (perceived intensity of others’ pain) and affective (own unpleasantness induced by perceived pain in others) components of empathy. Decreasing BOP also inhibited following altruistic behavior that was quantified by the amount of monetary donations to those who showed pain expressions. By contrast, reassuring patient identities in Experiment 1 or by noting the failure of medical treatment related to target faces in Experiment 2 increased subjective feelings of others’ pain and own unpleasantness and prompted more monetary donations to target faces. The increased monetary donations might be due to repeatedly confirming patient identity or knowing the failure of medical treatment increased the belief of authenticity of targets’ pain and thus enhanced cognitive and affective components of empathy. Alternatively, repeatedly confirming patient identity or knowing the failure of medical treatment might activate other emotional responses to target faces such as pity or helplessness, which might also influence altruistic decisions. The increased empathy rating scores and monetary donations might also reflect a contrast effect due to rating patient and actor/actress targets alternately. These possible accounts can be clarified in future work by asking participants to report their emotions and performing rating tasks on patient and actor/actress targets in separate blocks of trials. In consistent with the effects of manipulated BOP on empathy and altruism across the participants, the results of Experiment 2 showed that individuals’ intrinsic BOP related to each target face predicted their self-report of empathy and altruistic behavior across different target faces. Moreover, decreased (or increased) intrinsic BOP also predicted changes in empathy/altruistic behavior across different target faces. These converging behavioral findings across different participants and across different target faces provide evidence for causal relationships between BOP and empathy/altruism.

Our results showed that self-reports of others’ pain intensity and own unpleasantness elicited by perception of others’ pain were able to positively predict altruistic behavior across individuals. Previous research using questionnaire measures of empathy ability found that empathy as a trait is positively correlated with the amount of money shared with others in economic games (Edele et al., 2013; Li et al., 2019). Taken together, these findings are consistent with the proposition that empathy, as either an instant emotional response to others’ suffering (e.g., estimated in our study) or a personality trait (e.g., estimated in Edele et al., 2013 and Li et al., 2019), plays a key role in driving altruistic behavior (Batson, 1987; Batson et al., 2015; Eisenberg et al., 2010; Hofman, 2008; Penner et al., 2005). Our mediation analyses of the behavioral data in both Experiments 1 and 2 further revealed that the effects of decreased BOP on monetary donations were mediated by self-report of others’ pain intensity. These results further suggest empathy as an intermediate mechanism of the BOP effects on altruistic behavior.

Our neuroimaging experiments went beyond the subjective estimation of the relationships between BOP and empathy/altruism by investigating neural mechanisms underlying BOP effects on empathy for others’ pain. It is necessary to conduct the objective estimation of empathy to examine BOP effects because self-report measures of empathy can be influenced by social contexts and are unable to unravel brain mechanisms underlying BOP effects on empathy (e.g., Sheng and Han, 2012). Our EEG results in Experiments 3 and 5 repeatedly showed that neural responses to pain (vs. neutral) expressions over the frontal regions within 200 ms after face onset (indexed by the P2 amplitude over the frontal/central electrodes) were significantly reduced to faces with actor/actress identities compared to those with patient identities. The results in Experiments 3 and 4 indicate that BOP concomitant with face identity (i.e., patients’ pain expressions manifest their actual painful emotional states whereas actors/actresses’ pain expressions do not) rather than face identity (e.g., Tiger or Lion team identities) alone resulted in modulations of the P2 amplitudes to pain expressions in the direction as expected. Numerous EEG studies have shown that the frontal P2 component responds with enlarged amplitudes to various facial expressions such as fear, anger, happy (Williams et al., 2006; Luo et al., 2010; Calvo et al., 2013), and pain (Sheng and Han, 2012; Sheng et al., 2013; Sheng et al., 2016) expressions compared to neutral faces. These findings uncovered early affective processing by differentiating emotional and neutral expressions. ERPs to others’ pain within 200 ms post-stimulus occur regardless of task demands and are associated with spontaneous empathy for pain (Fan and Han, 2008). Our ERP results indicate that BOP may provide a cognitive basis for early spontaneous neural responses to others’ suffering reflected in pain expressions. Moreover, the results in Experiment five showed that the early spontaneous empathic neural responses in the P2 time window mediated the BOP effect on self-report of others’ pain intensity, which further mediated the relationship between the P2 empathic responses and the amount of monetary donations. These results highlight both early spontaneous neural responses to others’ pain and subjective feelings of others’ pain as intermediate mechanisms by which BOP influences altruistic behavior.

To identify neural architectures underlying BOP effects on empathy, we recorded BOLD responses, using fMRI, to perceived painful and non-painful stimuli applied to individuals with patient or actor/actress identities in Experiment 6. We showed that the contrast of perceived painful (vs. non-painful) stimulations activated the sensory (i.e., post-central gyrus), affective (i.e., insula), and cognitive (i.e., mPFC) nodes of the empathy network, similar to the findings of previous studies (Singer et al., 2004; Jackson et al., 2005; Saarela et al., 2007; Shamay-Tsoory et al., 2009; Han et al., 2009; Fan et al., 2011; Lamm et al., 2011; Zhou and Han, 2021; Luo et al., 2014). Viewing non-painful stimulations applied to neutral faces with patient versus actor/actress identities revealed increased activity in the mPFC and bilateral TPJ, suggesting the possible neural representation of facial identities in the brain regions. Most importantly, the results of searchlight RSA that was sensitive to both stimuli and subjective feelings evoked by the stimuli revealed significant variations of activities in the insula, post-central gyrus, and lateral frontal cortex in correspondence with the patterns of self-reports of empathy for patients and actors/actresses’ pain. In other words, the patterns of the activities in the insula, post-central gyrus, and lateral frontal cortex were able to predict distinct subjective feelings of patients’ and actors/actresses’ pain. Moreover, the results of our VPS analyses showed consistent evidence for decreased neural activities in the empathy-related neural network due to lack of BOP. These fMRI results together suggest that activities in the brain regions supporting affective sharing (e.g., insula; Shamay-Tsoory et al., 2009; Fan et al., 2011; Lamm et al., 2019), empathic sensorimotor resonance (e.g., post-central gyrus; Avenanti et al., 2005; Zhou and Han, 2021), and emotion regulation (e.g., lateral frontal cortex; Ochsner and Gross, 2005; Etkin et al., 2015) may provide intermediate mechanisms underlying variations of subjective feelings of others’ pain intensity due to lack of BOP.

Numerous studies have shown evidence for modulations of empathy by social contexts. Contextual variables that influence the perception of others’ pain and empathy include empathy targets’ posture (Martel et al., 2008), identifiable pain pathology (Twigg and Byrne, 2015), moral valence (Cui et al., 2016; Nicolardi et al., 2020), and so on. Empathizers’ prior exposure to pain (Prkachin and Rocha, 2010), socioeconomic status (Varnum et al., 2015), and cultural experiences (Wang et al., 2015; Hampton and Varnum, 2018) also influence empathy and its underlying brain activities. Perceived information about social relationships between observers and empathy targets also modulates empathic neural responses such that, relative to viewing own-race or own-team individuals’ pain, viewing other-race or opponent-team individuals’ pain decreased empathic neural responses in the affective (e.g., ACC and AI), cognitive (e.g., mPFC and TPJ), and sensorimotor (e.g., motor cortex) nodes of the empathy network (Xu et al., 2009; Avenanti et al., 2010; Hein et al., 2010; Mathur et al., 2010; Sheng and Han, 2012; Sheng et al., 2014; Sheng et al., 2016; Han, 2018; Zhou and Han, 2021). The perceived intergroup (racial) relationships between empathizers and empathy targets also influenced altruistic behavior such as medical treatment (Drwecki et al., 2011). These findings uncovered how social information perceived from stimuli and social experience modulate empathic neural responses to others’ suffering and subsequent social behavior. The results of our current work complemented the findings of previous studies by uncovering how beliefs, as preexisting internal mental representations of something that is not immediately present to the scenes (Fuentes, 2019), also modulate people’s empathy and following altruistic behavior. Specifically, in the current study, participants’ beliefs (i.e., pain expressions of patients manifest their actual feelings whereas pain expressions performed by actors/actresses do not) weakened the participants’ empathy for others’ pain and reduced their monetary donations to those who appeared suffering. BOP effects on empathy and altruistic behavior can be understood as modulations of empathy by preexisting internal information (e.g., beliefs) whereas previous findings revealed modulations of empathy by instantly perceived social information in a specific social context. These findings together help to construct neurocognitive models of empathy that take into consideration of both perceived social information and preexisting internal information and their interactions that lead to modulations of empathy and altruistic behavior during real-life social interactions.

It should be noted that our experimental manipulations changed the participants’ minds about the models’ identities (e.g., patient vs. actor/actress) rather than explicitly asking them to alter their BOP. BOP altered implicitly with target persons’ identities due to observers’ knowledge about individuals with different identities (e.g., painful stimuli applied to actors/actresses do not really hurt them and they show facial expressions to pretend a specific emotional state). Therefore, the BOP effects on empathy and altruistic behavior identified in our study might take place implicitly. This is different from the placebo effects on first-hand pain experiences that are produced by explicitly perceived verbal, conditioned, and observational cues that induce expectations of effective analgesic treatments (Meissner et al., 2011). Similar explicit manipulations of making individuals believe receiving oxytocin also promote social trust and preference for close social distances (Yan et al., 2018). Moreover, the placebo treatment relative to a control condition significantly attenuated activations in the ACC, AI, and subcortical structures (e.g., the thalamus) in response to painful electric shocks but increased the prefrontal activity during anticipation of painful stimulations possibly to inhibit activity in pain processing regions (Wager et al., 2004; Wager and Atlas, 2015). The brain regions in which empathic neural responses altered due to BOP (e.g., the lateral frontal cortex) as unraveled in the current study do not overlap with those in which activities are modulated by placebo analgesia (Atlas and Wager, 2014). These results suggest that there may be distinct neural underpinnings of BOP effects on empathic brain activity and placebo effects on brain responses to first-hand pain experiences.

Do beliefs also provide a cognitive basis for the widely documented ingroup bias in empathy for pain? Previous studies suggest that multiple neurocognitive mechanisms are involved in ingroup bias in empathy for pain such as lack of attention (Sheng and Han, 2012) and early group-based categorization of outgroup faces (Zhou et al., 2020b, see Han, 2018 for review). There have been behavioral evidence that White individuals who more strongly endorsed false beliefs about biological differences between Blacks and Whites (e.g., ‘Black people’s skin is thicker than White people’s skin’) reported lower pain ratings for a Black (vs. White) target and suggested less accurate treatment recommendations (Hoffman et al., 2016). These behavioral findings suggest that other beliefs may also provide a basis for modulations of empathy for others’ pain and relevant altruistic behavior. The underlying brain mechanisms, however, remain unknown. The paradigms developed in the current study may be considered in future research to examine neural underpinnings of the effects of false beliefs on empathy for pain.

Another question arising from the findings of the current study is whether the belief effect is specific to neural underpinnings of empathy for pain or is also evident for neural responses to other facial expressions. To address this issue, we conducted an additional EEG experiment in which we tested (1) whether beliefs of authenticity of others’ happiness influence brain responses to perceived happy expressions, and (2) whether lack of beliefs of others’ happiness also modulates neural responses to happy expressions in the P2 time window, similar to the BOP effect on ERPs to pain expressions (see Appendix 1 for methods). Similar to the paradigm used in Experiment 3, participants in the additional experiment had to first remember face identities (awardees or actors/actresses). Thereafter these faces with happy or neutral faces were presented with contextual information that the awardees showed happy expressions when receiving awards whereas actors/actresses imitated others’ happy expressions. The participants also performed identity judgments on the faces while EEG was recorded. Behavioral results in this experiment showed that participants reported less feelings of actors’ happiness compared to awardees’ happiness. ERP results in this experiment showed that lack of beliefs of authenticity of others’ happiness (e.g., actors simulating others’ happy expressions vs. awardees smiling when receiving awards) reduced the amplitudes of a long-latency positive component (i.e., P570) over the frontal region in response to happy expressions. However, the face identities did not affect the P2 amplitudes in response to happy (vs. neutral) expressions (see Appendix 1 for statistical details). These findings suggest that belief effects are evident for subjective feelings and brain activities in response to happy expressions. However, BOP or happiness affect neural responses to facial expressions in different time windows after face onset. Future research should examine neural mechanisms underlying belief effects on neural responses to other emotions to deepen our understanding of general belief effects on neural processes of others’ emotional states.

Our behavioral and neuroimaging findings have implications for how we understand the general functional role of beliefs in social cognition and interaction. Empathy is supposed to originate from an evolved adaptation to quickly and automatically respond to others’ emotional states during parental care that is necessary for offspring survival in humans and other species (de Waal, 2008; Decety, 2011). In most cases of interactions among family members (i.e., between parents and offspring or between siblings) perceived cues signaling pain in a person manifest his/her actual emotional states that urge help from other family members. Such life experiences may set up a default belief that perceived painful stimulation to others and their facial expressions reflect individuals’ actual emotional states. This default belief provides a fundamental cognitive basis of reflexive and automatic empathy and empathic brain activity that further generates autonomic and somatic responses, as suggested by the perception-action model of empathy (Preston and de Waal, 2002). Nevertheless, when social interactions expand beyond family members to non-kin members and even strangers, perceived pain expressions or painful stimuli applied to others may not always manifest others’ actual emotional states because perceived painful cues may be fake in some cases. BOP in such situations may function as cognitive gate-control to modulate neural responses to perceived pain in others. This is necessary for monitoring social interactions to determine whether to help or to coordinate with those who appear suffering. Our findings illustrate how the perception-emotion-behavior reactivity occurs under the cognitive constraint of BOP to keep empathy and altruistic decision/behavior for the right target who is really in need of help. In this sense, BOP also provides an important cognitive basis for survival and social adaption during social interactions.

Some limitations of the current work create future research opportunities. For example, a recent approach to hierarchical Bayesian models of cognition assumes that the brain represents information probabilistically and people represent a state or feature of the world not using a single computed value but a conditional probability density function (Knill and Pouget, 2004; Friston, 2005; Clark, 2013; Tappin and Gadsby, 2019). Our manipulations of BOP, however, had only two conditions (patient vs. actor/actress) and thus lack a model of effects of probability-based belief-updating on empathy and relevant altruistic behavior. Future research should examine how empathy and relevant altruistic behavior vary as a function of the degree of BOP. Other interesting research questions arising from our work include how the brain represents BOP. It has been proposed that different types of beliefs (e.g., empirical beliefs, conceptual beliefs, and relational beliefs) exist in the human mind and may have distinct neural underpinnings (Harris et al., 2009; Seitz and Angel, 2020). To address neural representations of BOP will allow researchers to further explore and construct neural models of the interaction between beliefs and empathic brain activity in the key nodes of the empathy network. Another interesting issue related to our findings is individual differences in BOP and BOP effects on empathy and altruism. Since specific degrees of beliefs differ widely across individuals (Ais et al., 2016), it is crucial to examine what personality/psychopathic traits or biological factors make individuals hold strong or weak BOP and exhibit large or small BOP effects on empathy and altruistic behavior. It is also important to clarify what environmental factors modify individuals’ default BOP and consequently change their motivations to help those who appear suffering. To clarify these issues will advance our understanding of individual and contextual factors that shape the functional role of BOP in modulations of empathy and altruistic behavior. Finally, a general issue arising from the current work is whether beliefs affect the processing of other emotions such as fear, sad, and happy, and, if yes, whether there are common underlying psychological and neural mechanisms.

Our results in Experiments 1–6 showed consistent evidence for modulations of both subjective (self-report) and objective (EEG/fMRI) measures of empathy for others’ suffering. An interesting question arising from these findings is whether the belief effects are specific to neural underpinnings of empathy for pain. We addressed this issue by examining belief effects on neural responses to other facial expressions in an additional experiment. Specifically, in this experiment, we sought to test (1) whether beliefs of authenticity of others’ happiness influence brain responses to perceived happy expressions and (2) whether beliefs also modulate neural responses to happy expressions in the P2 time window, similar to the BOP effect on ERPs to pain expressions. The paradigm used in the additional experiment was the same as that used in Experiment 3 except the following. We asked an independent sample of participants to remember identities (awardees or actors/actresses) of neutral faces. Thereafter, EEG signals to happy and neutral expressions of awardees or actors/actresses were recorded after informing the participants that photos of happy faces were taken from awardees who were smiling when receiving awards whereas actors/actresses imitated others’ smiling and showed happy expressions. We predicted that beliefs that actors/actresses’ expressions do not reflect their actual emotional states would decrease brain response to happy expressions. We tested this prediction by comparing ERPs to happy/neutral faces with awardee or actor/actress identities.

We recorded EEG signals from an independent sample of healthy young adults (N=30 males, mean age±s.d.=22.30±2.73 years). Face stimuli with happy or neutral expressions were adopted from the previous study (Wang and Han, 2021). There were photos of 16 Chinese models (half males) and each model contributed one photo with happy expression and one with neutral expression.

The participants were first presented with the faces with neutral expressions and were informed that these photos were taken from eight awardees who recently obtained awards and from eight actors/actresses. After the identity memory task, in which the participants were able to correctly recognize all faces with awardee or actor/actress identities, they were asked to perform identity judgments on faces with neutral or happy expressions by pressing one of two buttons while EEG was recorded. After EEG recording, the participants were presented with each happy face again and had to rate how happy the person is feeling (i.e., happiness intensity) by rating on a Likert-type scale (1=not happy at all, 7=extremely happy).

An ANOVA of the mean rating scores of happiness intensity with Identity (awardee vs. actor/actress) and Expression (happy vs. neutral) as within-subject variables revealed significant main effects of Identity (F(1,29)=19.512, p<0.001, η_p²=0.402, 90% CI=[0.166, 0.560]) and Expression (F(1,29)=422.774, p<0.001, η_p²=0.936, 90% CI=[0.889, 0.953]), and a significant Identity×Expression interaction (F(1,29)=6.610, p=0.016, η_p²=0.186, 90% CI=[0.021, 0.372], see Appendix 1—figure 1a, and Appendix 1—table 1 for details). The results suggest weaker subjective feelings of happiness intensity for faces with actor/actress identities compared to awardee identities.

Appendix 1—figure 1

Download asset Open asset

Appendix 1—figure 1—source data 1

Happy intensity rating scores and Mean differential P570 amplitudes.

https://cdn.elifesciences.org/articles/66043/elife-66043-app1-fig1-data1-v3.xlsx

Download elife-66043-app1-fig1-data1-v3.xlsx

The participants responded to face identities with high accuracies during EEG recording (>88% across all conditions, see Appendix 1—table 1 for details). Similarly, ERPs to face stimuli in this experiment were characterized by an early negative activity at 90–120 ms (N1) and a positive activity at 175–195 ms (P2) at the frontal/central regions, which were followed by two positive activities at 280–340 ms (P310) over the parietal region and 500–700 ms (P570) over the frontal area (Appendix 1—figure 1b). ANOVAs of the P2 amplitudes with Identity (awardee vs. actor/actress) and Expression (happy vs. neutral) as within-subject variables did not reveal a significant Identity×Expression interaction (F(1,29)=0.441, p=0.512, η_p²=0.015, 90% CI=[0, 0.145], Bayes factors=0.303).

Importantly, ANOVAs of the later P570 amplitudes showed a significant Identity×Expression interaction (F(1,29)=4.832, p=0.036, η_p²=0.143, 90% CI=[0.005, 0.328], Appendix 1—figure 1b and 1c, see Appendix 1—table 1 for statistical details). Simple effect analyses indicated significantly larger P570 amplitudes in response to happy versus neutral expressions of awardees’ faces (F(1,29)=20.880, p<0.001, η_p²=0.419, 90% CI=[0.181, 0.573]), but not of actors/actresses’ faces (F(1,29)=3.375, p=0.076, η_p²=0.104, 90% CI=[0, 0.285], Bayes factor=0.858).

Our behavioral and ERP results in this experiment suggest reduced subjective feelings and brain responses to happy (vs. neutral) expressions of actors/actresses’ faces compared to awardees’ faces. These results support the prediction that beliefs that actors/actresses’ expressions do not reflect their actual emotional states decrease brain response to happy expressions. However, belief effects on brain responses to happy expressions were observed on the P570 amplitudes but not on the P2 amplitudes. This is different from our ERP results in Experiments 3–5, in which we showed evidence that BOP modulated the P2 amplitudes. These results suggest general belief modulation effects on brain activities involved in processing of facial expressions. In addition, our results suggest that the time window in which beliefs modulate brain responses to facial expressions depends on the nature of facial expressions (e.g., pain or happiness expressions).

Source data files have been provided for Figures 1-6 and Appdendix 1 Figure 1.

1. 1. Ais J
   2. Zylberberg A
   3. Barttfeld P
   4. Sigman M

   (2016) Individual consistency in the accuracy and distribution of confidence judgments

   Cognition 146:377–386.

   https://doi.org/10.1016/j.cognition.2015.10.006

   • PubMed
   • Google Scholar
2. Book

   1. Atlas LY
   2. Wager TD

   (2014) A meta-analysis of brain mechanisms of placebo analgesia: consistent findings and unanswered questions

   In: Atlas L. Y, editors. Handbook of Experimental Pharmacology. Springer. pp. 37–69.

   https://doi.org/10.1007/978-3-662-44519-8_3

   • Google Scholar
3. 1. Avenanti A
   2. Bueti D
   3. Galati G
   4. Aglioti SM

   (2005) Transcranial magnetic stimulation highlights the sensorimotor side of empathy for pain

   Nature Neuroscience 8:955–960.

   https://doi.org/10.1038/nn1481

   • PubMed
   • Google Scholar
4. 1. Avenanti A
   2. Sirigu A
   3. Aglioti SM

   (2010) Racial Bias reduces empathic sensorimotor resonance with other-race pain

   Current Biology 20:1018–1022.

   https://doi.org/10.1016/j.cub.2010.03.071

   • PubMed
   • Google Scholar
5. 1. Azevedo RT
   2. Macaluso E
   3. Avenanti A
   4. Santangelo V
   5. Cazzato V
   6. Aglioti SM

   (2013) Their pain is not our pain: brain and autonomic correlates of empathic resonance with the pain of same and different race individuals

   Human Brain Mapping 34:3168–3181.

   https://doi.org/10.1002/hbm.22133

   • PubMed
   • Google Scholar
6. 1. Batson CD

   (1987) Prosocial motivation: is it ever truly altruistic?

   Advances in Experimental Social Psychology 20:65–122.

   https://doi.org/10.1016/S0065-2601(08)60412-8

   • Google Scholar
7. Book

   1. Batson CD
   2. Lishner DA
   3. Stocks EL

   (2015) The Oxford Handbook of Prosocial Behavior

   In: Schroeder D. A, Graziano W. G, editors. The Oxford Handbook. Oxford, UK: Oxford University Press. pp. 259–268.

   https://doi.org/10.1093/oxfordhb/9780195399813.001.0001

   • Google Scholar
8. 1. Bieri D
   2. Reeve RA
   3. Champion DG
   4. Addicoat L
   5. Ziegler JB

   (1990) The faces pain scale for the self-assessment of the severity of pain experienced by children: development, initial validation, and preliminary investigation for ratio scale properties

   Pain 41:139–150.

   https://doi.org/10.1016/0304-3959(90)90018-9

   • PubMed
   • Google Scholar
9. 1. Botvinick M
   2. Jha AP
   3. Bylsma LM
   4. Fabian SA
   5. Solomon PE
   6. Prkachin KM

   (2005) Viewing facial expressions of pain engages cortical Areas involved in the direct experience of pain

   NeuroImage 25:312–319.

   https://doi.org/10.1016/j.neuroimage.2004.11.043

   • PubMed
   • Google Scholar
10. 1. Calvo MG
    2. Marrero H
    3. Beltrán D

    (2013) When does the brain distinguish between genuine and ambiguous smiles? an ERP study

    Brain and Cognition 81:237–246.

    https://doi.org/10.1016/j.bandc.2012.10.009

    • PubMed
    • Google Scholar
11. 1. Clark A

    (2013) Whatever next? predictive brains, situated agents, and the future of cognitive science

    Behavioral and Brain Sciences 36:181–204.

    https://doi.org/10.1017/S0140525X12000477

    • PubMed
    • Google Scholar
12. 1. Coll MP

    (2018) Meta-analysis of ERP investigations of pain empathy underlinesmethodological issues in ERP research

    Social Cognitive and Affective Neuroscience 13:.

    https://doi.org/10.1093/scan/nsy072

    • PubMed
    • Google Scholar
13. 1. Colloca L
    2. Benedetti F

    (2005) Placebos and painkillers: is mind as real as matter?

    Nature Reviews Neuroscience 6:545–552.

    https://doi.org/10.1038/nrn1705

    • PubMed
    • Google Scholar
14. 1. Cui F
    2. Ma N
    3. Luo YJ

    (2016) Moral judgment modulates neural responses to the perception of other’s pain: an ERP study

    Scientific Reports 6:1–8.

    https://doi.org/10.1038/srep20851

    • Google Scholar
15. 1. Davis MH

    (1983) Measuring individual differences in empathy: evidence for a multidimensional approach

    Journal of Personality and Social Psychology 44:113–126.

    https://doi.org/10.1037/0022-3514.44.1.113

    • Google Scholar
16. 1. de Waal FB

    (2008) Putting the altruism back into altruism: the evolution of empathy

    Annual Review of Psychology 59:279–300.

    https://doi.org/10.1146/annurev.psych.59.103006.093625

    • PubMed
    • Google Scholar
17. 1. Decety J

    (2011) The neuroevolution of empathy

    Annals of the New York Academy of Sciences 1231:35–45.

    https://doi.org/10.1111/j.1749-6632.2011.06027.x

    • PubMed
    • Google Scholar
18. 1. Decety J
    2. Bartal IB-A
    3. Uzefovsky F
    4. Knafo-Noam A

    (2016) Empathy as a driver of prosocial behaviour: highly conserved neurobehavioural mechanisms across species

    Philosophical Transactions of the Royal Society B: Biological Sciences 371:20150077.

    https://doi.org/10.1098/rstb.2015.0077

    • Google Scholar
19. 1. Decety J
    2. Jackson PL

    (2004) The functional architecture of human empathy

    Behavioral and Cognitive Neuroscience Reviews 3:71–100.

    https://doi.org/10.1177/1534582304267187

    • PubMed
    • Google Scholar
20. 1. Drwecki BB
    2. Moore CF
    3. Ward SE
    4. Prkachin KM

    (2011) Reducing racial disparities in pain treatment: the role of empathy and perspective-taking

    Pain 152:1001–1006.

    https://doi.org/10.1016/j.pain.2010.12.005

    • PubMed
    • Google Scholar
21. 1. Edele A
    2. Dziobek I
    3. Keller M

    (2013) Explaining altruistic sharing in the dictator game: the role of affective empathy, cognitive empathy, and justice sensitivity

    Learning and Individual Differences 24:96–102.

    https://doi.org/10.1016/j.lindif.2012.12.020

    • Google Scholar
22. 1. Eisenberg N
    2. Eggum ND
    3. Di Giunta L

    (2010) Empathy-related responding: associations with prosocial behavior, aggression, and intergroup relations

    Social Issues and Policy Review 4:143–180.

    https://doi.org/10.1111/j.1751-2409.2010.01020.x

    • PubMed
    • Google Scholar
23. 1. Etkin A
    2. Büchel C
    3. Gross JJ

    (2015) The neural bases of emotion regulation

    Nature Reviews Neuroscience 16:693–700.

    https://doi.org/10.1038/nrn4044

    • PubMed
    • Google Scholar
24. 1. Fan Y
    2. Duncan NW
    3. de Greck M
    4. Northoff G

    (2011) Is there a core neural network in empathy? an fMRI based quantitative meta-analysis

    Neuroscience & Biobehavioral Reviews 35:903–911.

    https://doi.org/10.1016/j.neubiorev.2010.10.009

    • PubMed
    • Google Scholar
25. 1. Fan Y
    2. Han S

    (2008) Temporal dynamic of neural mechanisms involved in empathy for pain: an event-related brain potential study

    Neuropsychologia 46:160–173.

    https://doi.org/10.1016/j.neuropsychologia.2007.07.023

    • PubMed
    • Google Scholar
26. 1. Faul F
    2. Erdfelder E
    3. Lang AG
    4. Buchner A

    (2007) G*power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences

    Behavior Research Methods 39:175–191.

    https://doi.org/10.3758/bf03193146

    • PubMed
    • Google Scholar
27. 1. Fodor JA

    (1985) Précis of the modularity of mind

    The Behavioral and Brain Sciences 8:1–42.

    https://doi.org/10.1017/S0140525X0001921X

    • Google Scholar
28. 1. Friston K

    (2005) A theory of cortical responses

    Philosophical Transactions of the Royal Society B: Biological Sciences 360:815–836.

    https://doi.org/10.1098/rstb.2005.1622

    • PubMed
    • Google Scholar
29. Book

    1. Fuentes A

    (2019) Why We Believe: Evolution and the Human Way of Being

    New Haven, CA: Yale University Press.

    https://doi.org/10.1111/rirt.13934

    • Google Scholar
30. 1. Garcés M
    2. Finkel L

    (2019) Emotional theory of rationality

    Frontiers in Integrative Neuroscience 13:11–35.

    https://doi.org/10.3389/fnint.2019.00011

    • PubMed
    • Google Scholar
31. 1. Greenwald AG
    2. McGhee DE
    3. Schwartz JL

    (1998) Measuring individual differences in implicit cognition: the implicit association test

    Journal of Personality and Social Psychology 74:1464–1480.

    https://doi.org/10.1037/0022-3514.74.6.1464

    • PubMed
    • Google Scholar
32. 1. Greenwald AG
    2. Nosek BA
    3. Banaji MR

    (2003) Understanding and using the implicit association test: I. an improved scoring algorithm

    Journal of Personality and Social Psychology 85:197–216.

    https://doi.org/10.1037/0022-3514.85.2.197

    • PubMed
    • Google Scholar
33. 1. Gu X
    2. Han S

    (2007) Attention and reality constraints on the neural processes of empathy for pain

    NeuroImage 36:256–267.

    https://doi.org/10.1016/j.neuroimage.2007.02.025

    • PubMed
    • Google Scholar
34. 1. Hampton RS
    2. Varnum MEW

    (2018) The cultural neuroscience of emotion regulation

    Culture and Brain 6:130–150.

    https://doi.org/10.1007/s40167-018-0066-2

    • Google Scholar
35. 1. Han S
    2. Fan Y
    3. Xu X
    4. Qin J
    5. Wu B
    6. Wang X
    7. Aglioti SM
    8. Mao L

    (2009) Empathic neural responses to others' pain are modulated by emotional contexts

    Human Brain Mapping 30:3227–3237.

    https://doi.org/10.1002/hbm.20742

    • PubMed
    • Google Scholar
36. 1. Han X
    2. Luo S
    3. Han S

    (2016) Embodied neural responses to others' suffering

    Cognitive Neuroscience 7:114–127.

    https://doi.org/10.1080/17588928.2015.1053440

    • PubMed
    • Google Scholar
37. 1. Han X
    2. He K
    3. Wu B
    4. Shi Z
    5. Liu Y
    6. Luo S
    7. Wei K
    8. Wu X
    9. Han S

    (2017) Empathy for pain motivates actions without altruistic effects: evidence of motor dynamics and brain activity

    Social Cognitive and Affective Neuroscience 12:893–901.

    https://doi.org/10.1093/scan/nsx016

    • Google Scholar
38. 1. Han S

    (2018) Neurocognitive basis of racial ingroup Bias in empathy

    Trends in Cognitive Sciences 22:400–421.

    https://doi.org/10.1016/j.tics.2018.02.013

    • PubMed
    • Google Scholar
39. Preprint

    1. Han X
    2. Ashar YK
    3. Kragel P
    4. Petre B
    5. Schelkun V
    6. Atlas LY
    7. Chang LJ
    8. Jepma M
    9. Koban L
    10. Losin ERA
    11. Roy M
    12. Woo CW
    13. Wager TD

    (2021) Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature

    bioRxiv.

    https://doi.org/10.1101/2021.05.29.445964

    • Google Scholar
40. 1. Harris S
    2. Kaplan JT
    3. Curiel A
    4. Bookheimer SY
    5. Iacoboni M
    6. Cohen MS

    (2009) The neural correlates of religious and nonreligious belief

    PLOS ONE 4:e7272.

    https://doi.org/10.1371/journal.pone.0007272

    • Google Scholar
41. Book

    1. Hayes AF

    (2017) Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach

    The Guilford Press.

    https://doi.org/10.1111/jedm.12050

    • Google Scholar
42. 1. Hein G
    2. Silani G
    3. Preuschoff K
    4. Batson CD
    5. Singer T

    (2010) Neural responses to ingroup and outgroup members' suffering predict individual differences in costly helping

    Neuron 68:149–160.

    https://doi.org/10.1016/j.neuron.2010.09.003

    • PubMed
    • Google Scholar
43. 1. Hein G
    2. Morishima Y
    3. Leiberg S
    4. Sul S
    5. Fehr E

    (2016) The brain's functional network architecture reveals human motives

    Science 351:1074–1078.

    https://doi.org/10.1126/science.aac7992

    • PubMed
    • Google Scholar
44. 1. Hoffman KM
    2. Trawalter S
    3. Axt JR
    4. Oliver MN

    (2016) Racial Bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites

    PNAS 113:4296–4301.

    https://doi.org/10.1073/pnas.1516047113

    • PubMed
    • Google Scholar
45. Book

    1. Hofman ML

    (2008)

    Psychology, Psychiatry, & Social Work 

    In: Lewis M, Haviland-Jones J. M, Barrett L. F, editors. Handbook of Emotions. New York, NY: The Guilford Press. pp. 440–455.

    • Google Scholar
46. 1. Jackson PL
    2. Meltzoff AN
    3. Decety J

    (2005) How do we perceive the pain of others? A window into the neural processes involved in empathy

    NeuroImage 24:771–779.

    https://doi.org/10.1016/j.neuroimage.2004.09.006

    • PubMed
    • Google Scholar
47. 1. Jauniaux J
    2. Khatibi A
    3. Rainville P
    4. Jackson PL

    (2019) A meta-analysis of neuroimaging studies on pain empathy: investigating the role of visual information and observers' perspective

    Social Cognitive and Affective Neuroscience 14:789–813.

    https://doi.org/10.1093/scan/nsz055

    • PubMed
    • Google Scholar
48. Book

    1. Kenny DA
    2. Kashy DA
    3. Bolger N

    (1998) Data analysis in social psychology

    In: Gilbert D. T, Fiske S. T, Lindzey G, editors. The Handbook of Social Psychology. Oxford, UK: Oxford University Press. pp. 233–265.

    https://doi.org/10.1002/9780470561119.socpsy001004

    • Google Scholar
49. 1. Knill DC
    2. Pouget A

    (2004) The bayesian brain: the role of uncertainty in neural coding and computation

    Trends in Neurosciences 27:712–719.

    https://doi.org/10.1016/j.tins.2004.10.007

    • PubMed
    • Google Scholar
50. 1. Kriegeskorte N
    2. Goebel R
    3. Bandettini P

    (2006) Information-based functional brain mapping

    PNAS 103:3863–3868.

    https://doi.org/10.1073/pnas.0600244103

    • PubMed
    • Google Scholar
51. 1. Krishnan A
    2. Woo CW
    3. Chang LJ
    4. Ruzic L
    5. Gu X
    6. López-Solà M
    7. Jackson PL
    8. Pujol J
    9. Fan J
    10. Wager TD

    (2016) Somatic and vicarious pain are represented by dissociable multivariate brain patterns

    eLife 5:e15166.

    https://doi.org/10.7554/eLife.15166

    • PubMed
    • Google Scholar
52. 1. Lamm C
    2. Nusbaum HC
    3. Meltzoff AN
    4. Decety J

    (2007) What are you feeling? using functional magnetic resonance imaging to assess the modulation of sensory and affective responses during empathy for pain

    PLOS ONE 2:e1292.

    https://doi.org/10.1371/journal.pone.0001292

    • PubMed
    • Google Scholar
53. 1. Lamm C
    2. Decety J
    3. Singer T

    (2011) Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain

    NeuroImage 54:2492–2502.

    https://doi.org/10.1016/j.neuroimage.2010.10.014

    • PubMed
    • Google Scholar
54. 1. Lamm C
    2. Rütgen M
    3. Wagner IC

    (2019) Imaging empathy and prosocial emotions

    Neuroscience Letters 693:49–53.

    https://doi.org/10.1016/j.neulet.2017.06.054

    • PubMed
    • Google Scholar
55. 1. Li Z
    2. Yu J
    3. Yang X
    4. Zhu L

    (2019) Associations between empathy and altruistic sharing behavior in chinese adults

    The Journal of General Psychology 146:1–16.

    https://doi.org/10.1080/00221309.2018.1510826

    • PubMed
    • Google Scholar
56. 1. Li W
    2. Han S

    (2010) Perspective taking modulates event-related potentials to perceived pain

    Neuroscience Letters 469:328–332.

    https://doi.org/10.1016/j.neulet.2009.12.021

    • PubMed
    • Google Scholar
57. 1. Li W
    2. Han S

    (2019) Behavioral and electctrophysiological evidence for enhanced sensitivity to subtle variations of pain expressions of same-race than other-race faces

    Neuropsychologia 129:302–309.

    https://doi.org/10.1016/j.neuropsychologia.2019.04.008

    • PubMed
    • Google Scholar
58. 1. Luck SJ
    2. Gaspelin N

    (2017) How to get statistically significant effects in any ERP experiment (and why you shouldn't)

    Psychophysiology 54:146–157.

    https://doi.org/10.1111/psyp.12639

    • PubMed
    • Google Scholar
59. 1. Luo W
    2. Feng W
    3. He W
    4. Wang NY
    5. Luo YJ

    (2010) Three stages of facial expression processing: erp study with rapid serial visual presentation

    NeuroImage 49:1857–1867.

    https://doi.org/10.1016/j.neuroimage.2009.09.018

    • PubMed
    • Google Scholar
60. 1. Luo S
    2. Shi Z
    3. Yang X
    4. Wang X
    5. Han S

    (2014) Reminders of mortality decrease midcingulate activity in response to others’ suffering

    Social Cognitive and Affective Neuroscience 9:477–486.

    https://doi.org/10.1093/scan/nst010

    • Google Scholar
61. 1. Luo S
    2. Li B
    3. Ma Y
    4. Zhang W
    5. Rao Y
    6. Han S

    (2015) Oxytocin receptor gene and racial ingroup Bias in empathy-related brain activity

    NeuroImage 110:22–31.

    https://doi.org/10.1016/j.neuroimage.2015.01.042

    • PubMed
    • Google Scholar
62. 1. Luo S
    2. Han X
    3. Du N
    4. Han S

    (2018) Physical coldness enhances racial in-group Bias in empathy: electrophysiological evidence

    Neuropsychologia 116:117–125.

    https://doi.org/10.1016/j.neuropsychologia.2017.05.002

    • PubMed
    • Google Scholar
63. 1. Mackinnon DP
    2. Lockwood CM
    3. Williams J

    (2004) Confidence limits for the indirect effect: distribution of the product and resampling methods

    Multivariate Behavioral Research 39:99–128.

    https://doi.org/10.1207/s15327906mbr3901_4

    • PubMed
    • Google Scholar
64. 1. Martel MO
    2. Thibault P
    3. Roy C
    4. Catchlove R
    5. Sullivan MJL

    (2008) Contextual determinants of pain judgments

    Pain 139:562–568.

    https://doi.org/10.1016/j.pain.2008.06.010

    • PubMed
    • Google Scholar
65. 1. Mathur VA
    2. Harada T
    3. Lipke T
    4. Chiao JY

    (2010) Neural basis of extraordinary empathy and altruistic motivation

    NeuroImage 51:1468–1475.

    https://doi.org/10.1016/j.neuroimage.2010.03.025

    • PubMed
    • Google Scholar
66. 1. McKay RT
    2. Dennett DC

    (2009) The evolution of misbelief

    Behavioral and Brain Sciences 32:493–510.

    https://doi.org/10.1017/S0140525X09990975

    • Google Scholar
67. 1. Meissner K
    2. Bingel U
    3. Colloca L
    4. Wager TD
    5. Watson A
    6. Flaten MA

    (2011) The placebo effect: advances from different methodological approaches

    Journal of Neuroscience 31:16117–16124.

    https://doi.org/10.1523/JNEUROSCI.4099-11.2011

    • PubMed
    • Google Scholar
68. Book

    1. Millikan RG

    (1995) White Queen Psychology and Other Essays for Alice

    Cambridge, MA: MIT Press.

    https://doi.org/10.1111/j.1468-0149.1995.tb02467.x

    • Google Scholar
69. Software

    1. Morey RD
    2. Rouder JN

    (2015) BayesFactor: Computation of Bayes Factors for common designs, version 0.9.11-11

    R Package .

    https://richarddmorey.github.io/BayesFactor/
70. 1. Nicolardi V
    2. Panasiti MS
    3. D'Ippolito M
    4. Pecimo GL
    5. Aglioti SM

    (2020) Pain perception during social interactions is modulated by self-related and moral contextual cues

    Scientific Reports 10:1–12.

    https://doi.org/10.1038/s41598-019-56840-x

    • PubMed
    • Google Scholar
71. 1. Nili H
    2. Wingfield C
    3. Walther A
    4. Su L
    5. Marslen-Wilson W
    6. Kriegeskorte N

    (2014) A toolbox for representational similarity analysis

    PLOS Computational Biology 10:e1003553.

    https://doi.org/10.1371/journal.pcbi.1003553

    • PubMed
    • Google Scholar
72. 1. Ochsner KN
    2. Gross JJ

    (2005) The cognitive control of emotion

    Trends in Cognitive Sciences 9:242–249.

    https://doi.org/10.1016/j.tics.2005.03.010

    • PubMed
    • Google Scholar
73. 1. Penner LA
    2. Dovidio JF
    3. Piliavin JA
    4. Schroeder DA

    (2005) Prosocial behavior: multilevel perspectives

    Annual Review of Psychology 56:365–392.

    https://doi.org/10.1146/annurev.psych.56.091103.070141

    • PubMed
    • Google Scholar
74. 1. Petrovic P
    2. Dietrich T
    3. Fransson P
    4. Andersson J
    5. Carlsson K
    6. Ingvar M

    (2005) Placebo in emotional processing--induced expectations of anxiety relief activate a generalized modulatory network

    Neuron 46:957–969.

    https://doi.org/10.1016/j.neuron.2005.05.023

    • PubMed
    • Google Scholar
75. 1. Preacher KJ
    2. Rucker DD
    3. Hayes AF

    (2007) Addressing moderated mediation hypotheses: theory, methods, and prescriptions

    Multivariate Behavioral Research 42:185–227.

    https://doi.org/10.1080/00273170701341316

    • PubMed
    • Google Scholar
76. 1. Preacher KJ
    2. Hayes AF

    (2008a) Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

    Behavior Research Methods 40:879–891.

    https://doi.org/10.3758/BRM.40.3.879

    • PubMed
    • Google Scholar
77. Book

    1. Preacher KJ
    2. Hayes AF

    (2008b) Contemporary approaches to assessing mediation in communication research

    In: Hayes A. F, Slater M. D, Snyder L. B, editors. The SAGE Sourcebook of Advanced Data Analysis Methods for Communication Research. Thousand Oaks, CA: SAGE Publications. pp. 13–54.

    https://doi.org/10.4135/9781452272054.n2

    • Google Scholar
78. 1. Preston SD
    2. de Waal FB

    (2002) Empathy: its ultimate and proximate bases

    Behavioral and Brain Sciences 25:1–20.

    https://doi.org/10.1017/S0140525X02000018

    • PubMed
    • Google Scholar
79. 1. Prkachin KM
    2. Rocha EM

    (2010) High levels of vicarious exposure Bias pain judgments

    The Journal of Pain 11:904–909.

    https://doi.org/10.1016/j.jpain.2009.12.015

    • PubMed
    • Google Scholar
80. 1. Régner I
    2. Thinus-Blanc C
    3. Netter A
    4. Schmader T
    5. Huguet P

    (2019) Committees with implicit biases promote fewer women when they do not believe gender Bias exists

    Nature Human Behaviour 3:1171–1179.

    https://doi.org/10.1038/s41562-019-0686-3

    • Google Scholar
81. 1. Saarela MV
    2. Hlushchuk Y
    3. Williams AC
    4. Schürmann M
    5. Kalso E
    6. Hari R

    (2007) The compassionate brain: humans detect intensity of pain from another's face

    Cerebral Cortex 17:230–237.

    https://doi.org/10.1093/cercor/bhj141

    • PubMed
    • Google Scholar
82. 1. Schnell K
    2. Bluschke S
    3. Konradt B
    4. Walter H

    (2011) Functional relations of empathy and mentalizing: an fMRI study on the neural basis of cognitive empathy

    NeuroImage 54:1743–1754.

    https://doi.org/10.1016/j.neuroimage.2010.08.024

    • PubMed
    • Google Scholar
83. 1. Seitz RJ
    2. Angel HF

    (2020) Belief formation - A driving force for brain evolution

    Brain and Cognition 140:105548.

    https://doi.org/10.1016/j.bandc.2020.105548

    • PubMed
    • Google Scholar
84. 1. Semlitsch HV
    2. Anderer P
    3. Schuster P
    4. Presslich O

    (1986) A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP

    Psychophysiology 23:695–703.

    https://doi.org/10.1111/j.1469-8986.1986.tb00696.x

    • PubMed
    • Google Scholar
85. 1. Shamay-Tsoory SG
    2. Aharon-Peretz J
    3. Perry D

    (2009) Two systems for empathy: a double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions

    Brain 132:617–627.

    https://doi.org/10.1093/brain/awn279

    • PubMed
    • Google Scholar
86. 1. Shamay-Tsoory SG

    (2011) The neural bases for empathy

    The Neuroscientist 17:18–24.

    https://doi.org/10.1177/1073858410379268

    • PubMed
    • Google Scholar
87. 1. Sheng F
    2. Liu Y
    3. Zhou B
    4. Zhou W
    5. Han S

    (2013) Oxytocin modulates the racial Bias in neural responses to others’ suffering

    Biological Psychology 92:380–386.

    https://doi.org/10.1016/j.biopsycho.2012.11.018

    • Google Scholar
88. 1. Sheng F
    2. Liu Q
    3. Li H
    4. Fang F
    5. Han S

    (2014) Task modulations of racial Bias in neural responses to others' suffering

    NeuroImage 88:263–270.

    https://doi.org/10.1016/j.neuroimage.2013.10.017

    • PubMed
    • Google Scholar
89. 1. Sheng F
    2. Han X
    3. Han S

    (2016) Dissociated neural representations of pain expressions of different races

    Cerebral Cortex 26:1221–1233.

    https://doi.org/10.1093/cercor/bhu314

    • PubMed
    • Google Scholar
90. 1. Sheng F
    2. Han S

    (2012) Manipulations of cognitive strategies and intergroup relationships reduce the racial Bias in empathic neural responses

    NeuroImage 61:786–797.

    https://doi.org/10.1016/j.neuroimage.2012.04.028

    • PubMed
    • Google Scholar
91. 1. Shrout PE
    2. Bolger N

    (2002) Mediation in experimental and nonexperimental studies: new procedures and recommendations

    Psychological Methods 7:422–445.

    https://doi.org/10.1037/1082-989X.7.4.422

    • PubMed
    • Google Scholar
92. 1. Singer T
    2. Seymour B
    3. O'Doherty J
    4. Kaube H
    5. Dolan RJ
    6. Frith CD

    (2004) Empathy for pain involves the affective but not sensory components of pain

    Science 303:1157–1162.

    https://doi.org/10.1126/science.1093535

    • PubMed
    • Google Scholar
93. 1. Steiger JH

    (2004) Beyond the F test: effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis

    Psychological Methods 9:164–182.

    https://doi.org/10.1037/1082-989X.9.2.164

    • PubMed
    • Google Scholar
94. 1. Sterzer P
    2. Frith C
    3. Petrovic P

    (2008) Believing is seeing: expectations alter visual awareness

    Current Biology 18:R697–R698.

    https://doi.org/10.1016/j.cub.2008.06.021

    • Google Scholar
95. 1. Tappin BM
    2. Gadsby S

    (2019) Biased belief in the bayesian brain: a deeper look at the evidence

    Consciousness and Cognition 68:107–114.

    https://doi.org/10.1016/j.concog.2019.01.006

    • PubMed
    • Google Scholar
96. 1. Twigg OC
    2. Byrne DG

    (2015) The influence of contextual variables on judgments about patients and their pain

    Pain Medicine 16:88–98.

    https://doi.org/10.1111/pme.12587

    • PubMed
    • Google Scholar
97. 1. Varnum MEW
    2. Blais C
    3. Hampton RS
    4. Brewer GA

    (2015) Social class affects neural empathic responses

    Culture and Brain 3:122–130.

    https://doi.org/10.1007/s40167-015-0031-2

    • Google Scholar
98. 1. Völlm BA
    2. Taylor AN
    3. Richardson P
    4. Corcoran R
    5. Stirling J
    6. McKie S
    7. Deakin JF
    8. Elliott R

    (2006) Neuronal correlates of theory of mind and empathy: a functional magnetic resonance imaging study in a nonverbal task

    NeuroImage 29:90–98.

    https://doi.org/10.1016/j.neuroimage.2005.07.022

    • PubMed
    • Google Scholar
99. 1. Wager TD
    2. Rilling JK
    3. Smith EE
    4. Sokolik A
    5. Casey KL
    6. Davidson RJ
    7. Kosslyn SM
    8. Rose RM
    9. Cohen JD

    (2004) Placebo-induced changes in FMRI in the anticipation and experience of pain

    Science 303:1162–1167.

    https://doi.org/10.1126/science.1093065

    • PubMed
    • Google Scholar
100. 1. Wager TD
     2. Atlas LY

     (2015) The neuroscience of placebo effects: connecting context, learning and health

     Nature Reviews Neuroscience 16:403–418.

     https://doi.org/10.1038/nrn3976

     • Google Scholar
101. 1. Wang C
     2. Wu B
     3. Liu Y
     4. Wu X
     5. Han S

     (2015) Challenging emotional prejudice by changing self-concept: priming independent self-construal reduces racial in-group bias in neural responses to other’s pain

     Social Cognitive and Affective Neuroscience 10:1195–1201.

     https://doi.org/10.1093/scan/nsv005

     • Google Scholar
102. 1. Wang X
     2. Han S

     (2021) Processing of facial expressions of same-race and other-race faces: distinct and shared neural underpinnings

     Social Cognitive and Affective Neuroscience 16:576–592.

     https://doi.org/10.1093/scan/nsab027

     • Google Scholar
103. 1. Williams LM
     2. Palmer D
     3. Liddell BJ
     4. Song L
     5. Gordon E

     (2006) The ‘when’ and ‘where’ of perceiving signals of threat versus non-threat

     NeuroImage 31:458–467.

     https://doi.org/10.1016/j.neuroimage.2005.12.009

     • Google Scholar
104. 1. Wright TL
     2. Tedeschi RG

     (1975) Factor anlaysis of the Interpersonal Trust Scale

     Journal of Consulting and Clinical Psychology 43:470–477.

     https://doi.org/10.1037/h0076844

     • Google Scholar
105. 1. Xu X
     2. Zuo X
     3. Wang X
     4. Han S

     (2009) Do you feel my pain? racial group membership modulates empathic neural responses

     Journal of Neuroscience 29:8525–8529.

     https://doi.org/10.1523/JNEUROSCI.2418-09.2009

     • PubMed
     • Google Scholar
106. 1. Yan X
     2. Yong X
     3. Huang W
     4. Ma Y

     (2018) Placebo treatment facilitates social trust and approach behavior

     PNAS 115:5732–5737.

     https://doi.org/10.1073/pnas.1800779115

     • Google Scholar
107. 1. Zhao X
     2. Lynch JG
     3. Chen Q

     (2010) Reconsidering Baron and Kenny: myths and truths about mediation analysis

     Journal of Consumer Research 37:197–206.

     https://doi.org/10.1086/651257

     • Google Scholar
108. 1. Zhou F
     2. Li J
     3. Zhao W
     4. Xu L
     5. Zheng X
     6. Fu M
     7. Yao S
     8. Kendrick KM
     9. Wager TD
     10. Becker B

     (2020a) Empathic pain evoked by sensory and emotional-communicative cues share common and process-specific neural representations

     eLife 9:e56929.

     https://doi.org/10.7554/eLife.56929

     • Google Scholar
109. 1. Zhou Y
     2. Gao T
     3. Zhang T
     4. Li W
     5. Wu T
     6. Han X
     7. Han S

     (2020b) Neural dynamics of racial categorization predicts racial bias in face recognition and altruism

     Nature Human Behaviour 4:69–87.

     https://doi.org/10.1038/s41562-019-0743-y

     • Google Scholar
110. 1. Zhou Y
     2. Han S

     (2021) Neural dynamics of pain expression processing: Alpha-band synchronization to same-race pain but desynchronization to other-race pain

     NeuroImage 224:117400.

     https://doi.org/10.1016/j.neuroimage.2020.117400

     • Google Scholar

Author details

1. Taoyu Wu

   School of Psychological and Cognitive Sciences, PKU-IDG/MGovern Institute for Brain Research, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

   Contribution

   Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Shihui Han

   School of Psychological and Cognitive Sciences, PKU-IDG/MGovern Institute for Brain Research, Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   shan@pku.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3350-5104

• 13

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.