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

Acceptance summary:

This article represents a series of behavioral and imaging experiments investigating the effect of cognitive manipulation of beliefs on the explicit perception of other individual's pain and altruistic behavior. The results indicate that manipulations of people's beliefs regarding how much another person suffers alters the way participants rate the pain of others as well as the amount of money participants are willing to donate to the other person. Neuroimaging experiments, using EEG and fMRI, show that manipulating beliefs modulates neural activity in an early time window (P2 component) in response to the emotional expression of the other individual, involving temporo-parietal, post-central, insular, and frontal cortices. The results are overall clear and consistent throughout the six experiments, integrate with existing data and are of broad interest to the researchers studying the neurobiology of empathy.

Decision letter after peer review:

Thank you for submitting your article "Neural mechanisms underlying regulation of empathy and altruism by beliefs of others' pain" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Floris de Lange as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential Revisions (for the authors):

1) Framing and interpretation of the data.

Both reviewers question whether some of the data are correctly interpreted. This will require either additional evidence (new data) or a reframing of some of the reported results (particularly the pain intensity ratings).

2) Couching of the current results in the literature.

Both reviewers make several recommendations how to better connect your current study to the previous extensive literature on this topic.

3) Methodological issues.

The reviewers raise several methodological issues that will need to be clarified in a revised version of the paper.

Reviewer #1 (Recommendations for the authors):

I really appreciate and value the effort and amount of work the authors put in the six experiments. The method and analyses are clear and do not present major concerns. The results of the six experiment nicely integrate and replicate.

My main concern is in the interpretation of the results. Below I will try in detail to explain the problems I see.

In most of the experiments, participants are told the half of the pictures depict actors reproducing painful facial expressions. By doing so, the experimenter is basically telling participants that half of the pictures show a "fake" expression of pain, in other words, participant in the second round now know that the actor does not feel any pain. That participants' ratings of pain go down is then perhaps rather trivial. That the ratings do not go down to zero might instead be more interesting. However, asking to rate in how much pain an actor is that pretends to be in pain is an odd question to begin with. Rationally, one knows the actor has no somatic pain. So, a nuanced analysis of what the participants believe the experimenter to want them to report would be essential here, but appears to be missing. Do participants take the question to probe how much pain they now perceive to be expressed in the facial expression (disregarding their knowledge) or how much negative emotions the actors conjured in his mind to generate authentic facial expressions, or how much somatic pain the actor feels, or an average of all these? I find it rather frustrating, for the interpretation of the results, to not quite know how the participants interpret this question in the second session. By detaching the facial expressions from somatic pain through the belief manipulation, the authors create a situation in which reported painfulness becomes a question of reporting abstract beliefs, much as one would in typical false belief tasks, rather than reporting sensory perception. Similarly, asking about the unpleasantness experienced by the participant now becomes a compound report. Is it unpleasant to know that an emotion was faked? Is a negative feeling now reflecting empathy with pain, a feeling of being lied to, or a combination of both? (similar reasoning apply for the treatment manipulation, in which it is not clear whether participants now feel empathy or pity or something else). The authors continue to equate these measures with pain empathy, but I find it difficult to take that equation at face value.

The core of the issue is then what the paper is about. If the paper remains entirely in the domain of beliefs, and the question is whether telling people that someone doesn't really feel pain allows them to report the belief that the person is in less pain than originally thought, I think the data is sound, but the conclusion somewhat trivial. If the question is whether telling someone that an actor doesn't really experience pain reduces an automatic affective empathy for pain, which then alters reports of perceived pain, I do not see how the data would inform that question. There is no measure in the report that can be taken as a clear measure of affective empathy: it is unclear whether the rating reflects cognitive empathy or other cognitive processes (see questions above), the N2 is not only present when witnessing pain, but is typical of many cognive control tasks and the fMRI data reflecting the modulations are areas involved in cognitive tasks and beliefs beyond pain.

I will give an example of an experiment that could help explaining my thinking. Imaging an experiment in which I tell participants to rate how green is the apple I show them. After the rating I tell participants the apple was actually yellow, but was painted green before the experiment. Now I ask them again to rate the greenness. What do we expect participants to do? To rate the color they actually see (green) or to rate what they now know to be the real color of the apple (yellow), the one they cannot see? Without clear specification, some participants will interpret the question as to rate the color they know, and others to rate color they see. In both cases, the average rating might decrease, but would this decrease allow us to say that the cognitive manipulation has permeated the early stages of colour perception? Without knowing how participants interpreted the question, the best we would be able to say is that the cognitive manipulation modulated the final color report.

Additional remarks:

– It is not clear to me why the authors refer to the patient-to-patient condition as an enhancement of BOP. The results do indeed suggest that the rating and donation increases, but the manipulation was not meant to enhance the BOP, but to stay stable (an example of enhanced BOP would be if participants would have been told that in the first run they saw actors, but then discovered they were actually patients). Authors cannot define their manipulation based on the results. In all their experiments, one identity remains the same, and only the other changes, therefore BOP is expected to remain the same in one case and decrease (or increase) in the other condition. I would advise to rename their BOP accordingly, and not based on the results.

– The authors focus on the N2 components, which makes the manuscript nicely focused. The rationale of this choice is though not very clear, as the literature suggests other components also to be modulated by task instructions (see for instance the following meta-review https://academic.oup.com/scan/article/13/10/1003/5077584?login=true). Authors should justify the focus or report the results for the other components. As mentioned above, authors should also be careful in equating N2 as pain-specific component.

– The ANOVA on N2 shows a nice Identity x Expression interaction, which the authors interpret as a reduction of empathic neural responses, as the difference between pain and neutral is bigger while watching patient's facial expression. Beside the already mentioned problem of taking N2 as a marker of pain empathy, by looking at the graph I seem to notice that the change in the difference, could be more driven by a change in the N2 amplitude in the response to neutral stimuli (increase of N2 amplitude while watching the actors), rather than a reduction of N2 to the painful stimuli, which seems to stay stable across conditions. If this were indeed the case, it would again suggest that what might have been modulated is not per-se the specific response to pain, but to something like saliency (someone not saying the truth becomes salient) or trust.

– The manipulation of the team-identity in Exp4 is weak. Of course, it is good that it does not change the believe of how much the different team-mate should feel pain (which is what needed indeed for such a control experiment), but unfortunately it also doesn't create a systematic bias of any kind. As it stands, it boils down to a lable permutation of the identities, which one would not expect to play any modulatory role on any beliefs. If the goal was to test the specificity for pain of the BOP, the authors should have chosen a believe manipulation that was not affecting the pain domain, but another domain, for instance choosing a team the participants was a fan of, and a team that participants do not care about or does not like. By choosing two irrelevant teams, the observed lack of an interaction is trivial, and the authors cannot conclude that BOP is necessary for the modulation of empathic activity.

– In Exp6 it is not clear why the authors do not present the results of the interaction, which is a central contrast throughout the manuscript. Again, the authors lack evidence for a modulation of pain-empathy. What the authors could do to tap into the questions of whether their BOP modulation affects empathy for pain, is to use some form of multivariate pattern that has higher specificity for pain processing. One way the author could try to use to be able to say something about the involvement of empathy for pain is by using the vicarious pain signature maps (Somatic and vicarious pain are represented by dissociable multivariate brain patterns eLife (elifesciences.org)). Author could multiply the VPS maps with their β weights from the SPM model for the four conditions: patient-actor pain, patient-actor neutral, patient-patient pain, patient-patient neutral, and run an ANOVA on the resulting values. In this way they could basically test the interaction within a network that has been reliably associated with pain-empathy, and the validity of which has been tested across experiments.

Here below a list of some of the statements unsupported by the data:

– "Manipulated BOP changes empathy and altruistic behavior". As I tried to explain, there is no clear measure of empathy, due to the interpretability of the tasks from the participants' point of view. The statement is correctly supported in the case of donation. The concern is only about empathy.

– "Intrinsic BOP predicts empathy and …". Again it is not clear what participants are reporting and interpreting after they know that the treatment did not have an effect (pity, despair, helplessness).

– "Manipulated BOP changes empathic brain activity". The presence of a N2 is not sufficient to claim that there is empathic brain activity, as N2 can be elicited by salient stimuli as well (even the fact that there is an N2 in response to the neutral stimuli, suggests that N2 might represent other activity than empathic).

– "Together, the results in Experiment 3 and 4 suggest a key role of BOP, but not of other cognitive processes involved in the experimental manipulations, in modulations of neuronal responses to other's pain." The control experiment does not contain any other manipulation of cognitive beliefs, and therefore the authors cannot exclude that other beliefs not related to pain could have equally modulated the N2 component.

– "Our behavioral and neuroimaging results revealed critical functional roles of BOP in modulation of the perception-emotion-behavior reactivity by showing how BOP predicted and affected empathy/empathic brain activity and monetary donation. Our finding provide evidence that BOP constitutes a fundamental cognitive basis for empathy and altruistic behavior." The neuroimaging results revealed that areas involved in cognitive processes are modulated by cognitive manipulation. There is no evidence that empathy-related neuronal activity was modulated by the BOP manipulation. BOP is a successful cognitive manipulation, but what authors cannot conclude that BOP is a fundamental cognitive basis for empathy.

– "These findings together support that empathy, as either an instant emotional response to other's suffering or a sustained personality trait of understanding and sharing other's pain, is one of the key motivation factors for altruistic behavior." I do not see how the results support such a claim, and there is no measure of sustained personality trait of understanding either.

Reviewer #2 (Recommendations for the authors):

Experiment 1 follow-up notes:

– As I noted in my public review, the "enhanced" responses towards the targets appearing as Patients in both Rounds 1 and 2 could simply be a contrast effect due to the Patient-Actor targets. I think there are a number of ways the authors could rule out this possibility. For example, you could show just 8 patients in Round 1, followed by 16 patients in Round 2 (8 new, 8 returning from Round 1). Alternatively, you could show 8 actors/actresses and 8 patients in Round 1, and then tell participants that all these targets are actually patients in Round 2. My prediction in both cases would be that you wouldn't see an increase in empathy/altruism/etc. for the targets appearing as patients in both rounds in either case.

– Moreover, I referred to some confusion about the charitable donations in this study. In Round 1, the charitable donation is described like this: "The participants were informed that the amount of one of their decisions would be selected randomly and endowed to a charity organization to help those who suffered from the same disease. " In Round 2, it's described like this: "The participants had to…report how much they would like to donate from the extra bonus payment to each model. The participants were told that an amount of money would be finally selected from their 2^nd round donation decisions and presented to a charity organization after the study." I wasn't sure if the difference in wording (e.g., "a charity organization to help those who suffered from the same disease" vs. just "a charity organization") was important and intentional – is it the same disease-specific charity in both rounds? (And is it the same charity for both target types in Round 2 – e.g., both patients and actors?) If the answer to either of these questions is no, more detail is necessary, since this could potentially explain differences in giving across condition. (Also, if participants know that these targets are not actually receiving their money, but rather that a trial will be selected at random to determine their donation, it's a little surprising that participants would vary their responses at all – IF the money goes to the same charity regardless of target type.)

Experiment 2 typographical notes:

– In lines 274-277, the authors report the results for intensity and unpleasantness ratings and donations in a manner that's a little hard to follow ("One-sample t-tests showed that the z values were significantly smaller than zero for decreased-BOP patients (correlations between changes in belief and pain intensity/unpleasantness/monetary donation: mean {plus minus} s.d. = -0.304 {plus minus} 0.370, -0.277 {plus minus} 0.455 and -0.236 {plus minus} 0.410; t(59) = -6.352, -4.706 277 and -4.465; ps < 0.001; Cohen's d = 0.822, 0.609 and 0.576; 95% CI = (-0.400, -0.208), 278 (-0.394, -0.159), and (-0.342, -0.130))") – I'd recommend splitting up these measures, rather than grouping by statistical value (e.g., all three means, all three p-values, etc.).

Experiment 4 open-ended question:

– This study did get me thinking about the considerable literature on intergroup empathy that uses minimal groups or real-world group affiliations (e.g., soccer teams, etc.) to demonstrate that people show an in-group bias in empathy for pain. How should we conceptualize those findings in terms of BOP? (e.g., Are differential expectancies about pain tolerance/sensitivity/etc. embedded in these group memberships?) Or are those group-based effects supported by fundamentally different mechanisms?

Essential Revisions (for the authors):

1) Framing and interpretation of the data.

Both reviewers question whether some of the data are correctly interpreted. This will require either additional evidence (new data) or a reframing of some of the reported results (particularly the pain intensity ratings).

Thanks for these suggestions. Accordingly, we conducted a new EEG experiment (i.e., Supplementary Experiment 1 in the revision) and reported new EEG data to examine belief effects on neural responses to happy expressions to address Reviewer #1's question regarding whether belief effects are specific to neural processing of pain expressions. We also reported new fMRI results of RSA and VPS analyses in response to Reviewer #1's comments and suggestions (page 37-39). We discussed the implications of our new EEG and fMRI results in the revised Discussion (page 43-44, 45-47). We also cited previous studies of empathy for pain in the revised Introduction to explain how researchers employed subjective (self-report of rating scores of others' pain intensity and own unpleasantness) and objective (EEG/fMRI) measures of empathy (page 6-7). We believe that these modifications help to reframe our results of both subjective estimation and objective estimation of empathy and BOP effects on empathy for pain.

2) Couching of the current results in the literature.

Both reviewers make several recommendations how to better connect your current study to the previous extensive literature on this topic.

In the revised Introduction we cited additional literatures of studies of pain expression to connect our work with previous findings (page 7-8). In the revised Discussion we included one paragraph to connect our work with previous findings regarding how empathy is modulated by preexisting internal information (e.g., beliefs) and by instantly perceived social information (page 46-47), one paragraph regarding the relationship between our brain imaging findings and previous behavioral findings of false belief effects on empathy and altruistic behavior (page 48-49), and one paragraph to discuss general effects of beliefs on neural processing of facial expressions (page 49-50).

3) Methodological issues.

The reviewers raise several methodological issues that will need to be clarified in a revised version of the paper.

We have now modified the Method section to clarify the reviewers’ concerns about measures in the first and second round tests (page 55-56). We also included an additional paragraph in the Method section about VPS analysis of fMRI signals (page 71-72).

Reviewer #1 (Recommendations for the authors):

I really appreciate and value the effort and amount of work the authors put in the six experiments. The method and analyses are clear and do not present major concerns. The results of the six experiment nicely integrate and replicate.

My main concern is in the interpretation of the results. Below I will try in detail to explain the problems I see.

In most of the experiments, participants are told the half of the pictures depict actors reproducing painful facial expressions. By doing so, the experimenter is basically telling participants that half of the pictures show a "fake" expression of pain, in other words, participant in the second round now know that the actor does not feel any pain. That participants' ratings of pain go down is then perhaps rather trivial. That the ratings do not go down to zero might instead be more interesting. However, asking to rate in how much pain an actor is that pretends to be in pain is an odd question to begin with. Rationally, one knows the actor has no somatic pain. So, a nuanced analysis of what the participants believe the experimenter to want them to report would be essential here, but appears to be missing. Do participants take the question to probe how much pain they now perceive to be expressed in the facial expression (disregarding their knowledge) or how much negative emotions the actors conjured in his mind to generate authentic facial expressions, or how much somatic pain the actor feels, or an average of all these? I find it rather frustrating, for the interpretation of the results, to not quite know how the participants interpret this question in the second session. By detaching the facial expressions from somatic pain through the belief manipulation, the authors create a situation in which reported painfulness becomes a question of reporting abstract beliefs, much as one would in typical false belief tasks, rather than reporting sensory perception. Similarly, asking about the unpleasantness experienced by the participant now becomes a compound report. Is it unpleasant to know that an emotion was faked? Is a negative feeling now reflecting empathy with pain, a feeling of being lied to, or a combination of both? (similar reasoning apply for the treatment manipulation, in which it is not clear whether participants now feel empathy or pity or something else). The authors continue to equate these measures with pain empathy, but I find it difficult to take that equation at face value.

The core of the issue is then what the paper is about. If the paper remains entirely in the domain of beliefs, and the question is whether telling people that someone doesn't really feel pain allows them to report the belief that the person is in less pain than originally thought, I think the data is sound, but the conclusion somewhat trivial. If the question is whether telling someone that an actor doesn't really experience pain reduces an automatic affective empathy for pain, which then alters reports of perceived pain, I do not see how the data would inform that question. There is no measure in the report that can be taken as a clear measure of affective empathy: it is unclear whether the rating reflects cognitive empathy or other cognitive processes (see questions above), the N2 is not only present when witnessing pain, but is typical of many cognive control tasks and the fMRI data reflecting the modulations are areas involved in cognitive tasks and beliefs beyond pain.

I will give an example of an experiment that could help explaining my thinking. Imaging an experiment in which I tell participants to rate how green is the apple I show them. After the rating I tell participants the apple was actually yellow, but was painted green before the experiment. Now I ask them again to rate the greenness. What do we expect participants to do? To rate the color they actually see (green) or to rate what they now know to be the real color of the apple (yellow), the one they cannot see? Without clear specification, some participants will interpret the question as to rate the color they know, and others to rate color they see. In both cases, the average rating might decrease, but would this decrease allow us to say that the cognitive manipulation has permeated the early stages of colour perception? Without knowing how participants interpreted the question, the best we would be able to say is that the cognitive manipulation modulated the final color report.

Many thanks for these important and detailed comments. We believe that there are two important points here. First, what is the central research question of our study? In the revised Introduction we clarified that our study tested the hypothesis that belief of others' pain (BOP) provides a cognitive basis for empathy and altruistic behavior by modulating brain activity in response to others' pain. Specifically, we tested whether lack of BOP or weakening BOP results in inhibition of altruistic behavior by decreasing empathy and its underlying brain activity (page 5-6). We explained the design of our experiments in details by clarifying that our behavioral and EEG/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 (page 8-10). We clarified that different beliefs were built during a learning procedure that assigned different identities (e.g., patient or actor/actress) to faces. We measured participants' self-reports of others' pain and own unpleasantness and brain responses related to the same sets of faces with pain or neutral expressions, which, however, were informed to be patients or actors/actresses. During EEG/fMRI recording the participants were asked to discriminate patient or actor/actress identities to activate different beliefs, i.e., 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. The revised Introduction clarified that we compared self-reports and brain activities related to pain (vs. neutral) expressions of actors/actresses' faces relative to those of patients' faces to assess whether lack of BOP reduced altruistic behavior and whether BOP effects were mediated by decreased empathy due to weakened BOP.

The second point is whether self-reports of rating scores used in our study.

measured empathy and whether self-report ratings used to estimate empathy are sufficient for addressing the research question of our study. In the revised Introduction, we addressed these issues by providing details of how previous studies conducted subjective and objective estimations of empathy for others' pain (page 6-7). Specifically, we clarified that previous studies collected self-reports of others' pain and own unpleasantness when observing others' pain as a subjective estimation of empathy for pain (e.g., Bieri et al., 1990; Jackson et al., 2005; Lamm et al., 2007; Fan and Han, 2008; Sheng and Han, 2012). These two measures correspond to understanding and sharing the two components of empathy.

However, researchers realized that self report can be influenced by social contexts and depend on participants' understanding of task instructions. Therefore, it is necessary to estimate empathy by recording brain activities that differentially respond to painful vs. non-painful stimuli applied to others (or pain vs. neutral expressions) as an objective estimation of empathy for pain (e.g., Singer et al., 2004; Jackson et al., 2005; Gu and Han, 2007; Fan and Han, 2008; Hein et al., 2010; Sheng and Han, 2012). More importantly, fMRI studies revealed brain regions such as the ACC, AI, sensorimotor cortices, and temporoparietal junction, which are involved in empathy and predict rating scores related to understanding (or cognitive) or sharing (affective) of others' pain. It is now widely agreed that measuring rating scores others' pain and own unpleasantness is not sufficient to assess empathy and brain imaging provides an objective estimation of empathy. This is why, in our study, we reported four EEG/fMRI experiments to address our research questions even though we first reported two behavioral experiments. Our Experiments 1 and 2 gave participants clear task instructions to report on a scale how painful others feel and how unpleasant the participants feel when viewing others' pain, as subjective estimation of empathy for pain, similar to numerous previous studies. The instructions of the rating tasks focused on emotional states of faces and had nothing to do with face identities (i.e., patients or actors). Under these task instructions, BOP effects on empathy in both our behavioral and EEG/fMRI experiments occurred implicitly and automatically. In our revised Method we made clear this point (page 55-56). As mentioned above, we examined BOP effects on empathy by comparing self-reports and brain activities related to pain (vs. neutral) expressions of patients' faces relative to those of actors/actresses' faces. We agree with Reviewer #1 that there might be individual differences in understanding of the task instructions. This why we further obtained objective estimation of empathy by recording brain activities in response to others' pain as objective estimations of empathy for pain, which provided consistent results across our EEG/fMRI studies. It should be noted that, our EEG/fMRI results replicated previous findings of empathic neural responses in the same neural network and time windows, and our behavioral and EEG/fMRI results together support our conclusion of BOP effects on empathy.

In addition, we conducted VPS analyses to examine specifically how neural activities in the empathy-related regions identified in the previous research (Krishnan et al., 2016, eLife) were modulated by beliefs of others’ pain. The results provide further evidence for our hypothesis. We also reported new results of RSA analyses that activities in the brain regions supporting affective sharing (e.g., insula), sensorimotor resonance (e.g., post-central gyrus), and emotion regulation (e.g., lateral frontal cortex) provide intermediate mechanisms underlying variations of subjective feelings of others' pain intensity due to lack of BOP. These were reported in the revision (page 37-39). We believe that, putting all these results together, our paper provides consistent evidence that empathy and altruistic behavior are modulated by BOP.

Additional remarks:

– It is not clear to me why the authors refer to the patient-to-patient condition as an enhancement of BOP. The results do indeed suggest that the rating and donation increases, but the manipulation was not meant to enhance the BOP, but to stay stable (an example of enhanced BOP would be if participants would have been told that in the first run they saw actors, but then discovered they were actually patients). Authors cannot define their manipulation based on the results. In all their experiments, one identity remains the same, and only the other changes, therefore BOP is expected to remain the same in one case and decrease (or increase) in the other condition. I would advise to rename their BOP accordingly, and not based on the results.

Thanks for this helpful comment. Indeed, we did not test how BOP changes in the patient-to-patient condition, although it is likely that repetition of the patient identity of faces in the 2^nd round test might enhance BOP compared to the 1^st round test. As suggested by Reviewer #1 we renamed the patient-to-patient condition as patient-identity repetition In Experiments 1 and 5. We also renamed the patient-to-actor/actress condition as patient-identity change. Patient-identity repetition and patient-identity change describe the two conditions objectively. In addition, we discussed possible reasons for increased empathy and donation in the patient-identity repetition condition in the revision (page 41-42).

– The authors focus on the N2 components, which makes the manuscript nicely focused. The rationale of this choice is though not very clear, as the literature suggests other components also to be modulated by task instructions (see for instance the following meta-review https://academic.oup.com/scan/article/13/10/1003/5077584?login=true). Authors should justify the focus or report the results for the other components. As mentioned above, authors should also be careful in equating N2 as pain-specific component.

Thanks for this comment. It should be clarified that our manipulation of beliefs modulates neural activity in the P2 (but not N2) time window in response to others' pain expressions. We believe that Reviewer #1 commented on the P2 results here. We cited Coll's paper of a meta-analysis of ERP investigations of pain empathy in the revision. In particular, we clarified in the revised Introduction (page 8) that the following ERP findings are particularly related to the current work, i.e., pain compared to neutral expressions increased the amplitude of a positive component at 128–188 ms (P2) after face onset over the frontal/central regions, and the P2 component is associated with the ACC activity in response to others' pain expressions (Sheng and Han, 2012; Sheng et al., 2013; 2016; Han et al., 2016; Li and Han, 2019). Moreover, the P2 amplitudes in response to others' pain expressions positively predicted subjective feelings of own unpleasantness induced by others' pain and self-report of one's own empathy traits (e.g., Sheng and Han, 2012). These brain imaging findings suggest the P2 component as a good candidate for objective measures of empathy when viewing others' pain expressions.

– The ANOVA on N2 shows a nice Identity x Expression interaction, which the authors interpret as a reduction of empathic neural responses, as the difference between pain and neutral is bigger while watching patient's facial expression. Beside the already mentioned problem of taking N2 as a marker of pain empathy, by looking at the graph I seem to notice that the change in the difference, could be more driven by a change in the N2 amplitude in the response to neutral stimuli (increase of N2 amplitude while watching the actors), rather than a reduction of N2 to the painful stimuli, which seems to stay stable across conditions. If this were indeed the case, it would again suggest that what might have been modulated is not per-se the specific response to pain, but to something like saliency (someone not saying the truth becomes salient) or trust.

Thanks for this comment. Again, we believe Reviewer #1 mentioned P2 (but not N2) component here. We'd like to clarify the results by giving the exact numbers of the P2 amplitudes in different conditions. In the Supplementary tables we reported the mean P2 amplitudes in different conditions in Exp. 3 (Supplementary Table 3_2) and Exp. 5 (Supplementary Table 5_1). In Exp.3 the P2 amplitudes to pain expressions of patient vs. actor faces were 3.7 vs. 3.1 µV, and to neutral expressions of patient vs. actor faces were 2.7 vs. 2.9 µV. In Exp.5 the P2 amplitudes to pain expressions of patient vs. actor faces were 3.9 vs. 3.3 µV, and to neutral expressions of patient vs. actor faces were 3.0 vs. 3.2 µV. These results indicate that modulation of the differential P2 amplitudes to pain (vs. neutral) expressions by BOP was actually more driven by the change of the P2 amplitude in the response to painful rather than neutral stimuli. Besides, in the revised Introduction (page 7), we further clarified how brain imaging studies identified empathic neural responses and that brain responses to perceived non-painful stimuli applied to others or neutral expressions were necessarily collected in previous neuroimaging studies to control empathy-unrelated perceptual or motor processes.

– The manipulation of the team-identity in Exp4 is weak. Of course, it is good that it does not change the believe of how much the different team-mate should feel pain (which is what needed indeed for such a control experiment), but unfortunately it also doesn't create a systematic bias of any kind. As it stands, it boils down to a lable permutation of the identities, which one would not expect to play any modulatory role on any beliefs. If the goal was to test the specificity for pain of the BOP, the authors should have chosen a believe manipulation that was not affecting the pain domain, but another domain, for instance choosing a team the participants was a fan of, and a team that participants do not care about or does not like. By choosing two irrelevant teams, the observed lack of an interaction is trivial, and the authors cannot conclude that BOP is necessary for the modulation of empathic activity.

Thanks for this comment. We're sorry for not making clear the goal of Exp. 4 in the original submission. The goal of Exp. 4 was not to test the specificity for pain of the BOP (we included Supplementary Experiment 1 to address this issue in the revision). In Exp. 3 we showed evidence that BOP modulated the P2 amplitude to pain (vs. neutral) expressions. However, the procedure to manipulate BOP by assigning patient or actor/actress identities to faces in Exp. 3 consisted of multiple processes, including learning, memory and recognition of face identities, assignment to different social groups (e.g., patient or actor groups), etc. Therefore, in Exp. 4, we examined whether these processes including learning, memory, identity recognition, etc. even without BOP differences produced through these processes, would be able to interpret the results in Exp. 3. To this end, we employed the learning procedure used in Exp. 3 to generate two categories of faces (from Tiger or Lion teams). It should be noted that the learning procedure was the same in Exp. 3 and Exp. 4. The only difference between the two experiments was that patient/actor/actress identifies activated different beliefs regarding authenticity of painful emotional states of faces with pain expression, whereas Tiger or Lion team identifies did not. The results of Exp. 3 and Exp. 4 together indicate that the processes involved in the learning procedure were not sufficient to affect the P2 amplitudes in response to pain (vs. neutral) expressions. However, the difference in BOP activated by patient/actor/actress identifies were necessary for the P2 modulations because the same learning procedure that did not change BOP was unable to modulate the P2 amplitudes in response to pain (vs. neutral) expressions. We clarified these in the revised Results section (page 27-28).

– In Exp6 it is not clear why the authors do not present the results of the interaction, which is a central contrast throughout the manuscript. Again, the authors lack evidence for a modulation of pain-empathy. What the authors could do to tap into the questions of whether their BOP modulation affects empathy for pain, is to use some form of multivariate pattern that has higher specificity for pain processing. One way the author could try to use to be able to say something about the involvement of empathy for pain is by using the vicarious pain signature maps (Somatic and vicarious pain are represented by dissociable multivariate brain patterns eLife (elifesciences.org)). Author could multiply the VPS maps with their β weights from the SPM model for the four conditions: patient-actor pain, patient-actor neutral, patient-patient pain, patient-patient neutral, and run an ANOVA on the resulting values. In this way they could basically test the interaction within a network that has been reliably associated with pain-empathy, and the validity of which has been tested across experiments.

We thank Reviewer #1 for the suggestion of using the VPS maps to explore the BOP effects on brain responses to others' pain. This is an excellent idea to test how empathic neural responses are modulated by BOP. Accordingly, we calculated the VPS pattern responses to pain expressions of faces with patient or actor/actress identities for each single-participant using the VPS map in response to perceived noxious stimulation of body limbs (Krishnan et al., 2016) or in response to both perceived noxious stimulation of body limbs and painful facial expressions (Zhou et al., 2020). The results of both analyses showed decreased activities in the VPS pattern in response to pain expressions of faces with actor/actress than patient identities. These results were reported in the revision (page 40). We appreciate very much for Reviewer #1's insightful suggestion.

We'd like to clarify that we did conducted a whole-brain interaction (Identity (patient vs. actor) x Expression (pain vs. neutral)) analysis but did not find significant interaction, and we reported this in the revised Results section (page 38). Since this univariate analysis failed to get reliable results, we further conducted a multivariate representational similarity analysis (RSA) of BOLD signals to assess neural correlates of BOP effects on subjective feeling of others' pain. This analysis revealed significant activations in the left anterior insula and inferior parietal cortex, the right anterior insula/frontal cortex, superior temporal gyrus, inferior post-central gyrus, and superior parietal cortex. We reported the RSA results in the revision (page 38-39) which revealed brain regions in which patterns of neural responses predicted patterns of self-reports of subjective feeling of patients' and actors' pain. The univariate whole-brain interaction (Identity (patient vs. actor) x Expression (pain vs. neutral)) analysis only considered stimulus attributes (e.g., pain or non-pain, patient vs. actor). RSA, however, considered both stimulus attributes and participants' feelings of others' pain in different conditions. This is possibly why RSA works better in uncovering the neural activities that correspond to distinct subjective feelings of patients' and actors/actresses’ pain. Taken together, our fMRI results supplement our EEG results by revealing the key nodes of the empathy network in which activities were activated in correspondence with distinct patterns of subjective feelings of patients' and actors/actresses’ pain. These provide additional evidence for BOP effects on empathic brain responses.

Here below a list of some of the statements unsupported by the data:

– "Manipulated BOP changes empathy and altruistic behavior". As I tried to explain, there is no clear measure of empathy, due to the interpretability of the tasks from the participants' point of view. The statement is correctly supported in the case of donation. The concern is only about empathy.

Thanks for this comment. As we clarified in the revised Introduction (page 6-7), previous studies of empathy employed both subjective and objective estimation of empathy for others' pain. Subjective estimation of empathy for pain depends on collection of self report of the degree of others' pain and ones' own unpleasant feelings when observing others' suffering. Objective estimation of empathy for pain relies on collection of brain activities (using fMRI, EEG, MEG, etc.) that differentially respond to painful or non-painful stimuli applied to others. fMRI studies revealed greater activations in the ACC, AI, and sensorimotor cortices in response to painful vs. non-painful stimuli applied to others. 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 subjective reports of others' pain and ones' own unpleasantness. Particularly related to the current study, previous research showed that pain compared to neutral expressions increased the amplitude of the frontal P2 component at 128–188 ms after face (Sheng and Han, 2012; Sheng et al., 2013; 2016; Han et al., 2016; Li and Han, 2019) and the P2 amplitudes in response to others' pain expressions positively predicted subjective feelings of own unpleasantness induced by others' pain and self-report of one's own empathy traits (e.g., Sheng and Han, 2012). These brain imaging findings indicate that brain responses to others' pain can (1) differentiate others' painful or non-painful emotional states to support understanding of others' pain and (2) predict subjective feelings of others' pain and one's own unpleasantness induced by others' pain to support sharing of others' painful feelings. These findings provide effective subjective and objective measures of empathy that allow the current study to investigate neural mechanisms underlying modulations of empathy and altruism by beliefs of others’ pain. Similarly, our current study employed both subjective and objective measures of empathy and showed consistent evidence for modulation of empathy by BOP. We also discussed these in the revised Discussion (page 41-44).

– "Intrinsic BOP predicts empathy and …". Again it is not clear what participants are reporting and interpreting after they know that the treatment did not have an effect (pity, despair, helplessness).

Thanks for this comment. We'd like to clarify that, in all the experiments of the current study, we gave clear task instructions to participants for estimation of their subjective feelings of others' pain and own unpleasantness. After the presentation of each photo the participants were asked to evaluate intensity of each patient’s pain by rating on a Likert-type scale ("How painful do you think this person is feeling?", 0 = not painful at all; 10 = extremely painful). The participants were also asked to report how unpleasant they were feeling when they viewed the photo (i.e., own unpleasantness) by rating on a Likert-type scale ("How unpleasant do you feel when viewing this person?" 0 = not unpleasant at all, 10 = extremely unpleasant). These rating tasks were adopted from previous research (Bieri et al., 1990; Jackson et al., 2005; Lamm et al., 2007; Fan and Han, 2008; Sheng and Han, 2012) as subjective measures of understanding and sharing of others’ pain feeling — the two key components of empathy. Because the same task instructions were given in the 1^st and 2^nd round tests, the participants interpreted the task instructions in the same way in both the 1^st and 2^nd round tests. Moreover, our study conducted both subjective and objective (i.e., neuroimaging) measures of empathy and the results of the two types of measures complemented each other and were consistent regarding BOP effects on empathy. Thus self-reports of rating scores and brain responses to pain (vs. neutral) expressions indeed provide two types of measures of empathy.

To describe the results of Exp. 2 more precisely, we changed the title of Exp. 2 to "Intrinsic BOP predicts subjective estimation of empathy and altruistic behavior". We appreciate this comment from Reviewer #1 because we agree that other kinds of emotions such as pity, despair, helplessness may influence the self-report results. However, these effects may help to explain why the participants reported greater pain intensity and unpleasantness for the 0% effective target faces but not for the 100% effective target faces. For example, 0% effectiveness of treatment might increase pity for targets whereas 100% effectiveness of treatment would not. Most importantly, empathy changes for 100% effective targets are the key results for testing our hypothesis. We thank again for Reviewer #1 for this comment and included additional discussion in the revision regarding possible changes of other emotions related to 0% effective target faces (page 41-42). Specifically, we discussed alternative emotional explanations of the increased donations to targets without effective treatment in addition to the empathy account.

– "Manipulated BOP changes empathic brain activity". The presence of a N2 is not sufficient to claim that there is empathic brain activity, as N2 can be elicited by salient stimuli as well (even the fact that there is an N2 in response to the neutral stimuli, suggests that N2 might represent other activity than empathic).

Thanks for this comment. We believe Reviewer #1 mentioned P2 (but not N2) component here. Indeed, we agree with Reviewer #1 that the P2 component can be elicited by various types of visual stimuli, and this is why we did not simply take the P2 amplitude in response to pain expressions as a neural marker of empathy. Instead, similar to previous ERP research (Sheng and Han, 2012; Sheng et al., 2013; 2016; Han et al., 2016; Li and Han, 2019; Zhou and Han, 2021), we calculate the difference in the P2 amplitudes in response to pain compared to neutral expressions as a neural marker of empathy for pain. As we mentioned in the revised Introduction, the difference in P2 amplitude in response to pain compared to neutral expressions is associated with self-report of others’ pain (Sheng and Han, 2012). Thus using the difference in P2 amplitude in response to pain compared to neutral expressions, rather than simply using the P2 amplitude in response to pain expressions, to assess empathic neural responses rule out empathy-unrelated processes such as facial structure and social attributes. In the revised Introduction, we made clear this logic for examination of brain activities related to empathy in our brain imaging experiments (page 7 and 9).

– "Together, the results in Experiment 3 and 4 suggest a key role of BOP, but not of other cognitive processes involved in the experimental manipulations, in modulations of neuronal responses to other's pain." The control experiment does not contain any other manipulation of cognitive beliefs, and therefore the authors cannot exclude that other beliefs not related to pain could have equally modulated the N2 component.

Thanks for this comment. We appreciate Reviewer #1's clarification that the control experiment (Exp. 4) did not contain any other manipulation of cognitive beliefs. The key point here is to compare the results in Exp. 4 with those in Exp. 3 rather than examining the results of Exp. 4 independently. Exp. 3 and Exp. 4 engaged similar learning, memory, identity recognition, and other processes but were different in that Exp. 3 manipulated BOP whereas Exp. 4 did not. This difference corresponds to the presence modulations of the P2 amplitudes in response to pain (vs. neutral) expressions in Exp. 3 but not in Exp.4. The results of the two experiments together suggest that the manipulation of BOP in Exp. 3 was necessary for modulations of empathic (P2) responses to pain (vs. neutral) expressions. We are sorry for not making clear the rationale of including Experiment 4. In the revised Results section, we made clear the goal and rationale of Experiment 4 (page 27-28).

– "Our behavioral and neuroimaging results revealed critical functional roles of BOP in modulation of the perception-emotion-behavior reactivity by showing how BOP predicted and affected empathy/empathic brain activity and monetary donation. Our finding provide evidence that BOP constitutes a fundamental cognitive basis for empathy and altruistic behavior." The neuroimaging results revealed that areas involved in cognitive processes are modulated by cognitive manipulation. There is no evidence that empathy-related neuronal activity was modulated by the BOP manipulation. BOP is a successful cognitive manipulation, but what authors cannot conclude that BOP is a fundamental cognitive basis for empathy.

Thanks for this comment. As mentioned above, our results of RSA and VPS analyses revealed additional evidence that empathy-related activity was modulated by BOP. These were reported in the revised Results section (page 37-40). We'd be happy to clarify again that there are both cognitive and affective components of empathy, which are mediated by distinct neural substrates. These have been supported by numerous brain imaging findings. Thus we examined BOP effects on both cognitive and affective processes involved in empathy. Our RSA examined whether activities in the empathic network were associated with the dissimilarity matrix constructed from scores of pain intensity in different conditions. This analysis revealed activation in the insula, suggesting that activity in the affective node of the empathy network is associated with BOP modulations of subjective feelings of others' pain. We agree with Reviewer #1 that "BOP is a fundamental cognitive basis for empathy" is probably a strong conclusion. We thus played down the tone in the revision and made a weaker conclusion by saying that "Our findings suggest that BOP may provide a cognitive basis for empathy and altruistic behavior ".

– "These findings together support that empathy, as either an instant emotional response to other's suffering or a sustained personality trait of understanding and sharing other's pain, is one of the key motivation factors for altruistic behavior." I do not see how the results support such a claim, and there is no measure of sustained personality trait of understanding either.

Thanks for this comment. We feel sorry for not making clear this statement. We clarified in the revision that previous work used questionnaires to estimate empathy as a personality trait and our current study used self-report/neuroimaging to examine instant empathic responses to others' suffering shown in their facial expressions. We modified these sentences in the revised Discussion as " Our results showed that self-report of other’s 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). 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; Hoffman, 2008; Penner et al., 2005) " (page 42-43).

Reviewer #2 (Recommendations for the authors):

Experiment 1 follow-up notes:

– As I noted in my public review, the "enhanced" responses towards the targets appearing as Patients in both Rounds 1 and 2 could simply be a contrast effect due to the Patient-Actor targets. I think there are a number of ways the authors could rule out this possibility. For example, you could show just 8 patients in Round 1, followed by 16 patients in Round 2 (8 new, 8 returning from Round 1). Alternatively, you could show 8 actors/actresses and 8 patients in Round 1, and then tell participants that all these targets are actually patients in Round 2. My prediction in both cases would be that you wouldn't see an increase in empathy/altruism/etc. for the targets appearing as patients in both rounds in either case.

Thanks again for suggestion. We agree with Reviewer #1 that there might be alternative accounts of our results of empathy rating scores and monetary donations observed in the behavioral experiments. Because our ERP results focused on decreased empathic neural responses to actor/actress compared to patient targets and our paper also focused on the issue of decreased empathy and altruism related to BOP, we did not include results of new experiments to explain the increased empathy rating scores and monetary donations observed in the behavioral experiments. However, we appreciate Reviewer #2's suggestions and included additional discussion in the revised Discussion regarding alternative accounts of the behavioral results (page 41-42). In particular, we noted that "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 reflect a contrast effect due to rating patient and actor 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 block of trials." We believe that future work will clarify these.

– Moreover, I referred to some confusion about the charitable donations in this study. In Round 1, the charitable donation is described like this: "The participants were informed that the amount of one of their decisions would be selected randomly and endowed to a charity organization to help those who suffered from the same disease. " In Round 2, it's described like this: "The participants had to…report how much they would like to donate from the extra bonus payment to each model. The participants were told that an amount of money would be finally selected from their 2^nd round donation decisions and presented to a charity organization after the study." I wasn't sure if the difference in wording (e.g., "a charity organization to help those who suffered from the same disease" vs. just "a charity organization") was important and intentional – is it the same disease-specific charity in both rounds? (And is it the same charity for both target types in Round 2 – e.g., both patients and actors?) If the answer to either of these questions is no, more detail is necessary, since this could potentially explain differences in giving across condition. (Also, if participants know that these targets are not actually receiving their money, but rather that a trial will be selected at random to determine their donation, it's a little surprising that participants would vary their responses at all – IF the money goes to the same charity regardless of target type.)

Thanks again for this comment. We made clear in the revised Method session that " The participants were then asked to conduct the 2^nd round test in which each photo was presented again with a word below to indicate patient or actor/actress identity of the face in the photo. The participants had to report again pain intensity of each face and how much they would like to donate to the person shown in the photo. The participants were informed that an amount of money would be finally selected randomly from their 2^nd round decisions and donated to one of the patients through the same charity organization."(page 56).

Experiment 2 typographical notes:

– In lines 274-277, the authors report the results for intensity and unpleasantness ratings and donations in a manner that's a little hard to follow ("One-sample t-tests showed that the z values were significantly smaller than zero for decreased-BOP patients (correlations between changes in belief and pain intensity/unpleasantness/monetary donation: mean {plus minus} s.d. = -0.304 {plus minus} 0.370, -0.277 {plus minus} 0.455 and -0.236 {plus minus} 0.410; t(59) = -6.352, -4.706 277 and -4.465; ps < 0.001; Cohen's d = 0.822, 0.609 and 0.576; 95% CI = (-0.400, -0.208), 278 (-0.394, -0.159), and (-0.342, -0.130))") – I'd recommend splitting up these measures, rather than grouping by statistical value (e.g., all three means, all three p-values, etc.).

Thanks for this suggestion. Accordingly, these measures were reported separately in the revision (page 19-20).

Experiment 4 open-ended question:

– This study did get me thinking about the considerable literature on intergroup empathy that uses minimal groups or real-world group affiliations (e.g., soccer teams, etc.) to demonstrate that people show an in-group bias in empathy for pain. How should we conceptualize those findings in terms of BOP? (e.g., Are differential expectancies about pain tolerance/sensitivity/etc. embedded in these group memberships?) Or are those group-based effects supported by fundamentally different mechanisms?

Thanks for this comment. This is a great question, and, indeed, a complicated one. Our 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 earlier group-based categorization of outgroup faces (Zhou et al., 2020). However, there has 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). While these behavioral findings suggest other beliefs may also modulate empathy for others’ pain and relevant altruistic behavior, the underlying brain mechanisms 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. We include these discussions in the revised Discussion section (page 48-49).

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

• 1,957

  Page views

• 315

  Downloads

• 2

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.