Tuesday, May 18, 2021

Mental disorders were 3- to 4-fold more prevalent in children with parents in the lowest income percentiles; parents’ own mental disorders, other socio-demographic factors etc. did not fully explain these associations

Parental income and mental disorders in children and adolescents: prospective register-based study. Jonas Minet Kinge et al. International Journal of Epidemiology, dyab066, May 11 2021. https://doi.org/10.1093/ije/dyab066


Background: Children with low-income parents have a higher risk of mental disorders, although it is unclear whether other parental characteristics or genetic confounding explain these associations and whether it is true for all mental disorders.

Methods: In this registry-based study of all children in Norway (n = 1 354 393) aged 5–17 years from 2008 to 2016, we examined whether parental income was associated with childhood diagnoses of mental disorders identified through national registries from primary healthcare, hospitalizations and specialist outpatient services.

Results: There were substantial differences in mental disorders by parental income, except for eating disorders in girls. In the bottom 1% of parental income, 16.9% [95% confidence interval (CI): 15.6, 18.3] of boys had a mental disorder compared with 4.1% (95% CI: 3.3, 4.8) in the top 1%. Among girls, there were 14.2% (95% CI: 12.9, 15.5) in the lowest, compared with 3.2% (95% CI: 2.5, 3.9) in the highest parental-income percentile. Differences were mainly attributable to attention-deficit hyperactivity disorder in boys and anxiety and depression in girls. There were more mental disorders in children whose parents had mental disorders or low education, or lived in separate households. Still, parental income remained associated with children’s mental disorders after accounting for parents’ mental disorders and other factors, and associations were also present among adopted children.

Conclusions: Mental disorders were 3- to 4-fold more prevalent in children with parents in the lowest compared with the highest income percentiles. Parents’ own mental disorders, other socio-demographic factors and genetic confounding did not fully explain these associations.

Keywords: Mental disorders, income, inequality, childhood, adolescence

Key Messages

- Mental disorders in children decreased continuously with increasing parental income for all mental disorders, except eating disorders.

- The parental-income gradient was largest for attention-deficit hyperactivity disorder, followed by anxiety and depression.

- Our study suggests that associations between lower parental income and children’s mental disorders were partly, but not fully, attributed to other socio-demographic factors, parents’ own mental disorders and genetic factors.


Three major conclusions can be drawn from this study. First, despite relatively equal access to health services, childhood mental disorders were found to decrease continuously with parental income and there was no dividing line above or below which additional income was no longer associated with mental disorders. The associations varied with child age and sex. Second, the association with parental income was present for all mental disorders except eating disorders and largest for ADHD. Third, the association of parental income with mental disorders could partly, but not fully, be attributed to parental mental disorder and socio-demographic factors. In addition, the associations were present, but less pronounced, in children genetically unrelated to their parents.

Association of parental income and mental disorders by sex and age

The observed patterns of association and sex differences are similar to those of differential life expectancy by income in adults aged ≥40 years in Norway.18 This supports the suggested link between childhood family income and the subsequent socio-economic inequalities in health in adults.33

Association of parental income and subcategories of mental disorders

Previous studies have found associations between parental income and selected mental disorders in children.1 However, studies covering a range of categories are lacking. This study found that the most pronounced associations with parental income were for ADHD in both boys and girls. The prevalence of eating disorders did not vary with parental income in girls. Although varying associations were detected, these findings may be related to the pervasive co-morbidity within mental disorders.34

Evaluation of factors associated with differences in mental disorders by parental income

This study replicates previous findings that one-parent households, low parental education and mental disorders in parents are factors associated with children’s mental disorders.1,35,36 Further, the results show that absolute differences in mental disorders by single-parent household status, parental education and parental mental disorders were greater in children with parents at lower income levels.

Associations between parental income and children’s mental disorders were attenuated when adjusted for household and parental characteristics such as age, education, employment status, mental disorders and one-parent household. Nonetheless, adjusted parental income remained an independent predictor for mental disorders in children, which is in line with previous findings.3

The influence of a genetic component is also suggested. Children of parents with mental illness are at a higher genetic and environmental risk of developing psychopathology.37,38 Low income can be a consequence of psychopathology in parents.37 The largest income difference was found for ADHD, a mental disorder with a strong heritable component, which is also associated with reduced income in adulthood.38 In contrast, the difference across the income spectrum was smaller for anxiety, which has been shown to have a large environmental component.38 These differences suggest confounding by underlying genetic susceptibility on the relationship between parental income and offspring mental disorders. In addition, the associations between parental income and mental disorders in adopted children were weaker compared with children living with their biological parents. The differences in the associations with parental income observed among adopted children and Norwegian-born children were also greater for ADHD than for anxiety disorders.

Although weaker than in children living with their biological parents, the statistically significant associations between parental income and mental disorders in adopted children support that at least some mental health problems are a result of social factors.3

Studies from other countries suggest that registries do not fully capture interview-based diagnoses in children from lower-income families.11 If parental income is associated with use of health services for mental disorders given equal need, diagnoses from health registries could be biased indicators of income gradients in mental disorders. To explore this, we conducted supplementary analyses of the association between psychological-distress score, from the SHLC Survey,17 and health service. This analysis did not suggest that this bias the estimates for Norway.

Also, a strength of our study was that we used primary-care data in addition to specialist-care data, whilst most prior studies have included only specialist services.5 Furthermore, comparisons of diagnostic data from the Composite International Diagnostic Interview with health registry diagnoses on major depressive and anxiety disorders in Norway have been published previously.8 As indicators, registry-based diagnoses have moderate sensitivity and excellent specificity, with 0.2–4.2% false positives.8 The health survey and registry data used in this study have been found to measure the same symptoms.8

This study has some limitations. First, as the diagnoses of mental disorders in children were obtained from health registries, information was only available for individuals in contact with health services. Individuals with less severe cases of depressive disorders and anxiety do not all seek care.8,39 Thus, children with mild or transient symptoms may be underrepresented. Second, primary and specialist healthcare use different standards of diagnostic codes. ICPC2, used in primary care, relies on broader diagnostic categories than the ICD-10 used in specialist care. Thus, some specific mental disorders, such as those in the autism spectrum, do not have specific codes in the primary-care database. In Norway, however, children with autism and other severe conditions are unlikely to not have been under specialist care during the study period. Third, particularities of the setting and potential non-random assignment of adopted children to adoptive parents can affect the interpretation of data on the association between income and mental disorders in adopted children (Part II in the Supplementary Material, available as Supplementary data at IJE online).

New normal for body size perception: Participants consistently mis-categorized overweight male bodies as normal weight, while accurately categorizing normal weight

Misalignment between perceptual boundaries and weight categories reflects a new normal for body size perception. Annie W. Y. Chan, Danielle L. Noles, Nathan Utkov, Oguz Akbilgic & Webb Smith. Scientific Reports volume 11, Article number: 10442. May 17 2021. https://www.nature.com/articles/s41598-021-89533-5

Abstract: Combatting the current global epidemic of obesity requires that people have a realistic understanding of what a healthy body size looks like. This is a particular issue in different population sub-groups, where there may be increased susceptibility to obesity-related diseases. Prior research has been unable to systematically assess body size judgement due to a lack of attention to gender and race; our study aimed to identify the contribution of these factors. Using a data-driven multi-variate decision tree approach, we varied the gender and race of image stimuli used, and included the same diversity among participants. We adopted a condition-rich categorization visual task and presented participants with 120 unique body images. We show that gender and weight categories of the stimuli affect accuracy of body size perception. The decision pattern reveals biases for male bodies, in which participants showed an increasing number of errors from leaner to bigger bodies, particularly under-estimation errors. Participants consistently mis-categorized overweight male bodies as normal weight, while accurately categorizing normal weight. Overweight male bodies are now perceived as part of an expanded normal: the perceptual boundary of normal weight has become wider than the recognized BMI category. For female bodies, another intriguing pattern emerged, in which participants consistently mis-categorized underweight bodies as normal, whilst still accurately categorizing normal female bodies. Underweight female bodies are now in an expanded normal, in opposite direction to that of males. Furthermore, an impact of race type and gender of participants was also observed. Our results demonstrate that perceptual weight categorization is multi-dimensional, such that categorization decisions can be driven by ultiple factors.


By providing corroborating evidence from univariate and multi-variate analyses to investigate body size perception, we are able to identify the complex relationship between gender and race types of the stimuli and of the participants, and the impact of these factors on body size categorization. In particular, we have revealed that performance (percent accuracy) for body stimuli is not uniform, whereby participants performed best for Normal weight and worst for Obese stimuli. We also revealed evidence of an interaction between weight category and gender of the stimuli; participants were more accurate for Underweight and Normal male stimuli (leaner size) relative to the same weight category of female bodies, but they were more accurate for overweight and obese female stimuli compared to male bodies of the same size.

Multi-variate decision tree analysis provided not only consistent results but has pinpointed the direction of estimation errors. Specifically, it revealed that while our participants were reliably making more under-estimation errors for Overweight and Obese male stimuli, they were quite accurate when categorizing Overweight and Obese female stimuli. The overall decision tree pattern (Fig. 2 and Supplemental Figure 2) suggests a strong bias for male stimuli where participants showed an increasing number of errors from leaner to bigger bodies, particularly under-estimation errors. Importantly, there was an expansion of Normal weight category, such that for male stimuli, while a high percent accuracy was found for Normal weight (Fig. 2B), there were also substantial under-estimating errors for identification of Overweight male bodies (Fig. 2C), where participants consistently mis-identified Overweight as Normal weight. Thus, it ndicated that the perceptual BMI for Normal male bodies is now higher than the recognized BMI. For female stimuli however, an expanded boundary for Normal size was found in the opposite direction. Participants have categorized Underweight as Normal (Fig. 2A) as well as accurately categorized Normal bodies (Fig. 2B), suggesting that the averaged perceptual BMI for ormal female bodies is now lower than the recognized BMI. The fact that both male and female participants shared many of the same biases also suggests that visual learning plays a critical role in developing these specific biases. Part of these results was consistent with previous findings; for example, it has been reported5,6 that participants (predominately Caucasian women) make more under-estimation errors when judging Caucasian Overweight or Obese male bodies. It is also worth highlighting that due to the diverse range of our stimuli and equal sampling across both gender and race of participants in the current study, we are able to capture opposite response patterns when judging male vs female bodies, thus, a more complex pattern than previously reported.

As mentioned earlier, prior work has primarily reported perceptual biases for own body weight that might associate with race or gender types, while others have reported biases when judging others’ bodies, but theyhave primarily focused on a particular gender, race, and/or weight category of the stimuli or participants. Our current study focused on assessing others’ body weight as observers, accounting for race and gender of both stimuli and participants. We found that perceptual errors could be associated with characteristics of the participants and the stimuli. For example, all participants, regardless of their race and gender, showed more under-estimating errors by mis-categorizing AA female overweight bodies as normal (Fig. 3). Intriguingly, (Fig. 2A) when judging female underweight bodies (all race types), AA participants (both genders), showed a stronger over-estimating bias for female underweight bodies, mis-categorizing those images as normal weight. CA participants, however, did not exhibit such bias.

It has been well-established in the face perception literature that people are more accurate in recognizing and identifying faces of their own race compared to other race groups. This discrepancy in performance is known as the “other-race effect32,33,34,35,36,37,38,39,40. In the context of body weight perception, our current results did not show any other-race effect; no interaction between stimulus and participants’ race types was found in the univariate analysis or multi-variate analysis. However, we identified various participant-specific race effects from the multi-variate results. For example, we found that AA participants were slightly better at categorizing Obese male bodies relative to CA participants. AA participants also over-estimated Underweight female bodies, as they had consistently mis-categorized Underweight female bodies as Normal size (and this effect was not present in CA participants). For normal male bodies, AA performed better than CA, but AA made more over-estimation errors than CA. Interestingly, some stimuli-specific race effects were also identified. Overall, categorization performance was slightly (but significantly) better for Avatar stimuli in terms of percent accuracy. Multi-variate analysis further revealed that during categorization of female overweight stimuli, participants showed higher accuracy for Avatar and CA bodies than for AA (Fig. 3). A recent study20 had used visual adaptation to study the after-effect following repeated exposure of Asian or Caucasian female bodies, and their results seemed to be consistent to our findings. They also reported a lack of “other-race effect” at the stimuli level, but they reported that Asian participants seemed to show a weaker adaptation effect relative to Caucasians; however, the effect was not specific to Asian or Caucasian stimuli.

“Own-gender biases” have also been reported in face perception literature41. People are better at recalling or recognizing faces of their own gender relative to faces of the opposite gender41,42. Limited work has been conducted regarding gender-biases in body perception. Our recent study43 investigated gaze-pattern during perception of upright vs inverted bodies, but observed no differences in eye-movement patterns between male and female participants during a same/different categorization task of male body images. Multi-variate analysis in the current study has identified significant differences in performance between viewing female and male body images. There is a stimuli- and participant-specific gender effect that is particularly prominent for Obese bodies. Specifically, a marked difference was found between male and female participants, where male participants showed significantly higher accuracy for female Obese bodies. For male Overweight bodies, male participants performed better than female, while female participants showed more under-estimation errors. This suggests that under-estimation bias for Overweight male bodies was primarily driven by female participants. A stimuli-specific gender effect was also observed whereby, consistent with the univariate analysis, participants performed more accurately for Underweight male bodies than female Underweight bodies. Overall performance for Normal weight images was also better for male than female bodies. For Obese bodies, performance was better for female bodies, and there were also more under-estimation errors for male Obese bodies. These findings demonstrated that, by increasing the diversity in the stimuli and participants tested and by adopting a multi-variate approach, a more complex categorization pattern can be revealed. Furthermore, our observations of behavioural biases for higher BMI male stimuli and for lower BMI female stimuli seem to be consistent with the idea that partial overlapping or multiple gender-specific neural mechanisms may be at play during body size perception24,25.

Two major theories have been adopted to elucidate perceptual weight biases: the Weber’s law and contraction bias12,13,44. Specifically, the Weber’s law would predict that since detection of change of one’s body size is in constant proportion with one’s own weight, it is more diffcult to notice the change when one is overweight/obese. Alternatively, contradiction bias predicts that one’s perceived own BMI is inversely correlated with their own actual BMI. It has been reported that such correlation was only found during size estimation of participants’ own avatar, but did not generalize to estimating others’ body size12. While these theories may be helpful for explaining error in estimating one’s own weight, it is rather difficult to apply them to explain errors/biases during identification of others’ weight, especially when there are a lot more variables (race, gender, body weight, etc.) when dealing with “other bodies”. As we have shown here, estimation accuracies and errors interact with the type of stimuli presented in the experiment, thus illustrating that with increasing diversity in the stimuli, it might not be possible to show an “one-to-one mapping” using the above theories, as estimation decisions might be more complex than previously thought. While it is important to recognize that people have different body sizes, shapes, and other physical characteristics19, and that even BMI cut-off points may not capture variations in physiological measurements across cultures45, our current approach aims demonstrated that it is possible to capture and quantify some of the multi-dimensional visual characteristics, and it is critical that future work should also harness similar approaches.

Our findings here certainly do not attempt to capture categorization patterns for all types of bodies, and despite the constraints in our well-controlled paradigm (in real life, people with the same BMI may have different body shapes, and we see bodies from many different viewpoints other than straight-on), we have taken an important first step to quantify complex patterns in body weight perception. Finally, we believe that providing a careful characterization of perceptual biases in body weight here may lead to better diagnostic decision-making and development of personalized intervention programmes in both clinical and non-clinical settings.

Quantifying collective intelligence in human groups

Quantifying collective intelligence in human groups. Christoph Riedl et al. Proceedings of the National Academy of Sciences, May 25, 2021 118 (21) e2005737118; https://doi.org/10.1073/pnas.2005737118

Significance: Collective intelligence (CI) is critical to solving many scientific, business, and other problems. We find strong support for a general factor of CI using meta-analytic methods in a dataset comprising 22 studies, including 5,279 individuals in 1,356 groups. CI can predict performance in a range of out-of-sample criterion tasks. CI, in turn, is most strongly predicted by group collaboration process, followed by individual skill and group composition. The proportion of women in a group is a significant predictor of group performance, mediated by social perceptiveness.

Abstract: Collective intelligence (CI) is critical to solving many scientific, business, and other problems, but groups often fail to achieve it. Here, we analyze data on group performance from 22 studies, including 5,279 individuals in 1,356 groups. Our results support the conclusion that a robust CI factor characterizes a group’s ability to work together across a diverse set of tasks. We further show that CI is predicted by the proportion of women in the group, mediated by average social perceptiveness of group members, and that it predicts performance on various out-of-sample criterion tasks. We also find that, overall, group collaboration process is more important in predicting CI than the skill of individual members.

Keywords: collective intelligencehuman groupsteam performance

Honoring Confucius’ Golden Mean philosophy, both Chinese males & females are supposed to avoid being either extremely emotional or extremely restrained, resulting in a diminished sex difference in empathy

Culture, Sex, and Group-Bias in Trait and State Empathy. Qing Zhao et al. Front. Psychol., April 28 2021. https://doi.org/10.3389/fpsyg.2021.561930

Abstract: Empathy is sharing and understanding others’ emotions. Recently, researchers identified a culture–sex interaction effect in empathy. This phenomenon has been largely ignored by previous researchers. In this study, the culture–sex interaction effect was explored with a cohort of 129 participants (61 Australian Caucasians and 68 Chinese Hans) using both self-report questionnaires (i.e., Empathy Quotient and Interpersonal Reactivity Index) and computer-based empathy tasks. In line with the previous findings, the culture–sex interaction effect was observed for both trait empathy (i.e., the generalized characteristics of empathy, as examined by the self-report questionnaires) and state empathy (i.e., the on-spot reaction of empathy for a specific stimulus, as evaluated by the computer-based tasks). Moreover, in terms of state empathy, the culture–sex interaction effect further interacted with stimulus traits (i.e., stimulus ethnicity, stimulus sex, or stimulus emotion) and resulted in three- and four-way interactions. Follow-up analyses of these higher-order interactions suggested that the phenomena of ethnic group bias and sex group favor in empathy varied among the four culture–sex participant groups (i.e., Australian female, Australian male, Chinese female, and Chinese male). The current findings highlighted the dynamic nature of empathy (i.e., its sensitivity toward both participant traits and stimulus features). Furthermore, the newly identified interaction effects in empathy deserve more investigation and need to be verified with other Western and Asian populations.


In this study, the culture–sex interaction effect in empathy was studied with Australian and Chinese participants. Moreover, this interaction effect was identified on both trait and state empathy. For trait empathy, the current observation was consistent with previous findings (Melchers et al., 2015Zhao et al., 2019). For state empathy, the culture–sex interaction effect further interacted with stimulus traits (e.g., stimulus ethnicity, stimulus sex, and stimulus emotion), resulting in three- or four-way interactions (see Table 5). Follow-up analyses of the higher-order interactions revealed that the impacts of stimulus traits varied among the culture–sex participant groups (i.e., Australian female, Australian male, Chinese female, and Chinese male). To conclude, the current results support the theory of culture–sex interaction effect in empathy (Zhao et al., 2019). Furthermore, the current results highlight that beyond the fundamental culture–sex interaction effect in empathy, there could be more intriguing interactions across participant traits and stimulus features.

Trait Empathy

The culture–sex interaction effect emerged as a clear trend in terms of trait empathy (see Table 4). This finding is in line with that of Zhao et al. (2019), who evaluated trait empathy with Australian Caucasian (n = 196) and Chinese Han (n = 211) university students. Specifically, in both the current and the previous study (Zhao et al., 2019), the cultural differences in trait empathy were significant in female participants (i.e., Australian female > Chinese female participant) but not in male participants. Furthermore, sex differences in trait empathy were only significant with Australian participants (i.e., Australian female > Australian male participant) but not with Chinese participants. Zhao et al. (2019) proposed that the culture–sex interaction in trait empathy might be germane to social expectations for emotional expressions. Generally, Western cultures encourage females to externalize their emotions more than males (i.e., the so-called emotional female and rational male; Merten, 2005). In contrast, honoring Confucius’ Golden Mean philosophy, both Chinese males and Chinese females are supposed to avoid being either extremely emotional or extremely restrained (Huang, 2006Zhao et al., 2019), resulting in a diminished sex difference in empathy (also see Zhao et al., 2020). Nevertheless, it should be noted that the above relationship between empathy and social expectations is only a theoretical proposal by Zhao et al. (2019), and future empirical studies are necessary to verify this proposal.

State Empathy

The current state empathy results were more complex, spanning significant two-, three-, and four-way interactions (see Table 5). For example, there were four-way interactions on overall and cognitive empathy for NimStim stimuli (i.e., participant culture × participant sex × stimulus ethnicity × stimulus sex), four-way interactions on overall and emotional empathy for the documentary stimuli (i.e., participant culture × participant sex × stimulus sex × stimulus emotion), as well as one three-way interaction on perspective-taking of the documentary stimuli (i.e., participant culture × participant sex × stimulus emotion).

The Culture–Sex Interaction Effect

Within each of the aforementioned three- and four-way interactions, there is a culture–sex interaction effect. Moreover, these three- and four-way interactions covered all forms of state empathy examined in this study (i.e., overall empathy, emotional empathy, cognitive empathy, and perspective-taking). On the one hand, the current findings suggest that culture–sex interaction effects in empathy are not restricted to trait empathy (e.g., Zhao et al., 2019) but can expand to state empathy. On the other hand, the current results are similar to the findings of Zhao et al. (2019), suggesting that the culture–sex interaction is significant for inclusive components of empathy (see Melchers et al., 2015 and Lachmann et al. (2018), both of them found the interaction was not significant on cognitive trait empathy). It is worth mentioning that Schmitt (2015) had a theory of “culturally variable sex difference”; as per Schmitt (2015), the culture–sex interaction effect could be a non-negligible phenomenon in a broad range of social and psychological subjects in addition to empathy. Therefore, the culture–sex interaction effect deserves attention from future cross-cultural researchers of sociology and psychology.

However, the culture–sex interaction effect has been ignored by most of the previous investigators of the Western–Asian cultural difference in trait and state empathy (see Tables 13). As noted by Zhao et al. (2019), the culture–sex interaction effect could be an explanation for the inconsistent results among the publications (see Tables 13). Moreover, Zhao et al. (2019) proposed that the magnitude of the Western–Asian cross-cultural differences in trait empathy could be enlarged along with the female ratio of a sample (i.e., a positive correlation with the female%). Both the current study and Zhao et al. (2019) presented supporting evidence for the above notion since the effect size of the cultural difference in trait empathy tends to be larger for female participants relative to male participants.

Participant Culture Effect

Referencing the results of culture–sex interaction in trait empathy (Zhao et al., 2019), the Australian females should be the most empathic among the four culture–sex participant cohorts. Nevertheless, the current findings for state empathy revealed a different trend; that is, the advantages and disadvantages of state empathy are relatively counterbalanced for the participant groups. First, in light of the NimStim stimuli (i.e., task I), Australian participants expressed more cognitive empathy for positive and neutral stimuli (i.e., happiness and neutral-peacefulness). In contrast, Chinese participants reported more emotional empathy for negative emotions (i.e., anger and fear). Second, in light of the documentary stimuli (i.e., task II), the Chinese participants commonly expressed more empathy (i.e., overall empathy, emotional empathy, cognitive empathy, and perspective-taking) than Australian participants. However, Australian participants specifically reported more cognitive empathy for stimuli of male anger than Chinese participants did.

The inconsistency among the findings of trait empathy and state empathy (for NimStim and for documentary stimuli) is intriguing and can be explained by a range of factors. The first factor is social expectation. On the one hand, as per Zhao et al. (2019), Australian females’ higher self-evaluated trait empathy could be largely due to the social expectation placed on them. However, the impact of social expectation on the computer-based evaluations (i.e., state empathy) could be weaker than that on self-report evaluations (i.e., trait empathy) (Baez et al., 2017). More importantly, in the current study, participants were explicitly required to answer each state empathy question according to their inner feelings rather than social justice (see the section “Materials and Methods”). This instruction might have minimized the impact of social expectation on the state empathy tasks. On the other hand, Chinese traditional cultures (e.g., Confucianism and Taoism) honor humility and modesty in individuals (Lin et al., 2018). Hence, Chinese participants could downplay themselves while answering the trait empathy items (i.e., the items enquire ‘‘how good the participant is in empathy’’)12 but might be more objective during responding to state empathy questions (i.e., the questions ask ‘‘how much the participant felt for a given stimulus’’)13. Therefore, Chinese participants may seem to be less empathic than Australian participants in light of trait empathy (i.e., the self-report scales assessed) but not state empathy (i.e., the computer-tasks evaluated).

The second factor is the background information of the stimuli. The current results suggest that when the emotional background information was withheld (i.e., the NimStim stimuli), Australian participants had higher cognitive empathy for neutral and happy stimuli, while Chinese participants showed more emotional empathy for negative emotions. This observation was in agreement with the distinct Asian and Western cultural requirements of emotional expression and suppression. Generally, in Asian societies, negative emotions are expected to be masked (e.g., by a neutral or smiling face) for maintaining interpersonal harmony (Wei et al., 2013). This social rule is different from Western societies, in which externalizing emotions is accepted as an honest way to express oneself (Gross and John, 2003Murata et al., 2012). Consequently, since childhood, Chinese individuals have been trained to decode others’ emotions according to contextual information, as well as trained to be alert to others’ subtle emotional downturns (i.e., watch the “face colors”) (Wang, 2001). Therefore, emotional understanding (i.e., cognitive empathy) for neutral and happy faces without emotional background information could be a challenge for Chinese participants (i.e., as per the Chinese culture, a neutral or happy face by itself could indicate neutral, happy, or masked negative feelings). However, the empathic sensitivity (i.e., emotional empathy) for negative emotions might be more intense for the Chinese than Australian participants (i.e., due to the necessity of watching others’ “face colors” in Chinese society) (e.g., Wang, 2001).

In contrast, when the background information was given (i.e., the documentary stimuli), empathy for most of the emotions was promoted for Chinese participants. One exception was the cognitive empathy for the stimuli of male anger. Anger is an intense emotion that disturbs the harmony of interpersonal relationships (de Greck et al., 2012). Influenced by the Confucian Golden Mean philosophy, the Chinese may value social harmony much more than Westerners (Drummond and Quah, 2001de Greck et al., 2012Liu, 2014). In light of Chinese culture, expressing anger could be labeled as lacking in self-control (Kornacki, 2001Kong et al., 2020). In contrast, for Westerners, sincerely expressing emotions could be deemed as a way to enhance interpersonal understanding (Gross and John, 2003Murata et al., 2012). Moreover, de Greck et al. (2012) decoded the neurological basis of Western–Asian cultural differences in empathy for anger. They found that facing ethnic in-group anger, German participants had more brain activation in the cognitive empathy-related brain regions (i.e., the inferior temporal gyrus and middle insula). In contrast, Chinese participants showed more brain activation in the emotional regulation and personal distress-related brain region (i.e., the dorsolateral prefrontal cortex). de Greck et al. (2012) claimed that the Western participants might try to understand the anger; meanwhile, the Chinese participants might attempt to inhibit their aversive feelings stirred up by the anger. Noticeably, some previous researchers of cultural differences in empathy (see Tables 23) adopted the concepts of “negative emotions” or “suffering” (i.e., mixed negative emotions) as emotional stimuli. However, the current results highlight that the participants’ cultural differences in empathy can be qualified by the subtypes of negative emotions.

Ethnic Group Bias

In this study, the dominant trend of ethnic group bias in state empathy was the ethnic in-group bias for negative emotions together with the ethnic out-group bias for positive emotions. These findings were in line with our hypothesis (see the section “Introduction”) as well as the previous observation by Neumann et al. (2013). Specifically, the current Chinese participants exhibited ethnic in-group biases on overall empathy (i.e., the holistic concept of emotional and cognitive empathy) for fear (NimStim stimuli) and sadness (documentary stimuli).

In contrast, the current Australian participants expressed an ethnic out-group bias on overall empathy for happiness (documentary stimuli). These findings cannot be fully explained by either the theory of in-group familiarity (Cao et al., 2015) or the one of out-group hate (Avenanti et al., 2010). Instead, as discussed in the Introduction section, being concerned about in-groups in need (i.e., the in-group bias for negative emotions) and out-groups in a triumphant mood (i.e., the out-group bias for happiness) could be two facets of the “reciprocal altruism” (Trivers, 1971Mathur et al., 2010).

Nevertheless, two exceptions of ethnic group bias in state empathy were identified with the current Chinese participants (both for NimStim stimuli). First, the Chinese participants showed an ethnic out-group bias for the NimStim sadness. Sadness may be perceived as a symbol of powerlessness and low self-esteem (Merten, 2005); an exposure of one’s weakness in front of others without a good reason could be interpreted by Chinese people as “losing face” (i.e., a Chinese word, describing the feeling of embarrassment and shame for oneself as a consequence of unsuitable conduct; Ho, 1976Zhang et al., 2011). Trommsdorff et al. (2007) coined the term “non-acting” to explain the same situation. They stated that in cultures that discourage emotional externalization, individuals might purposely inhibit their reactions to an emotional person so as to “save that person’s face” (Trommsdorff et al., 2007). Therefore, the current Chinese participants might refrain from empathy toward the Asian characters expressing sadness without a good reason (i.e., NimStim stimuli), leading to the out-group bias. However, as long as an emotional background was given for the sadness of the documentary stimuli (e.g., an earthquake or bushfire ruin), the ethnic group bias of the current Chinese participants turned into an ethnic in-group bias.

Second, there was a four-way interaction (i.e., participant culture × participant sex × stimulus ethnicity × stimulus sex) on the overall empathy for NimStim stimuli. Further examination of the four-way interaction showed an ethnic out-group bias with the Chinese female participants on NimStim male stimuli. The reasons for the ethnic out-group bias could be still due to the non-acting strategy (Trommsdorff et al., 2007). Relatively, Western cultures provide more freedom for individuals to express their emotions, while Asian cultures value emotion regulation more (Davis et al., 2012Wei et al., 2013). Moreover, with Chinese and American participants, Davis et al. (2012) found that Chinese male participants expressed the highest emotion regulation, which was in line with their concern that the social pressure on moderating emotions was stronger for Chinese males than the other culture–sex participant groups (i.e., a culture–sex interaction effect in emotion regulation). Hence, the current Chinese female participants might adopt the non-acting strategy to specifically “save the face” of the NimStim Asian male over the NimStim Caucasian male (Trommsdorff et al., 2007). This turned out to be the Chinese female participants’ ethnic out-group bias in empathy. Nonetheless, when the emotional background was illustrated with the emotion (i.e., the documentary stimuli), the ethnic out-group bias for male stimuli was absent from the Chinese female participants. The above results stress that the ethnic group bias may vary among the culture–sex participant groups, which can be moderated by the availability of the background information; however, these possibilities were overlooked by previous researchers (Tables 23).

Sex Group Favor

Sex group favor in empathy was not examined in previous studies summarized in Tables 13. The current results revealed that the main sex group favor was biased to female (i.e., female > male stimuli, see Table 5). This main favor is consistent with a common social consensus, namely, females are more vulnerable and should be treated with extra consideration (i.e., the “ladies first” ideology) (Tuleja, 2012). Nevertheless, some minor variations on the sex favor effect could still be identified among the four culture–sex participant groups. First, in light of the NimStim stimuli, the sex group favor (i.e., female > male stimuli) was only significant with Australian male participants (i.e., the overall empathy for both Caucasian and Asian stimuli, as well as cognitive empathy for Asian stimuli), but not with the other three culture–sex participant groups. Second, in light of the documentary stimuli, the main sex group favor (i.e., female > male stimuli) was identifiable with all culture–sex participant groups. However, this ‘ladies first’ favor in empathy for the documentary stimuli tended to be stronger for the Australian than Chinese participants; this result also supported the notion that sex differentiation is more pronounced in Western than in Asian cultures (Zhao et al., 2019).

Third, the opposite sex group favor (i.e., the “alpha male” ideology) was also presented in the current results, particularly with the Australian participants. On the one hand, Australian male participants expressed more overall empathy for male happiness of the documentary stimuli (i.e., a male runner in the marathon) than the female ones (i.e., a bride in the wedding ceremony). Intriguingly, toward the same stimuli, the Australian female participants’ sex favor on the overall empathy was biased to female (i.e., the bride’s happiness > the male runner’s happiness). In contrast, Chinese female and Chinese male participants showed non-significant sex favor on the overall empathy for happiness (i.e., the bride’s happiness = the male runner’s happiness). Besides further stressing that sex differentiation can be more polarized in Western than in Asian cultures, the above results are in line with the stereotype of Australian males (i.e., the ‘Sporting Manhood in Australia’; Adair et al., 19971998).

On the other hand, Australian female participants’ sex group favors on overall empathy for the documentary stimuli of anger and sadness were biased to male (i.e., male > female stimuli; see Supplementary Document 1 for the stimuli’s background information). Teague (2014) evaluated empathic accuracies with three ethnic groups of Americans (viz., Caucasian, African, and Chinese). Teague found that relative to the male participants, the female participants of all three ethnic groups tended to be more sensitive to negative emotions (e.g., anger and sadness) expressed by Caucasian characters (i.e., the main ethnicity of the country) (Teague, 2014, see pp. 107–108). Moreover, relative to African male participants, the African female participants were hypersensitive to in-group anger and sadness (i.e., expressed by African characters). In contrast, Chinese female and Chinese male participants’ reactions toward in-group anger and sadness (i.e., expressed by Chinese characters) were relatively similar. Results of Teague (2014) and the current study imply that sex group difference and sex group favor in empathy for negative emotions may be relevant to social vulnerability, and the female vulnerability may be more obvious in Western than Asian societies. Nevertheless, since Teague (2014) did not split the stimuli according to stimulus sex, whether females in Western societies were specifically sensitive to male negative emotions was not definitive. Nevertheless, the current results indicate that Western females may be more empathic toward male anger and sadness than female ones. The sex group favor in empathy, especially the sex favor against common consensus (i.e., the alpha male ideology), is worthy of further investigation.

Limitations and Further Studies

The current study has several limitations. First, the sample size was small. Conclusions regarding the interaction effects in state empathy need to be replicated based on a larger sample size. Second, only university students were recruited in this study, and hence, the current findings might not be extended to the general populations of Australia and China. Third, in this study, the ethnic group bias and sex group favor were only explored in terms of state empathy but not trait empathy (i.e., the EQ and IRI items do not examine these phenomena). Further researchers might consider investigating these phenomena in trait empathy using self-report questionnaires. However, it should be noted that participants can interpret questions regarding ethnic group bias and sex group favor as tapping into racism and sexism. Consequently, participants may respond to these questions according to social desirability (i.e., without racism and sexism). Fourth, it should be noted that the empathic accuracies of some emotions (e.g., fear, surprise, and neutral-peacefulness) were low in the current study (see Supplementary Document 1). Result interpretations for these emotions with a low empathic accuracy should be done with care. Fifth, questions of state empathy presented in the current computer-based tasks could still be categorized as subjective (e.g., “I felt _____the feeling of the main character”) although they were comparatively more objective than the self-report items of trait empathy (i.e., the EQ and IRI items). The culture–sex interaction, ethnic group bias, and sex group favor effects ought to be verified by more objective techniques, such as brain imaging or physiological measurements (see Neumann and Westbury, 2011Neumann et al., 2015). Sixth, to date, the culture–sex interaction effect in empathy with adult participants has been identified by Melchers et al. (2015) (i.e., Germans vs. Chinese), Zhao et al. (2019) (i.e., Australians vs. Chinese), as well as the current study (i.e., Australians vs. Chinese). It is noteworthy that the Asian participants of these three studies were all Chinese. Thereby, it is essential to verify in further investigations whether the culture–sex interaction in empathy can be generalized to other Asian cultures; in other words, whether the culture–sex interaction effect is a common phenomenon of the Western–Asian contrast or is a specific term to the Western–Chinese contrast14.

In addition, some limitations of the current computer-based tasks of state empathy should be elaborated. Firstly, the current participants’ attitudes toward the other ethnicity (e.g., whether they had out-group hate) were not collected. The current authors deemed that out-group hate might not be a serious issue in the current case since both Australian and Chinese participants expressed the ethnic out-group bias in state empathy. Nevertheless, it is highly recommended for further investigators to record participants’ attitudes toward other ethnicities to elaborate on this topic. Secondly, each component of state empathy was evaluated by a single item (e.g., “I felt _____ the feeling of the main character. 1 = not at all to 9 = very strongly”, for emotional state empathy). The single-item design (i.e., also used by all previous investigations, see Tables 23) could be criticized as not sufficiently reliable to capture the relatively stable psychological traits of empathy. A multi-item evaluation of state empathy ought to be considered in future investigations. Thirdly, participants’ state empathy could be confounded by stimulus traits (e.g., age, clothing, and attractiveness of the character), which were not controlled in the current examinations. Fourthly, we did not directly compare the results of state empathy for NimStim stimuli with that for the documentary stimuli (i.e., tasks I and II, respectively) to evaluate the impact of background information on empathy. It should be noted that the stimuli of tasks I and II were different in several important aspects, including the availability of background information, the facial expressivity of the main characters, and more importantly, whether the characters expressed an emotion naturally. To evaluate the impact of background information on empathy, a future investigation with better-manipulated stimuli is necessary (i.e., an identical facial expression with different background information). Fifthly, regarding the stimuli of task II, we chose documentary photos of naturally expressed emotions with matched background information across Western and Asian stimuli (see details in Supplementary Document 1). Alternatively, researchers can do a computer manipulation on the facial expressions of those main characters to get a standard facial expression across Western and Asian stimuli. However, we are concerned that computer-modified facial expressions may change the social meaning and the biological validity of the stimuli. It is because emotional expressivity naturally differs between cultures (Rychlowska et al., 2015). Under the same situation, Westerners’ facial expressions could be more exaggerated than Asians’ (e.g., laughing or smiling at their wedding party). Hence, a standard happy face deemed so by Westerners could seem ecstatic to Asians. Therefore, we recommend documentary photos (i.e., naturally expressed emotions) rather than computer-manipulated ones. Finally, it should be stressed that due to the small sample size, the current investigation may not provide enough statistical power to reveal all subtle interaction effects on state empathy. Further investigation with a larger sample size is highly recommended.