The effect of autistic traits on prosocial behavior: The chain mediating role of received social support and perceived social support

Abstract

There is growing evidence that the defining characteristics of autism spectrum disorder are distributed across the general population; therefore, understanding the correlates of prosocial behavior in individuals with high levels of autistic traits could shed light on autism spectrum disorder and prosocial behavior. In this study, the mechanism underlying the influence of individuals’ autistic traits on their prosocial behavior was explored by conducting a questionnaire survey of 414 Chinese college students. The results showed that autistic traits can influence individuals’ prosocial behavior not only through the separate effects of received social support and perceived social support but also through the chain mediating effects of received social support and perceived social support; however, the direct effect of autistic traits on individuals’ prosocial behavior is not significant. This study is conducive to understanding the internal mechanism underlying the relationship between autistic traits and prosocial behavior. Future work is required to further investigate the clinical autism spectrum disorder samples and cross-cultural applicability of the model found in this study.

Lay abstract

Autistic traits are known to be associated with a set of core symptoms of autism spectrum disorder. The impact of autistic traits on prosocial behavior, including a consideration of the role of social support, has never been explored. We investigated whether and how social support mediates the autistic trait–prosocial behavior relationship. We found that autistic traits can influence prosocial behavior not only through received social support and perceived social support but also indirectly through the chain mediating effects of received social support and perceived social support. This study contributes to the understanding of how and to what extent prosocial behavior is influenced by autistic traits. Future work is required to further investigate the clinical autism spectrum disorder samples and cross-cultural applicability of the model found in this study.

Keywords

autism spectrum disorder autistic traits chain mediation perceived social support prosocial behavior received social support

Introduction

Prosocial behavior was a term first coined by Wispé (1972), who added a Latin prefix “pro” meaning “for” to denote a type of helping act. Specifically, prosocial behavior is an action or attitude voluntarily exhibited by individuals in the interest of others and organizations without expectation of external compensation (Ackfeldt & Wong, 2006; Lacetera & Macis, 2010). Prosocial behavior represents a broad category of behaviors such as helping, sharing, cooperating, and comforting (Goh et al., 2021; Memmott-Elison et al., 2020). Prosocial behavior is often believed to be the basis of human relationships (Hay, 1994) in that it attracts positive attention from others and serves as a prerequisite for shaping desirable interpersonal relationships, making it one of the most important factors of an individual’s social competence (Hwang & Mi-sun, 2018). As a key aspect of an individual’s social competence, prosocial behavior can foster positive social adaptation, which is an essential indicator of individual socialization development, while for society, prosocial behavior is helpful for sustaining good relationships that are conducive to justice, harmony, and the development of society as a whole (Penner et al., 2005; Wittek & Bekkers, 2015). Given that encouraging people to be involved in more prosocial behavior can foster and develop positive attitudes and build a stable and harmonious society, the factors affecting prosocial behavior and the ways to promote individual prosocial behavior are also notable topics of research in psychology.

In recent years, numerous studies have examined prosocial behavior influenced by internal personal factors such as gratitude (Bartlett & DeSteno, 2006), social cognition (Bellucci et al., 2020), and an individual’s autistic traits (Jameel et al., 2014, 2015; Zhao et al., 2019) or external social factors such as social support (Ciarrochi et al., 2001; van den Bos et al., 2018), social exclusion (Grueneisen, 2022), and interpersonal synchrony (Lumsden et al., 2014; Mogan, 2017; Twenge et al., 2007). However, few studies have simultaneously examined the effects of internal and external factors on prosocial behavior; for example, little is known about the effects of intrinsic autistic traits on prosocial behavior when the role of extrinsic social support is considered, yet such an investigation is necessary because it may shed light on the extent to which autistic traits influence prosocial behavior and whether autistic traits can also have an impact on prosocial behavior by influencing social support. The primary purpose of this study was, therefore, to investigate whether and how social support mediates the relationship between autistic traits and prosocial behavior.

Autistic traits, or the broad autism phenotype (BAP), were initially studied in the context of family members and relatives of individuals with autism spectrum disorder (ASD) (Baron-Cohen & Hammer, 1997; Bolton et al., 1994; Piven et al., 1997). ASD is a neurodevelopmental disorder characterized by difficulties with social interaction and communication as well as restricted, repetitive patterns of behaviors, interests, or activities (American Psychiatric Association, 2013). In this spectrum, autistic traits are defined as elevated but nonclinical levels of symptoms associated with ASD, which expands them beyond the threshold of the disorder toward the general population levels (De Groot & Van Strien, 2017). Aware of the extension of autistic traits into the general population (e.g. Baron-Cohen, 2008), researchers embarked on dimensional studies involving participants with an absence of familial ties to autism. To the best of our knowledge, the possible association between autistic traits and prosocial behavior has been explored in as few as four studies (Jameel et al., 2014, 2015; Riccio et al., 2020; Zhao et al., 2019). In Jameel et al.’s (2014) study, participants were divided into low and high autistic trait groups according to the Autism Spectrum Quotient (AQ) scores they received, and then they completed the self-report measure of prosocial behavior developed by researchers. Participants with high levels of autistic traits were less prosocial and reported less desire to help others. Jameel et al. (2015) subsequently developed the Social Expectations Task to further investigate the prosocial behavior of participants with high levels of autistic traits in clear-cut contexts where endorsed social rules indicated an appropriate response (e.g. offering your seat to an elderly woman) and in ambiguous contexts where there was a lack of clear rules telling participants what should be done (e.g. helping a young woman carrying a large parcel). Participants with high levels of autistic traits reported fewer prosocial responses to people who needed help. In Zhao et al.’s (2019) study, participants completed the AQ and then played the empathic dictator game (DG) to measure prosocial helping behavior. The game requires the A character to allocate some money to the B character, and eventually A will receive 3 times the money given. Participants are randomly assigned either the A or B character. The results showed that enhanced autistic traits were associated with reduced self-reported prosocial behavior. In Riccio et al.’s (2020) study, the same task as Jameel et al. (2015) was used to measure prosocial behavior, but the AQ-Short and Social Responsiveness Scale–brief (SRS-brief) were used as measures of autistic traits. The researchers did not find evidence that high levels of autistic traits were associated with reduced self-reported prosocial behavior.

However, some issues remain in these studies. The first three studies (Jameel et al., 2014, 2015; Zhao et al., 2019) all demonstrated a significant relationship between high levels of autistic traits and reduced self-reported prosocial behavior, yet self-report measures of prosocial behavior in these studies lacked evidence of reliability or validity. In contrast, the measure was tested for reliability by Riccio et al. (2020), but the results indicated that the internal consistency reliability of the measure was questionable. The shortcomings of these studies on this point limit the interpretation of their results to some extent. Moreover, all of these previous studies focused only on a specific type of prosocial behavior (e.g. prosocial helping behavior), and their conclusions about the correlation or noncorrelation of autistic traits with specific prosocial behavior cannot be extended to other types of prosocial behavior. To address this issue, more types of prosocial behaviors should be included when investigating the relationship between autistic traits and prosocial behavior. In general, a relatively reliable measure of multiple types of prosocial behavior is needed in this study, such as the Prosocial Tendencies Measure (PTM), which has been proven to have good reliability and validity to assess six types of prosocial behaviors in China or elsewhere (Carlo & Randall, 2002; Rodrigues et al., 2017; Wang et al., 2021).

Social support has been defined as “those social interactions or relationships that provide individuals with actual assistance or that embed individuals within a social system believed to provide love, caring, or sense of attachment to a valued social group or dyad” (Hobfoll, 1988). Such a definition eloquently encompasses the two major aspects of social support with long dominance in research: received social support and perceived social support. Received social support involves the number of supportive behaviors an individual has received (Haber et al., 2007), whereas perceived social support relates to both the satisfaction with and availability of support (Sarason et al., 1990). The correlation between the two has consistently been found to be relatively mild. For example, a meta-analysis of 23 studies found that the average correlation between received and perceived social support was r = 0.35, p < 0.001 (Haber et al., 2007). Research on the direction of the received-perceived social support association was initiated by researchers who examined whether received social support could influence health through perceived social support, yet none of them found evidence that received social support played a mediating role (e.g. Kessler, 1992; Lakey & Cassady, 1990; Wethington & Kessler, 1986). Instead, it has become a consensus among researchers that received social support is upstream of perceived social support. Wethington and Kessler (1986) were the first to suggest that received social support facilitated perceptions of social support and found some evidence that the effects of received social support on psychological stress were mediated by perceived social support. Such a directional relationship between received and perceived social support has been further supported by the social support deterioration deterrence model proposed by Norris and Kaniasty (1996). This model suggested that the scope of disaster exposure exerted an effect on psychological distress partially through a chain mediating effect of received and perceived social support and emphasized that the direct outcome of received social support should be the maintenance of perceived social support, and its indirect outcome should be better psychological health. Likewise, Xia et al. (2012) found that received social support could positively predict perceived social support; participants’ self-supporting personality could influence perceived social support through social support they received. Following the evidence in the literature on the direction of the received-perceived social support association, this study intended to investigate the possibility of the chain mediating role of received-perceived social support between autistic traits and prosocial behavior by including received social support as upstream of perceived social support in the mediation chain.

Previous literature suggests that there may be negative associations between autistic traits and both received social support and perceived social support, although these linkages have seldom been directly investigated. To name but a few, among college students, autistic traits were found to be associated with lower odds of having a confidant (McLeod & Anderson, 2022) measured by a single item, “Is there anyone you can truly open up to about your most private feelings without having to hold them back?” which is a common indicator of social support taken from the National Comorbidity Survey (Kessler, 1994). Another study showed that pregnant women with moderate-to-high levels of autistic traits perceived less social support from family members (Hosozawa et al., 2021). Therefore, it is important to determine whether autistic traits are negatively correlated with both received social support and perceived social support in this study to better understand the potential mechanism underlying the associations between autistic traits, social support, and prosocial behavior. Furthermore, the social support that individuals receive or perceive has positive associations with prosocial behavior (Ciarrochi et al., 2001; Kristofferson et al., 2014; van den Bos et al., 2018). Specifically, support from social networks, including teacher support, parental encouragement, and peer support, is believed to be a protective factor for prosocial behavior in children and adolescents (Lee et al., 2014), and individuals’ perceived levels of social support can positively predict their prosocial behavior (Lenzi et al., 2012). Based on these findings, social support can promote and motivate prosocial behavior; hence, individuals who receive and perceive more social support will tend to engage in more prosocial behavior than those who do not. Nevertheless, although social support is an important resource, not everyone obtains and experiences the same degree of social support from their social network, especially for people who self-report difficulties with social interactions, such as individuals with high levels of autistic traits. As such, reduced prosocial behavior in individuals with high levels of autistic traits can probably be explained by the influence of social support; that is, enhanced autistic traits make it hard for individuals to receive and perceive social support from their social networks well, depriving them of the motivation to produce prosocial behavior. Thus, social support may mediate the effect of autistic traits on prosocial behavior in terms of received social support and perceived social support, which was examined in this study.

In summary, the aim of the current study was to test the possible relational model among autistic traits, social support, and prosocial behavior. The research hypotheses are as follows: Hypothesis 1 (H1): autistic traits are negatively correlated with received social support, perceived social support, and prosocial behavior; Hypothesis 2 (H2): received social support mediates the relationship between autistic traits and prosocial behavior; Hypothesis 3 (H3): perceived social support mediates the impact of autistic traits on prosocial behavior; and Hypothesis 4 (H4): received social support and perceived social support play a chain mediating role in the relationship between autistic traits and prosocial behavior. The hypothesized model appears in Figure 1. Evidence supporting these hypotheses will facilitate a fuller picture of the relationship between the variables of interest and provide a new perspective to enrich research that promotes prosocial behavior in individuals with high levels of autistic traits.

Figure 1.

Hypothetical chain mediating model.

Method

Participants and procedure

The cross-sectional study was conducted online through convenience sampling from 26 May until 15 June 2022. Inclusion criteria were (1) Chinese college students, (2) consenting to the study, (3) understanding of spoken and written Chinese, (4) complete responses and no significant pattern of repetition to the questionnaires, (5) correctly answering the screening questions, and (6) the absence of any current known diagnosis of neurological disorders, psychiatric problems, or head injury.

A study web link was provided to eligible and willing participants via social apps such as WeChat. After providing digital consent, the survey itself could be completed in 12–15 min. When completed, participants were instructed to click on the “Submit” button. Once participants clicked on the “Submit” button, their data were then forwarded to a researcher’s account, which ensured that participants’ responses were anonymous. In addition, the same IP address was restricted to a second submission to avoid repeated submissions from the same participant. Participants were ultimately provided RMB 12 for their active participation in this study.

The present sample included 414 participants in the online survey with a mean age of 21.11 (SD = 2.49; range = 17–30). The majority of participants were undergraduates (66.91%; n = 277) and not the only child in the family (67.63%; n = 280). A slight majority of participants were female (52.42%; n = 217). Full demographic characteristics of the sample can be found in Table 1.

Table 1.

Demographics.

Age, M (SD)	Gender		Educational level		Only child or not
Age, M (SD)	Male	Female	Undergraduate	Postgraduate	Yes	No
21.11 (2.49)	47.58%	52.42%	66.91%	33.09%	32.37%	67.63%

Valid percentages were reported for all categories, and there were no missing values for all categories.

Measures

Autistic traits

The AQ was specifically designed to capture autistic traits in a nonclinical population (Baron-Cohen et al., 2001). Its impressive psychometric properties in terms of test–retest reliability (r = 0.70) and internal consistency (Cronbach’s α = 0.82) have been reported (Baron-Cohen et al., 2001) and have been shown to be satisfactory for China (Lau et al., 2013). The AQ consists of 50 items designed to assess five areas associated with autism and the extended phenotype: imagination (e.g. “If I try to imagine something, I find it very easy to create a picture in my mind”), attention switching (e.g. “I prefer to do things the same way over and over again”), attention to detail (e.g. “I often notice small sounds when others do not”), social skill (e.g. “I prefer to do things with others rather than on my own”), and communication (e.g. “Other people frequently tell me that what I’ve said is impolite, even though I think it is polite”). Each response is scored “1” for “definitely agree” or “slightly agree” or “0” for “definitely disagree” or “slightly disagree.” Higher AQ scores indicate individuals with a higher autistic load. Cronbach’s alpha of the scale in this study was 0.76, indicating that the internal consistency for the scale was acceptable.

Received social support

The Chinese Social Support Rating Scale (SSRS) was used to measure received social support (Xiao & Yang, 1987), which is a widely used self-reported measure of received social support in China with high validity and reliability (Kong et al., 2013; Kong & You, 2013; Xia et al., 2012). It contains 10 items categorized into three dimensions: subjective support, objective support, and the utilization of social support. Subjective support refers to the emotional satisfaction of individuals to be respected, supported, and understood (e.g. “How many close friends you have who can help and support you”). Objective support refers to the amount of moral and material support individuals receive from family members, relatives, and friends (e.g. “What has been a source of comfort and concern for you in the past when you have encountered difficulties or emergencies”). The utilization of support refers to the use of individuals’ social support (e.g. “What do you turn to for help when you are in trouble”). For Items 1 to 4 and 8 to 10, the choice of the first, second, third, and fourth answers counts as 1, 2, 3, and 4 points, respectively. Item 5 is divided into four elements: A, B, C, and D. Each element is scored from 1 to 4 points from “none” to “fully supported.” For Items 6 and 7, if the answer is “no source,” score 0 points, and if the answer is “the following sources,” score as many points as the source chosen. The total score was used to assess the quantity of social support received. Higher SSRS scores represent individuals with a greater extent of social support. Cronbach’s alpha of the scale in this study was 0.77, indicating that the internal consistency for the scale was acceptable.

Perceived social support

The Multidimensional Scale of Perceived Social Support (MSPSS) was used to measure perceived social support (Zimet et al., 1988), as its translated version has been proven to have established reliability and validity in Chinese populations (Huang et al., 2020; Miao et al., 2016). It includes 12 items that assess three sources of perceived social support: family support (e.g. “My family can help me practically and concretely”), friend support (e.g. “My friends can share happiness and sadness with me”), and significant other support (e.g. “I can share happiness and sadness with some people (such as teachers, relatives, classmates)”). Items were rated on a 7-point Likert-type scale ranging from 1 (very strongly disagree) to 7 (very strongly agree). Higher scores reflect individuals with higher levels of perceived social support. Cronbach’s alpha of the scale in this study was 0.92, indicating that the internal consistency for the scale was good.

Prosocial behavior

A Chinese version of the PTM was used to measure multiple types of prosocial behaviors in Chinese college students (Carlo & Randall, 2002; Wei et al., 2017) and has demonstrated good reliability and validity (Wang et al., 2021). It contains 23 items measuring six forms of prosocial behaviors: public, anonymous, dire, emotional, compliant, and altruistic. Public prosocial behavior describes helping in situations where it would be observed (e.g. “I can help others best when people are watching me”). Anonymous prosocial behavior describes helping behavior that is not witnessed by others (e.g. “I prefer to donate money anonymously”). Dire prosocial behavior describes helping in a crisis or emergency situation (e.g. “I tend to help people who are in real crisis or need”). Emotional prosocial behavior describes the tendency to help others in situations that have an emotional component, such as when a person needs comfort (e.g. “I usually help others when they are very upset”). Compliant prosocial behavior describes helping when asked (e.g. “When people ask me to help, I don’t hesitate”). Altruistic prosocial behavior describes helping in situations where no benefit is expected for oneself (e.g. “I feel that if I help someone, they should help me in the future”—reverse-scored). Items were rated on a 5-point Likert-type scale ranging from 1 (completely inconsistent) to 5 (completely consistent). Higher scores indicate individuals with more pronounced prosocial behavior. Cronbach’s alpha of the scale in this study was 0.84, indicating that the internal consistency for the scale was good.

The full list of items for each instrument can be seen in the Supplementary Material.

Community involvement

There was no community involvement in this study.

Data analysis

SPSS 26.0 and R 4.2.2 were used for statistical analysis. Before analyzing the data, it is essential to check for serious common method bias (CMB) given that this study deployed self-report questionnaires (Hariguna, 2021). Following the suggestion of Podsakoff et al. (2003), Harman’s single-factor test was applied to examine the degree of CMB in SPSS 26.0. Based on the principal component factor analysis, the largest variance explained does not account for a majority of the variance (>50%), and in that case, it turns out that the CMB of this study does not pose a severe threat to our results. The analytic approach was then conducted in four steps, as recommended by Chi et al. (2022).

First, using R 4.2.2, general descriptive statistics and Pearson correlation analysis (two-sided test p < 0.05 was considered to be significantly correlated) were performed among the variables. To ensure the accuracy of the results, the variance inflation factor (VIF) method was further used for multicollinearity diagnostics, and if the correlation matrix table shows that the values for all the coefficients are less than the recommended value of 0.90 (Hair et al., 2018) and the VIF values are below the cut-off of 10 (O’brien, 2007), there is no serious issue of any multicollinearity problem in the current data.

Second, simple mediation analysis was carried out by taking one mediator at a time using the mediate () function in the R package mediation (). Bootstrap estimation with 5000 bootstrap samples and 95% confidence intervals (CIs) were used to test the indirect effect. The mediating effect is implied to be significant if the 95% CIs for a specific effect do not contain zero. The p values less than 0.05 were considered to be statistically significant (Preacher & Hayes, 2008).

Third, if significant mediating effects are found, the robustness of the average causal mediation effect (ACME) estimates to the potential violation of the assumption of no unmeasured confounders, that is, the sequential ignorability (SI) assumption, needs to be examined, which is achieved by conducting a sensitivity analysis to find the value for the sensitivity parameter ρ at which ACME = 0 (Imai, Keele, & Tingley, 2010; Imai, Keele, & Yamamoto, 2010; Tingley et al., 2014). The larger this value is, the more robust the ACME estimate will be. In the package mediation, sensitivity analysis of the estimated ACMEs to such confounding is conducted through the function medsens().

Fourth, PROCESS Model 6 was employed to perform chain mediation analysis using the PROCESS () function in the R package bruceR () (Bao, 2022). To determine the statistical significance of the indirect effect, 95% confidence intervals (CIs) were obtained. If the 95% CIs do not contain zero, then the indirect effect is considered significant at the p < 0.05 level (Preacher & Hayes, 2008).

In all analyses, variables, including gender, age, education level, and only child status, were treated as covariates, as participants’ gender and age were found to potentially influence autistic traits (Baron-Cohen et al., 2001; Mason et al., 2021), social support (Geckova et al., 2003; Kerr et al., 2006) and prosocial behavior (Pakaslahti et al., 2002); social support may also be influenced by educational level (Akkermans et al., 2013) and the number of children in the family (Hong & Liu, 2021; Xu et al., 2019).

Results

CMB testing

According to the suggestions of Schwarz et al. (2017), the problem of CMB control was acknowledged and controlled during the design stage of this study. For this, both procedural remedies and statistical tests of CMB were applied. To ensure procedural remedies, we arranged the scales separately, with some items scored in reverse, and emphasized the principle of anonymity to all participants. In the statistical tests for addressing CMB, we used Harman’s single factor test to examine the degree of CMB. The results demonstrated that the largest variance explained was 12.94%, which is far less than the 50% critical standard (Podsakoff et al., 2003). Hence, there is no serious concern about CMB in this study.

Correlation analysis

Pearson’s correlations for the variables are presented in Table 2. The results show that (1) autistic traits are significantly negatively correlated with received social support, perceived social support, and prosocial behavior (r = –0.27, p < 0.001; r = –0.25, p < 0.001; r = –0.11, p < 0.05), supporting H1; (2) received social support is significantly positively correlated with perceived social support and prosocial behavior (r = 0.57, p < 0.001; r = 0.28, p < 0.001); and (3) perceived social support is significantly positively correlated with prosocial behavior (r = 0.33, p < 0.001).

Table 2.

Descriptive statistics and correlations between variables.

Variables	M	SD	1.	2.	3.	4.	5.	6.	7.	8.
1. Gender	1.48	0.50	—
2. Age	21.09	2.36	0.15**	—
3. Educational level	1.67	0.47	−0.01	−0.76***	—
4. Only child or not	1.32	0.47	0.14**	0.01*	−0.08	—
5. Autistic traits	21.77	5.10	−0.05	−0.11*	0.08	0.00	—
6. Received social support	37.57	5.97	−0.02	0.03	−0.06	−0.09	−0.27***	—
7. Perceived social support	89.21	14.54	−0.11*	−0.05	−0.00	0.05	−0.25***	0.57***	—
8. Prosocial behavior	78.10	10.40	0.10	−0.03	0.05	0.05	−0.11*	0.28***	0.33***	—

p < 0.05. **p < 0.01. ***p < 0.001.

In addition, an examination of the data for multicollinearity problems was performed. The correlation matrix table, shown in Table 2, suggests that the values for all the coefficients are less than the recommended value of 0.90 (Hair et al., 2018), and the VIF values range from 1.096 to 1.521, which are all below the cut-off of 10 (O’brien, 2007). Hence, there was no serious issue of multicollinearity in the current data.

Simple mediation analysis

Table 3 depicts the results of the mediation analysis for each mediator. The total effect is the sum of the indirect effect and the direct effect. When received social support is the mediator, the indirect effect accounts for 68.40% of the total effect (95% CI = [–0.236, –0.090], SE = 0.038). This indicates that received social support alone has a significant mediation effect. Hence, H2 is supported. Similarly, when we consider only perceived social support as a mediator, 78.35% is explained by the indirect effect (95% CI = [–0.263, –0.113], SE = 0.038), which confirms the significant mediation effect of perceived social support. Hence, H3 is supported.

Table 3.

Results of simple mediation analysis.

	Estimate	SE	[Boot 95% CI]
Simple mediation (received social support)
Total effect	−0.231	0.095	[−0.415, −0.046]
Average indirect effect	−0.158	0.038	[−0.236, −0.090]
Average direct effect	−0.073	0.095	[−0.260, 0.117]
Simple mediation (perceived social support)
Total effect	−0.231	0.095	[−0.415, −0.046]
Average indirect effect	−0.181	0.038	[−0.263, −0.113]
Average direct effect	−0.050	0.092	[−0.226, 0.133]

Boot 95% CI: lower and upper limits of the 95% confidence interval estimated by the bias-corrected percentile bootstrap method.

Figure 2(a) plots the estimated ACME of the received social support mediator against different values of sensitivity parameter ρ. The ACME equals zero when ρ is 0.30, meaning that the negative mediation effect of received social support will be reversed only if ρ is greater than 0.30. This indicates a strong degree of robustness. Similarly, Figure 2(b) plots the estimated ACME of the perceived social support mediator against different values of sensitivity parameter ρ. The ACME equals zero when ρ is 0.30. The negative mediation effect of perceived social support will be reversed only if ρ is greater than 0.30, which again indicates a strong degree of robustness.

Figure 2.

(a) ACME of received social support as a function of the degree of violation of the SI assumption; (b) ACME of perceived social support as a function of degree of violation of SI assumption.

Chain mediation analysis

The PROCESS model 6 was used to examine the mediating effect of received social support and perceived social support on the impact of autistic traits on prosocial behavior with covariates being controlled, and the chain mediating model was established, as shown in Figure 3. The results show that the predictive effect of autistic traits on prosocial behavior is not significant (β = –0.006, p > 0.05) but that autistic traits can significantly negatively predict received social support and perceived social support (β = –0.273, p < 0.001; β = –0.111, p < 0.01); received social support can not only significantly predict perceived social support (β = 0.548, p < 0.001) but can positively predict prosocial behavior (β = 0.137, p < 0.05), and perceived social support could significantly positively predict prosocial behavior (β = 0.266, p < 0.001), which is generally consistent with the results of the correlation analysis.

Figure 3.

Path coefficients of the chain mediating model. Covariates were included in the model but are not presented for simplicity.

Further testing of the mediating effect (see Table 4) shows that the bootstrap 95% CI of the total effect of received social support and perceived social support on the impact of autistic traits on prosocial behavior is [–0.414, –0.048], which does not include zero; this indicates that received social support and perceived social support are the mediating variables in the impact of autistic traits on prosocial behavior, and they have a total indirect effect of –0.218, accounting for 94.37% of the total effect. This mediating effect is mainly composed of the following three paths: (1) autistic traits → received social support → prosocial behavior (95% CI = [–0.153, –0.006], SE = 0.037), the mediating effect is –0.076, accounting for 32.90% of the total effect, and H2 is supported, which is consistent with the simple mediation effect analysis; (2) autistic traits → perceived social support → prosocial behavior (95% CI = [–0.115, –0.018], SE = 0.025), the mediating effect is –0.060, accounting for 25.97% of the total effect, and H3 is supported, which is consistent with the simple mediation effect analysis; and (3) autistic traits → received social support → perceived social support → prosocial behavior (95% CI = [–0.128, –0.041], SE = 0.022), the mediating effect is –0.081, accounting for 35.06% of the total effect, and H4 is supported.

Table 4.

Results of chain mediation analysis.

	Estimate	SE	[Boot 95% CI]
Indirect_All	−0.218	0.043	[−0.307, −0.139]
Ind_X_M1_Y	−0.076	0.037	[−0.153, −0.006]
Ind_X_M2_Y	−0.060	0.025	[−0.115, −0.018]
Ind_X_M1_M2_Y	−0.081	0.022	[−0.128, −0.041]
Direct	−0.013	0.090	[−0.193, 0.164]
Total	−0.231	0.094	[−0.414, −0.048]

Boot 95% CI: lower and upper limits of the 95% confidence interval estimated by the bias-corrected percentile bootstrap method; Ind_X_M1_Y: autistic traits → received social support → prosocial behavior; Ind_X_M2_Y: autistic traits → perceived social support → prosocial behavior; Ind_X_M1_M2_Y: autistic traits → received social support → perceived social support → prosocial behavior.

Discussion

The relationship between autistic traits, social support, and prosocial behavior

Consistent with previous studies, the results show that prosocial behavior is positively correlated with both received social support and perceived social support (Lenzi et al., 2012; Twenge et al., 2007) and that received social support is mildly correlated with perceived social support (Haber et al., 2007). The negative association found between autistic traits and prosocial behavior is not in line with the results of Riccio et al. (2020). As mentioned earlier, Riccio et al. (2020) used the less reliable measure of prosocial behavior, which might explain to some extent their failure to detect a significant relationship between the two, whether they employed the autistic traits measure with higher (SRS-brief, α = 0.86) or lower (AQ-Short, α = 0.57) internal consistency. When compared, Cronbach’s alphas of both the prosocial behavior (α = 0.84) and autistic traits (α = 0.76) measures adopted in the present study were higher, reflecting better internal consistency. Therefore, to yield more robust findings, tests of reliability or validity for measurement instruments should be given importance in future studies of the autistic traits–prosocial behavior relationship.

The findings that autistic traits have a negative link to both received social support and perceived social support are relatively novel. Previous work on the autistic traits–social support relationship was not comprehensive in that researchers typically focused on the support received or perceived by individuals with high levels of autistic traits from their family members or friends (Hosozawa et al., 2021; McLeod & Anderson, 2022), with little research on other significant support from their social networks and different dimensions of social support (e.g. received social support and perceived social support). Our findings complement this gap and expand on it, and they help us gain a more global understanding of the link between autistic traits and social support in terms of both received social support and perceived social support.

The relational model of autistic traits, social support, and prosocial behavior

The primary purpose of our study was to examine the relational model of autistic traits, social support, and prosocial behavior shown in Figure 1. As hypothesized, social support mediated the effect of autistic traits on prosocial behavior. Specifically, autistic traits can not only influence prosocial behavior through received social support and perceived social support but also indirectly influence prosocial behavior through the chain mediating effect of received social support and perceived social support.

First, the results show that the direct effect of autistic traits on prosocial behavior is not significant, which is inconsistent with the results of Zhao et al. (2019). As noted earlier, Zhao et al. (2019) used the dictator behavior task to measure specific prosocial behavior, which appeared to be reciprocal prosocial behavior, as the dictator would ultimately be rewarded with 3 times the amount of money he or she had allocated to the recipient. Thus, their results could be used to explain the relationship between autistic traits and reciprocal prosocial behavior but could not be generalized to other types of prosocial behaviors. Comparatively, the self-report measure of prosocial behavior used in our present study, the PTM, was created by drawing inspiration from ecological theory (Bronfenbrenner, 1979), social cognitive theory (Bandura, 1986), and theories on prosocial development (Eisenberg, 1986; Latané & Darley, 1970; Staub, 1978, 1979), aiming to gain a better understanding of diverse types of prosocial behaviors. While the PTM was not developed to measure all possible types of prosocial behaviors and there are other types of prosocial behaviors (e.g. reciprocal prosocial behavior is not directly measured in this scale), the six types included in the PTM (public, anonymous, dire, emotional, compliant, and altruistic) are conceptually important types of prosocial behaviors that span a number of distinct motivations and situations in an effort to capture more aspects of the multidimensional nature of prosocial behavior. Overall, our study explored the effect of autistic traits on a broader range of prosocial behavior types, and this is also supported to some extent by findings in our further analyses that autistic traits can only negatively predict public, compliant, emotional, and dire prosocial behavior but not other types of prosocial behavior (see Supplementary Result 1 and Table S1), and they may be influenced by psychological factors such as social expectations; specifically, many people appear to engage in prosocial behavior not because of their intrinsic desires but because they are driven by the expectations of others or even themselves (Caviola & Faulmüller, 2014). Perhaps people try to meet social expectations to avoid unfair outcomes such as negative feelings like shame or guilt (Charness & Dufwenberg, 2006). In contrast, individuals with increased autistic traits may be less influenced by social expectations because of poor social functioning and therefore may be less likely to exhibit such prosocial behavior, which is also consistent with that of Jameel et al. (2015). Whether autistic traits are still a predictor in other types of prosocial behavior not addressed in this study will be an interesting topic to explore in future research.

Second, received social support mediates the impact of autistic traits on prosocial behavior. This can be explained by the fact that autistic traits are associated with a set of core symptoms of ASD such as social interaction difficulties (Constantino & Todd, 2003), which act as a barrier to the development of social relationships. Social support from parents, teachers, friends, and significant others through social interactions is a basic social need for individuals (Taylor, 2011; Thoits, 1982) and is an important interpersonal factor that promotes prosocial behavior (You et al., 2022). Poor social interactions affect not only the quantity of social support received but also its quality, thus making it difficult to maintain good interpersonal relationships to promote prosocial behavior well. For individuals with ASD, the quality of support from the family and social environment is highly dependent on environmental variables such as the adequacy of interactions and communication styles and parental characteristics. Empirical evidence suggests that children and adolescents with ASD are generally characterized by poor friendships, often lack social skills, and frequently experience peer rejection (Bauminger & Kasari, 2001; Orsmond et al., 2004), so they typically have fewer friends than their normally developing peers, and their friendships are of lower quality in terms of companionship, safety, and helpfulness (Bauminger & Kasari, 2000). In addition, parents of children with ASD play an important role in providing opportunities for their children to meet other children and to support the ongoing friendship process (Bauminger & Kasari, 2001). Thus, individuals with ASD or high levels of autistic traits report less prosocial behavior, possibly due to the lack of an “autism-friendly” environment and poor social skills that prevent good communication and interactions with others, which may affect the amount and quality of social support they receive and thus have difficulty developing and engaging in more prosocial behavior. However, this possibility warrants further exploration.

Third, this study shows that perceived social support has a mediating role in the relationship between autistic traits and prosocial behavior. Previous studies have revealed that when individuals perceive a favorable interpersonal environment, they will develop a strong sense of belonging, which promotes altruistic behavior (Guzman et al., 2013; Twenge et al., 2007). Individuals with high levels of autistic traits typically exhibit autism-like characteristics of impaired social cognitive processes (Gökçen et al., 2014) and lowered sensitivity to social information (Sevgi et al., 2020), so they may engage in less positive evaluations of their external environment and may therefore have increased difficulty perceiving social support from others. Thus, individuals who self-report enhanced autistic traits may suffer from poorer perception of the availability of social support from their social networks, in turn impeding the production of prosocial behavior, which, of course, needs to be examined in future studies.

Finally, received social support and perceived social support are found to act as chain mediators linking autistic traits and prosocial behavior, and our further replicated analysis found that such a chain mediation model also holds in the larger sample size subgroups of undergraduates (see Supplementary Result 2, Figure S1, and Tables S2–S4) and non-only children (see Supplementary Result 3, Figure S2, and Tables S5–S7). Autistic traits are negatively associated with individuals’ mental health (Lundstrom et al., 2015; Rosbrook & Whittingham, 2010) and physical health (Stewart et al., 2021). Social support, as an important personal resource, plays an important positive role in maintaining and promoting individuals’ physical and mental health (Cohen & Wills, 1985; Uchino, 2009). According to social support resource theory, social support is believed to be an external and protective resource that can provide individuals with sustained mental energy to maintain their physical and mental health and ultimately influence their behavioral responses (Hobfoll et al., 1990). From this theoretical perspective, when individuals have high levels of autistic traits, their physical and mental health may be negatively affected by their inability to receive and perceive enough social support from others, which in turn discourages them from engaging in more prosocial behavior. On another note, our results show that the effect of the mediation chain explains 35.06% of the total effect, which is stronger than the received social support direct effect on prosocial behavior (accounting for 32.90% of the total effect). This may be interpreted in the context of the received-perceived social support relationship. As mentioned earlier, an individual’s perception of social support is affected by the previous social support received by him or her (Haber et al., 2007; Norris & Kaniasty, 1996; Xia et al., 2012). In addition, perceived social support is thought to influence the pattern of one’s interpretation of received supportive behaviors; individuals with heightened perceived social support interpret supportive behaviors more positively, whereas for individuals with low perceived social support (e.g. those with high levels of autistic traits), concern and support from family and friends may be interpreted as sarcasm or humiliation (Calsyn et al., 2005). High levels of autistic traits may make it difficult for individuals to attract more social support from their social networks, affecting their sensitivity to social support or their ability to perceive social support. This leads to their inability to maintain good social relationships, which ultimately affects individuals’ participation in prosocial behavior. Furthermore, perceived social support, compared with received social support, requires cognitive and emotional involvement, which is consistent with the internal system of prosocial behavior (Hobfoll, 1988). When individuals perceive the social support they have received, they are embedded in a social system that is believed to provide love, care, or a sense of attachment to a valued social group (Hobfoll, 1988). Sufficient perception of received social support promotes individuals to experience an intense sense of security and values, which facilitates the improvement of their prosocial behavior (Memmott-Elison et al., 2020). Thus, if individuals with high levels of autistic traits receive and perceive sufficient support from the social environment, they may be encouraged and assured to externalize their internal cognitive and emotional states into prosocial behavior toward others.

Limitations and future research

Obviously, this study has some limitations. First, this study was cross-sectional in design, so correlations could be estimated, but attribution of causality was not possible. Future studies should apply a longitudinal approach or experimental designs to further test the model proposed in this study.

Second, administration of self-report questionnaires through convenience sampling may be driven by factors such as social desirability, self-report bias, contextual effects, and poor memory; therefore, CMB may exist (Lindell & Whitney, 2001). Although we tested the data from this study for CMB and found it to be negligible, future studies could still attempt to collect ratings from others to further validate our results and allow for interesting discussion if discrepancies were found through the use of different sources of ratings. In addition, convenience sampling may result in selection bias based on the following aspects. First of all, given that the data reported in this study are exclusively from the college student population with similar generational and demographic profiles, a different picture may emerge if noncollege students are included. Second, college student volunteers in this study were all recruited online, and they may have a higher probability of relying on media devices and neglecting real-life social activities to some extent, which, in turn, may alter their received and perceived social support through, for example, social isolation. Thus, future research needs to collect more information on contextual aspects related to social support, including the quality of peer networks, parental marital status, family economic status, parents’ education level, and the number of siblings, to provide further validation of our results. Last, the study was conducted in the context of Eastern cultures. Eastern cultures typically support collectivism and emphasize interdependence, harmony, and the connection of self to others (Varnum et al., 2010). In contrast, Western cultures typically endorse higher levels of individualism, emphasizing self-direction, autonomy, and the uniqueness of the self (Cheon et al., 2013). Individuals from Eastern cultures tend to be field-dependent in their attention and cognitive processes, whereas individuals from Western cultures tend to be field-nondependent (McKone et al., 2010). These differences in social orientation and cognitive style between Eastern and Western cultures seem to suggest that people in Eastern cultures are more susceptible to socially pragmatic “rules” because they may make full use of the resources of social networks, which help to build good social relationships. More future cross-cultural research efforts are warranted to examine whether our findings can be replicated across cultural differences. All these drawbacks resulting from convenience sampling may limit the generalizability of our results.

Finally, this study was conducted in a nonclinical sample, and thus, the population sample for this study should be extended by taking into account individuals with ASD to elucidate potential similarities and differences across the whole spectrum of autism conditions. This will be important, as while ASD is generally understood as extremes in the distribution regarding the quantity of autistic traits in the general population (Baron-Cohen et al., 2001; Constantino, 2011; Westwood et al., 2016) and typically developing individuals with high levels of autistic traits can be considered the analog model of ASD, in that research on the characteristics of AQ high scorers in terms of the big five personality dimensions found that they showed a profile of high Neuroticism, low Extraversion, and low Conscientiousness and that their most marked features on the AQ were high Attention switching (difficulty) and high Communication (difficulty) (Wakabayashi et al., 2006), which are almost identical to those actually diagnosed with ASD (Baron-Cohen et al., 2001), such a conceptualization has been questioned (Mottron & Bzdok, 2020).

Clinical implications

Despite the aforementioned limitations, this study has some clinical implications for the improvement and intervention of prosocial behavior in the context of ASD. Rodgers et al. (2019) found that the primary concerns of adults with ASD were related to their support needs, the impact of their ASD diagnosis, and the level of knowledge of others about ASD. Therefore, incorporating service models that directly facilitate more social support such as building a friendly community that fosters acceptance of ASD can be helpful in promoting social adjustment in individuals with ASD or high levels of autistic traits. In such an inclusive community, families of individuals with typical development can increase their insight and understanding of ASD, rather than ridiculing, rejecting, or blaming those with ASD. Individuals with ASD who benefit from a positive community environment will be better integrated into community life, which will lead to improvements in their prosocial behavior and may also alleviate, to some extent, the stress and anxiety of parents coping with raising children with ASD.

In addition, our findings respond to the need for the social work field to strengthen its research and practice capacity related to social interaction needs and ASD (Bishop-Fitzpatrick et al., 2019). Given that social support is a fundamental social need based on social interaction (Taylor, 2011; Thoits, 1982) and that a clear knowledge of social support is a prerequisite for its reception and perception, it is important to explicitly instruct individuals with ASD or high levels of autistic traits to recognize and that acknowledge social support. Davidson et al. (1999) examined the impact of participation in peer support groups and found that long-term participation in peer support groups was associated with having larger social networks, and that higher levels of groups were associated with lower rates of isolation and apprehension and greater support seeking. Similarly, Nelson and Lomotey (2006) found that participation in peer support programs was positively associated with higher ratings of social support, greater involvement in external community activities, and improved quality of life over time. These findings seem to suggest that efforts to increase the level of understanding of social support in individuals with ASD or high levels of autistic traits can be achieved through strategies that include participation in peer support programs, in which they are provided with more opportunities to learn and practice what social support is and will also benefit from the favorable atmosphere of high-quality peer groups.

Conclusion

This study is important for understanding the link between autistic traits and prosocial behavior in the general population and the underlying mediating mechanism of this association. Findings from this study indicate that autistic traits have a significantly negative link to prosocial behavior, received social support, and perceived social support. Furthermore, received social support and perceived social support not only mediate the effect of autistic traits on prosocial behavior but also form a chain mediating effect between autistic traits and prosocial behavior. The findings of this study underline how elevated autistic traits and low levels of social support impede the production of prosocial behavior. Future research is needed to examine the cross-cultural applicability of the model found in this study. Replication of these findings in a sample of individuals with ASD is also needed to determine how autistic traits influence social support and prosocial behavior to improve outcomes for individuals with an ASD diagnosis or high levels of autistic traits.

Supplemental Material

sj-docx-1-aut-10.1177_13623613231177776 – Supplemental material for The effect of autistic traits on prosocial behavior: The chain mediating role of received social support and perceived social support

Supplemental material, sj-docx-1-aut-10.1177_13623613231177776 for The effect of autistic traits on prosocial behavior: The chain mediating role of received social support and perceived social support by Shuhua Zhang, Hong Li, Hai Li and Shuo Zhao in Autism

Footnotes

Acknowledgements

The authors express their deepest gratitude to the participants involved in this study, without who this research would simply not be possible.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the Natural Science Foundation of Guangdong Province, China (Grant No. 2022A1515011167), Guangdong Key Project of China (2018B030335001), and Shenzhen Natural Science Fund (the Stable Support Plan Program 20200804111553001, Shenzhen Science and Technology Program GJHZ20190823115412789).

Ethics approval

All participants provided digital informed consent prior to participation in the study. This study was approved by the ethics committee of Shenzhen University Health Science Center, and all procedures complied with the ethical standards of the 1964 Declaration of Helsinki regarding the treatment of human participants in research.

ORCID iDs

Shuhua Zhang

Shuo Zhao

Data availability statement

The data analyzed in this study are available from the corresponding author upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

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