Measuring Individual Differences in Measures of Autism Spectrum Disorders
Recognizing the Role of General Intelligence
Abstract
Abstract. Previous research suggests that theory of mind tasks such as the Reading the Mind in the Eyes Test (RMET) are correlated with general intelligence (g). The present study replicated and extended this research by testing correlations between g, the RMET, and two related measures, the empathy quotient (EQ) and systematizing quotient (SQ). The RMET, EQ, and SQ were all significantly correlated with g (r = .27 with RMET; r = −.15 with EQ; r = .27 with SQ). To determine if the RMET, EQ, and SQ derive their predictive power from g, a hierarchical regression examined whether the RMET, EQ, and SQ predicted feelings toward STEM and humanities after controlling for g. The EQ and SQ continued to significantly predict feelings toward STEM (β = −.20 for EQ; β = .42 for SQ) after controlling for g, and the RMET and EQ continued to significantly predict feelings toward humanities (β = .10 for RMET; β = .20 for EQ) after controlling for g, suggesting that these measures do not entirely derive their predictive power from g.
In 1985, Baron-Cohen et al. asked if children with autism spectrum disorders (ASD) have a theory of mind (ToM). In children, ToM is the ability to recognize that other people have unique mental states which may or may not match one’s own (Baillargeon et al., 2010). In adults, ToM is expanded to include the ability to identify others’ mental states and to use this knowledge to make predictions about others’ actions (Premack & Woodruff, 1978). Whether children with ASD have a ToM started a line of research in which Baron-Cohen and colleagues developed several ToM measures which are commonly used for children with and without ASD. However, Baron-Cohen and colleagues recognized that many of the ToM measures created for children were not sensitive enough to detect individual differences in ToM abilities in adults with and without ASD. For this reason, Baron-Cohen et al. (1997) created, and Baron-Cohen et al. (2001) revised, the Reading the Mind in the Eyes Test (RMET).
The RMET is currently one of the most popular ToM measures for adults with and without ASD. However, research suggests that the RMET may at least partially derive its predictive validity from general intelligence (g). g is the variance shared across a variety of cognitive tests (Jensen & Weng, 1994). Although Baron-Cohen et al. (2001) suggest that the RMET is not (or is only weakly) related to g, a recent meta-analysis by Baker et al. (2014) suggests that the RMET correlates moderately with g (r = .24; see also Coyle et al., 2018). The moderate relationship between the RMET and g is concerning because g is largely responsible for much of the predictive validity of cognitive tests. By removing the variance attributable to g, one can limit the predictive power of cognitive tests (Jensen, 1984, 1998). If removing the variance attributable to g from the RMET greatly reduces the predictive power of the RMET, that would suggest that g and not ToM is responsible for the RMET’s predictive ability. This would call into question knowledge about ToM gained using the RMET. The present study attempts to replicate Baker et al. (2014) by examining the relationship between the RMET and g. Based on Baker et al. (2014; see also Coyle et al., 2018), the RMET was expected to correlate positively with g.
The present study also extends Baker et al. (2014) by examining the relationship between g and two related measures created by Baron-Cohen and colleagues, the Empathy Quotient (EQ) and the Systemizing Quotient (SQ) (Baron-Cohen et al., 2003; Baron-Cohen & Wheelwright, 2004; Wakabayashi et al., 2006). Like ToM, empathizing and systemizing were originally intended to explain behaviors associated with ASD (Baron-Cohen, 2009, 2010; Baron-Cohen et al., 2005; Baron-Cohen et al., 2003; Goldenfeld et al., 2007; Wheelwright et al., 2006), but have been expanded to explain behaviors of adults without ASD. Empathizing requires correctly identifying, responding to, and making predictions based on others’ mental states. This involves a cognitive component where one is aware of and predicts another’s mental state, which is theoretically similar to ToM (Baron-Cohen, 2010, p. 169). Based on the similarity between this cognitive component of empathizing and ToM, the EQ was expected to correlate positively with the RMET. Based on the EQ’s relationship with the RMET (a cognitive test), and the fact that cognitive tests are related to g (to varying degrees), the EQ was also expected to correlate positively with g.
On the other hand, systemizing requires correctly analyzing a system and making predictions based on the set rules of that system (Baron-Cohen, 2009; Goldenfeld et al., 2007). While systemizing is sometimes described as very different from or even opposite to empathizing (Baron-Cohen et al., 2005), analyzing complex systems seems intuitively related to g which increases in predictive validity as complexity increases (Gottfredson, 1997). For this reason, and because many cognitive tests derive their predictive validity from g, the SQ was also expected to correlate positively with g.
In addition to replicating and extending Baker et al. (2014), the present study also seeks to answer a fundamental question about the predictive validity of the RMET, EQ, and SQ. For many cognitive tests, removing the variance attributable to g limits the test’s predictive validity (Jensen, 1984, 1998). The present study explores whether the RMET, EQ, and SQ predict important, real-world outcomes after removing the variance attributable to g. Previous research suggests that the RMET, EQ, and SQ are all related to choosing either STEM or humanities majors in college. ToM and empathizing are related to choosing humanities majors (Billington et al., 2007; Focquaert et al., 2007; Thomson et al., 2015) and systemizing is related to choosing STEM majors (Billington et al., 2007; Focquaert et al., 2007). For this reason, the present study explores whether the RMET, EQ, and SQ predict feelings toward STEM and humanities majors after controlling for g. First, the present study examined whether the RMET, EQ, and SQ are related to feelings toward STEM and humanities majors without controlling for g. Based on previous research, the RMET and EQ were expected to correlate positively with liking humanities and negatively with liking STEM. On the other hand, the SQ was expected to correlate negatively with liking humanities and positively with liking STEM. Next, the present study explored whether these relationships would be significant after controlling for g.
The present study examines these predictions in an adult sample without ASD to determine whether or not the use of the RMET, EQ, and SQ in such samples is justified. This question is both timely and necessary as the EQ and SQ, like the RMET, are currently being used to explore individual differences in adult samples without ASD.
Methods
Participants
Participants were recruited through an undergraduate participant pool at a public university in Texas. Analyses included 431 participants (142 males and 289 females). Of these, 28.5% identified as White, 47.6% identified as Hispanic, 10.7% identified as Asian, 10.9% identified as Black, and 2.3% identified as Other. All participants provided informed consent.
Measures
General intelligence was based on the International Cognitive Ability Resource (ICAR, Condon & Revelle, 2014; The International Cognitive Ability Resource Team, 2014). The ICAR included 16 items, 4 items from each of the following sections: letter and number series, matrix reasoning, three-dimensional rotation, and verbal reasoning. g was calculated using principal axis factoring with no rotation on the total scores of the 4 ICAR sections and was based on the first factor on which the ICAR sections loaded highest. A g based on ACT test scores was used as a proxy for the g based on the ICAR in supplementary analyses as robustness checks for the primary and exploratory analyses. These are reported in Tables E2–E5 in the Electronic Supplementary Material 1, ESM 1.
Empathizing was measured using the Empathy Quotient Short Form (EQ, Wakabayashi et al., 2006). The EQ measures self-reported ability to make predictions by identifying and responding to others’ mental states. The EQ includes 22 items such as “I can tune into how someone else feels rapidly and intuitively.”
Systemizing was measured using the Systemizing Quotient Short Form (SQ, Wakabayashi et al., 2006). The SQ measures self-reported ability to use set rules to make predictions and analyze a system. The SQ includes 25 items such as “I am fascinated by how machines work.”
Theory of Mind was measured using the Reading the Mind in the Eyes Revised Version (RMET, Baron-Cohen et al., 1997; Baron-Cohen et al., 2001). The RMET presents participants with 36 pairs of eyes and asks them to select the emotion which best describes what the person in the picture is thinking or feeling.
Feelings toward STEM and humanities were measured using four Likert scales asking participants to rank the extent to which they like STEM, dislike STEM, like humanities, and dislike humanities. Difference scores were calculated for STEM and humanities by subtracting participants’ dislike scores from their like scores. Positive values indicate a greater degree of liking.
Results
Primary Analyses
Table 1 reports correlations between g, RMET, EQ, SQ, STEM difference scores, and humanities difference scores. Descriptive statistics are reported in Table E1, ESM 1. Robustness checks partially replicate the results reported in the primary analyses (see Tables E2–E5 in ESM 1).
g correlated positively with the RMET (r = .27, p < .001), negatively with the EQ (r = −.1 5, p = .001), and positively with the SQ (r = .27, p < .001). The RMET correlated positively with humanities difference scores (r = .11, p = .02) and was not related to the EQ (r = −.003, p = .95). The EQ correlated negatively with STEM difference scores (r = −.20, p < .001) and positively with humanities difference scores (r = .19, p < .001). The SQ correlated positively with STEM difference scores (r = .44, p < .001) but not with humanities difference scores (r = .02, p = .68). The EQ did not correlate with the SQ (r = .05, p = .27).
Exploratory Analyses
The present study also examined whether the RMET, EQ, and SQ predicted feelings toward STEM and humanities after controlling for g. To address this issue, we used a hierarchical regression, which entered g in step one and the remaining predictors in step two. Table 2 reports estimates predicting feelings toward humanities while Table 3 reports estimates predicting feelings toward STEM. A significant increase in ΔR2 would suggest that the RMET, EQ, and SQ predicted feelings toward STEM and humanities after controlling for (i.e., removing variance attributable to) g. Estimates in step two represent each variable’s ability to predict the outcome after controlling for g and all other predictors entered in step two.
In step one, g did not significantly predict (β = .05, p = .34) or account for any variance in feelings toward humanities. This was expected as g and humanities difference scores were not significantly correlated. Unsurprisingly, in step two, g continued to fail to significantly predict (β = .05, p = .35) feelings toward humanities. The RMET was a marginally significant predictor (β = .10, p = .06) and the EQ was a significant predictor (β = .20, p < .001) of feelings toward humanities. The SQ was not a significant predictor (β = .00, p = .98) of feelings toward humanities. Adding the RMET, EQ, and SQ to the model significantly increased the variance accounted for from 0% to 5% (p < .001).
In step one, g significantly predicted (β = .25, p < .001) and accounted for 6% of the variance in feelings toward STEM. Again, this is to be expected as g and STEM difference scores were significantly correlated. In step two, g continued to significantly predict (β = .12, p = .01) feelings toward STEM. The RMET was not a significant predictor (β = −.04, p = .30) of feelings toward STEM. The EQ and SQ were significant predictors (β = −.20, p < .001 for EQ; β = .42, p < .001 for SQ) of feelings toward STEM. Adding the RMET, EQ, and SQ to the model significantly increased the variance accounted for from 6% to 25% (p < .001).
Discussion
The present study explored the relationships between the RMET, EQ, SQ, and g and examined whether the RMET, EQ, and SQ predicted meaningful academic outcomes (feelings toward STEM and humanities) after controlling for g. The RMET, EQ, and SQ were expected to correlate positively with g. The RMET and EQ were expected to correlate positively with each other and with liking humanities and to correlate negatively with liking STEM. The SQ was expected to correlate negatively with liking humanities and positively with liking STEM.
Primary Analyses
As expected, the RMET correlated positively with g. Both the direction and size of this effect replicate previous research (Baker et al., 2014). However, the RMET did not correlate with the EQ. This is surprising as ToM is conceptually similar to the cognitive component of empathizing (Baron-Cohen, 2010, p. 169) and does not replicate previously reported effects (Thomson et al., 2015; Voracek & Dressler, 2006). The lack of correlation may be due to the fact that the RMET is a performance-based measure while the EQ is a self-report measure. Previous research suggests that self-report emotional intelligence is not related to either performance-based emotional intelligence or outcomes in social situations (Brackett et al., 2006). The lack of relationship between self-report and performance-based measures may occur because people are not making accurate judgments about their ability to empathize, similar to a Dunning-Kruger effect (Dunning et al., 2003; Kruger & Dunning, 1999; see also Burson et al., 2006; Krueger & Mueller, 2002). It is also possible that the lack of relationship between self-report and performance-based measures occurs because the two types of tests are intentionally measuring distinct concepts (e.g., trait and ability EI; O’Connor et al., 2019).
The EQ and SQ correlated significantly with g as expected. For the SQ, this relationship was in the anticipated direction, suggesting that as perceived systemizing increases, g also increases. However, the EQ unexpectedly correlated negatively with g. This suggests that as perceived empathizing increases, g decreases. As suggested above, this unexpected relationship could potentially be caused by people not making accurate judgments about their ability to empathize. On the other hand, this negative relationship could suggest a compensatory relationship. Perhaps it is necessary for people with lower g to cultivate greater empathizing tendencies. Previous research suggests that both g and social intelligence are necessary for optimal team performance (Baggio et al., 2019; Freeman et al., 2016). For this reason, it might be beneficial for those with lower g to cultivate greater social intelligence (such as empathizing tendencies) to achieve outcomes more effectively through collaboration. However, because the effect size between the EQ and g is relatively small, any conclusions based on this relationship should be tentative until the effect is replicated using different measures of both empathizing and g.
As expected, the RMET correlated positively with feelings toward humanities, replicating previous research (Billington et al., 2007). However, unexpectedly, the RMET did not significantly correlate with feelings toward STEM. This may suggest that ToM is necessary to be successful in humanities majors but not in STEM majors. Consistent with prior research (Billington et al., 2007; Focquaert et al., 2007; Thomson et al., 2015), the EQ was negatively related to feelings toward STEM, whereas the SQ was positively related to feelings toward STEM. However, while the EQ was positively related to feelings toward humanities as expected, the SQ was not related to feelings toward humanities. As with the relationship between the RMET and feelings toward STEM, the lack of a relationship between the SQ and feelings toward humanities may suggest that systemizing is necessary to be successful in STEM majors but not in humanities majors.
It is also possible that this pattern of relationships may suggest that feelings toward STEM and humanities can be explained by related but distinct concepts such as a preference for people or things or vocational interests (Lippa, 1998; Su, Rounds, & Armstrong, 2009). Those with systemizing tendencies are likely to prefer things to people because one can use systemizing to understand things but not people (Baron-Cohen et al., 2005). On the other hand, those with empathizing tendencies are likely to prefer people to things because one can use empathizing to understand people but not things (Baron-Cohen et al., 2005). This pattern of relationships between empathizing and systemizing and feelings toward STEM and humanities might suggest that a related variable, such as a preference for people or things or vocational interests, affects the relationships between the EQ and SQ and feelings toward STEM and humanities. Graziano et al. (2012) suggest that STEM majors relate to a preference for things. However, certain humanities majors may relate to people (e.g., English literature, creative writing) while other humanities majors relate to things (e.g., art, architecture). This diversity would introduce error into estimates involving humanities majors or may cause a suppression effect, potentially explaining the moderate to the large relationship between the SQ and feelings toward STEM and the small to moderate relationship between the EQ and feelings toward humanities. However, Graziano et al. (2012) does not suggest a relationship between a preference for people and non-STEM majors.
It is also possible that this pattern of relationships occurs because of the relative difficulty of STEM majors compared to non-STEM majors. Chen (2015) suggests that the difficulty of the coursework in STEM courses may lead some students to switch to majors with easier coursework. It is possible that because STEM is more difficult, only those students with a high ability or interest in STEM continue in STEM majors. Students with less ability or interest in STEM may be more likely to leave STEM majors. This interpretation is supported by previous research which suggests that a g above a certain threshold is necessary to be successful in upper-level physics and mathematics classes (Hsu & Schombert, 2010). This would explain why both the EQ and SQ significantly predicted feelings toward STEM. However, it is possible that because non-STEM majors are easier, a high level of ability or interest in humanities may not be as necessary for students to continue in humanities majors. This would also explain the relatively small (or absent) relationships between the EQ and SQ and feelings toward humanities.
Exploratory Analyses
Exploratory analyses examined whether the RMET, EQ, and SQ predict feelings toward STEM and humanities majors, after controlling for g. The RMET and EQ significantly predicted feelings toward humanities and the EQ and SQ significantly predicted feelings toward STEM after controlling for g. In addition, both hierarchical regressions reported a significant increase in the variance accounted for after adding the RMET, EQ, and SQ to the model. This suggests that while there were significant correlations between each of these measures and g, none of these measures derived their entire predictive validity from their relationship with g. Because the standardized β estimates were very similar to the correlation estimates, it is likely that g does not meaningfully contribute to the ability of the RMET, EQ, and SQ to predict feelings toward STEM and humanities.
Limitations and Future Research
Because all participants were in college, the present sample likely lacks participants with low g. This may bias the results of the present study. In particular, Spearman’s Law of Diminishing Returns (SLODR) suggests that test scores highly related to g (e.g., ICAR, SAT, ACT) should gradually become less predictive as ability level increases (Deary et al., 1996; see also Coyle, 2015; Coyle et al., 2011). Future research should attempt to replicate these findings with samples that include individuals with low g. The present study did not control for the presence of ASD or non-clinical ASD symptoms, which have been related to the RMET, EQ, and SQ. Future studies should replicate these results while tracking ASD diagnosis and symptoms. In addition, the study included only one measure of ToM and empathizing, the RMET and EQ, one of which is based on self-report. Future research should attempt to replicate these findings with other measures, preferably ones that are both performance-based.
Conclusion
The present study replicated research suggesting a relationship between the RMET and g while also showing relationships between g and two related measures, the EQ and SQ. Results indicated that ToM, empathizing, and systemizing correlated with g. Exploratory analyses suggested that the RMET and EQ predicted feelings toward humanities and that the EQ and SQ predicted feelings toward STEM after g was included in the model. Future research should consider the role of g when using these measures.
Electronic Supplementary Material
The electronic supplementary material is available with the online version of the article at https://doi.org/10.1027/1614-0001/a000351
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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