Abstract
We investigated the association between an aspect of Theory of Mind in childhood, false-belief understanding, and trajectories of internalising (emotional and peer) and externalising (conduct and hyperactivity) problems in childhood and adolescence. The sample was 8408 children from the UK’s Millennium Cohort Study, followed at ages 5, 7, 11, 14, and 17 years. Social cognitive abilities were measured at 5 and 7 years through a vignette version of the Sally–Anne task administered by an unfamiliar assessor in a socially demanding dyadic interaction. Internalising and externalising problems were measured via the Strengths and Difficulties Questionnaire at 7–17 years. Using latent growth modelling, and after controlling for sex, ethnicity, maternal education, verbal ability, and time-varying family income, we found that superior social cognitive abilities predicted a decrease in emotional problems over time. In sex-stratified analyses, they predicted decreasing conduct problem trajectories in females and lower levels of conduct problems at baseline in males.
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Introduction
Effective interpersonal relations rely on social cognitive abilities [31, 68] and are a core feature of adaptive functioning and social competence in childhood and adolescence [31, 51, 55], in turn inversely associated with psychopathology [9, 12, 20, 47, 53]. Early deficits in social competence can become evident in difficulties in both communication skills and social cognition [6, 83] as well as behavioural problems [82]. Deficits in social cognition specifically have been linked not only with later behavioural problems [23, 56], but also autism spectrum disorder and borderline personality disorder [77], attention-deficit / hyperactivity disorder [84], and social phobia [41]. In this study, we explore the role of a component of social cognition, Theory of Mind (ToM), in the trajectories of broad and specific mental health problems from middle childhood to late adolescence in the general population.
Theory of Mind and social cognitive abilities
Theory of Mind (ToM) encompasses a complex set of socio-cognitive abilities [27] enabling us to navigate the social world [1] and communicate more efficiently [18, 24]. It entails ‘reading’ others’ minds [86] by inferring their mental states [57]. Brain regions implicated in ToM include the temporoparietal junction [61, 70], associated with belief attribution [71], the superior temporal sulcus, involved in mental state inference [28] and social perception [2], and the ventromedial prefrontal cortex [43, 76], involved in the regulation of negative emotion [35].
Several measures have been proposed to assess ToM as a single, well-defined construct [7], all in turn used to address two questions: When is ToM first established during typical development [33, 74, 80]? And do difficulties in ToM imply psychopathology [5, 77]? One of the earliest measures of ToM used storytelling with puppets, and established that most neurotypical children aged around 4–5 years can understand false beliefs [87]. A modified version using dolls, known as the Sally–Anne task (SAT), indicated that around 80% of the general population passed the test by age 5 [5]. However, during the last decade, this view of ToM has been challenged on several fronts [4, 32, 63, 72]. Heyes [34], for example, has provided evidence in favour of a ‘submentalising’ model, where ToM is the result of multiple independent social cognitive components working together. Two significant challenges were also identified [63]: different tests meant to measure distinct constructs actually track the same ToM construct (heterogeneity), while a single test meant to be measuring one construct can track multiple social cognitive abilities (lack of specificity). For example, the SAT is an elicited-response task demanding executive functions [29, 73], while it has been also established that the performance of children on this dyadic assessment depends on factors beyond false-belief understanding, as children closely monitor the conduct of their assessor and react to it, thereby employing other social cognitive skills to complete the task successfully [40]. Furthermore, the explicit attribution of false beliefs is closely related to language [19, 62], among other factors affecting individual differences in ToM [37].
Poor social cognition and child psychopathology
The last decade has witnessed a renewed interest in the long-term effects of poor social cognition, with a 2022 systematic review synthesising the evidence from 12 longitudinal studies on the role of social communication in internalising and externalising problems [16]. Much of this evidence points to clear links between early deficits in social cognition and later internalising and externalising problems. For example, Oliver et al. [56] tracked conduct problems from age 4 to 13 years, identifying four trajectories (low problem levels, limited to childhood only, problems beginning in adolescence, early-onset persistent problems), and showed that all problem trajectory types, except the low problem type, were associated with social cognitive deficits. Miers et al. [52] found three groups during adolescence presenting social anxiety (high, varying; moderate, decreasing; low, decreasing), and provided evidence for an association between social skill deficits and interpersonal problems at school, especially in the case of moderate and high problem trajectories. In a large meta-analytic review, Trentacosta and Fine [82] established an association between early social cognition (in the form of emotion knowledge) and internalising and externalising problem trajectories with small to moderate effect sizes. Finally, in a recent study examining the developmental course of social cognitive skills rather than that of internalising and externalising problems, de la Osa et al. [17] tracked the trajectories of social cognitive abilities in a sample of 378 children from preschool to preadolescence (3–12 years) and found that preadolescents in the increasing social deficit trajectory presented with a higher level of interpersonal and behavioural problems at school.
Aims of the study
In this work, we focus on the role of ToM and social cognitive abilities established at ages 5 and 7 years (middle childhood) in mental health trajectories from age 7 to age 17 years (late adolescence). To this end, we use data from the UK’s Millennium Cohort Study (MCS), a large longitudinal birth cohort that follows around 19,000 children born during 2000–2002 to explore the role of children’s performance on the SAT in their course of their mental health (internalising and externalising problems) until late adolescence. At 5 years old, the MCS children were administered a vignette version of the SAT by an unfamiliar interviewer, this being the first task among several cognitive assessments at that age [49]. The protocol had 11 pointing-and-talking interactions and 3 final questions for the child. The same protocol was implemented when the children were 7 years old. The number of children who answered all three questions correctly in both sweeps was much lower than expected, and the survey team attributed this to the change of assessment mode (using vignettes) and the delivery of the protocol (using it to build rapport) [49]. However, we consider these specific characteristics of the SAT as an opportunity to study a group of children who passed the test and thus demonstrated both (1) false-belief understanding and (2) above-average social interaction skills in a demanding social situation. We refer to the particular combination of these outcomes as ‘superior’ social cognition. The guiding research question here is whether ‘superior’ ToM and social cognition in childhood, as defined above in the context of the MCS surveys, are associated with mental health from middle childhood through to late adolescence.
In particular, our hypothesis is that superior ToM and social cognitive abilities established in childhood would predict better mental health over time, as measured in MCS at ages 7, 11, 14 and 17 years via the parent-reported Strengths and Difficulties Questionnaire (SDQ) [30], even after adjustment for confounders. Therefore, we controlled for sex, ethnicity, cognitive ability at baseline (age 5 years), time-varying family income, and parental education. In a further sex-stratified analysis, given the well-documented gender differences in internalising and externalising problems [42, 45, 88] and ToM [14, 22], we explored further whether superior social cognitive abilities predict SDQ trajectories differently based on sex.
Methods
Participants and analytic sample
MCS followed more than 19,000 children born in 2000–2002 [39], starting from around 9 months (sweep 1) to 3, 5, 7, 11, 14, and 17 years (sweeps 2 to 7, correspondingly). As explained by Plewis et al. [60], the sampling frame for MCS was provided by 338 electoral wards, and was designed to over-represent (a) families living in areas of high child poverty across the UK, (b) wards with high proportions of ethnic minorities in England, and (c) the smaller UK countries. Most of the information was collected through interviews with and self-completion questionnaires for the main adult respondent (overwhelmingly the mother) in the child’s home. Ethical approval was obtained from NHS Multi-Centre Ethics Committees, and all parents gave informed consent before interviews took place (the cohort children themselves gave their assent at age 11 years and their consent from age 14 years onwards). At the age 7 sweep, over 13,000 families took part. Our study’s analytic sample included cohort members that were singletons or first-born twins or triplets who (a) had valid data on the SDQ at age 7, and (b) had participated at both age 5 and age 7 sweeps, so that they had the chance to participate in the Sally–Anne task (SAT) assessment on both occasions. Of the 8,408 children (51% female) in the sample, all but 107 of them had complete data on the SAT at ages 5 and 7. Figure 1 shows the sample selection process.
Measures and procedures
Mental health from childhood to adolescence (7–17 years)
The 25-point SDQ [30], with each item rated on a 3-point scale, was completed for the child by the parent (the mother in the vast majority of cases) at ages 7, 11, 14, and 17 years. The items combine into five broad scales: (1) emotional symptoms; (2) peer relationship problems; (3) prosocial behaviour; (4) conduct problems; and (5) hyperactivity/inattention. Each of these can take values from 1 to 11 in the MCS dataset. The first two scales constitute internalising (emotional) problems, and the last two scales correspond to externalising (behavioural) problems. Taken together (that is, without the prosocial scale) the four scales are used to calculate a total ‘difficulties’ SDQ score, which in the MCS dataset ranges from 1 to 41, after rescaling. As an established psychometric instrument [78], the SDQ has been shown to have good concurrent [54] and discriminant [46] validity, and is routinely used as a screening and assessment tool for mental health problems in this age group [48].
Sally–Anne task (SAT)
In this task, the child is introduced to two characters, Sally and Anne. Sally has a box, and Anne has a basket. Sally places a ball in her basket, and then leaves the room. In her absence, Anne takes the ball from the basket and moves it into the box. Children are asked to predict, on Sally's return to the room, (Q1) where Sally will look for the object (or, where she thinks the object is). In addition, children are asked two control questions: (Q2) a reality question (Where is the object, really?) and (Q3) a memory question (Where did Sally put the object at the beginning?) These three questions were asked at both age 5 and 7. In the present study, we require that a child had fully passed the test (Q1 to Q3 were answered correctly) in both interviews. Therefore, our predictor variable is whether a child had passed the SAT questions in both sweeps (therefore, the child’s SAT performance is given here in a dichotomous variable: ‘Passed’ or ‘Failed’).
Covariates
We adjusted for the following potential confounders. The family’s social background was approximated by the MCS sampling ‘Stratum’ (type of electoral ward within a UK country), which indexes the socioeconomic deprivation of each family’s area at the beginning of MCS. There are two strata in each country (England, Wales, Scotland, and Northern Ireland): advantaged and disadvantaged. In England, however, there is an additional, ‘Ethnic minority’, stratum, which includes wards that had an ethnic minority indicator of at least 30% in the 1991 Census, that is, at least 30% of their total population fell into the two categories ‘Black’ (Black Caribbean, Black African and Black Other) or ‘Asian’ (Indian, Pakistani, and Bangladeshi). The ‘Disadvantaged' stratum in England includes wards which were not part of the ethnic minority stratum, and which fell into the upper quartile (poorest 25% of wards) of the ward-based Child Poverty Index (CPI). Finally, the ‘Advantaged' stratum includes wards which were neither a part of the ethnic minority stratum nor in the top quartile of the CPI. Maternal education (‘Mat Edu’) was the educational level of the main respondent attained by the age 5 sweep. This is an interval variable, ranging from 1 (no qualifications) to 6 (corresponding to the UK’s National Vocational Qualifications Level 5). In terms of individual characteristics, ‘Ethnicity’ is a covariate with 6 possible values, derived from the main respondent questionnaire at the age 5 sweep: White, mixed, Indian, Pakistani and Bangladeshi, Black or Black British, other ethnic group (including Chinese or other). ‘Sex’ (male/female) is a binary variable as reported by the main respondent. We have also considered the child’s expressive language ability (‘Verbal ability’) as assessed at age 5 (with values that range from 20–80) with a picture-naming cognitive test (ability and age adjusted based on British Ability Scales II age-normed data). Finally, ‘income’ is a household-level covariate, given in OECD equivalised income quintiles. It is tracked on every sweep in the MCS, and we treat it as a time-dependent variable. As any change in family income arguably takes time to influence mental health outcomes [79], we follow a time-lagged approach and consider the influence of family income from sweep k on SDQ measures at sweep k + 1.
Analytic strategy
Sample bias and missing data
Sample bias analysis was performed using unweighted descriptive statistics to identify the profile of our analytic sample in comparison to the non-analytic sample (‘rest of MCS’) at age 7 years. The volume of missing data was also identified at this stage, and this informed the imputation process.
Difference of means and correlations
The difference of SDQ means between the two groups for SAT (those children who passed the SAT and those who did not) was tested for independence in order to establish a main effect for SDQ at age 7, 11, 14, and 17 yeas. We also calculated the (unweighted) pairwise correlations between SDQ and the continuous numerical covariates, namely, income, maternal education, and verbal ability.
Latent growth model (LGM)
Latent growth curve modelling [25] is a powerful tool in longitudinal research for tracking changes over time [11]. We use it here to understand the role of ToM in SDQ (a) at the starting point (baseline) at age 7 and (b) over time (across ages 7, 11, 14, and 17 years). Taking into consideration the covariates described previously, we formulate a structural equation model as depicted in Fig. 2.
To examine the link between passing the false-belief task in childhood (‘SAT’) and the overall growth of mental health problems (‘total SDQ’) from childhood to adolescence, that is, for \(t\in [\mathrm{1,4}]\) survey sweeps, we fitted 3 LGMs, as explained below. We follow the latest best practices [58] and assess model fit using the robust standardised root mean squared residual (SRMR) for each of our models, considering a good fit only when SRMR < 0.08, based on accepted recommendations [36]. We started from a core, minimally adjusted, model for each cohort member in the sample (represented by \(m\in [1, 8408])\), with only sex and stratum as covariates.
We adjusted this core model by adding ethnicity, level of maternal education, and the cohort member’s standardised verbal ability score (at age 5 sweep):
Finally, we added to the adjusted model (2) the time-varying family income:
Supplementary analysis by sex
In an additional step, we stratified our analysis by sex. We fitted only the fully adjusted LGM (3) without the sex covariate on the four subscales for internalising and externalising problems as well as on the total SDQ scale.
Imputation process and data analysis
Missing data on all the covariates were imputed using multiple imputation by chained equations (MICE) for mixed data, on the assumption that they were missing at random [65]. We generated 100 imputed datasets based on the classification and regression trees (CART) algorithm, also known as decision trees [13], and used Rubin’s combination rules to consolidate the obtained individual estimates into a single set of multiply imputed estimates [67]. All numerical calculations were performed using R (R.Core.Team, 2021) with the ‘mice’ package and the ‘cart’ method [85], while ‘lavaan.survey’ was used as a convenient wrapper for the ‘lavaan’ package for structural equation modelling [66]. For reproducibility, we note that the random seed was set to 357, and imputation was performed on our dataframe ‘df’ via the command: mice(df, m = 100, seed = 357, method = "cart") to obtain the survey design with an imputation list (‘df_survey’) prior to fitting the latent growth model [fit <—growth(model, data = df)] and [output <—lavaan.survey(fit, df_survey)]. Our findings were reproduced and checked for convergence with a different random seed (123) and increasing imputation numbers (25, 50, 75, 100).
Results
Sample bias
Compared to the rest of MCS at age 7 sweep, our analytic sample was slightly over-indexed in girls, children of White ethnic background, and those from less disadvantaged areas. Income was moderately higher (Cohen’s d = 0.40) as were maternal education (d = 0.46) and verbal ability (d = 0.32), as seen in Table 1.
Missing values
The analytic sample of 8408 children was made up of MCS cohort members who were present at both age 5 and age 7 sweeps, and who had complete SDQ data at age 7. However, for subsequent sweeps, the SDQ variables had missing values of around 10.8% at age 17, 2.7% at age 14, and 2.5% at age 11. The ‘SAT’ variable had only 107 (1.3%) of its values missing. Maternal education had 3.7%, verbal ability had 1%, while income variables in different sweeps had between 3.1% and 8.9% missing values. There was zero missingness in sex and stratum, and only 2 (0.02%) values were missing for ethnicity.
Difference of means and correlations
Scores on the outcome variable (total ‘SDQ’) at baseline (age 7 sweep) were lower (M = 6.87, SD = 4.41) for those who had passed the SAT compared to those who had not (M = 8.10, SD = 5.22), where t(728.99) = 6.468, p < 0.001, Cohen’s d = 0.25, 95% CI = [0.18, 0.33]. The same holds true for the remaining survey sweeps in our study, with an effect size of d = 0.22, 95% CI = [0.14, 0.30] at age 11, d = 0.27, 95% CI = [0.19, 0.35] at age 14, and d = 0.24, 95% CI = [0.16, 0.32] at age 17. Figure 3 depicts the differences with additional visual information included in violin box plots.
To complete the bivariate analysis and depict the change over time (from age 7 to 11, 14, and 17 years), we present the results for each subscale for (A) emotional, (B) peer, (C) conduct, and (D) hyperactivity problems against pass–fail values for SAT in Fig. 4. We find that, in our analytic sample, (A) emotional problems increase over time for all from M = 2.47 (SD = 1.72) at age 7 years to 2.98 (2.24) at age 17 years; (B) peer problems increase from 2.12 (1.48) to 2.70 (1.77); (C) conduct problems decrease from 2.26 (1.45) to 2.11 (1.45); and (D) hyperactivity problems decrease from 4.16 (2.44) to 3.37 (2.21). For all the subscales, mean scores for those in the ‘Pass’ group (black bars in Fig. 4) are, in every age group, below those in the ‘Fail’ group (grey bars). In addition, emotional problems increase more for the ‘Fail’ group (red line in subplot A) compared to those in the ‘Pass’ group (green line).
We also calculated the pairwise correlations between the dependent variable (total SDQ) [at both baseline (age 7) and endpoint (age 17)] and the numerical covariates (income at age 5, verbal ability, and maternal education), as in Table 2. Correlation strength was weak to moderate, with the strongest association found for family income and maternal education (\(r=.51,\mathrm{ t}(7824)=52.18, 95\mathrm{\% CI}=[.49, .52], p<.001\)). We note that, in each survey sweep, the total difficulties SDQ scale had acceptable internal consistency: Cronbach’s \(\alpha =.71\) (age 7); \(\alpha =.73\) (age 11); \(\alpha =.73\) (age 14); and \(\alpha =.73\) (age 17 years). The consistency of all the subscales improved over time; however, at baseline (age 7), the subscales for peer (\(\alpha =.51\)) and conduct (\(\alpha =.57\)) problems had poor consistency, whereas those for emotional (\(\alpha =.62\)) and hyperactivity (\(\alpha =.75\)) problems had better consistency.
Latent growth models for total SDQ
In all three LGMs (weighted, imputed), having passed the SAT predicted decreasing trajectories (negative slope). Even in the fully adjusted case (3), ‘Pass’ was a significant predictor of decreasing SDQ over time (\({b}_{1}=\)− \(0.171, se=0.077, z=\)− \(2.216, p=.027\)). The covariances between the LGM intercept and slope are related in all models (for instance, in model 3 we have \(Cov\left(i,s\right)=\) − \(1.562, se=0.187, z=\)− \(8.371, p<.001\)). All our models have a robust SRMR of up to 0.022, or less, which indicates a very good fit for a structural equation model. Tables 3, 4 include the regression coefficient estimates for the LGM slope and intercept, respectively. We note that, even in the fully adjusted model, males start out with higher total SDQ scores at baseline, as expected (\({a}_{2}=1.293, se=0.129, z=10.048, p<.001\)), compared to females, but they decrease over time (\({b}_{2}=-0.432, se=0.053, z=-8.199, p<.001\)).
Latent growth model for internalising and externalising subscales
The fully adjusted LGM (weighted, imputed) was fitted for each of the SDQ subscales of emotional/peer (internalising) and conduct/hyperactivity (externalising) problems. The SAT-pass group had decreasing trajectories (negative slope) for emotional problems (\({b}_{1}=\)− \(0.089, se=0.034, z=\)− \(2.594, p=.009\)). Tables 5, 6 include the regression coefficient estimates for the LGM slope and intercept, respectively. Here too, we found that males start out with higher internalising and externalising problems compared to females, with hyperactivity scores showing the greatest difference (\({a}_{2}=0.866, se=0.059, z=14.591, p<.001\)), decreasing over time (\({b}_{2}=\)− \(0.064, se=0.023, z=\)− \(2.788, p=.005\)).
Supplementary sex-stratified analysis
The fully adjusted LGM (3)—without the sex covariate—was fitted for the SDQ subscales of internalising and externalising problems stratified by sex. In the case of boys (4129 cohort members, with 6% SAT ‘Pass’), we found that passing the SAT predicted fewer conduct problems at baseline (\({a}_{1}=-0.202, se=0.080, z=-2.510, p=.012)\). In the case of girls (4279 cohort members, with 8% SAT ‘Pass’), it predicted a negative slope in the conduct problems’ trajectory (\({b}_{1}=-0.058, se=0.027, z=-2.184, p=.029)\). Results of the LGM (3) for the SDQ subscales, and for the total SDQ as well, are presented in Table 7.
Discussion
The results of the present study support the hypothesis that superior social cognitive abilities as measured in middle childhood (ages 5 and 7) predict fewer mental health problems from middle childhood through to late adolescence in the general youth population. In the context of the MCS surveys, we employed the term ‘superior’ social cognitive abilities to mean that children had established (a) false-belief understanding, as demonstrated through answering the SAT questions correctly, first at age 5 and again at 7 years, and (b) social competence skills that allowed them to navigate a demanding social interaction with an unfamiliar interviewer-assessor. Using latent growth modelling, we found that these social cognitive abilities predicted decreasing trajectories (negative slope) of emotional problems over time. This association persisted even after controlling for sex, ethnicity, parental education, time-dependent family income across sweeps, and verbal ability at baseline.
The hypothesis was drawn from the evidence about the role of deficits in ToM and social cognition in youth psychiatric conditions. Here we wanted to understand whether those who had established superior social cognitive skills in middle childhood may be ‘protected’ from internalising and externalising problems later on. Our results suggest that this was indeed the case, at least with respect to emotional symptoms. Furthermore, for male cohort members, we found that conduct problems for those in the superior social cognitive abilities group were lower at baseline. For female cohort members in this group, the trajectory of conduct problems was found to be decreasing over time.
These findings extend previous work, which has linked deficits in social cognition with particularly conduct problems [56, 75]. They also extend previous finding showing that both internalising and externalising problems are linked with impairments in the broader construct of social competence. For example, testing developmental cascades in a sample of 117 children, Bornstein et al. [10] provided evidence that less socially competent children at age 4 years exhibit more externalising and internalising problems at age 10 years and more externalising problems at age 14 years, even after controlling for intelligence and maternal education. Our study contributes to this evidence by showing links of superior ToM with mental health across development in the general population. It would appear that superior social cognitive abilities permit a more skilful navigation of the social worlds in which children and adolescents find themselves in, thus protecting against emotional and behavioural problems [3, 8, 21].
Our study has several limitations. First, it is correlational, so we cannot determine whether the association between social cognitive abilities and mental health symptom trajectories is causal and not due to residual confounding. Second, our measure for ToM was based on a single false-belief task (SAT). Our analysis did not examine other false-belief tasks, or indeed other ToM measures [7]. Crucially, the SAT was not administered in an enacted storytelling format but relied on a vignette delivered in demanding dyadic interaction between the child and an unfamiliar interviewer-assessor. Third, we controlled for cognitive (verbal) ability in addition to maternal education, income, and demographic variables, but not a measure of executive function as a confounder at age 5 or 7 years, as this was not available in MCS in those survey sweeps. Fourth, the trajectories of mental health symptom scores were tracked through parent-reported SDQ scales; ideally, these would be complemented with teacher and self-reports and assessments by mental health professionals. Nonetheless, the present study also has significant strengths, including the use of data from a large and nationally representative UK birth cohort and the longitudinal recording of both our key measures (SAT and SDQ). The survey also allowed us to consider a variety of potential confounders at both family and child levels. Additionally, we were able to use four survey sweeps, in which the young person’s internalising and externalising problems were consistently tracked across 10 years and for a period that includes two important transitions, to puberty and secondary school.
The significant association between superior social cognitive skills in middle childhood and decreasing emotional symptoms over time suggests the possibility of impactful early interventions [38]. Social cognition and ToM abilities can be supported from an early age, both at home and in educational settings, through the use of mirroring and imitation [50, 59], eye contact [26], joint attention [81], mental state talk [69], and pretend play (Charman, 2000; [44]. Early years and primary school curricula can be expanded to include more of these activities.
Data availability
The data that support the findings of this study are available from the Millennium Cohort Study, UK Data Service of the University of Essex, University of Manchester and Jisc (https://ukdataservice.ac.uk/). The dataset is available from the UK Data Service by application, under licence.
References
Adolphs R (1999) Social cognition and the human brain. Trends Cognitive Sci 3(12):469–479
Allison T, Puce A, McCarthy G (2000) Social perception from visual cues: role of the STS region. Trends Cogn Sci 4(7):267–278
Andrews K, Lariccia L, Talwar V, Bosacki S (2021) Empathetic concern in emerging adolescents: the role of theory of mind and gender roles. J Early Adolescence 41:027243162110022. https://doi.org/10.1177/02724316211002258
Apperly IA (2012) What is “theory of mind”? Concepts, cognitive processes and individual differences. Quart J Exp Psychol 65(5):825–839
Baron-Cohen S, Leslie AM, Frith U (1985) Does the autistic child have a “theory of mind” ? Cognition 21(1):37–46
Beauchamp MH, Anderson V (2010) SOCIAL: an integrative framework for the development of social skills. Psychol Bull 136(1):39–64. https://doi.org/10.1037/a0017768
Beaudoin C, Leblanc É, Gagner C, Beauchamp MH (2020) Systematic review and inventory of theory of mind measures for young children [systematic review]. Frontiers Psychol. https://doi.org/10.3389/fpsyg.2019.02905
Bialecka-Pikul M, Stępień-Nycz M, Szpak M, Grygiel P, Bosacki S, Devine R, Hughes C (2021) Theory of mind and peer attachment in adolescence. J Res Adolescence. https://doi.org/10.1111/jora.12630
Bornstein MH, Hahn C-S, Suwalsky JT (2013) Developmental pathways among adaptive functioning and externalizing and internalizing behavioral problems: Cascades from childhood into adolescence. Appl Dev Sci 17(2):76–87
Bornstein MH, Hahn CS, Haynes OM (2010) Social competence, externalizing, and internalizing behavioral adjustment from early childhood through early adolescence: developmental cascades. Dev Psychopathol 22(4):717–735. https://doi.org/10.1017/s0954579410000416
Burant CJ (2016) Latent growth curve models: tracking changes over time. Int J Aging Hum Dev 82(4):336–350. https://doi.org/10.1177/0091415016641692
Burt KB, Obradović J, Long JD, Masten AS (2008) The interplay of social competence and psychopathology over 20 years: testing transactional and cascade models. Child Dev 79(2):359–374
Buuren SV (2018) Flexible Imputation of Missing Data, 2nd edn. Chapman and Hall/CRC, Florida
Calero CI, Salles A, Semelman M, Sigman M (2013) Age and gender dependent development of theory of mind in 6-to 8-years old children. Front Hum Neurosci 7:281
Charman T, Baron-Cohen S, Swettenham J, Baird G, Cox A, Drew A (2000) Testing joint attention, imitation, and play as infancy precursors to language and theory of mind. Cogn Dev 15(4):481–498
Dall M, Fellinger J, Holzinger D (2022) The link between social communication and mental health from childhood to young adulthood: a systematic review. Front Psychiatry. https://doi.org/10.3389/fpsyt.2022.944815
de la Osa N, Penelo E, Navarro JB, Trepat E, Ezpeleta L (2022) Developmental trajectories of social cognition from preschool to adolescence. Eur Child Adolesc Psychiatry 31(5):819–828. https://doi.org/10.1007/s00787-021-01719-4
de Villiers JG (2021) The role(s) of language in theory of mind. In: Gilead M, Ochsner KN (eds) The neural basis of mentalizing springer international publishing. Cham
de Villiers PA, de Villiers JG (2012) Deception dissociates from false belief reasoning in deaf children: implications for the implicit versus explicit theory of mind distinction. Br J Dev Psychol 30(Pt 1):188–209. https://doi.org/10.1111/j.2044-835X.2011.02072.x
Deighton J, Humphrey N, Belsky J, Boehnke J, Vostanis P, Patalay P (2018) Longitudinal pathways between mental health difficulties and academic performance during middle childhood and early adolescence. Br J Dev Psychol 36(1):110–126
Devine RT, Apperly IA (2022) Willing and able? theory of mind, social motivation, and social competence in middle childhood and early adolescence. Dev Sci 25(1):e13137. https://doi.org/10.1111/desc.13137
Devine RT, Hughes C (2013) Silent films and strange stories: theory of mind, gender, and social experiences in middle childhood. Child Dev 84(3):989–1003. https://doi.org/10.1111/cdev.12017
Dickerson, B. C. (2015). Dysfunction of social cognition and behavior. Continuum (Minneap Minn), 21(3 Behavioral Neurology and Neuropsychiatry) https://doi.org/10.1212/01.CON.0000466659.05156.1d
Dunbar, R. (1998). Theory of mind and the evolution of language. Approaches to the Evolution of Language, 92–110.
Duncan, T. E., Duncan, S. C., Strycker, L. A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications, 2nd ed. Lawrence Erlbaum Associates Publishers.
Farroni T, Csibra G, Simion F, Johnson MH (2002) Eye contact detection in humans from birth. Proc Natl Acad Sci 99(14):9602–9605
Frith CD, Frith U (2007) Social cognition in humans. Curr Biol 17(16):724–732. https://doi.org/10.1016/j.cub.2007.05.068
Gallagher HL, Frith CD (2003) Functional imaging of ‘theory of mind.’ Trends Cogn Sci 7(2):77–83. https://doi.org/10.1016/s1364-6613(02)00025-6
Ghrear S, Baimel A, Haddock T, Birch SAJ (2021) Are the classic false belief tasks cursed? Young children are just as likely as older children to pass a false belief task when they are not required to overcome the curse of knowledge. PLoS ONE 16(2):e0244141
Goodman R (1997) The strengths and difficulties questionnaire: a research note. J Child Psychol Psychiatry 38(5):581–586. https://doi.org/10.1111/j.1469-7610.1997.tb01545.x
Gresham FM, Elliott SN (1987) The relationship between adaptive behavior and social skills: Issues in definition and assessment. J Special Education 21(1):167–181. https://doi.org/10.1177/002246698702100115
Happé F, Cook JL, Bird G (2017) The structure of social cognition: In (ter) dependence of sociocognitive processes. Annu Rev Psychol 68(1):243–267
Happé FG (1995) The role of age and verbal ability in the theory of mind task performance of subjects with autism. Child Dev 66(3):843–855
Heyes C (2014) Submentalizing: I am not really reading your mind. Perspect Psychol Sci 9(2):131–143. https://doi.org/10.1177/1745691613518076
Hiser J, Koenigs M (2018) The multifaceted role of the ventromedial prefrontal cortex in emotion, decision making, social cognition, and psychopathology. Biol Psychiat 83(8):638–647. https://doi.org/10.1016/j.biopsych.2017.10.030
Hu L-T, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model 6(1):1–55. https://doi.org/10.1080/10705519909540118
Hughes C, Jaffee SR, Happé F, Taylor A, Caspi A, Moffitt TE (2005) Origins of individual differences in theory of mind: from nature to nurture? Child Dev 76(2):356–370. https://doi.org/10.1111/j.1467-8624.2005.00850.x
Hughes, C., Lecce, S. (2010). Early social cognition.
Joshi H, Fitzsimons E (2016) The millennium cohort study: the making of a multi-purpose resource for social science and policy. Longitudinal Life Course Stud Inter J 7(4):409–430
Korkiakangas T, Dindar K, Laitila A, Kärnä E (2016) The Sally–Anne test: an interactional analysis of a dyadic assessment. Int J Lang Commun Disord 51(6):685–702. https://doi.org/10.1111/1460-6984.12240
Lavoie M-A, Battaglia M, Achim AM (2014) A meta-analysis and scoping review of social cognition performance in social phobia, posttraumatic stress disorder and other anxiety disorders. J Anxiety Disord 28(2):169–177
Leadbeater BJ, Kuperminc GP, Blatt SJ, Hertzog C (1999) A multivariate model of gender differences in adolescents’ internalizing and externalizing problems. Dev Psychol 35(5):1268–1282. https://doi.org/10.1037//0012-1649.35.5.1268
Leopold A, Krueger F, dal Monte O, Pardini M, Pulaski SJ, Solomon J, Grafman J (2012) Damage to the left ventromedial prefrontal cortex impacts affective theory of mind. Soc Cogn Affect Neurosci 7(8):871–880. https://doi.org/10.1093/scan/nsr071
Lillard A (2001) Pretend play as twin earth: a social-cognitive analysis. Develop Rev 21(4):495–531. https://doi.org/10.1006/drev.2001.0532
Loeber R, Keenan K (1994) Interaction between conduct disorder and its comorbid conditions: effects of age and gender. Clin Psychol Rev 14(6):497–523
Lundh LG, Wångby-Lundh M, Bjärehed J (2008) Self-reported emotional and behavioral problems in Swedish 14 to 15-year-old adolescents: a study with the self-report version of the strengths and difficulties questionnaire. Scand J Psychol 49(6):523–532. https://doi.org/10.1111/j.1467-9450.2008.00668.x
Masten, A. S., Burt, K. B., Coatsworth, J. D. (2015). Competence and psychopathology in development. Developmental psychopathology: Volume three: risk, disorder, and adaptation, 696–738.
Mathai J, Anderson P, Bourne A (2002) The Strengths and difficulties questionnaire (SDQ) as a screening measure prior to admission to a child and adolescent mental health service (CAMHS). Australian J Adva Mental Health 1(3):235–246. https://doi.org/10.5172/jamh.1.3.235
MCS. (2020). Millennium Cohort Study (Surveys 1–5) (9th ed.). Centre for Longitudinal Studies, UCL Institute of Education. https://cls.ucl.ac.uk/wp-content/uploads/2020/09/MCS1-5_User_Guide_ed9_2020-08-07.pdf
Meltzoff AN, Decety J (2003) What imitation tells us about social cognition: a rapprochement between developmental psychology and cognitive neuroscience. Philos Trans R Soc Lond B Biol Sci 358(1431):491–500. https://doi.org/10.1098/rstb.2002.1261
Merrell KW, Popinga MR (1994) The alliance of adaptive behavior and social competence: an examination of relationship between the scales of independent behavior and the social skills rating system. Res Dev Disabil 15(1):39–47. https://doi.org/10.1016/0891-4222(94)90037-x
Miers AC, Blöte AW, de Rooij M, Bokhorst CL, Westenberg PM (2013) Trajectories of social anxiety during adolescence and relations with cognition, social competence, and temperament. J Abnorm Child Psychol 41:97–110. https://doi.org/10.1007/s10802-012-9651-6
Moilanen KL, Shaw DS, Maxwell KL (2010) Developmental cascades: externalizing, internalizing, and academic competence from middle childhood to early adolescence. Dev Psychopathol 22(3):635–653
Muris P, Meesters C, van den Berg F (2003) The Strengths and difficulties questionnaire (SDQ)–further evidence for its reliability and validity in a community sample of Dutch children and adolescents. Eur Child Adolesc Psychiatry 12(1):1–8. https://doi.org/10.1007/s00787-003-0298-2
Obradovic J, Burt KB, Masten AS (2006) Pathways of adaptation from adolescence to young adulthood: antecedents and correlates. Ann N Y Acad Sci 1094:340–344. https://doi.org/10.1196/annals.1376.046
Oliver BR, Barker ED, Mandy WP, Skuse DH, Maughan B (2011) Social cognition and conduct problems: A developmental approach. J Am Acad Child Adolesc Psychiatry 50(4):385–394. https://doi.org/10.1016/j.jaac.2011.01.006
Ondobaka S, Kilner J, Friston K (2017) The role of interoceptive inference in theory of mind. Brain Cognition. https://doi.org/10.1016/j.bandc.2015.08.002
Pavlov G, Maydeu-Olivares A, Shi D (2021) Using the standardized root mean squared residual (SRMR) to assess exact fit in structural equation models. Educ Psychol Meas 81(1):110–130. https://doi.org/10.1177/0013164420926231
Piaget J (2013) Play, dreams and imitation in childhood. Routledge
Plewis, I., Calderwood, L., Hawkes, D., Hughes, G., & Joshi, H. (2004). MCS: Technical report on sampling (UK: Centre for Longitudinal Studies, Issue.
Poulin-Dubois D (2020) Theory of mind development: state of the science and future directions. Prog Brain Res 254:141–166. https://doi.org/10.1016/bs.pbr.2020.05.021
Pyers JE, Senghas A (2009) Language promotes false-belief understanding: evidence from learners of a new sign language. Psychol Sci 20(7):805–812. https://doi.org/10.1111/j.1467-9280.2009.02377.x
Quesque F, Rossetti Y (2020) What do theory-of-mind tasks actually measure? theory and practice. Perspect Psychol Sci 15(2):384–396. https://doi.org/10.1177/1745691619896607
R.Core.Team. (2021). R: a language and environment for statistical computing. In R Foundation for Statistical Computing. URL https://www.R-project.org/. Vienna, Austria.
Raghunathan TE, Lepkowski JM, Hoewyk V, J., S., P. (2001) A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol 27(1):85–96
Rosseel Y (2012) lavaan: An R package for structural equation modeling. J Stat Softw. https://doi.org/10.18637/jss.v048.i02
Rubin DB (1987) Multiple imputation for nonresponse in surveys. John Wiley
Rubin, K., Rose-Krasnor, L. (1992). Interpersonal Problem-Solving and Social Competence in Children. https://doi.org/10.1007/978-1-4899-0694-6_12
Ruffman T, Slade L, Crowe E (2002) The relation between children’s and mothers’ mental state language and theory-of-mind understanding. Child Dev 73(3):734–751. https://doi.org/10.1111/1467-8624.00435
Saxe R (2006) Uniquely human social cognition. Curr Opin Neurobiol 16(2):235–239. https://doi.org/10.1016/j.conb.2006.03.001
Saxe, R., & Kanwisher, N. (2013). People thinking about thinking people: the role of the temporo-parietal junction in “theory of mind”. In Social Neuroscience Psychology Press.
Schaafsma SM, Pfaff DW, Spunt RP, Adolphs R (2015) Deconstructing and reconstructing theory of mind. Trends Cogn Sci 19(2):65–72
Scott RM (2017) The developmental origins of false-belief understanding. Curr Dir Psychol Sci 26(1):68–74. https://doi.org/10.1177/0963721416673174
Scott RM, Baillargeon R (2017) Early false-belief understanding. Trends Cognitive Sci 21(4):237–249
Scourfield J, Martin N, Eley TC, McGuffin P (2004) The genetic relationship between social cognition and conduct problems. Behav Genet 34(4):377–383. https://doi.org/10.1023/B:BEGE.0000023643.49413.df
Shamay-Tsoory SG, Tibi-Elhanany Y, Aharon-Peretz J (2006) The ventromedial prefrontal cortex is involved in understanding affective but not cognitive theory of mind stories. Soc Neurosci 1(3–4):149–166. https://doi.org/10.1080/17470910600985589
Sharp, C., Fonagy, P., & Goodyer, I. (2008). Social cognition and developmental psychopathology. OUP Oxford. https://books.google.co.uk/books?id=Ug3bAAAAMAAJ
Stone LL, Janssens JMAM, Vermulst AA, Van Der Maten M, Engels RCME, Otten R (2015) The strengths and difficulties questionnaire: psychometric properties of the parent and teacher version in children aged 4–7. BMC psychology 3(1):4–4. https://doi.org/10.1186/s40359-015-0061-8
Su Y, D’Arcy C, Caron J, Meng X (2021) Increased income over time predicts better self-perceived mental health only at a population level but not for individual changes: an analysis of a longitudinal cohort using cross-lagged models. J Affective Disorders. https://doi.org/10.1016/j.jad.2021.05.118
Surian L, Caldi S, Sperber D (2007) Attribution of beliefs by 13-month-old infants. Psychol Sci 18(7):580–586
Tomasello M (1995) Joint attention as social cognition. In: Dunham CMPJ (ed) Joint attention: Its origins and role in development. Lawrence Erlbaum Associates Inc, pp 103–130
Trentacosta CJ, Fine SE (2010) Emotion knowledge, social competence, and behavior problems in childhood and adolescence: a meta-analytic review. Soc Dev 19(1):1–29
Tuerk C, Anderson V, Bernier A, Beauchamp MH (2021) Social competence in early childhood: an empirical validation of the social model. J Neuropsychol 15(3):477–499. https://doi.org/10.1111/jnp.12230
Uekermann J, Kraemer M, Abdel-Hamid M, Schimmelmann BG, Hebebrand J, Daum I, Wiltfang J, Kis B (2010) Social cognition in attention-deficit hyperactivity disorder (ADHD). Neurosci Biobehav Rev 34(5):734–743
van Buuren S, Groothuis-Oudshoorn K (2011) Mice: multivariate imputation by chained equations in R. J Statistical Software 45(3):1–67. https://doi.org/10.18637/jss.v045.i03
Whiten A (2013) Culture and the evolution of interconnected minds. In: Baron-Cohen S, Tager-Flusberg H, Lombardo M (eds) Understanding other minds. Oxford University Press, pp 431–447
Wimmer H, Perner J (1983) Beliefs about beliefs: representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition 13(1):103–128. https://doi.org/10.1016/0010-0277(83)90004-5
Zoccolillo M (1993) Gender and the development of conduct disorder. Dev Psychopathol 5(1–2):65–78. https://doi.org/10.1017/S0954579400004260
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Tsomokos, D.I., Flouri, E. The role of social cognition in mental health trajectories from childhood to adolescence. Eur Child Adolesc Psychiatry 33, 771–786 (2024). https://doi.org/10.1007/s00787-023-02187-8
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DOI: https://doi.org/10.1007/s00787-023-02187-8