Skip to main content
NCBI home page
Affective Science logoLink to Affective Science
. 2021 May 3;2(3):289–300. doi: 10.1007/s42761-021-00041-1

Genetic and Environmental Contributions to Positive Affect: Insights from Adolescent Twins

Diane C Gooding 1,2,✉, Mollie N Moore 1, Madeline J Pflum 1, Nicole L Schmidt 1,3, H Hill Goldsmith 1,3
PMCID: PMC8939818  NIHMSID: NIHMS1727757  PMID: 35330700

Abstract

Disturbances in positive affect and reductions in social reward/interpersonal pleasure are common across a range of clinical disorders and are often related. We examined the relationship between the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS-A), and other measures of positive affect in adolescents in a genetically informative research design. The sample consisted of 177 MZ and 136 same-sex DZ twins drawn from a study of adolescent twins (M = 16.4 ± .97 years) who were part of the Wisconsin Twin Project. The self-report questionnaires included the Behavioral Activation Scale (BAS), Psychological Well-Being Scale, revised Early Adolescent Temperament Questionnaire (EATQR), and the adolescent version of the ACIPS (ACIPS-A). Structural equation modeling estimated the relative contribution of genetic and environmental factors to the phenotypic variance in each of the measures. Follow-up bivariate analyses parsed the genetic and environmental contributions to the phenotypic covariances between the ACIPS-A and each of the other measures of positive affect. We found evidence of moderate heritability for the ACIPS-A scale scores. Overall, models specifying additive genetic and unique environmental effects (AE models) were the most parsimonious models for each of the measures. Several of the measures showed moderate positive phenotypic intercorrelations, and all but one of these intercorrelations showed significant partial genetic underpinnings. Moreover, the bivariate biometric analyses indicated that the ACIPS-A also captures unique heritable variation. Thus, the ACIPS-A captures unique heritable contributions to social/interpersonal pleasure, as well as shared genetic variance with other measures of positive affectivity.

Keywords: Social/interpersonal pleasure, Reward responsiveness, Affiliation, Social anhedonia, Well-being

Introduction

Positive affect, the extent to which an individual engages positively with the environment (Watson, 1988), encompasses aspects of positive emotionality such as enthusiasm, reward responsiveness, sociability, and happiness. High levels of positive affect are associated with positive outcomes, e.g., life satisfaction, longevity, and self-esteem (Danner et al., 2001; Coffey & Warren, 2020), whereas dysregulation of positive affect has been linked to low trait positive affect, or anhedonia (Heininga et al., 2017). Drawing from the original descriptions of Meehl (1975) and Rado (1956), social anhedonia can be best conceptualized as a loss of interest; it signifies an absence of pleasure, i.e., something passive, rather than the presence of displeasure. Individual differences in social and interpersonal pleasure range from low to high levels of enjoyment. Understanding associations between various aspects of positive affect and social/interpersonal pleasure enhance appreciation of individual differences in personality and personality development. Additionally, disturbances in positive affect and reductions in social reward are associated, such as in major depression (Healey et al., 2014; Kupferberg et al., 2016) and schizophrenia (Catalano et al., 2018). Given that deficits in social pleasure are common across a range of clinical disorders (Barkus & Badcock, 2019; Gooding, 2019), clarifying the nature of the association between various aspects of positive affect and social/interpersonal pleasure is a research priority.

The Anticipatory and Consummatory Interpersonal Pleasure Scale, or ACIPS (Gooding & Pflum, 2014a) was specifically developed to measure individual differences in self-reported capacity to experience social and interpersonal pleasure. The ACIPS remains the sole measure of anhedonia or pleasure that has been developed and validated on large independent samples of children, adolescents, and adults.

Adolescence is a developmental period characterized by considerable physiological, affective, behavioral, and psychosocial changes (Davey et al., 2008; Casey et al., 2008). It is an especially sensitive period for social development and neuroplasticity (Casey et al., 2008), as well as for the development of approach behaviors and responsiveness to reward (Davey et al., 2008; Crone & Dahl, 2012; Urosevic et al., 2012; Wahlstrom et al., 2010; Walker et al., 2017). Based on functional MRI studies of affective processing, Crone & Dahl, (2012) have suggested that adolescence may be a sensitive period for learning and valuing reward, especially social reward.

During adolescence, peer networks enlarge, and the importance of close friendships becomes more salient. In Western cultures, adolescence is also the period when most individuals experience their first romantic relationships (La Greca & Harrison, 2005). Thus, studying social and interpersonal pleasure during adolescence offers an opportunity to investigate sensitivity to social reward at perhaps its developmental peak. Despite the unique aspects of adolescence, relatively few investigations of normative adolescent pleasure and its relation to social reward exist.

Using the broadly conceived Snaith–Hamilton Assessment of Pleasure Scale (SHAPS; Snaith et al., 1995), Leventhal et al., (2015) observed significant positive correlations between the SHAPS and other measures of positive affect, such as subjective happiness, pleasure sensitivity, engagement in pleasant events, positive mood, and pleasantness ratings of developmentally appropriate pictures. However, only 5 of the 14 items on the SHAPS contain social/interpersonal content. Using the adolescent version of the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS-A; Gooding et al., 2016) in two previous independent investigations, Gooding and collaborators (Gooding et al., 2016; Gooding, Chan, et al., 2017) revealed that social/interpersonal pleasure was significantly associated with measures of general pleasure as measured by the Temporal Experience of Pleasure Scale (TEPS; Gard et al., 2006).

Study Rationale and Hypotheses

We examined the genetic contributions to positive affect, particularly social and interpersonal pleasure, in adolescents. We studied monozygotic (MZ) and dizygotic (DZ) twins to estimate genetic and environmental effects on observed differences. By administering a battery of self-report measures assessing positive affect to MZ and DZ twins, we sought to investigate the extent of genetic covariation between the ACIPS-A and more established measures of positive affect. Because prior research indicates that gender differences in emotion expressiveness exist, whereby adolescent girls are more likely to display positive emotions than adolescent boys (Chaplin & Aldao, 2013), we limited our analyses to same-sex twin pairs.

We expected associations between several measures of positive affect and social/interpersonal pleasure, as measured by the ACIPS-A. We intended to extend the literature by focusing on adolescents, and by including some measures that heretofore had been rarely used in adolescent samples. In addition to replicating the association between these measures of positive affect, we also decomposed the covariance between the measures of positive affect into genetic and environmental components using quantitative genetic modeling.

Method

Participants

All the participants were drawn from the Wisconsin Twin Project, a birth record-based set of longitudinal twin studies examining the etiology of emotions, temperament, and psychology (see Schmidt et al., 2013; Schmidt et al., 2019 for full sample details). We analyzed data from 177 MZ (56.5%) and 136 same-sex DZ (43.5%) twins. Approximately half (50.8%) of the sample was female. Eighty-five percent of the twins were of Caucasian origin, 4.8 % reported being of multiethnic origin, and 3.5% were of African-American origin. Twin zygosity was classified with multiple methods across testing occasions. Initially, zygosity was classified with the Zygosity Questionnaire for Young Twins (Goldsmith, 1991). Observational ratings of zygosity were collected during in-person assessments. Ambiguous zygosity was resolved by genotyping.

Procedure

Briefly, the sample was recruited from an ongoing study of adolescent twins who were undergoing an extensive assessment. Participants were recontacted and mailed questionnaire packets; they received monetary remuneration for their participation. All participants provided their assent and their parents provided written informed consent. All procedures were approved a priori by the Educational and Social and Behavioral Sciences Institutional Review Board of the University of Wisconsin–Madison.

The following self-report measures were collected: the adolescent version of the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS-A; Gooding et al., 2016); the Child Depression Inventory (Kovacs, 1992); Behavioral Inhibition/Behavioral Activation System Scales (Carver & White, 1994); Psychological Well-Being Scale (Ryff, 1989); and revised Early Adolescent Temperament Questionnaire (Ellis & Rothbart, 2001). Each of the measures is described below.

Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS-A)

Our focal measure was the Anticipatory and Consummatory Interpersonal Pleasure Scale–Adolescent Version (ACIPS-A; Gooding et al., 2016), a 17-item self-report scale that assesses individual differences in one’s capacity to experience pleasure in social and interpersonal situations. The adolescent version is a developmentally sensitive modification of the original Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS; Gooding & Pflum, 2014a, 2014b), a valid and reliable measure used in adult samples. A sample item from this scale is “I appreciate being able to hang out with people I know after school or work.” Adolescent respondents are instructed to rate how true the statement is for them on a Likert-type scale from 1 (“really false for me”) to 4 (“really true for me”). In the present sample, internal consistency, as assessed by Cronbach’s alpha, was 0.89. Lower scores on the ACIPS-A indicate greater likelihood of social anhedonia, while higher scores indicate greater likelihood of social/interpersonal pleasure.

Behavioral Inhibition System/Behavioral Activation System Scale (BIS/BAS)

The Behavioral Inhibition System/Behavioral Activation System Scale (BIS/BAS; Carver & White, 1994) was administered to assess reinforcement sensitivity. The BAS-Total scale is comprised of three subscales, namely, BAS-Reward Responsiveness, BAS-Drive, and BAS-Fun Seeking. Although it was originally developed for adults, Cooper et al., (2007) indicated that the measure is suitable for use in adolescents as well. We used the BAS-Reward Responsiveness (BAS-RR) subscale to assess positive affective reactivity to rewarding stimuli. An example of a BAS-RR item is “When I see an opportunity for something I like, I get excited right away.” In our sample, the BAS-RR subscale had an internal consistency of 0.61.

Psychological Well-Being (PWB)

The Psychological Well-Being Scale, developed by Ryff (1989; Ryff & Keyes, 1995), has been administered to adults across the age span, ranging from young adulthood through old age. The Wisconsin Twin Project is one of the few investigations to administer the measure to a younger sample. For the purpose of this investigation, we focused on the Positive Relations with Others (PWB-PRO) subscale because it is the subscale most directly associated with the constructs of positive affect and affiliation. The Positive Relations with Others subscale measures the level of one’s satisfaction with one’s interpersonal relationship, as well as one’s capacity to maintain close interpersonal relationships; an example of an item is “I feel that I get a lot out of my friendships.” Internal consistency for this 14-item subscale was moderately high, α = 0.87.

Early Adolescent Temperament Questionnaire-Revised (EATQ-R)

The revised Early Adolescent Temperament Questionnaire (EATQ-R; Ellis & Rothbart, 2001) is a revision of the EATQ (Capaldi & Rothbart, 1992), which measures reactive and regulatory traits in adolescents. Although we administered the full EATQ, we focused on two subscales, the Affiliation (EATQR-Aff) and Pleasure Sensitivity (EATQR-PS) subscales. The Affiliation subscale assesses the adolescent’s desire for closeness with others; a sample item from this subscale is “It is important to me to have close relationships with other people.” In this sample, the internal consistency for the EATQR-Aff subscale was marginally acceptable, α = 0.68. The Pleasure Sensitivity subscale assesses the level of pleasure related to low intensity activities and low-complexity stimuli; an example of an item is “I like to feel a warm breeze blowing on my face.” In this sample, the internal consistency for the subscale was slightly better, α= 0.75.

Child Depression Inventory (CDI)

We administered a modified version of the Child Depression Inventory (CDI; Kovacs, 1992), a 27-item self-report measure that assesses the cognitive, affective, and behavioral symptoms of depression in children and adolescents. Based on the Beck Depression Inventory (Beck et al., 1961), youths are asked to endorse the statement that best describes themselves for the past 2 weeks. In this investigation, the measure was modified in that the suicide question was removed for the adolescent sample. Higher scores indicate more severe depressive symptoms. We focused on the Anhedonia subscale, in which 2 of the 7 subscale items have explicitly social/interpersonal content. For example, one such item ranges from “I have plenty of friends” to “I do not have any friends.” Coefficient alpha for this subscale was 0.75.

Data Analysis

After inspecting descriptive statistics and correlations among scales, we calculated twin intraclass correlations, followed by univariate and bivariate biometric model fitting, the latter using the Cholesky decomposition (Neale & Maes, 1998, Chapter 10).

Twin intraclass correlations (ICCs) provided an easily understood descriptive measure of twin similarity, which can also be used to estimate heritability using Falconer’s model (Falconer, l989), i.e., the heritability is estimated as twice the difference between MZ and DZ ICCs. Univariate analyses used the structural equation modeling program Mplus (version 7.3; Muthén & Muthén, 2010). These models decompose the phenotypic variance (and covariance across MZ and separately DZ twins) in a single measure, such as the ACIPS, into genetic and environmental components. The models are superior to the Falconer method because they incorporate sample sizes, use variances as well as covariances, estimate standard errors of estimates, allow testing of model fits, and test the effects of dropping parameters (Rijsdijk, 2002). The models estimate the latent additive genetic (A), shared (common) environmental (C), and nonshared (unique) environmental factors contributing to variation for each of the measures of positive affect, based on patterns of observed covariances from the twin pairs (Neale & Maes, 1998). Unique environmental factors include individual experiences, as well as measurement error. The full ACE model includes all three parameters and the significance of each parameter can be tested by comparing the reduced models (e.g., AE) to determine whether removal of a parameter (e.g., C) significantly reduces the chi-square goodness-of-fit statistic. Akaike’s Information Criterion (AIC; Akaike, 1987) is one of the several measures of overall fit provided by Mplus. The univariate models also provide candidate submodels for each measure to use in the bivariate models.

The follow-up bivariate analyses examine the genetic and environmental bases of phenotypic covariances between the measures of positive affect. We used a Cholesky decomposition of the variance-covariance matrices because we needed a specific ordering of variables to examine our focal measure, the ACIPS. That is, we were interested in the extent to which genetic and environmental variance for the ACIPS was shared with each of the other measures, as well as how much residual genetic and environmental variance was unique to the ACIPS. Figure 1 depicts part of the bivariate biometric model.

Fig. 1.

Fig. 1

Open in a new tab

Bivariate Biometric Model. The figure depicts the full bivariate biometric model, for only one of the twins for simplicity’s sake. The paths (a21, c21, e21) indicate the extent to which the genetic and environmental influences on phenotype 1 (in this case, the other measure of positive affect) also influence phenotype 2 (the ACIPS-A). Latent variable A reflects genetic influences, so A1= genetic factors for the other measure of positive affect and A2= residual genetic factors for the ACIPS-A; C1 and C2 are the common (shared) environmental factors for the other measure of positive affect (PA) and the factors for the other measure of PA and the residual ACIPS-A, respectively

As for the univariate approach, model fitting was carried out using Mplus (version 7.3; Muthén & Muthén, 2010). This program uses a maximum likelihood estimation procedure and operates on the raw data rather than simply on the variance-covariance matrix.

Results

Preliminary Analyses

The final sample included 313 adolescent twins whose mean age was 16.38 years (SD = 0.97, range: 13–18 years). Age did not differ significantly between zygosity groups, t(311) = 1.81, p= 0.07.

We found no significant association between age and total score on the ACIPS-A, for either the MZ twins, r = 0.03, or same-sex DZ twins, r = 0.04, n.s., respectively. Neither male nor female twins showed any significant association between age and total ACIPS-A score, r =−0.05 and r = 0.06, n.s., respectively. However, significant gender differences existed for several of the self-report measures. The females reported higher mean levels of social/interpersonal pleasure as measured by the ACIPS-A (mean = 3.50 ± .38), compared to males (mean = 3.29 ± .45), t(300)= 4.40, p < 0.001.

Female adolescents also scored significantly higher than males on the EATQ-R Affiliation subscale [means = 4.16 ± .55 and 3.78 ± .69, respectively, t(292)=5.30, p < 0.001] and Pleasure Sensitivity subscale [means = 3.74 ± .72 and 3.40 ± 3.40 ± .82, respectively, t(311) = 3.93, p < 0.001]. Females also had higher CDI-Anhedonia scores (mean = 0.50 ± .35) than males (0.30 ± .30), t (308) = 5.46, p < 0.001. Females (mean = 3.52 ± .33) did not differ from males (mean = 3.48 ± .40) in terms of scores on the BAS-Reward Responsiveness subscale, t(296) = 0.98, n.s. Similarly, we found no differences between females (mean = 4.35 ± .86) and males (mean = 4.38 ± .77) in terms of the PWB-Positive Relations with Others subscale, t(311) = -0.30, n.s. As a result of these observed differences, gender was controlled for in subsequent genetic analyses.

Associations Between the Measures

Table 1 provides the associations between the ACIPS-A and the other measures, after partialling out gender. Higher levels of social/interpersonal pleasure in adolescents were significantly associated with greater positive response to rewards, as measured by the BAS-Reward Responsiveness subscale (r = 0.40, p < 0.01), and more satisfaction with close interpersonal relationships, as measured by the PWB-Positive Relations with Others subscale (r =0.49, p < 0.01). Higher levels of social/interpersonal pleasure were also significantly associated with higher levels of affiliativeness and more sensitivity to low-intensity pleasure, as measured by the EATQ-R, r’s = 0.52 and 0.23, p< 0.01, respectively. Greater social and interpersonal pleasure was also related to lower levels of anhedonia, as measured by the CDI, r = −0.23, p < 0.01.

Table 1.

Descriptive statistics and associations between ACIPS-A and other measures

Measure Mean (± SD) Pearson correlation with ACIPS-A
Anticipatory and Consummatory Interpersonal Pleasure Scale-Adolescent version 3.39 (.43) –
BAS-Reward Responsiveness 3.50 (.36) 0.399**
PWB-Positive Relations with Others 4.36 (.82) 0.493**
EATQR-Affiliation 3.97 (.65) 0.517**
EATQR-Pleasure Sensitivity 3.57 (.79) 0.232**
CDI-Anhedonia 0.40 (.34) −0.230**
Open in a new tab

Means and (standard deviations) are provided for the ACIPS-A and the other self-report measures in the center column; the right-hand column provides the Pearson correlations between the ACIPS-A and the self-report measures, after controlling for gender. Acronyms: ACIPS-A, Anticipatory and Consummatory Interpersonal Pleasure Scale, Adolescent version; BAS-Reward Responsiveness, Behavioral Activation System Scale-Reward Responsiveness subscale; PWB-Positive Relations with Others, Psychological Well-Being Scale Positive Relations with Others subscale; EATQR-Affiliation, revised Early Adolescent Trait Questionnaire Affiliation subscale; EATQR-Pleasure Sensitivity, revised Early Adolescent Trait Questionnaire Pleasure Sensitivity subscale; CDI-Anhedonia, Child Depression Inventory Anhedonia subscale

**p < 0.01, two-tailed

Twin Similarity

Table 2 provides the intraclass correlations (ICCs) for the MZ and same-sex DZ twins. On the basis of the ICCs for MZ pairs being significant and consistently higher than for DZ pairs, a heritable component associated with each of the self-report measures is implicated. The MZ intraclass correlation for the ACIPS-A provides an upper bound heritability estimate for the measure.

Table 2.

Intraclass correlations for MZ and DZSS twin pairs

Self-report measure MZ-ICC (N = 86 pairs) Same-sex DZ-ICC (N = 64 pairs)
ACIPS-A 0.548 0.054
BAS-Reward Responsiveness 0.240 −0.010
PWB-Positive Relations with Others 0.600 0.287
EATQR-Affiliation 0.352 0.000
EATQR-Pleasure Sensitivity 0.398 0.212
CDI-Anhedonia 0.504 0.272
Open in a new tab

Acronyms: MZ, monozygotic; DZ, dizygotic; ICC, intraclass correlation; ACIPS-A, Anticipatory and Consummatory Interpersonal Pleasure Scale, Adolescent version; BAS-Reward Responsiveness, Behavioral Activation System Scale Reward Responsiveness subscale; PWB-Positive Relations with Others, Psychological Well-Being Scale Positive Relations with Others subscale; EATQR-Affiliation, revised Early Adolescent Temperament Questionnaire Affiliation subscale; EATQR-Pleasure Sensitivity, revised Early Adolescent Temperament Questionnaire Pleasure Sensitivity subscale; CDI-Anhedonia, Child Depression Inventory Anhedonia subscale

Univariate Model Fitting

Using scores residualized on gender, we fit univariate ACE models estimating genetic and environmental influences on social/interpersonal pleasure, reward responsiveness, positive relations with others, affiliation, pleasure sensitivity, and anhedonia. Table 3 shows the fit measures and genetic and environmental parameter estimates, with standard errors, for the various models that were tested. For all the scales, with the exception of anhedonia, the results of the model fitting revealed that models specifying additive genetic and unique environmental effects (AE models) were the most parsimonious models.

Table 3.

Fit indices for univariate models fitted to self-report measures of positive affect and parameter estimates (with 95% confidence intervals) for ACE and selected reduced models

Selected fit indices Unsquared standardized parameter estimates (standard error)
Measure Model χ2 df p AIC a c e
ACIPS-A ACE 3.686 6 .719 828.382 .711 (.054) .000 (.268) .703 (.055)
AE 3.686 7 .815 826.382 .711 ( .054) Fixed @ 0 .703 (.055)
BAS-Reward Responsiveness ACE 7.420 6 .284 854.220 .463 (.114) .000 (.315) .887 (.060)
AE 7.420 7 .387 852.220 .463 (.114) Fixed @ 0 .887 (.060)
PWB-Positive Relations with Others ACE 2.835 6 .829 811.229 .711 (.186) .261 (.467) .653 (.050)
AE 2.910 7 .893 809.304 .760 (.042) Fixed @ 0 .650 (.049)
EATQ-R-Affiliation ACE 10.931 6 .091 845.220 .567 (.089) .000 (.266) .824 (.061)
AE 10.931 7 .142 843.220 .567 (.089) Fixed @ 0 .824 (.061)
EATQ-R-Pleasure Sensitivity ACE 4.726 6 .579 840.121 .607 (.236) .180 (.684) .774 (.057)
AE 4.742 7 .691 838.136 .636 (.065) Fixed @ 0 .772 (.054)
CDI-Anhedonia ACE 3.557 6 .736 819.905 .508 (.287) .462 (.288) .727 (.052)
AE 4.124 7 .765 818.472 .694 (.052) Fixed @ 0 .720 (.050)
CE 4.422 7 .731 818.770 Fixed @ 0 .657 (.052) .754 (.045)
E 33.745 8 <.0001 846.092 Fixed @ 0 Fixed @ 0 1.00
Open in a new tab

Best-fitting models are in bold font. The a, c, and e parameter estimates may be squared to produce standard variance estimates (e.g., a2 = heritability)

N = 86 MZ pairs and 64 same-sex DZ pairs for all analyses

As the first row of Table 3 shows, in the univariate ACE model for the ACIPS, the C parameter was forced to zero (its lower bound). Thus, we dropped C from the model, resulting in an improvement in model fit. The reduced AE model indicated that additive genetic influences explained 50.4% (.7102) of the variance in social/interpersonal pleasure measured by the ACIPS (i.e., the program yields nonsquared parameter estimates, such as .710, and squaring these estimates provides variance estimates). This heritability estimate was not much smaller than the upper bound estimate obtained from the MZ intraclass correlation (.548) in Table 2.

The next four measures in Table 3 each showed either a zero estimate for shared environments (C) or a C estimate that was much smaller than its standard error. Thus, C could be dropped for these four scales; that is, all of the phenotypic variance was associated with genetic and nonshared (unique) environmental effects. Heritabilities were small to moderate in size, ranging from 21.3% for BAS-RR to 56.3% for PWB-Positive Relations.

CDI Anhedonia showed a more complex pattern, with neither the A nor the C latent variables having estimates that were more than twice their standard errors in the ACE model. Either C or A could be dropped, and the AE model fit slightly better than the full ACE model. Clearly, both A and C could not simultaneously be dropped (see the very poor fit of the E only model in the last row of Table 3). The problem here is likely that we had insufficient power to distinguish both A and C in the ACE model (Martin et al., 1978). Thus, the statistical criteria favor the AE model slightly, but we would be reluctant to discount the presence of C effects.

The best-fitting univariate models can be carried into the bivariate models.

Bivariate Biometric Model Fitting

We used the Cholesky decomposition model, which is a computationally efficient method for examining the genetic and environmental contributions to the covariance between two related variables. The Cholesky model is useful when the two correlated variables are ordered in some way. Here, the ordering is conceptual, based on our focus on whether the ACIPS-A adds distinctive information (etiological information in this application) to that provided by extant measures. See Loehlin (1996) for further explanation of the allowable inferences from the bivariate Cholesky decompositions. Five bivariate ACE Cholesky decomposition models estimated the extent to which genetic and environmental factors for five other scales contributed to variation in the ACIPS-A. Like the earlier analyses, scores were residualized on gender before model-fitting. Recall from Table 1 that the EATQ-R Pleasure Sensitivity and CDI Anhedonia scales had only low Pearson correlations with the ACIPS-A; thus, for these two pairings with the ACIPS-A, little phenotypic covariance is available to parse into its genetic and environmental components.

We describe the bivariate biometric model results first with the example of EATQ-R Affiliation and the ACIPS-A (third row of Table 4) because their correlations were the highest among the measures of positive affect (r = .517 in Table 1). The best-fitting univariate models for both ACIPS-A and EATQ-R Affiliation were AE models; thus, AE models were used in the bivariate approach. Figure 2 depicts the model results by showing the standardized path coefficients for the bivariate model. Considering first additive genetic factors, the estimate of .554 for the square root of heritability of EATQ-R Affiliation is very close to the .566 estimate for the univariate model (Table 3, fourth row). Turning to genetic effects on ACIPS-A, we see two paths of almost equal size: .499 shared with EATQ-R Affiliation and .504 unique to ACIPS-A. Squaring these two paths and adding them together (.4992 + .5042 = .503) provides the estimate of heritability for ACIPS scores. This value is very close to sqrt(.710) = .504 for the univariate heritability of ACIPS-A (Table 3, first row. Parallel considerations apply to the analysis of the cross-trait and unique nonshared environmental factors in Fig. 2. In summary, both genetic and nonshared (E) environmental effects contribute to the phenotypic overlap of EATQ-R Affiliation with ACIPS-A, and residual ACIPS-A variance is similarly divided into A and E components.

Table 4.

Bivariate Cholesky models for the prediction of ACIPS-A by the other five measures of positive affect: model fits and parameter estimates

Predictor scale Model for predictor Overall model fits (selected indices) Effectsa on predictor Effectsa from predictor to ACIPS-A Effectsa unique to ACIPS-A
χ2 df p AIC a e a* e* au eu
BAS-Reward Responsiveness AE 17.6 20 .612 1629.4

.466

(.113)

.885

(.060)

.297

(.142)

.293

(.070)

.641

(.061)

.644

(.050)
PWB-Positive Relations. AE 11.1 20 .943 1565.3

.761

(.041)

.649

(.048)

.418

(.077)

.262

(.069)

.573

(.060)

.654

(.051)
EATQ-R–Affiliation AE 23.0 20 .290 1587.1

.556

(.090)

.831

(.060)

.500

(.108)

.284

(.070)

.506

(.092)

.643

(.051)
EATQ-R-Pleasure Sensitivity AE 16.5 20 .687 1652.6

.636

(.065)

.771

(.054)

.182

(.108)ns

.149

(.070)

.688

(.055)

.687

(.054)
CDI-Anhedonia AEb 10.8 20 .951 1635.5

.693

(.052)

.721

(.050)

.246

(.095)

.074

(.070)ns

.668

(.060)

.699

(.054)
Open in a new tab

aAll effects are unsquared standardized parameter estimates. Standard errors are in parentheses after parameter estimates

bA model in which the C parameter for CDI-Anhedonia was incorporated was also fit, but the C parameter estimate was driven to zero for both effects on CDI-Anhedonia and on ACIPS-A (cross-trait). AIC, Akaike’s Information Criterion (Akaike, 1987), a measure of overall fit. Ns, 86 MZ pairs and 64 same-sex DZ pairs for all five models

Fig. 2.

Fig. 2

Open in a new tab

Fitted bivariate biometric model: standardized parameter estimates. The figure displays a fitted bivariate biometric model illustrating the shared genetic and environmental covariance for the revised EATQR-Affiliation scale (Ellis & Rothbart, 2001) and the ACIPS-A (Gooding & Pflum, 2014a; Gooding et al., 2016). Note that in this bivariate model, the C factors reflecting common shared environmental influence are not depicted because we fit the reduced AE model

We fit the bivariate Cholesky models for the other four scales with ACIPS-A. Rather than providing figures similar to Fig. 2 for each pairing, we provide the model fits and parameter estimates for bivariate models in Table 4. A small proportion of the genetic variance in the ACIPS-A was predicted by BAS-Reward Responsiveness; our analyses revealed that 82% of the genetic variance was unique to the ACIPS-A. The PWB-Positive Relations with Others subscale shared both genetic and unique environmental covariance with the ACIPS-A. Although 35% of the genetic variance in the ACIPS-A is shared with the Positive Relations with Others scale, the rest of the genetic variance is unique. The EATQR Pleasure Sensitivity scale made a small but nonsignificant contribution to the genetic variance of the ACIPS-A. Finally, almost 12% of the genetic variance in the ACIPS-A was predicted by the genetic variance associated with the CDI-Anhedonia scale; 88% of the genetic variance was unique to the ACIPS-A.

Discussion

Summary of Key Findings

Using a sample of male and female adolescent twins, we found significant associations among six self-report measures of positive affect, with correlations ranging from 0.23 to 0.52. The pattern of correlations is consistent with prior evidence that the ACIPS correlates with, but differs significantly from, other assays of pleasure, such as the Temporal Experience of Pleasure Scale (TEPS; Gard et al., 2006), whether in adolescents (Gooding et al., 2016; Gooding, Chan, et al., 2017) or adults (Gooding & Pflum, 2014a, 2014b; Gooding et al., 2015). The results were also consistent with prior reports of social/interpersonal pleasure being positively and significantly associated with reward responsiveness (Gooding & Pflum, 2014a), and social connectedness (Gooding et al., 2015) in adults.

We detected additive genetic and unique environmental components for each of the measures of positive affect. This initial biometric analysis of the ACIPS-A indicated a substantial heritable component—51% of the variance—for social/interpersonal pleasure. The bivariate biometric analyses indicated shared genetic variance between the ACIPS-A and other scales, but also unique genetic variance for the ACIPS-A in each analysis.

Our results add substantially to the extant construct validation of the ACIPS (Gooding & Pflum, 2014a, 2014b; Gooding et al., 2015; Gooding, Padrutt, & Pflum, 2017). The modest-to-moderate correlation values in Table 2 indicate that the ACIPS does not overlap too extensively with the other positive affect-related scales and thus taps some distinct content.

Furthermore, the low correlation of ACIPS-A with CDI-Anhedonia ( r = −0.230) suggests that these two scales are assessing something different. Only one of the eight CDI Anhedonia items is explicitly social in nature, while two others have social implications. Moreover, high scores on the CDI Anhedonia scale convey quite substantial problems in functioning, which may differ from the implication of low scores on the ACIPS-A. Despite these distinctions, our finding of a significant negative association of ACIPS-A and CDI-Anhedonia is wholly consistent with previous findings. ACIPS is negatively and significantly associated with social anhedonia scales, such as the Social Anhedonia scale on the Oviedo Schizotypy Assessment Questionnaire (Fonseca-Pedrero et al., 2010) in adolescents (Fonseca-Pedrero et al., 2016) and the revised Social Anhedonia Scale (Eckblad et al., 1982) in adults (Gooding & Pflum, 2014a, 2014b; Gooding, Padrutt, & Pflum, 2017). Adolescence may be one of the periods when social anhedonia is at its peak (Dodell-Feder & Germine, 2018).

Genetics and the ACIPS

One issue preliminary to genetic issues is whether studying social pleasure in twins generalizes to nontwins. Might not twins’ social world hinge on features of their dyad rather than individual features? Although being a twin surely holds some relevance to social relationships, we find no evidence that, at least by adolescence, twins are unrepresentative of the broader population. Twins as a group do not respond significantly differently from singletons on the ACIPS. In this study, the ACIPS-A sum score for twins is 57.80 (SD=7.35), whereas we previously reported from a community sample of singletons from Europe that the ACIPS-A sum score was a very similar 54.83 (SD = 7.47) (Gooding et al., 2016).

Next, we consider the univariate biometric analyses. Our finding of moderate heritability for the ACIPS is consistent with findings indicating that social anhedonia is influenced by additive genetic effects, with heritability estimates ranging widely from 27 to 67% (Kendler & Hewitt, 1992; MacDonald et al., 2001; Linney et al., 2003). Whereas our ACIPS findings were based on an adolescent sample, prior heritability estimates for social anhedonia have relied upon adult samples tested with the revised Social Anhedonia scale (RSAS; Eckblad et al., 1982). (Notably, the RSAS contains aspects of social anxiety, as well as reduced pleasure in social/interpersonal pleasure.) Thus, our findings extend this line of research to a new developmental period, adolescence.

Second, we may consider the bivariate biometric analyses as analogous to the bivariate phenotypic analyses discussed above. Rather than considering the evidence for convergent validity and discriminant properties that accrues from phenotypic correlations (as above), we can consider the degree to which phenotypic convergence of two scales is affected by common genetic factors. Similarly, demonstration of genetic effects that are unique to one scale (in our case, the ACIPS-A) is a type of evidence for distinctiveness, that is, evidence for etiological distinctiveness rather than simply phenotypic distinctiveness.

This biometric parsing of genetic effects into common (to ACIPS-A and another scale) versus unique (to ACIPS-A) parts is more meaningful for the three scales (EATQ-R Affiliation, PWB-Positive Relations with Others, and BAS-Reward Responsiveness) that are phenotypically more highly correlated with ACIPS-A. In other words, substantial phenotypic overlap indicates the presence of more covariance to parse. These same bivariate biometric analyses also indicated that substantial unique genetic variance was associated with the ACIPS-A.

The Reward Responsiveness subscale of the BAS contributed 18% of the genetic variance to the ACIPS-A, leaving 82% of the genetic variance of the ACIPS-A unique. Approximately 35% of the genetic variance is shared with the Positive Relations with Others scale, but the rest of the genetic variance, nearly 65%, is unique to the ACIPS-A. Finally, the EATQ-R Affiliation scale, the measure of positive affect that was most highly correlated with the ACIPS-A, contributed half of the genetic variance of the ACIPS-A, leaving half of the genetic variance of ACIPS-A as unique. (Here, the term “unique” means “not shared with the other scale being analyzed in conjunction with the ACIPS-A”). Taken together, these findings provide additional evidence for the distinctiveness and utility of the ACIPS-A.

Heritability of Social/Interpersonal Pleasure: Our Findings in Context

Next, we consider our main finding in the context of extant findings on the heritability of positive affect. Presently, most research on positive affect has focused on broad constructs such as “well-being” or “positive emotionality,” though a few studies have focused on particular traits. One study of the genetic architecture of trait optimism in adolescent twins revealed heritability estimates between 29 and 38% for the subscale and full scale, respectively (Mavioglu et al., 2015). The results of an investigation of subjective happiness among adolescent twins (mean age of 16) revealed heritability estimate of 34% (Haworth et al., 2017). Another investigation of 17-year old twins revealed that fluctuations in positive affect were significantly heritable (i.e., 34% of the variance was due to genetic effects; Zheng et al., 2016). According to Bouchard’s (2004) survey of broad heritability influences, most heritability estimates for major personality traits are in the range of 40 to 50%; for example, the Big Three trait of positive emotionality has a heritability of 50%. Furthermore, Weinberg et al., (2015) observed heritability in the range of 45 to 55% for event-related potential (ERP) responses to visual affective stimuli. Thus, our finding that 51% of the variance in social/interpersonal pleasure is heritable is comparable with previous reports of genetic contributions to self-reported personality trait characteristics, as well as findings of genetic contributions to affectively-modulated psychophysiological responses.

Children, adolescents, and adults may differ in terms of the genetic and environmental contributions of a trait or behavior across various domains, such as conservatism, antisocial behavior, and intelligence (Bouchard, 2004). Thus, one might expect changes in the magnitude of genetic influences of social/interpersonal pleasure over the course of the lifespan. A difference in heritability estimates between adolescents and adults might reflect changes in the significance of experiential factors. However, extant data regarding subjective well-being, typically conceptualized as a broad category of positive affective experiences (Diener et al., 1999) suggest no differences in heritability estimates between adolescents and adults (Lykken & Tellegen, 1996; Bartels et al., 2010; Bartels, 2015). Indeed, meta-analyses indicate that the weighted average heritability for well-being ranges from 32 to 41% (Bartels, 2015; Nes & Røysamb, 2015). Thus, although we used an adolescent sample and the adolescent version of the ACIPS, it is likely that this heritability estimates comparable to the value of 51% that we estimated would be found for adult samples using the adult version of the ACIPS.

However, we would caution extrapolating the heritability findings based on adolescents to children. Consider the example of intelligence. As reported in the survey by Bouchard (2004), a study of Dutch twins suggests that the heritability of intelligence steadily increases over childhood from 22% at age 5 to 85%, at age 12, decreases around age 16 to 62% and then is over 80% until old age (over 75 years old). A more recent investigation of intelligence revealed a similar finding, i.e., the heritability of general cognitive ability increases linearly from childhood to young adulthood (Haworth et al., 2010). In the case of social/interpersonal pleasure, a difference in heritability estimates between children and adolescents might reflect changes in the importance of environmental influences beyond the family (e.g., school, peers).

Limitations and Future Directions

We relied upon a predominantly Caucasian, nonclinical sample. Replicating with a larger, more ethnically diverse sample of twins would be useful. Also, generalizability to individuals with frank psychiatric conditions (e.g., those found in inpatient or clinically ascertained samples) might yield different findings. Future longitudinal studies, encompassing early-, middle-, and late adolescence would also provide the opportunity to investigate developmental changes in the valuation of, and responsivity to, social reward.

Future research might benefit from inclusion of other questionnaires, such as the Strengths and Difficulties Questionnaire (SDQ; Goodman, 2001), which focuses on peer problems, as well as prosocial behavior. Future research might also include other forms of measurement, such as behavioral observations and/or peer reports. Also, future research should investigate developmental continuity between self-reported social anhedonia from early to late adolescence and from adolescence to adulthood.

Despite these limitations, the present investigation furthers our understanding of the underlying nature of pleasure, particularly social and interpersonal pleasure, by clarifying the nature of associations between various aspects of positive affect. Moreover, these findings have implications for the understanding of genetic and environmental contributions to adolescents’ positive affect overall.

Conclusion

The ACIPS, a relatively new measure of social/interpersonal pleasure, demonstrates promise as a measure of positive affect. Adolescents can reliably self-report on this social pleasure measure. The ACIPS taps genetic variance that is partially distinct from other measures of positive affect and sociability.

Additional Information

Acknowledgements

We are grateful to the twin families for their participation and the Waisman Center for infrastructure support (P30 HD03352 and U54 HD090256). This work was supported by the National Institute of Mental Health at the National Institutes of Health (R01 MH059785 to Goldsmith & Lemery-Chalfant, R01 MH084051 to HHG) and Conte Neuroscience Centers (P50 MH0845051 and P50 MH100031).

Data Availability

Study data are available through the Mendeley Data Repository.

Conflict of Interest

The authors declare no competing interests.

Ethical Approval

The study was approved by the Educational and Social and Behavioral Sciences Institutional Review Board of the University of Wisconsin-Madison.

Informed Consent

All participants gave their assent, and their parents provided their written informed consent.

Open Practices Statement

The deidentified data that were generated and/or analyzed during the current study are available in the Mendeley data repository, DOI: 10.17632/fm5cb343cr.1.

The investigation and analyses described herein were not preregistered.

References

  1. Akaike H. Factor analysis and AIC. Psychometrika. 1987;52:317–332. doi: 10.1007/BF02294359. [DOI] [Google Scholar]
  2. Barkus, E., & Badcock, J. C. (2019). A transdiagnostic perspective on social anhedonia. Frontiers in Psychiatry, 10, No. 216. 10.3389/fpsyt.2019.00216. [DOI] [PMC free article] [PubMed]
  3. Bartels M. Genetics of wellbeing and its components satisfaction with life, happiness, and quality of life: A review and meta-analysis of heritability studies. Behavior Genetics. 2015;45:137–156. doi: 10.1007/s10519-015-9713-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bartels M, Savioul V, de Moor MHM, Willelmsen AHM, van Beijsterveldt CEM, Hottenga JJJ, de Geus EJC, Boomsma DI. Heritability and genome-wide linkage scan for subjective happiness. Twin Research and Human Genetics. 2010;13(2):135–142. doi: 10.1375/twin.13.2.135. [DOI] [PubMed] [Google Scholar]
  5. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Archives of General Psychiatry. 1961;4:561–571. doi: 10.1001/archpsyc.1961.01710120031004. [DOI] [PubMed] [Google Scholar]
  6. Bouchard TJ. Genetic influence on human psychological traits: A survey. Current Directions in Psychological Science. 2004;13(4):148–151. doi: 10.1111/j.0963-7214.2004.00295.x. [DOI] [Google Scholar]
  7. Capaldi DM, Rothbart MK. Development and validation of an early adolescent temperament measure. Journal of Early Adolescence. 1992;12:153–173. doi: 10.1177/0272431692012002002. [DOI] [Google Scholar]
  8. Carver CS, White TL. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS scales. Journal of Personality and Social Psychology. 1994;67:310–333. doi: 10.1037/0022-3514.67.2.319. [DOI] [Google Scholar]
  9. Casey BJ, Jones RM, Hareb TA. The adolescent brain. Annals of New York Academy of Science. 2008;1124:111–126. doi: 10.1196/annals.1440.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Catalano LT, Heerey EA, Gold JM. The valuation of social rewards in schizophrenia. Journal of Abnormal Psychology. 2018;127(6):602–611. doi: 10.1037/abn0000366. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chaplin, T. M., & Aldao, A. (2013). Gender differences in emotion expression in children: A meta-analytic review. Psychological Bulletin, 139, 735–765. [DOI] [PMC free article] [PubMed]
  12. Coffey JK, Warren MT. Comparing adolescent positive affect and self-esteem as precursors to adult self-esteem and life satisfaction. Motivation and Emotion. 2020;44:707–718. doi: 10.1007/s11031-020-09825-7. [DOI] [Google Scholar]
  13. Cooper A, Gomez R, Aucote H. The Behavioural Inhibition System and Behavioural Approach System (BIS/BAS) scales: Measurement and structural invariance across adults and adolescents. Personality and Individual Differences. 2007;43(7):295–305. doi: 10.1016/j.paid.2006.11.023. [DOI] [Google Scholar]
  14. Crone EA, Dahl RE. Understanding adolescence as a period of social-affective engagement and goal flexibility. Nature Neuroscience Reviews. 2012;13:636–650. doi: 10.1038/nrn3313. [DOI] [PubMed] [Google Scholar]
  15. Danner DD, Snowdon DA, Friesen WV. Positive emotions in early life and longevity: Findings from the Nun Study. Journal of Personality and Social Psychology. 2001;80(5):804–813. doi: 10.1037/0022-3514.80.5.804. [DOI] [PubMed] [Google Scholar]
  16. Davey, C. G., Yücel, M., & Allen, N. B. (2008). The emergence of depression in adolescence: Development of the prefrontal cortex and the representation of reward. Neuroscience and Biobehavioral Reviews, 32, 1–19. [DOI] [PubMed]
  17. Diener E, Suh EM, Lucas RE, Smith HL. Subjective well-being: Three decades of progress. Psychological Bulletin. 1999;125:276–302. doi: 10.1037/0033-2909.125.2.276. [DOI] [Google Scholar]
  18. Dodell-Feder D, Germine L. Epidemiological dimensions of social anhedonia. Clinical Psychological Science. 2018;6(5):735–743. doi: 10.1177/2167702618773740. [DOI] [Google Scholar]
  19. Eckblad ML, Chapman LJ, Chapman JP, Mishlove M. The Revised Social Anhedonia Scale. Madison, WI: University of Wisconsin–Madison; 1982. [Google Scholar]
  20. Ellis, L. K., & Rothbart, M. K. (2001). Revision of the Early Adolescent Temperament Questionnaire. Poster presented at the 2001 biennial meeting of the Society of Research in Child Development. Minneapolis, MN.
  21. Fonseca-Pedrero E, Gooding DC, Ortuño-Sierra J, Pflum MJ, Paino M, Muñiz J. Classifying risk status of non-clinical adolescents using psychometric indicators for psychosis spectrum disorders. Psychiatry Research. 2016;243:246–254. doi: 10.1016/j.psychres.2016.06.049. [DOI] [PubMed] [Google Scholar]
  22. Fonseca-Pedrero, E., Muñiz, J., Lemos-Giráldez, S., Paino M., Villazón-García, U., (2010). ESQUIZO-Q: Cuestionario Oviedo para la Evaluación de la Esquizotipia [ESQUIZO-Q: Oviedo Questionnaire for Schizotypy Assessment]. TEA ediciones, Madrid.
  23. Gard DE, Gard MG, Kring AM, John OP. Anticipatory and consummatory components of the experience of pleasure: A scale development study. Journal of Research in Personality. 2006;40:1086–1102. doi: 10.1016/j.jrp.2005.11.001. [DOI] [Google Scholar]
  24. Goldsmith HH. A zygosity questionnaire for young twins: A research note. Behavior Genetics. 1991;21(3):257–269. doi: 10.1007/BF01065819. [DOI] [PubMed] [Google Scholar]
  25. Gooding DC. The transdiagnostic nature of social anhedonia: Review of recent research findings using the ACIPS. International Journal of Psychiatric and Mental Health. 2019;1:1–3. [Google Scholar]
  26. Gooding DC, Pflum MJ. The assessment of interpersonal pleasure: Introduction of the Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS) and preliminary findings. Psychiatry Research. 2014;215(1):237–243. doi: 10.1016/j.psychres.2013.10.012. [DOI] [PubMed] [Google Scholar]
  27. Gooding DC, Pflum MJ. Further validation of the ACIPS as a measure of social hedonic response. Psychiatry Research. 2014;215:771–777. doi: 10.1016/j.psychres.2013.11.009. [DOI] [PubMed] [Google Scholar]
  28. Gooding, D. C., Chan, R. C. K., Zhou, H.-Y., Li, Z., & Cheung, E. F. C. (2017). The indirect assessment of social anhedonia in Chinese adolescents: Preliminary findings. Psychiatry Research, 257, 418–423. [DOI] [PubMed]
  29. Gooding, D. C., Padrutt, E. R., & Pflum, M. J. (2017). The predictive value of the NEO-FFI items: Parsing the nature of social anhedonia using the Revised Social Anhedonia Scale and the ACIPS. Frontiers in Psychology, 8, 147. 10.3389/fpsyg.2017.00147. [DOI] [PMC free article] [PubMed]
  30. Gooding DC, Pflum MJ, Fonseca-Pedrero E, Paino M. Assessing social anhedonia in adolescence: The ACIPS-A in a community sample. European Psychiatry. 2016;37:49–55. doi: 10.1016/j.eurpsy.2016.05.012. [DOI] [PubMed] [Google Scholar]
  31. Gooding DC, Winston TM, Pflum MJ, Burgin CJ. Individual differences in hedonic experience: Further evidence for the construct validity of the ACIPS. Psychiatry Research. 2015;229:524–532. doi: 10.1016/j.psychres.2015.05.061. [DOI] [PubMed] [Google Scholar]
  32. Goodman R. Psychometric properties of the Strengths and Difficulties Questionnaire. Journal of the American Academy of Child and Adolescent Psychiatry. 2001;40:1337–1345. doi: 10.1097/00004583-200111000-00015. [DOI] [PubMed] [Google Scholar]
  33. Haworth CM, Carter K, Eley TC, Plomin R. Understanding the genetic and environmental specificity and overlap between well-being and internalizing symptoms in adolescence. Developmental Science. 2017;20:e12376. doi: 10.1111/desc.12376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Haworth CM, Wright MM, Luciano M, Martin NG, de Geus EJ, van Beijsterveldt CEM, Bartels M, et al. The heritability of general cognitive ability increases linearly from childhood to young adulthood. Molecular Psychiatry. 2010;15(11):112–1120. doi: 10.1038/mp.2009.55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Healey KL, Morgan J, Musselman SC, Olino TM, Forbes EE. Social anhedonia and medial prefrontal responses to mutual liking in late adolescents. Brain and Cognition. 2014;89:39–50. doi: 10.1016/j.bandc.2013.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Heininga VE, Van Roekel E, Ahles JJ, Oldehinkel AJ, Mezulis AH. Positive affective functioning in anhedonic individuals’ daily life anything but flat and blunted. Journal of Affective Disorders. 2017;218:437–445. doi: 10.1016/j.jad.2017.04.029. [DOI] [PubMed] [Google Scholar]
  37. Kendler K, Hewitt J. The structure of self-report schizotypy in twins. Journal of Personality Disorders. 1992;6:1–17. doi: 10.1521/pedi.1992.6.1.1. [DOI] [Google Scholar]
  38. Kovacs M. Children’s Depression Inventory. New York: Multi-Health Systems; 1992. [Google Scholar]
  39. Kupferberg A, Bicks L, Hasler G. Social functioning in major depressive disorder. Neuroscience and Biobehavioral Reviews. 2016;69:313–332. doi: 10.1016/j.neubiorev.2016.07.002. [DOI] [PubMed] [Google Scholar]
  40. La Greca AM, Harrison HM. Adolescent peer relations, friendships, and romantic relationships: Do they predict social anxiety and depression? Journal of Clinical Child and Adolescent Psychology. 2005;34(1):49–61. doi: 10.1207/s15374424jccp3401_5. [DOI] [PubMed] [Google Scholar]
  41. Leventhal AM, Unger JB, Audrain-McGovern J, Sussaman S, Volk HE, Strong DR. Measuring anhedonia in adolescents: A psychometric analysis. Journal of Personality Assessment. 2015;97(5):1–9. doi: 10.1080/00223891.2015.1029072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Linney YM, Murray RM, Peters ER, MacDonald AM, Rijsdijk F, Sham PC. A quantitative genetic analysis of schizotypal personality traits. Psychological Medicine. 2003;33:803–816. doi: 10.1017/S0033291703007906. [DOI] [PubMed] [Google Scholar]
  43. Loehlin JC. The Cholesky approach: A cautionary note. Behavior Genetics. 1996;26:65–69. doi: 10.1007/BF02361160. [DOI] [Google Scholar]
  44. Lykken D, Tellegen A. Happiness is a stochastic phenomenon. Psychological Science. 1996;7(3):186–189. doi: 10.1111/j.1467-9280.1996.tb00355.x. [DOI] [Google Scholar]
  45. MacDonald AW, Pogue-Geile MF, Debski TT, Manuck S. Genetic and environmental influences on schizotypy: A community-based twin study. Schizophrenia Bulletin. 2001;27(1):47–58. doi: 10.1093/oxfordjournals.schbul.a006859. [DOI] [PubMed] [Google Scholar]
  46. Martin NG, Eaves LJ, Kearsey MJ, Davies P. The power of the classical twin study. Heredity. 1978;40(1):97–116. doi: 10.1038/hdy.1978.10. [DOI] [PubMed] [Google Scholar]
  47. Mavioglu, Mavioglu RN, Boomsma DI, Bartels M. Causes of individual differences in adolescent optimism: A study in Dutch twins and their siblings. European Child and Adolescent Psychiatry. 2015;24:1381–1388. doi: 10.1007/s00787-015-0680-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Meehl PE. Hedonic capacity: Some conjectures. Bulletin of the Menninger Clinic. 1975;39:295–307. [PubMed] [Google Scholar]
  49. Muthén, L. K., & Muthén, B. O. (2010). Mplus User's Guide (7th ed.). Muthén & Muthén.
  50. Neale, M. C., & Maes, H. H. M. (1998). Methodology for genetic studies of twins and families. Kluwer Academic Publishers.
  51. Nes, R. B., & Røysamb, E. (2015). The heritability of subjective well-being: Review and meta-analysis. In M. Pluess (Ed.), Genetics of psychological well-being (pp. 75–96). Oxford University Press.
  52. Rado, S. (1956). Psychoanalysis of behavior: Collected papers. Grune & Stratton.
  53. Rijsdijk, F. V. (2002). Analytic approaches to twin data using structural equation models. Briefings in Bioinformatics, 3, 119–133. 10.1093/bib/3.2.119. [DOI] [PubMed]
  54. Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069–1087.
  55. Ryff CD, Keyes CLM. The structure of psychological well-being revisited. Journal of Personality and Social Psychology. 1995;69(4):719–727. doi: 10.1037/0022-3514.69.4.719. [DOI] [PubMed] [Google Scholar]
  56. Schmidt NL, Lemery-Chalfant K, Goldsmith HH. Wisconsin Twin Project overview: Temperament and affective neuroscience. Twin Research and Human Genetics. 2019;22(6):794–799. doi: 10.1017/thg.2019.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Schmidt NL, Van Hulle CA, Brooker RL, Meyer LR, Lemery-Chalfant K, Goldsmith HH. Wisconsin Twin Research: Early development, child psychopathology, autism, and sensory overresponsivity. Twin Research and Human Genetics. 2013;16(01):376–384. doi: 10.1017/thg.2012.105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Snaith, R. P., Hamilton, M., Morley, S., Humayan, A., Hargreaves, D. Trigwell, P. (1995). A Scale for the Assessment of Hedonic Tone the Snaith–Hamilton Pleasure Scale. British Journal of Psychiatry 167(1):99–103. [DOI] [PubMed]
  59. Urosevic S, Collins P, Muetzel R, Lim K, Luciana M. Longitudinal change in Behavioral Approach System sensitivity and brain structures involved in reward processing during adolescence. Developmental Psychology. 2012;48(5):1488–1500. doi: 10.1037/a0027502. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Wahlstrom D, White T, Luciana M. Neurobehavioral evidence for changes in dopamine system activity during adolescence. Neuroscience Biobehavioral Reviews. 2010;34:631–648. doi: 10.1016/j.neubiorev.2009.12.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Walker DM, Bell MR, Flores C, Gulley JM, Willing J, Paul MR. Adolescence and reward: Making sense of neural and behavioral changes amid the chaos. Journal of Neuroscience. 2017;37(45):10855–10866.3.2. doi: 10.1523/JNEUROSCI.1834-17.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Watson D. Intraindividual and interindividual analyses of positive and negative affect: Their relation to health complaints, perceived stress, and daily activities. Journal of Personality and Social Psychology. 1988;54:1020–1030. doi: 10.1037/0022-3514.54.6.1020. [DOI] [PubMed] [Google Scholar]
  63. Weinberg A, Venables NC, Proudfit GH, Patrick CJ. Heritability of the neural response to emotional pictures: Evidence from ERPs in an adult twin sample. SCAN. 2015;10:424–434. doi: 10.1093/scan/nsu059. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Zheng Y, Plomin R, von Stumm S. Heritability of intraindividual mean and variability of positive and negative affect: Genetic analysis of daily affect ratings over a month. Psychological Science. 2016;27(12):1611–1619. doi: 10.1177/0956797616669994. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Study data are available through the Mendeley Data Repository.

Articles from Affective Science are provided here courtesy of Springer

RESOURCES