International Journal of Psychology, 2014 Vol. 49, No. 5, 409–414, DOI: 10.1002/ijop.12032
Effects of verbal ability and fluid intelligence on children’s emotion understanding Simona De Stasio1 , Caterina Fiorilli2 , and Carlo Di Chiacchio3 1
Department of Movement, Human and Health Sciences, Division of Human and Social Sciences, Foro Italico University of Rome, Rome, Italy 2 Department of Human Sciences, Libera Universit`a Maria SS. Assunta, Rome, Italy 3 National Institute for the Evaluation of the Educational System, Rome, Italy
T
his study investigated the role of verbal ability and fluid intelligence on children’s emotion understanding, testing the hypothesis that fluid intelligence predicts the development of emotion comprehension over and above age and verbal ability. One hundred and two children (48 girls) aged 3.6–6 years completed the Test of Emotion Comprehension (TEC) that comprised external and mental components, the Coloured Progressive Matrices and the Test for Reception of Grammar. Regression analysis showed that fluid intelligence was not equally related to the external and mental components of the TEC (Pons & Harris, 2000). Specifically, the results indicated that the external component was related to age and verbal ability only, whereas recognition of mental emotional patterns required abstract reasoning skills more than age and verbal ability. It is concluded that the development of fluid intelligence has a significant role in the development of mental component of emotion comprehension. Keywords: Emotion understanding; Language; Fluid intelligence; Preschool children.
Existing studies refer to children’s emotion understanding as a set of skills that increase in complexity throughout the preschool and school years. According to Pons and colleagues (Pons & Harris, 2000; Pons, Harris, & de Rosnay, 2004), the development of emotion understanding may be divided into three hierarchical phases: external, mental and reflective. In the first phase (3–4 years), children acquire understanding of the public aspect of emotions: their situational causes, their outward expression and events or objects that serve as external prompts of reactive emotions (Cutting & Dunn, 1999; Denham, 1986; Izard, 1990). In the mental phase (4–6 years), children develop an understanding of how the intensity of an emotion decreases over time, and how elements of a current situation can serve as a reminder of past experiences (Flavell, 2000); gain an appreciation of hidden affects; and become aware that beliefs produce emotional reactions. During this phase, children display increasing competence in making emotion attributions on the basis of beliefs and desires (Ensor, Spencer, & Hughes, 2011; Rieffe, Terwogt, & Cowan, 2005). In the final phase of emotion understanding development (6–9 years), children’s understanding of the reflective
components of emotion expands to include attribution of emotion on the basis of moral judgements, the negative feelings produced by a morally reprehensible action (e.g., distress at a failure to confess) and understanding of ambivalent emotions. Collectively, this research suggests that children initially understand emotions based on facial and situational cues, whereas later in their development they are able to consider emotions based on psychological factors. These developmental changes in the abilities of emotion comprehension imply a very close relationship between children’s emotion and cognitive competences. Therefore, it appears worthy to consider the different contributions of cognitive factors on emotion comprehension. The role of verbal ability and fluid intelligence in children’s emotion understanding Children’s linguistic abilities play a significant role in predicting their level of emotion understanding. Evidence of this relationship has come from studies using simple tasks, such as affective labelling as well as more complex belief-based emotion attribution tasks (e.g., de Rosnay,
Correspondence should be addressed to Simona De Stasio, University “Foro Italico” of Rome, P.zza Lauro de Bosis, 15, 00135 Rome, Italy. (E-mail: [email protected]).
© 2014 International Union of Psychological Science
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Pons, Harris, & Morrell, 2004; Ensor et al., 2011; Farina, Albanese, & Pons, 2007). Dunn, Brown, and Beardsall (1991) showed that in 36-month-old children the number of utterances per hour containing a reference to emotion was correlated with a later ability to recognize emotions at 6 years, as evaluated by affective labelling and affective perspective-taking tasks. Cutting and Dunn (1999) found a significant relationship between the receptive and expressive language ability of 3- and 4-year-olds and their understanding of the expression and causes of emotion. Furthermore, de Rosnay and Harris (2002) found that the language ability of 3–6-year-olds was a significant predictor of their emotion understanding. Despite evidence showing the predictive role of language ability in the development of children’s emotion understanding, it has been argued that most of the tasks used to evaluate emotion understanding are verbal, and require cognitive operations based on language (Lewis & Osborne, 1990; Nelson, 2005). Thus, there is a risk that language skills might overlap with those more appropriately pertaining to emotion understanding. Surprisingly, not many researches have explored how non-verbal ability (such as fluid intelligence) might affect emotion comprehension. The few studies reported in the literature generally examine the adult population, finding IQ to have a significant effect on emotion understanding (Mayer, Caruso, & Salovey, 1999). It is therefore of interest to study the effect of fluid intelligence on children’s emotion comprehension. One of the very few existing studies on this topic carried out by Albanese, De Stasio, Di Chiacchio, Fiorilli, and Pons (2010), specifically focused on the relationship between children’s non-verbal ability and the different components of their emotion understanding, suggesting that children’s non-verbal ability enables them to take into account multiple causes of emotions and recognize the presence of mixed emotions, thanks to conceptual structures allowing them to coordinate two or more aspects of a situation. In summary, these two distinct lines of research focusing respectively on the role of children’s verbal and non-verbal abilities in their emotion understanding present some limitations. First, most of the previous studies only examined the more rudimentary components of emotion understanding. Second, children have typically been administered emotion understanding tasks requiring advanced receptive and expressive skills. Third, it is well-established that children’s fluid intelligence affects their cognitive skills, whereas little is known about its influence on emotion understanding development. Finally, past research has examined the relationships between verbal ability and emotion understanding and between non-verbal intelligence and emotion understanding separately. In this study, we set out to overcome these limitations by analysing emotion understanding with the Test of Emotion Comprehension (Pons & Harris, 2000) that includes both basic and
complex aspects of emotion. This test was also chosen because its linguistic simplicity (i.e., simple items, short questions and non-verbal-answers) limits the involvement of children’s linguistic expression skills. In addition, in order to evaluate children’s fluid intelligence we used the Coloured Progressive Matrices (CPM) developed by Raven (1984), thus minimizing the effect of language, and of formal education. Rationale of the present study The crucial feature of this study was the investigation of the roles of both verbal and fluid intelligence in children’s emotion understanding. First, we expected our data to confirm positive association of emotion understanding with children’s age as well as with fluid intelligence and verbal ability. Second, we expected children’s fluid intelligence to incrementally predict their understanding of the mental components of emotion, over and above verbal ability and age. This literature suggests that the mental understanding of emotions, for example, appreciating that a person’s emotions are based more on his or her expectations than on the true state of the world, and working out how the intensity of an emotion decreases over time, requires children to have abstract reasoning skills. On this basis, we evaluated abstract reasoning using CPM, considering abstract reasoning and representational capacity to be the core abilities of fluid intelligence. METHOD Participants A total of 102 children (48 girls) aged 3.6–6 years (M = 4.27, SD = .89) were recruited from two public kindergartens in Rome, Italy. The schools were located in areas with mixed socioeconomic profiles. The participants were typically developing children without any known learning, language or behavioural problems. Measures Test of Emotion Comprehension The Test of Emotion Comprehension (TEC; Pons & Harris, 2000, Italian version by Albanese & Molina, 2008) assesses understanding of emotions in children aged 3–11 years and comprises nine components: (1) recognition, (2) external cause, (3) desire, (4) belief, (5) reminder, (6) regulation, (7) hiding, (8) mixed and (9) morality. It consists of a booklet (male and female versions) with a cartoon scenario and four possible emotional outcomes depicted by facial expressions displayed on each page. Overall level of emotion © 2014 International Union of Psychological Science
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TABLE 1 Descriptives, reliablities and Pearson correlation coefficients among emotion understanding, age, fluid intelligence and verbal ability (n = 102)
1. External 2. Mental 3. TEC total 4. Age 5. CPM 6. TROG M SD α
1
2
3
4
— 0.456** 0.817** 0.551** 0.247** 0.497** 2.11 0.98 0.62
— 0.778** 0.453** 0.410** 0.390** 1.61 0.96 0.65
— 0.596** 0.346** 0.547** 4.75 2.01 0.72
— 0.509** 0.466** 4.27 0.89 —
5
— 0.213* 8.65 2.40 0.72
6
— 13.85 4.55 0.76
Note: **p < .01. *p < .05.
understanding is determined by assigning a maximum of 1 point for each component for which a correct answer is provided. This yields a maximum score of 9 points (success on all components) and a minimum of 0 point (failure on all components) (see Pons et al., 2004 for a detailed presentation of this test). In the present study, we only took the external and mental levels into account; thus the total score for the TEC ranged from 0 to 6. In order to calculate the score for the external phase of emotion understanding, we summed the correct answers for the components recognition, external cause and desire; for the mental phase we summed the correct answers for the components belief , reminder and hiding. The resulting score for each phase varied from a minimum of 0 to a maximum of 3. Coloured Progressive Matrices The CPM (Raven, 1984) assesses fluid intelligence and consists of 36 items presenting a series of non-meaningful geometrical figures. It is designed to measure the ability to form perceptual relations and to reason by analogy, independently of language and formal schooling. The test consists of three sets (A, Ab and B) of 12 items each. The test increases in difficulty across sets A, Ab and B. Each item contains a figure with a missing piece. The total score is the total number of correct answers. Test of receptive grammar The TROG (Bishop, 1989), translated into Italian by Suraniti, Ferri, and Neri (2009), assesses understanding of 20 Italian grammatical contrasts in children (4–12 years) with and without specific language disorders (see Bishop, 1989, for a detailed description of the measure). This test requires the subject to match a sentence to one of four coloured line drawings. The items are divided into blocks of four, with each block measuring a specific aspect of syntactic ability. The total score is the number of blocks passed (range = 0–20). © 2014 International Union of Psychological Science
Procedure Two female researchers tested the children individually in a quiet room at their school. Tests were administered in counterbalanced order and took place over two consecutive sessions in the same week, each session lasting a maximum of 30 min. Ethics approval for this study was granted by the Local Research Ethics Committee. RESULTS Descriptive statistics and correlations Table 1 shows the descriptive statistics (mean and standard deviation), reliabilities (Cronbach’s alpha) and Pearson’s correlation coefficients among the emotion understanding scores (external, mental and overall), CPM, TROG and age. All correlations were statistically significant, indicating a positive association among the variables. The alphas showed acceptable values ranging from .62 (external) to .76 (TROG). Given that the variable age displayed a skewed distribution, children were split into two groups on the basis of their median age (51 months) in order to ensure a linear relation with the other variables and, in the case of the subsequent regression models, a more accurate estimation of the regression weights. The dichotomized age groups were then compared in the analyses, with children below the median taken as the reference group. Multiple regression Three sequential multiple regression models were performed. In each model, scores for each of the TEC components (i.e., external and mental) and total TEC score were regressed onto the scores for CPM and age, as well as onto TROG. Following our hypotheses, the regression models involved the following steps: Step 1—CPM scores were entered; Step 2—age was entered;
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TABLE 2 Hierarchical multiple regression analyses predicting external component of emotion comprehension from fluid intelligence, age and verbal ability
TABLE 3 Hierarchical multiple regression analyses predicting mental component of emotion comprehension from fluid intelligence, age and verbal ability
External component of emotion understanding Predictor Step 1 Fluid intelligencea Step 2 Fluid intelligence Ageb Step 3 Fluid intelligence Age Verbal abilityc Total R2 N
R2
β
0.06* 0.25* 0.24** −0.05 0.57** 0.07** −0.04 0.43** 0.31** 0.38** 100
Mental component of emotion understanding β
R2
Predictor Step 1 Fluid intelligencea Step 2 Fluid intelligence Ageb Step 3 Fluid intelligence Age Verbal abilityc Total R2 n
0.17** 0.41* 0.08** 0.24* 0.33** 0.04** 0.249* 0.217* 0.236* 0.29** 100
Note: **p < .01. *p < .05. a Fluid intelligence is assessed by the Coloured Progressive Matrices. b Age was split into two groups in relation with the median age (51 months). Children below the median were the reference group. c Verbal ability is assessed by the Test for Reception of Grammar.
Note: **p < .01. *p < .05. a Fluid intelligence is assessed by the Coloured Progressive Matrices. b Age was split into two groups in relation with the median age (51 months). Children below the median were the reference group. c Verbal ability is assessed by the Test for Reception of Grammar.
and Step 3—TROG scores closed the sequence. The results of the sequential regression of the external component of children’s emotion understanding (see Table 2) revealed a statistically significant contribution of each of the predictors. Age showed the highest contribution (unique contribution of 24% at Step 2) R2 = .31, F(2, 99) = 21.69, p < 001, as compared to CPM (6% of explained variance at Step 1) and TROG (unique contribution of 7% at Step 3) R2 = .38, F(3, 98) = 19.85, p < 001. With regard to the regression coefficients, at Step 2 the inclusion of age made fluid intelligence insignificant. At Step 3, linguistic ability was added to the equation and after controlling for effects of age and fluid intelligence, the result was statistically significant. With regard to the mental components of emotion understanding, the regression analysis showed that each of the predictors significantly increased explained variance. In this model, fluid intelligence showed the highest contribution, Model 1 (17% of explained variance at Step 1), continuing to be significantly associated with the mental component after the influence of age, R2 = .25, F(2, 99) = 16.39, p < 001 (unique contribution of 8% at Step 2) and linguistic ability (R2 = .29, F(3, 98) = 13.48, p < 001 (unique contribution of 4% at Step 3), had been controlled for (see Table 3). Finally, with regard to the total TEC score (see Table 4), the sequential regression analysis again showed that each predictor contributed significantly to the prediction of emotion comprehension, with age making the greatest contribution, R2 = .36, F(2, 99) = 27.60, p < 001 (unique contribution of 24% at Step 2). Moreover, in this model the effect of fluid intelligence was no longer significant, once age had been introduced into
TABLE 4 Hierarchical multiple regression analyses predicting total emotion comprehension from fluid intelligence, age and verbal ability Total emotion comprehension β
R2
Predictor Step 1 Fluid intelligencea Step 2 Fluid intelligence Age Step 3 Fluid intelligence Ageb Verbal abilityc Total R2 N
0.12
**
0.35** 0.24** 0.06 0.57** 0.09* 0.07 0.40** 0.35** 0.45** 100
Note: **p < .01. *p < .05. a Fluid intelligence is assessed by the Coloured Progressive Matrices. b Age was split into two groups in relation with the median age (51 months). Children below the median were the reference group. c Verbal ability is assessed by the Test for Reception of Grammar.
the equation (Step 2). At Step 3, linguistic ability had a significant effect, R2 = .45, F(3, 98) = 26.93, p < 001 (unique contribution of 9%). DISCUSSION The present study had two aims: the first was to assess whether verbal ability, fluid intelligence and age were associated with emotion understanding; the second was © 2014 International Union of Psychological Science
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to examine whether children’s emotion understanding was predicted by fluid intelligence over age and verbal ability. As expected, children who were older and/or obtained higher scores on the language and analytical intelligence measures displayed more advanced emotion understanding. With regard to the first research goal, age was strongly correlated with both cognitive and emotion understanding abilities. This finding was consistent with that of Pons et al. (2004) who found, in a sample of British children, a clear improvement in ability to pass the nine components of the TEC with increasing age. Regarding the second goal, our findings showed that children’s emotion understanding was predicted by their fluid intelligence over age and verbal ability. More specifically, the results indicated that the external component was related to age and verbal ability only; whereas recognition of mental emotional patterns required, more than age and verbal ability, abstract reasoning skills. The effect of verbal ability on the external and the mental phases of emotion understanding Our results confirmed previous research suggesting that as age increases, syntactic language skills may affect the manifestation of the external understanding of emotions (Dunn & Cutting, 1999). Children with superior receptive language skills are able to comprehend emotional engagement in social interactions more easily and are thus better able to recognize the external causes of a given emotion. Language skills also play a significant part in the understanding of the mental aspects of emotion. Two different explanations for this have been proposed (Harris et al., 2005). First, language may be viewed as an instrument of cognitive representation. Emotions may be considered objects for language to represent, just like any another concrete or abstract object. Therefore, the more children are able to represent such objects, the better their understanding of them. Second, understanding others makes a child a more attractive conversation partner. Thus, children with greater language ability secure more opportunities to represent mental states—including emotions—more extensively.
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emotion understanding over and above their verbal ability and age. We assume that fluid intelligence is instrumental in promoting a representational understanding of the emotions of self and others. The abilities involved in the mental components of emotion understanding such as false belief-based emotion attribution, appreciation of the role of memory and distinguishing between expressed emotion and experienced emotion, require the capacity for abstract reasoning. We may say that children who pass mental component items demonstrate the ability to maintain representations of two different emotions (Wimmer & Perner, 1983). Success on mental component tasks may imply that children are able to identify the source of information and take into account the other person’s access to the same information (Wimmer, Hogrefe, & Sodian, 1988). The abilities required to resolve complex emotional tasks appear to be similar to those called for in the Coloured Progressive Matrices. The CPM (having a nonverbal format) measure processes that are central to fluid intelligence (i.e., dealing with novelty, reasoning and problem solving). Our results show that older children are better at solving problems and integrating multiple sources of information, as assessed by the CPM, and these enable them to consider multiple causes of emotion and recognize the difference between expressed and experienced emotions. Overall, these findings suggest that children’s fluid intelligence is an important predictor of the mental component of emotion understanding, over age and language ability. While fluid intelligence is well-known to play a role in cognitive activities, our results provide new and more detailed information about its contribution to emotion understanding. Specifically, it appears that more advanced abstract reasoning skills, such as ability to identify patterns and relations and infer and implement rules, make preschool children more competent in interpreting situations involving the mental components of emotion understanding. These are original findings that contribute to the existing literature by providing support for different explanations of causal mechanisms between fluid intelligence, language and emotion understanding and suggesting paths for future research. LIMITATIONS
The effect of fluid intelligence on the external and the mental phases of emotion understanding The results implied that fluid intelligence does not have a significant effect on the external components of emotion understanding. In contrast, children’s fluid intelligence incrementally predicted their mastery of the mental components of © 2014 International Union of Psychological Science
Although our study was informative and novel, it displayed a number of weaknesses. Among the most critical limitations was the use of a single measure per construct and the fact that other types of intelligence were not assessed. Relevant control factors that may need to be further analysed include the educational status of parents’ and the number of siblings in participants’ families. Another limitation relates to the small sample
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size: given that participants were typically developing 3–6-year-olds from a central Italian town, the findings require replication with larger and different kinds of sample. FUTURE DIRECTIONS Finally we believe that these findings, if replicated in future longitudinal studies, could have key implications for intervention with typically and atypically developing children, in terms of refining efforts to enhance emotion understanding through both verbal and fluid intelligence. Manuscript received December 2011 Revised manuscript accepted November 2013 First published online January 2014
REFERENCES Albanese, O., & Molina, P. (Eds.) (2008). Lo sviluppo della comprensione delle emozioni e la sua valutazione. Milano, Italy: Edizioni Unicopli. Albanese, O., De Stasio, S., Di Chiacchio, C., Fiorilli, C., & Pons, F. (2010). Emotion comprehension: The impact of nonverbal intelligence. Journal of Genetic Psychology, 171, 101–115. Bishop, D. V. M. (1989). Test for reception of grammar (2nd ed.). Department of Psychology, Manchester, University of Manchester: Author. Cutting, A. L., & Dunn, J. (1999). Theory of mind, emotion understanding, and family background: Individual differences and interrelations. Child Development, 70, 853–865. de Rosnay, M., & Harris, P. L. (2002). Individual differences in children’s understanding of emotion: The role of attachment and language. Attachment and Human Development, 4, 39–45. de Rosnay, M., Pons, F., Harris, P. L., & Morrell, J. (2004). A lag between understanding false belief and emotion attribution in young children: Relationships with linguistic ability and mothers’ mental state language. British Journal of Developmental Psychology, 22, 197–218. Denham, S. A. (1986). Social cognition, prosocial behavior, and emotion in preschoolers: Contextual validation. Child Development, 57, 194–201. Dunn, J., & Cutting, A. L. (1999). Understanding others, and individual differences in friendship interactions in young children. Social Development, 8, 201–219. Dunn, J., Brown, J., & Beardsall, L. (1991). Family talk about feeling states and children’s later understanding of others’ emotions. Developmental Psychology, 27, 448–455.
Ensor, R., Spencer, D., & Hughes, C. (2011). ‘You feel sad?’ Emotion understanding mediates predictors of prosocial behaviour: Findings from 2-to 4-years. Social Development, 20, 93–110. Farina, E., Albanese, O., & Pons, F. (2007). Making inferences and comprehension of emotions in children of 5–7 years of age. Psychology of Language and Communication, 11(2), 3–19. Flavell, J. H. (2000). Development of children’s knowledge about the mental world. International Journal of Behavioural Development, 24, 15–23. Harris, P. L., de Rosnay, M., & Pons, F. (2005). Language and children’s understanding of mental states. Current Directions in Psychological Science, 14(1), 69–73. Izard, C. E. (1990). Facial expressions and the regulation of emotions. Journal of Personality and Social Psychology, 58(3), 487–498. Lewis, C., & Osborne, A. (1990). Three-year-olds’ problems with false belief: Conceptual deficit or linguistic artefact? Child Developmental, 61, 1514–1519. Mayer, J. D., Caruso, D., & Salovey, P. (1999). Emotional intelligence meets traditional standards for an intelligence. Intelligence, 27, 267–298. Nelson, K. (2005). Language pathways into the community of minds. In J. W. Astington & J. Baird (Eds.), Why language matters for theory of mind (pp. 26–49). New York, NY: Oxford University Press. Pons, F., & Harris, P. L. (2000). TEC (test of emotion comprehension). Oxford, England: Oxford University Press. Pons, F., Harris, P. L., & de Rosnay, M. (2004). Emotion comprehension between 3 and 11 years: Developmental periods and hierarchical organization. European Journal of Developmental Psychology, 2, 127–152. Raven, J. C. (1984). Matrici progressive CMP. Firenze, Italy: Edizioni OS. Rieffe, C., Terwogt, M. M., & Cowan, R. (2005). Children’s understanding of mental states as causes of emotions. Infant and Child Development, 14(3), 259–272. Suraniti, S., Ferri, R., & Neri, V. (2009). TROG-2, test for reception of grammar by D.V.M. Bishop. Firenze, Italy: Giunti. Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13, 103–128. Wimmer, H., Hogrefe, G. J., & Sodian, B. (1988). A second stage in children’s conception of mental life: Understanding informational accesses as origins of knowledge and belief. In J. W. Astington, P. L. Harris, & D. R. Olson (Eds.), Developing theories of mind (pp. 173–192). Cambridge, England: Cambridge University Press.
© 2014 International Union of Psychological Science
Effects of verbal ability and fluid intelligence on children’s emotion understanding Simona De Stasio1 , Caterina Fiorilli2 , and Carlo Di Chiacchio3 1
Department of Movement, Human and Health Sciences, Division of Human and Social Sciences, Foro Italico University of Rome, Rome, Italy 2 Department of Human Sciences, Libera Universit`a Maria SS. Assunta, Rome, Italy 3 National Institute for the Evaluation of the Educational System, Rome, Italy
T
his study investigated the role of verbal ability and fluid intelligence on children’s emotion understanding, testing the hypothesis that fluid intelligence predicts the development of emotion comprehension over and above age and verbal ability. One hundred and two children (48 girls) aged 3.6–6 years completed the Test of Emotion Comprehension (TEC) that comprised external and mental components, the Coloured Progressive Matrices and the Test for Reception of Grammar. Regression analysis showed that fluid intelligence was not equally related to the external and mental components of the TEC (Pons & Harris, 2000). Specifically, the results indicated that the external component was related to age and verbal ability only, whereas recognition of mental emotional patterns required abstract reasoning skills more than age and verbal ability. It is concluded that the development of fluid intelligence has a significant role in the development of mental component of emotion comprehension. Keywords: Emotion understanding; Language; Fluid intelligence; Preschool children.
Existing studies refer to children’s emotion understanding as a set of skills that increase in complexity throughout the preschool and school years. According to Pons and colleagues (Pons & Harris, 2000; Pons, Harris, & de Rosnay, 2004), the development of emotion understanding may be divided into three hierarchical phases: external, mental and reflective. In the first phase (3–4 years), children acquire understanding of the public aspect of emotions: their situational causes, their outward expression and events or objects that serve as external prompts of reactive emotions (Cutting & Dunn, 1999; Denham, 1986; Izard, 1990). In the mental phase (4–6 years), children develop an understanding of how the intensity of an emotion decreases over time, and how elements of a current situation can serve as a reminder of past experiences (Flavell, 2000); gain an appreciation of hidden affects; and become aware that beliefs produce emotional reactions. During this phase, children display increasing competence in making emotion attributions on the basis of beliefs and desires (Ensor, Spencer, & Hughes, 2011; Rieffe, Terwogt, & Cowan, 2005). In the final phase of emotion understanding development (6–9 years), children’s understanding of the reflective
components of emotion expands to include attribution of emotion on the basis of moral judgements, the negative feelings produced by a morally reprehensible action (e.g., distress at a failure to confess) and understanding of ambivalent emotions. Collectively, this research suggests that children initially understand emotions based on facial and situational cues, whereas later in their development they are able to consider emotions based on psychological factors. These developmental changes in the abilities of emotion comprehension imply a very close relationship between children’s emotion and cognitive competences. Therefore, it appears worthy to consider the different contributions of cognitive factors on emotion comprehension. The role of verbal ability and fluid intelligence in children’s emotion understanding Children’s linguistic abilities play a significant role in predicting their level of emotion understanding. Evidence of this relationship has come from studies using simple tasks, such as affective labelling as well as more complex belief-based emotion attribution tasks (e.g., de Rosnay,
Correspondence should be addressed to Simona De Stasio, University “Foro Italico” of Rome, P.zza Lauro de Bosis, 15, 00135 Rome, Italy. (E-mail: [email protected]).
© 2014 International Union of Psychological Science
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Pons, Harris, & Morrell, 2004; Ensor et al., 2011; Farina, Albanese, & Pons, 2007). Dunn, Brown, and Beardsall (1991) showed that in 36-month-old children the number of utterances per hour containing a reference to emotion was correlated with a later ability to recognize emotions at 6 years, as evaluated by affective labelling and affective perspective-taking tasks. Cutting and Dunn (1999) found a significant relationship between the receptive and expressive language ability of 3- and 4-year-olds and their understanding of the expression and causes of emotion. Furthermore, de Rosnay and Harris (2002) found that the language ability of 3–6-year-olds was a significant predictor of their emotion understanding. Despite evidence showing the predictive role of language ability in the development of children’s emotion understanding, it has been argued that most of the tasks used to evaluate emotion understanding are verbal, and require cognitive operations based on language (Lewis & Osborne, 1990; Nelson, 2005). Thus, there is a risk that language skills might overlap with those more appropriately pertaining to emotion understanding. Surprisingly, not many researches have explored how non-verbal ability (such as fluid intelligence) might affect emotion comprehension. The few studies reported in the literature generally examine the adult population, finding IQ to have a significant effect on emotion understanding (Mayer, Caruso, & Salovey, 1999). It is therefore of interest to study the effect of fluid intelligence on children’s emotion comprehension. One of the very few existing studies on this topic carried out by Albanese, De Stasio, Di Chiacchio, Fiorilli, and Pons (2010), specifically focused on the relationship between children’s non-verbal ability and the different components of their emotion understanding, suggesting that children’s non-verbal ability enables them to take into account multiple causes of emotions and recognize the presence of mixed emotions, thanks to conceptual structures allowing them to coordinate two or more aspects of a situation. In summary, these two distinct lines of research focusing respectively on the role of children’s verbal and non-verbal abilities in their emotion understanding present some limitations. First, most of the previous studies only examined the more rudimentary components of emotion understanding. Second, children have typically been administered emotion understanding tasks requiring advanced receptive and expressive skills. Third, it is well-established that children’s fluid intelligence affects their cognitive skills, whereas little is known about its influence on emotion understanding development. Finally, past research has examined the relationships between verbal ability and emotion understanding and between non-verbal intelligence and emotion understanding separately. In this study, we set out to overcome these limitations by analysing emotion understanding with the Test of Emotion Comprehension (Pons & Harris, 2000) that includes both basic and
complex aspects of emotion. This test was also chosen because its linguistic simplicity (i.e., simple items, short questions and non-verbal-answers) limits the involvement of children’s linguistic expression skills. In addition, in order to evaluate children’s fluid intelligence we used the Coloured Progressive Matrices (CPM) developed by Raven (1984), thus minimizing the effect of language, and of formal education. Rationale of the present study The crucial feature of this study was the investigation of the roles of both verbal and fluid intelligence in children’s emotion understanding. First, we expected our data to confirm positive association of emotion understanding with children’s age as well as with fluid intelligence and verbal ability. Second, we expected children’s fluid intelligence to incrementally predict their understanding of the mental components of emotion, over and above verbal ability and age. This literature suggests that the mental understanding of emotions, for example, appreciating that a person’s emotions are based more on his or her expectations than on the true state of the world, and working out how the intensity of an emotion decreases over time, requires children to have abstract reasoning skills. On this basis, we evaluated abstract reasoning using CPM, considering abstract reasoning and representational capacity to be the core abilities of fluid intelligence. METHOD Participants A total of 102 children (48 girls) aged 3.6–6 years (M = 4.27, SD = .89) were recruited from two public kindergartens in Rome, Italy. The schools were located in areas with mixed socioeconomic profiles. The participants were typically developing children without any known learning, language or behavioural problems. Measures Test of Emotion Comprehension The Test of Emotion Comprehension (TEC; Pons & Harris, 2000, Italian version by Albanese & Molina, 2008) assesses understanding of emotions in children aged 3–11 years and comprises nine components: (1) recognition, (2) external cause, (3) desire, (4) belief, (5) reminder, (6) regulation, (7) hiding, (8) mixed and (9) morality. It consists of a booklet (male and female versions) with a cartoon scenario and four possible emotional outcomes depicted by facial expressions displayed on each page. Overall level of emotion © 2014 International Union of Psychological Science
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TABLE 1 Descriptives, reliablities and Pearson correlation coefficients among emotion understanding, age, fluid intelligence and verbal ability (n = 102)
1. External 2. Mental 3. TEC total 4. Age 5. CPM 6. TROG M SD α
1
2
3
4
— 0.456** 0.817** 0.551** 0.247** 0.497** 2.11 0.98 0.62
— 0.778** 0.453** 0.410** 0.390** 1.61 0.96 0.65
— 0.596** 0.346** 0.547** 4.75 2.01 0.72
— 0.509** 0.466** 4.27 0.89 —
5
— 0.213* 8.65 2.40 0.72
6
— 13.85 4.55 0.76
Note: **p < .01. *p < .05.
understanding is determined by assigning a maximum of 1 point for each component for which a correct answer is provided. This yields a maximum score of 9 points (success on all components) and a minimum of 0 point (failure on all components) (see Pons et al., 2004 for a detailed presentation of this test). In the present study, we only took the external and mental levels into account; thus the total score for the TEC ranged from 0 to 6. In order to calculate the score for the external phase of emotion understanding, we summed the correct answers for the components recognition, external cause and desire; for the mental phase we summed the correct answers for the components belief , reminder and hiding. The resulting score for each phase varied from a minimum of 0 to a maximum of 3. Coloured Progressive Matrices The CPM (Raven, 1984) assesses fluid intelligence and consists of 36 items presenting a series of non-meaningful geometrical figures. It is designed to measure the ability to form perceptual relations and to reason by analogy, independently of language and formal schooling. The test consists of three sets (A, Ab and B) of 12 items each. The test increases in difficulty across sets A, Ab and B. Each item contains a figure with a missing piece. The total score is the total number of correct answers. Test of receptive grammar The TROG (Bishop, 1989), translated into Italian by Suraniti, Ferri, and Neri (2009), assesses understanding of 20 Italian grammatical contrasts in children (4–12 years) with and without specific language disorders (see Bishop, 1989, for a detailed description of the measure). This test requires the subject to match a sentence to one of four coloured line drawings. The items are divided into blocks of four, with each block measuring a specific aspect of syntactic ability. The total score is the number of blocks passed (range = 0–20). © 2014 International Union of Psychological Science
Procedure Two female researchers tested the children individually in a quiet room at their school. Tests were administered in counterbalanced order and took place over two consecutive sessions in the same week, each session lasting a maximum of 30 min. Ethics approval for this study was granted by the Local Research Ethics Committee. RESULTS Descriptive statistics and correlations Table 1 shows the descriptive statistics (mean and standard deviation), reliabilities (Cronbach’s alpha) and Pearson’s correlation coefficients among the emotion understanding scores (external, mental and overall), CPM, TROG and age. All correlations were statistically significant, indicating a positive association among the variables. The alphas showed acceptable values ranging from .62 (external) to .76 (TROG). Given that the variable age displayed a skewed distribution, children were split into two groups on the basis of their median age (51 months) in order to ensure a linear relation with the other variables and, in the case of the subsequent regression models, a more accurate estimation of the regression weights. The dichotomized age groups were then compared in the analyses, with children below the median taken as the reference group. Multiple regression Three sequential multiple regression models were performed. In each model, scores for each of the TEC components (i.e., external and mental) and total TEC score were regressed onto the scores for CPM and age, as well as onto TROG. Following our hypotheses, the regression models involved the following steps: Step 1—CPM scores were entered; Step 2—age was entered;
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TABLE 2 Hierarchical multiple regression analyses predicting external component of emotion comprehension from fluid intelligence, age and verbal ability
TABLE 3 Hierarchical multiple regression analyses predicting mental component of emotion comprehension from fluid intelligence, age and verbal ability
External component of emotion understanding Predictor Step 1 Fluid intelligencea Step 2 Fluid intelligence Ageb Step 3 Fluid intelligence Age Verbal abilityc Total R2 N
R2
β
0.06* 0.25* 0.24** −0.05 0.57** 0.07** −0.04 0.43** 0.31** 0.38** 100
Mental component of emotion understanding β
R2
Predictor Step 1 Fluid intelligencea Step 2 Fluid intelligence Ageb Step 3 Fluid intelligence Age Verbal abilityc Total R2 n
0.17** 0.41* 0.08** 0.24* 0.33** 0.04** 0.249* 0.217* 0.236* 0.29** 100
Note: **p < .01. *p < .05. a Fluid intelligence is assessed by the Coloured Progressive Matrices. b Age was split into two groups in relation with the median age (51 months). Children below the median were the reference group. c Verbal ability is assessed by the Test for Reception of Grammar.
Note: **p < .01. *p < .05. a Fluid intelligence is assessed by the Coloured Progressive Matrices. b Age was split into two groups in relation with the median age (51 months). Children below the median were the reference group. c Verbal ability is assessed by the Test for Reception of Grammar.
and Step 3—TROG scores closed the sequence. The results of the sequential regression of the external component of children’s emotion understanding (see Table 2) revealed a statistically significant contribution of each of the predictors. Age showed the highest contribution (unique contribution of 24% at Step 2) R2 = .31, F(2, 99) = 21.69, p < 001, as compared to CPM (6% of explained variance at Step 1) and TROG (unique contribution of 7% at Step 3) R2 = .38, F(3, 98) = 19.85, p < 001. With regard to the regression coefficients, at Step 2 the inclusion of age made fluid intelligence insignificant. At Step 3, linguistic ability was added to the equation and after controlling for effects of age and fluid intelligence, the result was statistically significant. With regard to the mental components of emotion understanding, the regression analysis showed that each of the predictors significantly increased explained variance. In this model, fluid intelligence showed the highest contribution, Model 1 (17% of explained variance at Step 1), continuing to be significantly associated with the mental component after the influence of age, R2 = .25, F(2, 99) = 16.39, p < 001 (unique contribution of 8% at Step 2) and linguistic ability (R2 = .29, F(3, 98) = 13.48, p < 001 (unique contribution of 4% at Step 3), had been controlled for (see Table 3). Finally, with regard to the total TEC score (see Table 4), the sequential regression analysis again showed that each predictor contributed significantly to the prediction of emotion comprehension, with age making the greatest contribution, R2 = .36, F(2, 99) = 27.60, p < 001 (unique contribution of 24% at Step 2). Moreover, in this model the effect of fluid intelligence was no longer significant, once age had been introduced into
TABLE 4 Hierarchical multiple regression analyses predicting total emotion comprehension from fluid intelligence, age and verbal ability Total emotion comprehension β
R2
Predictor Step 1 Fluid intelligencea Step 2 Fluid intelligence Age Step 3 Fluid intelligence Ageb Verbal abilityc Total R2 N
0.12
**
0.35** 0.24** 0.06 0.57** 0.09* 0.07 0.40** 0.35** 0.45** 100
Note: **p < .01. *p < .05. a Fluid intelligence is assessed by the Coloured Progressive Matrices. b Age was split into two groups in relation with the median age (51 months). Children below the median were the reference group. c Verbal ability is assessed by the Test for Reception of Grammar.
the equation (Step 2). At Step 3, linguistic ability had a significant effect, R2 = .45, F(3, 98) = 26.93, p < 001 (unique contribution of 9%). DISCUSSION The present study had two aims: the first was to assess whether verbal ability, fluid intelligence and age were associated with emotion understanding; the second was © 2014 International Union of Psychological Science
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to examine whether children’s emotion understanding was predicted by fluid intelligence over age and verbal ability. As expected, children who were older and/or obtained higher scores on the language and analytical intelligence measures displayed more advanced emotion understanding. With regard to the first research goal, age was strongly correlated with both cognitive and emotion understanding abilities. This finding was consistent with that of Pons et al. (2004) who found, in a sample of British children, a clear improvement in ability to pass the nine components of the TEC with increasing age. Regarding the second goal, our findings showed that children’s emotion understanding was predicted by their fluid intelligence over age and verbal ability. More specifically, the results indicated that the external component was related to age and verbal ability only; whereas recognition of mental emotional patterns required, more than age and verbal ability, abstract reasoning skills. The effect of verbal ability on the external and the mental phases of emotion understanding Our results confirmed previous research suggesting that as age increases, syntactic language skills may affect the manifestation of the external understanding of emotions (Dunn & Cutting, 1999). Children with superior receptive language skills are able to comprehend emotional engagement in social interactions more easily and are thus better able to recognize the external causes of a given emotion. Language skills also play a significant part in the understanding of the mental aspects of emotion. Two different explanations for this have been proposed (Harris et al., 2005). First, language may be viewed as an instrument of cognitive representation. Emotions may be considered objects for language to represent, just like any another concrete or abstract object. Therefore, the more children are able to represent such objects, the better their understanding of them. Second, understanding others makes a child a more attractive conversation partner. Thus, children with greater language ability secure more opportunities to represent mental states—including emotions—more extensively.
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emotion understanding over and above their verbal ability and age. We assume that fluid intelligence is instrumental in promoting a representational understanding of the emotions of self and others. The abilities involved in the mental components of emotion understanding such as false belief-based emotion attribution, appreciation of the role of memory and distinguishing between expressed emotion and experienced emotion, require the capacity for abstract reasoning. We may say that children who pass mental component items demonstrate the ability to maintain representations of two different emotions (Wimmer & Perner, 1983). Success on mental component tasks may imply that children are able to identify the source of information and take into account the other person’s access to the same information (Wimmer, Hogrefe, & Sodian, 1988). The abilities required to resolve complex emotional tasks appear to be similar to those called for in the Coloured Progressive Matrices. The CPM (having a nonverbal format) measure processes that are central to fluid intelligence (i.e., dealing with novelty, reasoning and problem solving). Our results show that older children are better at solving problems and integrating multiple sources of information, as assessed by the CPM, and these enable them to consider multiple causes of emotion and recognize the difference between expressed and experienced emotions. Overall, these findings suggest that children’s fluid intelligence is an important predictor of the mental component of emotion understanding, over age and language ability. While fluid intelligence is well-known to play a role in cognitive activities, our results provide new and more detailed information about its contribution to emotion understanding. Specifically, it appears that more advanced abstract reasoning skills, such as ability to identify patterns and relations and infer and implement rules, make preschool children more competent in interpreting situations involving the mental components of emotion understanding. These are original findings that contribute to the existing literature by providing support for different explanations of causal mechanisms between fluid intelligence, language and emotion understanding and suggesting paths for future research. LIMITATIONS
The effect of fluid intelligence on the external and the mental phases of emotion understanding The results implied that fluid intelligence does not have a significant effect on the external components of emotion understanding. In contrast, children’s fluid intelligence incrementally predicted their mastery of the mental components of © 2014 International Union of Psychological Science
Although our study was informative and novel, it displayed a number of weaknesses. Among the most critical limitations was the use of a single measure per construct and the fact that other types of intelligence were not assessed. Relevant control factors that may need to be further analysed include the educational status of parents’ and the number of siblings in participants’ families. Another limitation relates to the small sample
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size: given that participants were typically developing 3–6-year-olds from a central Italian town, the findings require replication with larger and different kinds of sample. FUTURE DIRECTIONS Finally we believe that these findings, if replicated in future longitudinal studies, could have key implications for intervention with typically and atypically developing children, in terms of refining efforts to enhance emotion understanding through both verbal and fluid intelligence. Manuscript received December 2011 Revised manuscript accepted November 2013 First published online January 2014
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