Social anxiety disorder (SAD) is a prevalent disorder in adolescents. It manifests itself in avoidance of social situations, difficulties forming relationships and an overall increased impairment...Show moreSocial anxiety disorder (SAD) is a prevalent disorder in adolescents. It manifests itself in avoidance of social situations, difficulties forming relationships and an overall increased impairment in social functioning. In adulthood, individuals with SAD report an overall poorer quality of life. Intolerance of uncertainty (IU) and coping strategies have been associated with SAD in recent literature. However, information on this subject is scarce in adolescents. This study investigates the relationship between IU and traits of social anxiety (SA) in adolescents, incorporating age as a moderating variable and adaptive and maladaptive coping strategies as a mediating variable. Data on SA traits, IU and coping strategies was collected within a larger study from a non-clinical sample using questionnaires. This study included 233 participants (Mage = 18.6, SD = 3.3) of which 81% were female. Results of this study show a positive relationship between IU and SA traits. This positive relationship weakens with age during adolescence. Additionally, maladaptive coping strategies mediate the relationship between SA and IU, strengthening this relationship. These findings indicate IU, age and coping are important factors to be considered in relation to the development and maintenance of SAD. Future research should continue on this subject to provide additional practical implications for the prevention and treatment of SADShow less
In this study a social prediction game was used to investigate expectations about others’ prosocial behavior in adolescence. Prosocial behavior can be seen as a voluntary action of a person to help...Show moreIn this study a social prediction game was used to investigate expectations about others’ prosocial behavior in adolescence. Prosocial behavior can be seen as a voluntary action of a person to help others. There are different motivations to behave prosocially. This study investigated the motivations of selfishness; always wanting more, and risk aversion; avoiding risks. The goal of this study was to investigate adolescents’s expectations about the prosocial behavior of others and if there was a difference in gender and age in these expectations. First, an improvement in the number of correct predictions in the risk averse motivation over age was found. There was no significant improvement of the number of correct predictions in the selfish motivation found. Second, there were no gender differences within the number of correct predictions of both motivations. Third, a significant correlation was found between gender, age and the number of correct predictions, which showed that the number of correct predictions of the risk averse motivation increased with age for girls and the number of correct predictions of the selfish motivation increased with age for boys. In conclusion, there is a gender difference between the expectations of the use of selfish or risk averse motivations of other players in a social prediction game and age has an influence on this difference during adolescence. This means that within the classroom, a different approach for boys and girls is needed to adopt more prosocial and cooperative motivations and expectations.Show less
Adolescence is a period of many changes, a shift in focus appears from parents to peers. A greater focus on peer relationships can bring many insecurities. Therefore, adolescence is a period of...Show moreAdolescence is a period of many changes, a shift in focus appears from parents to peers. A greater focus on peer relationships can bring many insecurities. Therefore, adolescence is a period of heightened risk for the development of mental health problems. Using economic behavioural games, the social behaviour of others can be predicted by recognising the underlying motivations of others. The underlying motivations researched in this study are greediness and risk aversion. The aim of the study was to learn if social anxiety traits influenced the prediction of the social behaviour of others. It was investigated if a higher level of social anxiety influenced the accuracy of the predictions, based on greedy and risk averse motivations, of participants. In addition, the effects of age and motivation may influence the accuracy of the predictions of participants. No significant results were found during the study. Since the study is conducted with a small sample size, this may have affected the statistical power. Also, participants were not specifically recruited for the requirement of social anxiety. However, the results suggested a relation between age and the number of accurate predictions. Furthermore, the results suggested a relation between the level of social anxiety and motivation. These results should be further investigated in future research. To conclude, this study contributes to a better understanding of social anxiety. The ability of being able to predict when individuals are prosocial or not may prevent the onset of mental health disorders.Show less
Does Planning Ability (PA) improve with age, is PA advanced in adolescents who are less impulsive and what is the effect of both age and impulsivity on PA? This study aimed to find answers on above...Show moreDoes Planning Ability (PA) improve with age, is PA advanced in adolescents who are less impulsive and what is the effect of both age and impulsivity on PA? This study aimed to find answers on above mentioned questions. The study has an experimental cross-sectional design and is part of a larger research project about identifying computational components that underlie planning skills. Participants were 157 adolescent citizens from the United States (range = 8.02 – 25.90, M = 15.83, SD = 5.08), of which 78 women, 77 men and 2 missing genders. Data of participants playing Four-in-a-row, an alternative form of tic-tac-toe, has been analysed to measure PA. Impulsivity is measured with unidimensional Barratt Impulsiveness Scale–Brief (BIS-Brief), the shortened version of the Barratt Impulsiveness Scale-11 (BIS-11). Age (Wilks’ Lambda: F (2,146) = .321; p = .726) and Impulsivity (Wilks’ Lambda: F (2,146) = .701; p = .498) both showed non-significant effects on the outcome measures Mean Difficulty Level (MDL) and Mean Reaction Time (MRT) (indicators of PA). The interaction effect was not significant either (Wilks’ Lambda: F (2,146) = .475; p = .623). Age was significantly and positively correlated with MDL, MRT and negatively correlated with impulsivity. MDL and MRT were significantly moderately correlated and impulsivity was negatively correlated with MRT but not significantly with MDL. All in all, with age, impulsivity does not affect PA differently. However, the correlations suggest that with age people improved in MDL, made slower decisions and became less impulsive. Probably, MDL and MRT were not suitable outcome measures for PA, but measuring PA by means of the computational models and when Four-in-a-Row meets the COTAN standards, it is possible to reach a more ecologically valid measurement tool that can measure accurate PA.Show less
The four-in-a-row task is a planning task that was recently developed with the aim to measure the psychological construct of complex planning. Planning becomes more complex with an increase in the...Show moreThe four-in-a-row task is a planning task that was recently developed with the aim to measure the psychological construct of complex planning. Planning becomes more complex with an increase in the number of futures to anticipate, which consequently makes it more difficult to measure. Previous measurements have been proven insufficient in measuring complex planning. The current study aims to establish the construct validity of the four-in-a-row task, by investigating if performance on the task can be predicted by age related improvements in planning abilities, as measured by the subscale ‘anticipation of future consequences’ (AFC) of the Future Orientation Scale (FOS) (Steinberg, 2009). In the current study 150 American participants took part in an online testing session, in which they played 35 four-in-a-row games against a computer opponent that adjusted the difficulty level to the playing strength of the participant using a staircasing algorithm, and filled out the FOS. The participants’ reaction times and the difficulty setting of the computer opponents during their final three games were used as a measure of performance on the task. It was found that both reaction times and the difficulty setting of the computer opponent increased with age. Furthermore, it was found that age in interaction with the AFC score was able to predict both reaction times and the difficulty setting of the computer opponent, indicating a difference in how different age groups with strong planning abilities perform on the four-in-a-row task. As the findings of the current study underline the promise and importance of the four-in-a-row task as a measuring method for complex planning, several recommendations for future research are offered.Show less
