Page 96 - Like me, or else... - Michelle Achterberg

Chapter 4
of homoscedasticity. Although the estimator of the regression parameters is not influenced when this assumption is violated, the estimator of the covariance matrix can be biased, resulting in too liberal or too conservative significance tests (Hayes & Chai, 2007). Therefore, we used heteroscedasticity-consistent standard error (HCSE) estimators, by using the HCSE macro of Hayes and Cai (2007). As recommended by Long and Ervin (2000), we used the HC3 method. Moreover, we performed genetic modeling of behavioral responses (noise blast difference scores) and neural responses (ROI activation) to social feedback using the OpenMX package (Neale et al., 2016) in R (R Core Team, 2015).
Behavioral analyses
Social feedback retaliation
Results
The linear mixed-effect model showed a significant main effect of type of social feedback on noise blast duration, F(2, 505) = 300.8754, p<.001. Pairwise comparisons revealed that noise blast duration after negative feedback (M=2688, SD=736) was significantly longer than noise blast duration after neutral feedback (M=1906, SD=648, p<.001), and after positive feedback (M=1459, SD=852, p<.001). Noise blast duration was significantly longer after neutral feedback than after positive feedback (p<.001). There were also significant noise blast x age F(2, 505) = 10.57, p<.001) and noise blast x IQ interaction effects F(2, 505) = 12.27, p<.001), showing larger condition effects for older children and for children with higher IQ. To control for possible confounding effects of age and IQ, we included these variables as regressors in further models. There were no significant gender differences in noise blast duration after positive, neutral or negative feedback (independent sample T-tests, all p’s>.05). Results did not change after exclusion of children with an Axis-I disorder.
Twin analyses
To investigate twin-effects in (imagined) aggression after social feedback we calculated the differences in noise blast duration between negative versus positive feedback, negative versus neutral feedback; and neutral versus positive feedback. Next, we performed Pearson’s correlations between these differences scores within MZ (n=138) and DZ (n=115) twin pairs (Table 2). Behavioral genetic analyses revealed that aggression following negative relative to positive social feedback was moderately influenced by genetics (A= 20%, 95% CI: 0-37%), and to a lesser extent influenced by shared environment (C=6%, 95% CI: 0-34%). Unique environment and measurement error explained the largest part of the variance in aggression after negative feedback (E=74%, 95% CI:0.63-0.90), see Table 2. The best fitting model was an ACE-model, see Table S1. Aggression following negative
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