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  • br Results Fig a shows the general cognitive

    2018-11-03


    Results Fig. 1a shows the general cognitive ability heritability estimates for children, adolescents, and adults, re-plotted from Brant et al. (2013). Given the reported high heritability of cognitive ability, we focused on the simulated population where heritability was highest, that is, the population where genetic variation was wide and environmental variation was narrow. Fig. 1b plots the heritability proxy, the difference between MZ and DZ correlations, for high ability and low ability groups, which were split at the mean ability score of the population based on the average ability of each pair. Measures were taken at an early (100 epochs), mid (500 epochs), and late (990) point in training. The robustness of the measure was assessed by considering values for the 10 previous and 10 following epochs in training, and these were used to compute statistical reliability. As with the empirical data, there was an overall increase in heritability with age (main effect of developmental stage: F(1,40)=304.35, p<0.001, ηp2=0.884). Heritability increased due to a reduction in the correlation between DZ twins (see later Fig. 5). In other words, as with the empirical data, heritability increased due to a reduction in the influence of shared environment on behavior. The pattern of change in heritability was different between ability groups (F(1,40)=25.08, p<0.001, ηp2=0.385). As with the empirical data, for the high ability group, there was no significant difference in heritability between early and mid points (t(20)=0.38, p=0.707) but a significant rise between mid and late points (t(20)=4.79, p<0.001). As with the empirical data, for the low ability group, heritability showed a significant increase between early and mid points (t(20)=31.02, p<0.001) but no change between mid and late points (t(20)=1.47, p=0.157). In NS-398 to the empirical data, there were reliable differences both at the early measurement point (high ability>low ability, t(40)=2.78, p=0.008) and the late measurement point (low ability>high ability, t(40)=5.41, p<0.001). Within the simulation framework, it was possible that higher ability could have been generated by an extended sensitive period, consistent with Brant et al.’s (2013) hypothesis. That hypothesis would be supported if it should turn out that connection NS-398 pruning (which reduced plasticity) had a later onset for the high ability group than the low. While the high ability group did indeed have a later onset (mean epoch 103.6 versus 98.7 for the low ability group), this effect was not reliable (t(998)=1.15, p=0.251); nor did pruning onset predict ability as a continuous variable (Pearson correlation=0.043, p=0.172). While the qualitative pattern of Brant et al.’s (Brant et al. 2013) heritability data was captured, the main mechanistic difference between high and low ability groups in the simulations was not, therefore, an extended sensitive period in plasticity. Moreover, as with real children, in the simulation, differences in the performance of high ability and low ability networks were observable from early in development (Fig. S4). The above simulation data were drawn from the population where the heritability of behavior was the highest. Fig. 2 displays equivalent data from the other conditions, with differing ranges of genetic and environmental variation. Fig. 2 plots the heritability proxy for ability groups across the full period of development, indicating the points of measurement used in Fig. 1b. Comparison indicates that two patterns, the increasing heritability across development, and the overlapping heritability for the ability groups followed by divergence followed by convergence, were unique to the highest heritability condition. In the other conditions, where environmental influences were relatively more important, heritability reduced with age, and lower heritability was consistently observed for the high ability group. As described in Section 2.5, ability was assessed using a method that reduced the influence of environmental variation, in line with the contemporary use of ‘culture fair’ intelligence tests. The same data contrasts in Fig. 2 are included in Fig. S5, but calculated using untransformed performance on exception verbs at 50 epochs. The use of this different measure to define ability groups modulated the interaction of ability and heritability, mainly for conditions of wide genetic variation. For the target condition with high heritability, it served to reduce the heritability of performance in the high ability group beyond the early stage of development. This is because the conditions of the environment in which simulated individuals were raised increasingly contributed to how well they scored in ability tests, reducing the manifestation of their intrinsic ability. In other words, the model predicted that the observed empirical data of Brant et al. (2013) are dependent on using ability tests that are relatively insensitive to environmental influence.