Why was the model able to capture these

Why was the model able to capture these qualitative effects? That is, what was the mechanistic account of the empirical data? In the model, there is experimental control over the constraints that shape development. We can therefore plot predictive power of these constraints across development, split by ability groups. Fig. 5 plots observed correlations between neurocomputational parameter values and behavior, and between environmental quality and behavior. In addition, it perk inhibitor plots MZ and DZ behavioral correlations. Lastly, it plots the total percentage variance explained by all the neurocomputational parameters together, summing the linear fits. It is apparent that the earlier increase in heritability in the low ability group is driven by an earlier decrease in the DZ twin correlation compared to the high ability group. While some individual neurocomputational parameters (which are more likely to have different values between DZ twins) increase their predictive power over development, the total predictive power of all genetic/neurocomputational properties is approximately constant. It does not therefore explain the divergence of DZ performance. The divergence of DZ performance occurs because of two factors, both concerning the characteristics of non-linear learning systems. First, the relationship of neurocomputational parameters to behavior is typically non-linear. Second, neurocomputational parameters show higher order interactions (i.e., non-additive effects) in their influence on behavior. Both of these factors have been proposed as explanations of ‘missing heritability’, that is, the reason why the summed linear predictive power of measured genetic similarity in genome-wide association studies tends to fall short of the level of heritability estimated from twin study designs (Thomas et al., 2016). Together these factors cause genetic differences between DZ twins to be exaggerated in their neurocomputational influence on behavior. Non-linear neurocomputational influences become greater as the systems approach ceiling performance. Low ability networks have lower dimensional internal representational spaces and as a consequence tend to adopt non-linear processing states sooner during training. Researchers have highlighted the notable finding that ability is related to the rates of change of structural properties of the brain, such as cortical thickness and surface area (Shaw et al., 2006). The model demonstrated similar ability-related modulation of rates of change of structural properties of the ANNs, derived from measures of their connectivity (total number and total magnitude). We know in the model that differences in ability were generated by small differences over a large set of general neurocomputational properties, some of which directly influenced measures of connectivity (architecture, hidden unit numbers, sparseness), some of which influenced connectivity only indirectly via experience-dependent change. Ability modulated reductions in connection numbers because larger networks tended to be more computationally powerful but also lost connections more quickly during pruning (in the sense that higher mountains tend to have steeper sides). Ability modulated increases in connection strength because late in development, low ability networks had to buttress their smaller networks more to extract the best performance gains they could manage. In short, the model provides a way to conceptualize how structural differences between networks, and between their dynamic rates of change, may be related to differences in the quality of behavior via low-level neurocomputational properties. Aside from reconciling these key data, the model also exhibited some other attractive properties that support its validity at a qualitative level. General cognitive ability is captured as the outcome of many small differences in general neurocomputational properties; these properties are mainly under genetic control, but the mapping of genes to these properties and these properties to behavior is many to one (Plomin et al., 2013; Plomin and Kovas, 2005). The genetic influence on behavior via the neurocomputational properties remains relatively consistent across development, even while heritability is increasing (Thomas et al., 2016; Trzaskowski et al., 2014). Since some but not all neurocomputational parameters influence structural indices of the networks, while all contribute to the ability of the networks, the genes determining ability and structure are partially overlapping (Brans et al., 2010; see Thomas et al., 2016, Figs. 9 and 10). Structural indices correlate highly with each other, behavioral measures correlate highly with each other, but the correlation between the two is weaker (Posthuma et al., 2003; Thomas et al., 2016). If differences in the richness of the training set implement one causal pathway of SES effects on development (Thomas et al., 2013), the model demonstrates relatively small effects of SES on structural indices (Table S1), in line with recent data (Lawson et al., 2013; Noble et al., 2015). When SES serves as a limiting factor on development, low SES can reduce the heritability of behavior and has detectable impact on network structure (Fig. S1; Noble et al., 2015; Turkheimer et al., 2003; Thomas & Coecke, in prep.).