Multiple Causality in Developmental Disorders

Computational modeling can allow scientists to observe the behavioral effects of many different types of network failure. In many cases, one observes "multiple causality" (Thomas, 2003) in which multiple types of disruptions to the network will result in similar behavioral deficits. This observation is important not just for understanding computational models, but for applied clinical psychology as well.

For example, ADHD-like behavior may have multiple orthogonal sets of underlying causes; these causes may also be behaviorally indistinguishable from one another. Thomas refers to this as a heterogenous disorder, in contrast to something like Williams Syndrome (which is understood genetically and therefore appears to be a homogenous disorder). The problem arises when one treats a heterogenous disorder; because different mechanisms may be causing the same behavioral deficit, the effects of therapy may be unpredictable, or in some cases even counterproductive.

How then, can we tell a heterogenous disorder from a homogenous one? One obvious option is to get additional behavioral measures, preferably of a different type: one would expect to find greater similarity among homogenous disorders than hetergenous disorders. Unfortunately, Thomas showed that this is not necessarily the case. First, he implemented a network that was capable of learning past tense formation on the basis of a large language corpus, and identified 10 "homogenously disordered" networks that performed poorly on tests of overgeneralization (sleep-sleeped) for a single reason (they used localist as opposed to distributed coding). Next, he created 10 "heterogenous" networks that showed increased overgeneralization of past tense formation, but for different reasons: some networks suffered from localist as opposed to distributed representations; others used a slower learning rate parameter, while others were missing a hidden layer. As a group, the heterogenous networks did not differ significantly from networks that were homogenously impaired on 4 other measures of past tense formation (even using non-parametric tests).

Even if the average performance of heterogenous and homogenous groups on multiple behavioral measures is not different at a single point in time, one might expect that these groups would show different developmental trajectories. Again, Thomas shows that this is not necessarily the case; by comparing the "longitudinal performance" (across 5000 training sets) of the homogenous and heterogenous disordered networks, there was no significant difference between them on any of the behavioral measures in one study. In a second study, one behavioral measure showed a difference between the groups, but this was not the disorder's diagnostic measure.

However, all is not lost: Thomas showed that even if heterogenous and homogenous networks do not necessarily differ in mean behavioral scores, they do differ in the variability of those scores relative to one another, and the change in variability over time. Homogenous groups in fact show higher variability on the diagnostic measure than heterogenous groups, and show lower variability in the other measures than on the diagnostic measure. In addition, the variability of the homogenous group's measures decreases over time, whereas this trend is much less apparent in hetergenous group measures.

In an analysis of two groups of children, those with Williams syndrome and those with Word-Finding Difficulties (homogenous and heterogenous disorders, respectively), Thomas showed that these trends hold in real data as well.

Unfortunately, this study bears several caveats (several of which Thomas himself mentions). First, individual differences can confound these results. Second, differences in measurement error between groups can also contribute to variability. Third, the homogeneity of underlying disorders is unlikely to be so black-and-white in actual practice, and may actually be more graded.

But the largest limitation of this study is that diagnosing individuals on the basis of variability requires that you already have baseline variability measures for the homogenous and hetergenous groups. In other words, to diagnose an individual on the basis of his/her variability, you need to be able to compare his/her variability to the variability of the heterogenous and homogenous groups. Unfortunately, it is precisely this distinction between groups which doesn't currently exist for many disorders. Hypothetically speaking, one might use a classifier (such as a neural network, or a support vector machine) to separate those individuals manifesting the characteristics of homogenous disorders from those with heterogenous disorders, but this approach was not mentioned by Thomas and still involves the caveats mentioned above.

Reference:

Michael S.C. Thomas (2003) Multiple causality in developmental disorders: methodological implications from computational modelling. Developmental Science 6:5 (2003), pp 537–556