Being Fully Human: Feral Children and Developmental Critical Periods

Most people have heard stories of "wild children", children who grew without exposure to language, so that by the time they were finally exposed to a human language, their brains were no longer capable of learning to speak or understand. These true stories highlight the concept of a cortical "critical period", a time period in the child's life when he must either learn a skill, or the skill will be lost to him for the rest of his life. The concept of the feral child is also common in myth and fiction.

The stories of the feral children demonstrate the phenomenon of the critical period for language skills. Similar critical periods for mathematics, music, poetry, art, and spatial visualization skills are also likely, although the following discussion will explain why the concept is not as simple as it first appears.

Chris Chatham at Develintel Blog posts a fascinating examination of the cognitive modeling of "critical periods" using computational models.


A central theme of several articles in the May issue of Developmental Psychobiology is that future research must strive to explain the mechanisms that give rise to critical periods in development, rather than merely describing a relationship between plasticity and age. While some argue for the use of converging behavioral, neuropsychological, ERP, and fMRI techniques to achieve this goal, an article by Thomas & Johnson suggests that computational modeling is a particularly effective tool for any such attempt to causally link neural development with behavioral change.

The authors emphasize that computational simulations of critical or sensitive periods force theorists to become explicit about the precise nature of the representations in that problem domain or modality, and how those representations may change with age. Computational models also require that theorists indicate the exact kind of "input" required for a developing system to illustrate the critical period effect, as well as the frequencies with which those inputs are encountered.


....Sometimes, sensitive period effects can seem to result from increased competition for mental resources. For example, some children who appear to recover from brain damage will not manifest any particular deficit, but will show an overall decrement in cognitive performance

....The learning algorithms of neural network simulations also suggest other ways in which sensitive periods might emerge. For example, the Hebbian learning algorithm can be described as "fire together, wire together," and based on this description, it becomes clear that the more "active" brain would manifest more plasiticity. The authors go on to describe that both electrical activity and brain metabolism appear to peak in early to mid childhood, and that children's hemodynamic response tends to be more widespread than that in adults for the same tasks.

....In a simple model of imprinting, O'Reilly and Johnson illustrated how Hebbian learning can cause a self-organized termination of sensitivity to input. Their model was trained on Object A for 100 presentations, and then trained with 150 presentations of a very different looking object, Object D. Preference for an object was interpreted as the amount of activity on the output layer, and by the end of training, the network "preferred" Object D. However, if the network was trained on just 25 more presentations of A (bringing the total to 125), the network would never show a preference for Object D, despite over 900 presentations of that object. In this case, the connection weights within the network became entrenched as a result of a specific type and frequency of input, ultimately resulting in reduced sensitivity to further training. In other words, the "sensitive period" for this network closed between 100 and 125 presentations of Object A.

....Thomas & Johnson also describe how sensitive periods may seem to end as a result of endogenous factors, in which the potential for plasticity is reduced according to a strict developmental timeline. They used a three-layer model of past-tense acquisition, trained through backpropagation, with an input layer of 90 units, an output layer of 100 units, and a hidden layer of 100 units. Two pathways existed between input and ouput: a direct pathway between input and output, and an indirect pathway which connected the input to the output via the hidden layer.

The networks were damaged after 10, 50, 100, 250, 400, 450 or 490 training presentations by removing 75% of the connections between both pathways. After sustaining this damage, each network was trained for an additional 500 trials, and then tested on past-tense formation for both regular mappings (walk - walked) and three types of irregular mappings (run - ran, sleep - slept, go - went). Critically, the authors simulated reduced plasticity by including a small probability that any low connection weight would be destroyed after 100 presentations, which is roughly equivalent to the developmental time-course of synaptic overproduction and subsequent synaptic pruning throughout late childhood and adulthood. More at the source.

Computational modeling of neural development using neural nets and other models allows the sort of experiments that would be extremely difficult to monitor and interpret using animal models. Over time, these methods get more sophisticated and useful. Critical periods arise naturally from developmental phenomenon, including pruning of networks and evolutionary competition for neural resources in the developing brain.

By learning to utilise the critical periods in the upbringing of young humans, the inborn capacities of the young can be developed more fully. Although the "g" value on IQ tests might not be changed appreciably post-adolescence, the power of the "g" which is present could conceivably be multiplied considerably. This is one possible escape valve from the "genetics determines g -------> genetics is destiny" argument.

But only by approaching the issues honestly can the potential of newer generations of humans be augmented. The current denial of the genetic contribution to "g", and the current denial of the importance of "g", makes it very difficult to approach the subject clearly and soberly. Political Correctness has the effect of dooming the disadvantaged to more generations of disadvantage. Is that paradoxical? Is it by design? Ask the Nutzis who continue to push the PC agenda.