How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with recent neurobiological evidence (...) that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning. (shrink)

Recent work in biology and cognitive science depicts a variety of target phenomena as the products of a tangled web of causal influences. Such influences may include both internal and external factors as well as complex patterns of reciprocal causal interaction. Such twisted tales are sometimes seen as a threat to explanatory strategies that invoke notions such as inner programs, genes for and sometimes even internal representations. But the threat, I shall argue, is more apparent than real. Complex causal influence, (...) in and of itself, provides no good reason to reject these familiar explanatory notions. To believe otherwise, I suggest, is generally to commit (at least) one of two seductive errors. The first error is to think that the general notion of a state x coding for an outcome y involves the state's constituting a full description of y. This is what I call the myth of the self-contained code. The second error is to think that the practice of treating certain factors as special (e.g., seeing genes as coding for outcomes in a way environmental factors do not) depends on the (often mistaken) belief that the singled out factor is somehow doing the most real work. Where the amounts of causal influence are evenly spread, it is assumed there can be no reason to treat one factor in a special way. This is what I term the Myth of Explanatory Equality. Avoiding these errors involves reminding ourselves of (1) the rich context-dependence of even standard, unproblematic uses of the notions of code, program and information content (all three make sense only relative to an assumed ecological backdrop) and (2) the difference between explaining why an event occurred and displaying the full workings of a complex causal system. (shrink)