Contrary to assumptions that changes in brain networks are possible only during crucial periods of development, research in the past decade has supported the idea of a permanently plastic brain. Novel experience, altered afferent input due to environmental changes and learning new skills are now recognized as modulators of brain function and underlying neuroanatomic circuitry. Given findings in experiments with animals and the recent discovery of increases in gray and white matter in the adult human brain as a result of (...) learning, the old concept of cognitive reserve, that is the ability to reinforce brain volume in crucial areas and thus provide a greater threshold for age-dependent deficits, has been reinforced. The challenge we face is to unravel the exact nature of the dynamic structural alterations and, ultimately, to be able to use this knowledge for disease management. Understanding normative changes in brain structure that occur as a result of environmental changes and demands is pivotal to understanding the characteristic ability of the brain to adapt. (shrink)

Saffran, Newport, and Aslin (1996a) found that human infants are sensitive to statistical regularities corresponding to lexical units when hearing an artificial spoken language. Two sorts of segmentation strategies have been proposed to account for this early word‐segmentation ability: bracketing strategies, in which infants are assumed to insert boundaries into continuous speech, and clustering strategies, in which infants are assumed to group certain speech sequences together into units (Swingley, 2005). In the present study, we test the predictions of two computational (...) models instantiating each of these strategies i.e., Serial Recurrent Networks: Elman, 1990; and Parser: Perruchet & Vinter, 1998 in an experiment where we compare the lexical and sublexical recognition performance of adults after hearing 2 or 10 min of an artificial spoken language. The results are consistent with Parser’s predictions and the clustering approach, showing that performance on words is better than performance on part‐words only after 10 min. This result suggests that word segmentation abilities are not merely due to stronger associations between sublexical units but to the emergence of stronger lexical representations during the development of speech perception processes. (shrink)

Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...) the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. (shrink)