Critical periods and catastrophic interference effects in the development of self-organizing feature maps

Learning nonnative speech sounds changes local encoding in the adult human cortex

Adults can learn to identify nonnative speech sounds with training, albeit with substantial variability in learning behavior. Increases in behavioral accuracy are associated with increased separability for sound representations in cortical speech areas. However, it remains unclear whether individual auditory neural populations all show the same types of changes with learning, or whether there are heterogeneous encoding patterns. Here, we used high-resolution direct neural recordings to examine local population response patterns, while native English listeners learned to recognize unfamiliar vocal pitch patterns in Mandarin Chinese tones. We found a distributed set of neural populations in bilateral superior temporal gyrus and ventrolateral frontal cortex, where the encoding of Mandarin tones changed throughout training as a function of trial-by-trial accuracy ("learning effect"), including both increases and decreases in the separability of tones. These populations were distinct from populations that showed changes as a function of exposure to the stimuli regardless of trial-by-trial accuracy. These learning effects were driven in part by more variable neural responses to repeated presentations of acoustically identical stimuli. Finally, learning effects could be predicted from speech-evoked activity even before training, suggesting that intrinsic properties of these populations make them amenable to behavior-related changes. Together, these results demonstrate that nonnative speech sound learning involves a wide array of changes in neural representations across a distributed set of brain regions.

Applying cognitive training to target executive functions during early development

Developmental psychopathology is increasingly recognizing the importance of distinguishing causal processes (i.e., the mechanisms that cause a disease) from developmental outcomes (i.e., the symptoms of the disorder as it is eventually diagnosed). Targeting causal processes early in disordered development may be more effective than waiting until outcomes are established and then trying to reverse the pathogenic process. In this review, I evaluate evidence suggesting that neural and behavioral plasticity may be greatest at very early stages of development. I also describe correlational evidence suggesting that, across a number of conditions, early emerging individual differences in attentional control and working memory may play a role in mediating later-developing differences in academic and other forms of learning. I review the currently small number of studies that applied direct and indirect cognitive training targeted at young individuals and discuss methodological challenges associated with targeting this age group. I also discuss a number of ways in which early, targeted cognitive training may be used to help us understand the developmental mechanisms subserving typical and atypical cognitive development.

Self-organizing map models of language acquisition

Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories.

Computational perspectives on cognitive development

This article reviews the efforts to develop process models of infants' and children's cognition. Computational process models provide a tool for elucidating the causal mechanisms involved in learning and development. The history of computational modeling in developmental psychology broadly follows the same trends that have run throughout cognitive science-including rule-based models, neural network (connectionist) models, ACT-R models, ART models, decision tree models, reinforcement learning models, and hybrid models among others. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

Studying development in the 21st Century

In this response, we consider four main issues arising from the commentaries to the target article. These include further details of the theory of interactive specialization, the relationship between neuroconstructivism and selectionism, the implications of neuroconstructivism for the notion of representation, and the role of genetics in theories of development. We conclude by stressing the importance of multidisciplinary approaches in the future study of cognitive development and by identifying the directions in which neuroconstructivism can expand in the Twenty-first Century.