Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment

THE CHILD AND THE WORLD: How Children acquire Language

HOW CHILDREN ACQUIRE LANGUAGE Over the last few decades research into child language acquisition has been revolutionized by the use of ingenious new techniques which allow one to investigate what in fact infants (that is children not yet able to speak) can perceive when exposed to a stream of speech sound, the discriminations they can make between different speech sounds, differentspeech sound sequences and different words. However on the central features of the mystery, the extraordinarily rapid acquisition of lexicon and complex syntactic structures, little solid progress has been made. The questions being researched are how infants acquire and produce the speech sounds (phonemes) of the community language; how infants find words in the stream of speech; and how they link words to perceived objects or action, that is, discover meanings. In a recent general review in Nature of children's language acquisition, Patricia Kuhl also asked why we do not learn new languages as easily at 50 as at 5 and why computers have not cracked the human linguistic code. The motor theory of language function and origin makes possible a plausible account of child language acquisition generally from which answers can be derived also to these further questions. Why computers so far have been unable to 'crack' the language problem becomes apparent in the light of the motor theory account: computers can have no natural relation between words and their meanings; they have no conceptual store to which the network of words is linked nor do they have the innate aspects of language functioning - represented by function words; computers have no direct links between speech sounds and movement patterns and they do not have the instantly integrated neural patterning underlying thought - they necessarily operate serially and hierarchically. Adults find the acquisition of a new language much more difficult than children do because they are already neurally committed to the link between the words of their first language and the elements in their conceptual store. A second language being acquired by an adult is in direct competition for neural space with the network structures established for the first language

EEG complexity as a biomarker for autism spectrum disorder risk

BACKGROUND: Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD. METHODS: Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months. RESULTS: Multiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter. CONCLUSIONS: This proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder.This research was supported by a grant from Autism Speaks (to HTF), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN and WJB). We thank the following people for their help in data collection: Tara Augenstein, Leah Casner, Laura Kasparian, Nina Leezenbaum, Vanessa Vogel-Farley and Annemarie Zuluaga. We are especially grateful to the families who participated in this study. (Autism Speaks; R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - National Institute on Deafness and Other Communication Disorders (NIDCD); Simons Foundation

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Knowing who likes who: The early developmental basis of coalition understanding

Group biases based on broad category membership appear early in human development. However, like many other primates humans inhabit social worlds also characterised by small groups of social coalitions which are not demarcated by visible signs or social markers. A critical cognitive challenge for a young child is thus how to extract information concerning coalition structure when coalitions are dynamic and may lack stable and outwardly visible cues to membership. Therefore, the ability to decode behavioural cues of affiliations present in everyday social interactions between individuals would have conferred powerful selective advantages during our evolution. This would suggest that such an ability may emerge early in life, however, little research has investigated the developmental origins of such processing. The present paper will review recent empirical research which indicates that in the first 2 years of life infants achieve a host of social-cognitive abilities that make them well adapted to processing coalition-affiliations of others. We suggest that such an approach can be applied to better understand the origins of intergroup attitudes and biases. Copyright © 2010 John Wiley & Sons, Ltd

Reduced neural sensitivity to social stimuli in infants at risk for autism

In the hope of discovering early markers of autism, attention has recently turned to the study of infants at risk owing to being the younger siblings of children with autism. Because the condition is highly heritable, later-born siblings of diagnosed children are at substantially higher risk for developing autism or the broader autism phenotype than the general population. Currently, there are no strong predictors of autism in early infancy and diagnosis is not reliable until around 3 years of age. Because indicators of brain functioning may be sensitive predictors, and atypical social interactions are characteristic of the syndrome, we examined whether temporal lobe specialization for processing visual and auditory social stimuli during infancy differs in infants at risk. In a functional near-infrared spectroscopy study, infants aged 4–6 months at risk for autism showed less selective neural responses to social stimuli (auditory and visual) than low-risk controls. These group differences could not be attributed to overall levels of attention, developmental stage or chronological age. Our results provide the first demonstration of specific differences in localizable brain function within the first 6 months of life in a group of infants at risk for autism. Further, these differences closely resemble known patterns of neural atypicality in children and adults with autism. Future work will determine whether these differences in infant neural responses to social stimuli predict either later autism or the broader autism phenotype frequently seen in unaffected family members

Infant cortex responds to other humans from shortly after birth

A significant feature of the adult human brain is its ability to selectively process information about conspecifics. Much debate has centred on whether this specialization is primarily a result of phylogenetic adaptation, or whether the brain acquires expertise in processing social stimuli as a result of its being born into an intensely social environment. Here we study the haemodynamic response in cortical areas of newborns (1–5 days old) while they passively viewed dynamic human or mechanical action videos. We observed activation selective to a dynamic face stimulus over bilateral posterior temporal cortex, but no activation in response to a moving human arm. This selective activation to the social stimulus correlated with age in hours over the first few days post partum. Thus, even very limited experience of face-to-face interaction with other humans may be sufficient to elicit social stimulus activation of relevant cortical regions