Modeling developmental cognitive neuroscience

Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks

Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles.

Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.

Toward a Neuroscience of Adult Cognitive Developmental Theory

Piaget's genetic epistemology has provided the constructivist approach upon which child developmental theories were founded, in that infants are thought to progress through distinct cognitive stages until they reach maturity in their early 20's. However, it is now well established that cognition continues to develop after early adulthood, and several “neo-Piagetian” theories have emerged in an attempt to better characterize adult cognitive development. For example, Kegan's Constructive Developmental Theory (CDT) argues that the thought processes used by adults to construct their reality change over time, and reaching higher stages of cognitive development entails becoming objectively aware of emotions and beliefs that were previously in the realm of the subconscious. In recent years, neuroscience has shown a growing interest in the biological substrates and neural mechanisms encompassing adult cognitive development, because psychological and psychiatric disorders can arise from deficiencies therein. In this article, we will use Kegan's CDT as a framework to discuss adult cognitive development in relation to closely correlated existing constructs underlying social processing, such as the perception of self and others. We will review the functional imaging and electrophysiologic evidence behind two key concepts relating to these posited developmental changes. These include self-related processing, a field that distinguishes between having conscious experiences (“being a self”) and being aware of oneself having conscious experiences (“being aware of being a self”); and theory of mind, which is the objective awareness of possessing mental states such as beliefs and desires (i.e., having a “mind”) and the understanding that others possess mental states that can be different from one's own. We shall see that cortical midline structures, including the medial prefrontal cortex and cingulate gyrus, as well as the temporal lobe, are associated with psychological tasks that test these models. In addition, we will review computational modeling approaches to cognitive development, and show how mathematical modeling can provide insights into how sometimes continuous changes in the neural processing substrate can give rise to relatively discrete developmental stages. Because deficiencies in adult cognitive development can result in disorders such as autism and depression, bridging the gaps between developmental psychology, neuroscience, and modeling has potential implications for clinical practice. As neuromodulation techniques such as deep brain and transcranial stimulation continue to advance, interfacing with these systems may lead to the emergence of novel investigational methods and therapeutic strategies in adults suffering from developmental disorders.

Mechanisms of Hierarchical Cortical Maturation

Cortical information processing is structurally and functionally organized into hierarchical pathways, with primary sensory cortical regions providing modality specific information and associative cortical regions playing a more integrative role. Historically, there has been debate as to whether primary cortical regions mature earlier than associative cortical regions, or whether both primary and associative cortical regions mature simultaneously. Identifying whether primary and associative cortical regions mature hierarchically or simultaneously will not only deepen our understanding of the mechanisms that regulate brain maturation, but it will also provide fundamental insight into aspects of adolescent behavior, learning, neurodevelopmental disorders and computational models of neural processing. This mini-review article summarizes the current evidence supporting the sequential and hierarchical nature of cortical maturation, and then proposes a new cellular model underlying this process. Finally, unresolved issues associated with hierarchical cortical maturation are also addressed.

From perceptual to language-mediated categorization

From at least two months onwards, infants can form perceptual categories. During the first year of life, object knowledge develops from the ability to represent individual object features to representing correlations between attributes and to integrate information from different sources. At the end of the first year, these representations are shaped by labels, opening the way to conceptual knowledge. Here, we review the development of object knowledge and object categorization over the first year of life. We then present an artificial neural network model that models the transition from early perceptual categorization to categories mediated by labels. The model informs a current debate on the role of labels in object categorization by suggesting that although labels do not act as object features they nevertheless affect perceived similarity of perceptually distinct objects sharing the same label. The model presents the first step of an integrated account from early perceptual categorization to language-based concept learning.

To Model or Not to Model? A Dialogue on the Role of Computational Modeling in Developmental Science

All sciences use models of some variety to understand complex phenomena. In developmental science, however, modeling is mostly limited to linear, algebraic descriptions of behavioral data. Some researchers have suggested that complex mathematical models of developmental phenomena are a viable (even necessary) tool that provide fertile ground for developing and testing theory as well as for generating new hypotheses and predictions. This paper explores the concerns, attitudes, and historical trends that underlie the tension between two cultures: one in which computational simulations of behavior are an important complement to observation and experimentation, and another which emphasizes evidence from behavioral experiments and linear models enhanced by verbal descriptions. This tension is explored as a dialogue between three characters: Ed (Experimental Developmentalist), Mira (Modeling Inclusive Research Advocate), and Phil (Philosopher of Science).

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.

On the mental representations originating during the interaction between language and vision

The interaction between vision and language processing is clearly of interest to both cognitive psychologists and psycholinguists. Recent research has begun to create understanding of the interaction between vision and language in terms of the representational issues involved. In this paper, we first review some of the theoretical and methodological issues in the current vision-language interaction debate. Later, we develop a model that attempts to account for effects of affordances and visual context on language-scene interaction as well as the role of sensorimotor simulation. The paper addresses theoretical issues related to the mental representations that arise when visual and linguistic systems interact.

Changes in the interaction of resting-state neural networks from adolescence to adulthood

This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12-30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks.

Age-related cognitive gains are mediated by the effects of white matter development on brain network integration

A fundamental, yet rarely tested premise of developmental cognitive neuroscience is that changes in brain activity and improvements in behavioral control across adolescent development are related to brain maturational factors that shape a more efficient, highly-interconnected brain in adulthood. We present the first multimodal neuroimaging study to empirically demonstrate that maturation of executive cognitive ability is directly associated with the relationship of white matter development and age-related changes in neural network functional integration. In this study, we identified specific white matter regions whose maturation across adolescence appears to reduce reliance on local processing in brain regions recruited for conscious, deliberate cognitive control in favor of a more widely distributed profile of functionally-integrated brain activity. Greater white matter coherence with age was associated with both increases and decreases in functional connectivity within task-engaged functional circuits. Importantly, these associations between white matter development and brain system functional integration were related to behavioral performance on tests of response inhibition, demonstrating their importance in the maturation of optimal cognitive control.

Unimodal experience constrains while multisensory experiences enrich cognitive construction

Mareschal and his colleagues argue that cognition consists of partial representations emerging from organismic constraints placed on information processing through development. However, any notion of constraints must consider multiple sensory modalities, and their gradual integration across development. Multisensory integration constitutes one important way in which developmental constraints may lead to enriched representations that serve more than immediate behavioural goals.

Neuroconstructivism

Neuroconstructivism is a theoretical framework focusing on the construction of representations in the developing brain. Cognitive development is explained as emerging from the experience-dependent development of neural structures supporting mental representations. Neural development occurs in the context of multiple interacting constraints acting on different levels, from the individual cell to the external environment of the developing child. Cognitive development can thus be understood as a trajectory originating from the constraints on the underlying neural structures. This perspective offers an integrated view of normal and abnormal development as well as of development and adult processing, and it stands apart from traditional cognitive approaches in taking seriously the constraints on cognition inherent to the substrate that delivers it.