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

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

The Laminar Architecture of Visual Cortex and Image Processing Technology

The mammalian neocortex is organized into layers which include circuits that form functional columns in cortical maps. A major unsolved problem concerns how bottom-up, top-down, and horizontal interactions are organized within cortical layers to generate adaptive behaviors. This article summarizes a model, called the LAMINART model, of how these interactions help visual cortex to realize: (1) the binding process whereby cortex groups distributed data into coherent object representations; (2) the attentional process whereby cortex selectively processes important events; and (3) the developmental and learning processes whereby cortex stably grows and tunes its circuits to match environmental constraints. Such Laminar Computing completes perceptual groupings that realize the property of Analog Coherence, whereby winning groupings bind together their inducing features without losing their ability to represent analog values of these features. Laminar Computing also efficiently unifies the computational requirements of preattentive filtering and grouping with those of attentional selection. It hereby shows how Adaptive Resonance Theory (ART) principles may be realized within the laminar circuits of neocortex. Applications include boundary segmentation and surface filling-in algorithms for processing Synthetic Aperture Radar images.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); Office of Naval Research (N00014-95-1-0657

Artificial ontogenesis: a connectionist model of development

This thesis suggests that ontogenetic adaptive processes are important for generating intelligent beha- viour. It is thus proposed that such processes, as they occur in nature, need to be modelled and that such a model could be used for generating artificial intelligence, and specifically robotic intelligence. Hence, this thesis focuses on how mechanisms of intelligence are specified.A major problem in robotics is the need to predefine the behaviour to be followed by the robot. This makes design intractable for all but the simplest tasks and results in controllers that are specific to that particular task and are brittle when faced with unforeseen circumstances. These problems can be resolved by providing the robot with the ability to adapt the rules it follows and to autonomously create new rules for controlling behaviour. This solution thus depends on the predefinition of how rules to control behaviour are to be learnt rather than the predefinition of rules for behaviour themselves.Learning new rules for behaviour occurs during the developmental process in biology. Changes in the structure of the cerebral 'cortex underly behavioural and cognitive development throughout infancy and beyond. The uniformity of the neocortex suggests that there is significant computational uniformity across the cortex resulting from uniform mechanisms of development, and holds out the possibility of a general model of development. Development is an interactive process between genetic predefinition and environmental influences. This interactive process is constructive: qualitatively new behaviours are learnt by using simple abilities as a basis for learning more complex ones. The progressive increase in competence, provided by development, may be essential to make tractable the process of acquiring higher -level abilities.While simple behaviours can be triggered by direct sensory cues, more complex behaviours require the use of more abstract representations. There is thus a need to find representations at the correct level of abstraction appropriate to controlling each ability. In addition, finding the correct level of abstrac- tion makes tractable the task of associating sensory representations with motor actions. Hence, finding appropriate representations is important both for learning behaviours and for controlling behaviours. Representations can be found by recording regularities in the world or by discovering re- occurring pat- terns through repeated sensory -motor interactions. By recording regularities within the representations thus formed, more abstract representations can be found. Simple, non -abstract, representations thus provide the basis for learning more complex, abstract, representations.A modular neural network architecture is presented as a basis for a model of development. The pat- tern of activity of the neurons in an individual network constitutes a representation of the input to that network. This representation is formed through a novel, unsupervised, learning algorithm which adjusts the synaptic weights to improve the representation of the input data. Representations are formed by neurons learning to respond to correlated sets of inputs. Neurons thus became feature detectors or pat- tern recognisers. Because the nodes respond to patterns of inputs they encode more abstract features of the input than are explicitly encoded in the input data itself. In this way simple representations provide the basis for learning more complex representations. The algorithm allows both more abstract represent- ations to be formed by associating correlated, coincident, features together, and invariant representations to be formed by associating correlated, sequential, features together.The algorithm robustly learns accurate and stable representations, in a format most appropriate to the structure of the input data received: it can represent both single and multiple input features in both the discrete and continuous domains, using either topologically or non -topologically organised nodes. The output of one neural network is used to provide inputs for other networks. The robustness of the algorithm enables each neural network to be implemented using an identical algorithm. This allows a modular `assembly' of neural networks to be used for learning more complex abilities: the output activations of a network can be used as the input to other networks which can then find representations of more abstract information within the same input data; and, by defining the output activations of neurons in certain networks to have behavioural consequences it is possible to learn sensory -motor associations, to enable sensory representations to be used to control behaviour

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

Visuospatial coding as ubiquitous scaffolding for human cognition

For more than 100 years we have known that the visual field is mapped onto the surface of visual cortex, imposing an inherently spatial reference frame on visual information processing. Recent studies highlight visuospatial coding not only throughout visual cortex, but also brain areas not typically considered visual. Such widespread access to visuospatial coding raises important questions about its role in wider cognitive functioning. Here, we synthesise these recent developments and propose that visuospatial coding scaffolds human cognition by providing a reference frame through which neural computations interface with environmental statistics and task demands via perception–action loops