Connectionist Models Of Development Research Articles

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

The use of self-organizing feature maps (SOFM) in models of cognitive development has frequently been associated with explanations of critical or sensitive periods. By contrast, error-driven connectionist models of development have been linked with catastrophic interference between new knowledge and old knowledge. We introduce a set of simulations that systematically evaluate the conditions under which SOFMs demonstrate critical/sensitive periods in development versus those under which they display interference effects. We explored the relative contribution of network parameters (for example, whether learning rate and neighbourhood reduce across training), the representational resources available to the network, and the similarity between old and new knowledge in determining the functional plasticity of the maps. The SOFMs that achieved the best discrimination and topographic organization also exhibited sensitive periods in development while showing lower plasticity and hence limited interference. However, fast developing, coarser SOFMs also produced topologically organized representations, while permanently retaining their plasticity. We argue that the impact of map organization on behaviour must be interpreted in terms of the cognitive processes that the map is driving.

Rule following and rule use in the balance-scale task

Quinlan et al. [Quinlan, p., van der Mass, H., Jansen, B., Booij, O., & Rendell, M. (this issue). Re-thinking stages of cognitive development: An appraisal of connectionist models of the balance scale task. Cognition, doi:10.1016/j.cognition.2006.02.004] use Latent Class Analysis (LCA) to criticize a connectionist model of development on the balance-scale task, arguing that LCA shows that this model fails to capture a torque rule and exhibits rules that children do not. In this rejoinder we focus on the latter problem, noting the tendency of LCA to find small, unreliable, and difficult-to-interpret classes. This tendency is documented in network and synthetic simulations and in psychological research, and statistical reasons for finding such unreliable classes are discussed. We recommend that LCA should be used with care, and argue that its small and unreliable classes should be discounted. Further, we note that a preoccupation with diagnosing rules ignores important phenomena that rules do not account for. Finally, we conjecture that simple extensions of the network model should be able to achieve torque-rule performance.

Beyond backprop: emerging trends in connectionist models of development: an introduction

Since the publication in 1986 of Rumelhart and McClelland’s Parallel distributed processing: Explorations in the microstructure of cognition , neural network or connectionist models have become an increasingly common method for studying learning and development. A wide range of developmental domains have been investigated with connectionist models, including language acquisition, perceptual development, object permanence, developmental psychopathology and motor skill acquisition. Many of these models rely on the backpropagation-of-error learning algorithm, a form of supervised learning in which a ‘teacher’ shapes the output of the network by providing it with desired responses.

Connectionist models of development

AbstractHow have connectionist models informed the study of development? This paper considers three contributions from specific models. First, connectionist models have proven useful for exploring nonlinear dynamics and emergent properties, and their role in nonlinear developmental trajectories, critical periods and developmental disorders. Second, connectionist models have informed the study of the representations that lead to behavioral dissociations. Third, connectionist models have provided insight into neural mechanisms, and why different brain regions are specialized for different functions. Connectionist and dynamic systems approaches to development have differed, with connectionist approaches focused on learning processes and representations in cognitive tasks, and dynamic systems approaches focused on mathematical characterizations of physical elements of the system and their interactions with the environment. The two approaches also share much in common, such as their emphasis on continuous, nonlinear processes and their broad application to a range of behaviors.

A connectionist model of development

We present a phenomenological modeling framework for development. Our purpose is to provide a systematic method for discovering and expressing correlations in experimental data on gene expression and other developmental processes. The modeling framework is based on a connectionist or "neural net" dynamics for biochemical regulators, coupled to "grammatical rules" which describe certain features of the birth, growth, and death of cells, synapses and other biological entities. We outline how spatial geometry can be included, although this part of the model is not complete. As an example of the application of our results to a specific biological system, we show in detail how to derive a rigorously testable model of the network of segmentation genes operating in the blastoderm of Drosophila. To further illustrate our methods, we sketch how they could be applied to two other important developmental processes: cell cycle control and cell-cell induction. We also present a simple biochemical model leading to our assumed connectionist dynamics which shows that the dynamics used is at least compatible with known chemical mechanisms.