Socially-distributed cognition and cognitive architectures: towards an ACT-R-based cognitive social simulation capability

ACT-R is one of the most widely used cognitive architectures, and it has been used to model hundreds of phenomena described in the cognitive psychology literature. In spite of this, there are relatively few studies that have attempted to apply ACT-R to situations involving social interaction. This is an important omission since the social aspects of cognition have been a growing area of interest in the cognitive science community, and an understanding of the dynamics of collective cognition is of particular importance in many organizational settings. In order to support the computational modeling and simulation of socially-distributed cognitive processes, a simulation capability based on the ACT-R architecture is described. This capability features a number of extensions to the core ACT-R architecture that are intended to support social interaction and collaborative problem solving. The core features of a number of supporting applications and services are also described. These applications/services support the execution, monitoring and analysis of simulation experiments. Finally, a system designed to record human behavioral data in a collective problem-solving task is described. This system is being used to undertake a range of experiments with teams of human subjects, and it will ultimately support the development of high fidelity ACT-R cognitive models. Such models can be used in conjunction with the ACT-R simulation capability to test hypotheses concerning the interaction between cognitive, social and technological factors in tasks involving socially-distributed information processing

A Comparative Study of Reservoir Computing for Temporal Signal Processing

Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir. A readout layer is then trained to reconstruct a target output from the reservoir's state. The multitude of RC architectures and evaluation metrics poses a challenge to both practitioners and theorists who study the task-solving performance and computational power of RC. In addition, in contrast to traditional computation models, the reservoir is a dynamical system in which computation and memory are inseparable, and therefore hard to analyze. Here, we compare echo state networks (ESN), a popular RC architecture, with tapped-delay lines (DL) and nonlinear autoregressive exogenous (NARX) networks, which we use to model systems with limited computation and limited memory respectively. We compare the performance of the three systems while computing three common benchmark time series: H{\'e}non Map, NARMA10, and NARMA20. We find that the role of the reservoir in the reservoir computing paradigm goes beyond providing a memory of the past inputs. The DL and the NARX network have higher memorization capability, but fall short of the generalization power of the ESN

Active Virtual Network Management Prediction: Complexity as a Framework for Prediction, Optimization, and Assurance

Research into active networking has provided the incentive to re-visit what has traditionally been classified as distinct properties and characteristics of information transfer such as protocol versus service; at a more fundamental level this paper considers the blending of computation and communication by means of complexity. The specific service examined in this paper is network self-prediction enabled by Active Virtual Network Management Prediction. Computation/communication is analyzed via Kolmogorov Complexity. The result is a mechanism to understand and improve the performance of active networking and Active Virtual Network Management Prediction in particular. The Active Virtual Network Management Prediction mechanism allows information, in various states of algorithmic and static form, to be transported in the service of prediction for network management. The results are generally applicable to algorithmic transmission of information. Kolmogorov Complexity is used and experimentally validated as a theory describing the relationship among algorithmic compression, complexity, and prediction accuracy within an active network. Finally, the paper concludes with a complexity-based framework for Information Assurance that attempts to take a holistic view of vulnerability analysis