R is a very powerful framework for statistical modeling. Thus, it is of high importance to integrate R with state-of-theart tools in NLP. In this paper, we present the functionality and architecture of such an integration by means of TextImager. We use the OpenCPU API to integrate R based on our own R-Server. This allows for communicating with R-packages and combining them with TextImager’s NLP...

This paper presents an alternative approach for obtaining a converse Lyapunov theorem for discrete-time systems. The proposed approach is constructive, as it provides an explicit Lyapunov function. The developed converse Lyapunov theorem establishes existence of global Lyapunov functions for globally exponentially stable (GES) systems and for globally asymptotically stable systems, Lyapunov fun...

While computational cognitive modeling has made great strides in addressing complex dynamic tasks, the modeling of individual differences in complex tasks remains a largely unexplored area of research. In this paper we present a straightforward approach to modeling individual differences, specifically age-related cognitive differences, in complex tasks, and illustrate the application of this ap...

This paper presents a cognitive model of a subliminal priming task, using Retrieval by ACcumulating Evidence (RACE), a model of declarative memory retrieval. RACE is implemented as an extension to the ACT-R architecture of cognition. First, we will discuss the exact implementation of RACE within the constraints imposed by ACT-R. Second, we will discuss the subliminal priming task that we modele...

Why do we build models? There are two basic reasons: explanation or prediction [Ripley, 2004]. Using large ensembles of models for prediction is commonplace, but is rarely used for explanation, where we typically choose one “best” model. When there are several equally good models, it is common sense to look at them too, but can the “bad” models tell us something as well? This paper describes ex...

This paper presents an implementation of a cognitive model of a complex real-world task in the cognitive architecture Soar. During the implementation process there were lessons learned on various aspects, such as the retrieval of working memory elements with relative values, alternative approaches to reasoning, and reasoning control. Additionally, the implementation is compared to an earlier im...