Cognitive modeling has not yet played much of a role in the study of sociotechnical systems. Arguably, this is because most cognitive modeling systems were originally created to model microcognitive results, not the types of macrocognitive behaviors that drive sociotechnical systems (Klein et al., 2003). However, this does not mean that cognitive modeling systems cannot be adapted to deal with macrocognitive activities in ways that are relevant to cognitive engineering. Previous research using GOMS in sociotechnical systems indicated that GOMS is problematic to use when interruptions and task switching are common; therefore, we added new theoretical structures to GOMS to deal with these issues. We tested the system by constructing a model of routine network maintenance and installation at a large telecommunications company.We then compared the model predictions with observations of the work. The results showed that the model results were useful in guiding the research and organizing the findings.

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Reliability of voice-controlled Ambient Assisted Living (AAL) systems depends mostly on real-time recognition of speech commands coming from a noisy environment. This paper discusses the aspects of constructing anytime algorithms for robust speech recognition in AAL systems. In this case, command recognition is a speaker-dependent classification on a limited set of isolated phrases. An interruptible search can rule out every command that is not a possible solution according to various parameters of speech, and can identify a spoken command with a sufficiently high probability, while keeping time constraints in mind.