There has been a proliferation of proposed mental modules in an attempt to account for different cognitive functions but so far there has been no successful account of their integration. ACT-R (Anderson & Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how they are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. Empirical examples are presented that illustrate the predictions of ACT-R’s modules. In addition, two models of complex tasks are described to illustrate how these modules result in strong predictions when they are brought together. One of these models is concerned with complex patterns of behavioral data in a dynamic task and the other is concerned with fMRI data obtained in a study of symbol manipulation.

Usability of complex dynamic human computer interfaces can be evaluated by cognitive modeling to investigate cognitive processes and their underlying structures. Even though the prediction of human behavior can help to detect errors in the interaction design and cognitive demands of the future user the method is not widely applied. The time-consuming transformation of a problem “in the world ” into a “computational model ” and the lack of fine-grained analysis of simulation data are mainly responsible for this. Having realized these drawbacks we developed HTAmap and SimTrA to simplify the development and analysis of cognitive models. HTAmap, a high-level framework for cognitive modeling, aims to reduce the modeling effort. Within HTAmap the process of building cognitive models is transformed into a pattern-oriented task, based on “cognitive activity patterns”. SimTrA supports the analysis of cognitive model data on an overall and microstructure level and enables the user to automatically compare simulated data with empirical data. This paper describes both concepts and first implementations. The practicability of both tools is shown using an example in the domain of process control.

The Problem. The driving task involves a host of relevant cognitive, perceptual, and motor abilities. Cognitive architectures—frameworks for modeling human cognition and behavior—have evolved to integrate known theories of human cognition into unified theories that account for a wide range of cognitive phenomena. Role of Driving Simulators. Models of driver behavior based on cognitive architectures can be linked into driving simulators to act as virtual drivers, using simulated perceptual and motor processes to drive in the simulator environment. Typically, the output of these virtual drivers is analogous to that for human drivers, thus facilitating comparison between model and human data. Key Results of Driving Simulator Studies. This chapter highlights one model in particular developed in the ACT-R cognitive architecture. This driver model has been shown to account for several important aspects of driver behavior, particularly in the context of curve negotiation and lane changing. Scenarios and Dependent Variables. The ACT-R driver model can be placed in various environments with driving as the only task or with driving while performing another secondary task (such as dialing a cellular phone). The model’s simulation output includes predictions of both driver behavior as well as behavior on the secondary task (e.g., total dialing time). Platform Specificity and Equipment Limitations. The ACT-R driver model currently runs on a Macintosh platform and LISP environment. However, a recent Java re-implementation of the model within the Distract-R prototyping system has made the model more widely available on a range of platforms, in addition to facilitating its use by non-modeling designers and practitioners. 1. Keywords, Key points

Abstract. The evaluation and design of user interfaces may be facilitated by using performance models based on cognitive architectures. A recent trend in HCI is the increased focus on perceptual and motor-related aspects of the interaction. With respect to this focus, we present the foundations of HiTEC, a new cognitive architecture based on recent findings of interactions between perception and action in the domain of cognitive psychology. This approach is contrasted with existing architectures.

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Treating simulations as theories by not sampling their behavior

Model comparison is vital to evaluating progress in the fields of artificial general intelligence (AGI) and cognitive architecture. As they mature, AGI and cognitive architectures will become increasingly capable of providing a single model that completes a multitude of tasks, some of which the model was not specifically engineered to perform. These models will be expected to operate for extended periods of time and serve functional roles in real-world contexts. Questions arise regarding how to evaluate such models appropriately, including issues pertaining to model comparison and validation. In this paper, we specifically address model validation across multiple levels of abstraction, using an existing computational process model of unmanned aerial vehicle basic maneuvering to illustrate the relationship between validity and timescales of analysis.