Introduction Integrating theory, data, and knowledge about cognitive psychology and human performance in a way that is useful for guiding design in HCI is still not a simple matter. However, there have been significant advances since Card, Moran, and Newell wrote the above passage. One of the key advances is the development of cognitive architecture, the subject of this chapter. The chapter will first consider the what it is to be cognitive architecture and why cognitive architecture is relevant for HCI. In order to detail the present state of cognitive architectures in HCI, it is important to consider some of the past use of cognitive architectures in HCI research. Then, four architectures actively in use in the research community (LICAI/CoLiDeS, Soar, EPIC, and ACT-R/ PM) and their application to HCI will be examined. The chapter will conclude with a discussion of the future of cognitive architectures in HCI. 1.1 What Are Cognitive Architectures? Most any di

ABSTRACT: We describe the CoJACK cognitive architecture. It is designed to provide an intuitive, high level behaviour representation language that exhibits variability across individuals and across time for use in synthetic environments. The included moderator layer enables modelling of physiological factors and affect. We report initial results of an investigation of variability and time-based moderation in the dTank simulation. The results show that moderation leads to interesting effects and that variability is generated in a principled and repeatable fashion. 1.

The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition—cognitively bounded rational analysis—that sharpens the predictive acuity of general, integrated theories of cognition and action. Such theories provide the necessary computational means to explain the flexible nature of human behavior but in doing so introduce extreme degrees of freedom in accounting for data. The new approach narrows the space of predicted behaviors through analysis of the payoff achieved by alternative strategies, rather than through fitting strategies and theoretical parameters to data. It extends and complements established approaches, including computational cognitive architectures, rational analysis, optimal motor control, bounded rationality, and signal detection theory. The authors illustrate the approach with a reanalysis of an existing account of psychological refractory period (PRP) dual-task performance and the development and analysis of a new theory of ordered dual-task responses. These analyses yield several novel results, including a new understanding of the role of strategic variation in existing accounts of PRP and the first predictive, quantitative account showing how the details of ordered dual-task phenomena emerge from the rational control of a cognitive system subject to the combined constraints of internal variance, motor interference, and a response selection bottleneck.

interaction, artificial intelligence. People make use of hidden external information, first recalling that it exists and then finding it. This dissertation investigates the memory phenomena involved in recalling that external information exists. We present data in which a programmer navigates to hidden features in a real-world task environment. We then present a model that accounts for this navigation by encoding and using simple episodic memories for having seen a feature. The model inherits constraints from its underlying cognitive architecture, which specify that learning is passive and pervasive, and that it creates simple memories that depend on the feature itself being present as a cue. The nature of these memories requires the model to recall features to its mind’s eye as cues in order to retrieve them. This retrieval process requires domain knowledge: familiarity with features in order to imagine them, and an idea of when it would be useful to recall having seen them. Recalling that a hidden feature exists prompts the model to scroll to that feature. Thus the model’s access to external information is a function of passively-encoded episodic memories, and retrieval of these memories using knowledge. As a claim applied to people, this appears to overlap with a recently-

Abstract. Posner and colleagues [38, 40] assert that attention comprises three distinct anatomical areas of the brain responsible for separate aspects of attention, namely alerting, orienting and executive control. Based on this view of attention, the work presented here computationally models the attentional networks task (ANT) which can be used to assess the efficiency and interactions of these disparate networks, collectively responsible for different functions related to attention mechanisms. The present research builds upon the model of ANT to show the modulation effects of one network on the other and suggests how the model can be used to simulate neglect conditions related to attention. The model is evaluated against data sets from experimental studies and the model’s fit to data is assessed statistically. Building such models of attention benefits computer vision research, as they are, well informed from both cognitive psychology and neuroscience perspectives.

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.

Abstract. Posner and colleagues [38,40] assert that attention comprises three distinct anatomical areas of the brain responsible for separate aspects of attention, namely alerting, orienting and executive control. Based on this view of attention, the work presented here computationally models the attentional networks task (ANT) which can be used to assess the efficiency and interactions of these disparate networks, collectively responsible for different functions related to attention mechanisms. The present research builds upon the model of ANT to show the modulation effects of one network on the other and suggests how the model can be used to simulate neglect conditions related to attention. The model is evaluated against data sets from experimental studies and the model’s fit to data is assessed statistically. Building such models of attention benefits computer vision research, as they are, well informed from both cognitive psychology and neuroscience perspectives.

Human factors is a disparate discipline. Academic programs in human factors are found in departments of industrial engineering, psychology, mechanical engineering, architecture, optometry, and elsewhere. Human factors professionals are employed in a variety of industries, at many levels in the organization chart, and hold an equally disparate array of job titles. However, many hold job titles along the lines of “human factors engineer, ” and many academic programs housed outside engineering departments still refer to an engineering component in their program – for instance, many human factors programs in psychology departments are termed engineering psychology. Thus, despite the fact that the field is far from uniform or unitary, there is clearly a strong engineering presence. Is this merely a label or is this how human factors is actually practiced? The Accreditation Board for Engineering and Technology (ABET, which accredits for engineering higher education in the United States) defines engineering as “the profession in which a knowledge of the mathematical and natural sciences gained by study, experience, and practice is applied with judgment to develop ways to utilize economically the materials and forces of nature for the benefit of mankind ” (ABET, 2003, back cover). For the purposes of this discussion, we will focus on the “mathematical and natural sciences” aspect of this definition. To what extent do mathematical and natural sciences guide work in human factors? It is our contention that the answer to this question has changed over time and, indeed, has been somewhat cyclical. Consider two relatively influential publications from two different points in time: 1984’s