Predictive engineering models using the EPIC architecture for a high-performance task

Kieras, David E.; Wood, Scott D.; Meyer, David E.

doi:10.1145/223904.223906

Cited by 20 publications

(21 citation statements)

References 9 publications

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“…Current research in cognitive architectures assumes both more detail and more variety of human mechanisms, and so can potentially account for a wider and deeper range of design issues. Representative examples of such work use architectures that represent perceptual-cognitive-motor interactions [Kieras and Meyer 1996;Kieras et al 1995;Nelson et al 1994], comprehension processes [Doane et al 1992;Kitajima and Polson 1992], and problem-solving and learning mechanisms [Altmann et al 1995;Anderson 1993;Bauer and John 1995;Howes 1994;Polson and Lewis, 1990;Rieman et al 1994]. 7 Because these research efforts are rigorous and make use of computational models, they should eventually lead to engineering-style design tools for additional aspects of interface design and usability.…”

Section: Discussionmentioning

confidence: 99%

The GOMS family of user interface analysis techniques

John

Kieras

1996

ACM Trans. Comput.-Hum. Interact.

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540

266

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Since the publication of The Psychology of Human-Computer Interaction, the GOMS model has been one of the most widely known theoretical concepts in HCI. This concept has produced several GOMS analysis techniques that differ in appearance and form, underlying architectural assumptions, and predictive power. This article compares and contrasts four popular variants of the GOMS family (the Keystroke-Level Model, the original GOMS formulation, NGOMSL, and CPM-GOMS) by applying them to a single task example.

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Section: Discussionmentioning

confidence: 99%

The GOMS family of user interface analysis techniques

John

Kieras

1996

ACM Trans. Comput.-Hum. Interact.

Self Cite

540

266

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“…Developed and empirically validated by psychologists, CAs (such as ACT-R and EPIC) are production-rule-based blueprints of the operational structure of cognition [82,83]. In contrast to all the other models reviewed in this paper, only CAs explicitly include the workings of human memory in simulations.…”

Section: Logical Control In Humansmentioning

confidence: 99%

Achieving Closed‐Loop Control Simulation of Human‐Artefact Interaction: A Comparative Review

Vegte

Horváth

2011

Modelling and Simulation in Engineering

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To include user interactions in simulations of product use, the most common approach is to couple human subjects to simulation models, using hardware interfaces to close the simulation-control loop. Testing with virtual human models could offer a lowcost addition to evaluation with human subjects. This paper explores the possibilities for coupling human and artefact models to achieve fully software-based interaction simulations. We have critically reviewed existing partial solutions to simulate or execute control (both human control and product-embedded control) and compared solutions from literature with a proof-of-concept we have recently developed. Our concept closes all loops, but it does not rely on validated algorithms to predict human decision making and low-level human motor control. For low-level control, validated solutions are available from other approaches. For human decision making, however, validated algorithms exist only to predict the timing but not the reasoning behind it. To identify decision-making schemes beyond what designers can conjecture, testing with human subjects remains indispensable.

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“…Generative models [22] describes a set of valid (or plausible) paths to task completion. The term generative is drawn from generative grammars in formal linguistics.…”

Section: Generative and Recognition Behavior Modelsmentioning

confidence: 99%

Using GOMS and Bayesian plan recognition to develop recognition models of operator behavior

Zaientz¹,

DeKoven²,

Piegdon³

et al. 2006

SPIE Proceedings

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Trends in combat technology research point to an increasing role for uninhabited vehicles in modern warfare tactics. To support increased span of control over these vehicles human responsibilities need to be transformed from tedious, errorprone and cognition intensive operations into tasks that are more supervisory and manageable, even under intensely stressful conditions. The goal is to move away from only supporting human command of low-level system functions to intention-level human-system dialogue about the operator's tasks and situation.A critical element of this process is developing the means to identify when human operators need automated assistance and to identify what assistance they need. Toward this goal, we are developing an unmanned vehicle operator task recognition system that combines work in human behavior modeling and Bayesian plan recognition. Traditionally, human behavior models have been considered generative, meaning they describe all possible valid behaviors. Basing behavior recognition on models designed for behavior generation can offers advantages in improved model fidelity and reuse. It is not clear, however, how to reconcile the structural differences between behavior recognition and behavior modeling approaches.Our current work demonstrates that by pairing a cognitive psychology derived human behavior modeling approach, GOMS, with a Bayesian plan recognition engine, ASPRN, we can translate a behavior generation model into a recognition model. We will discuss the implications for using human performance models in this manner as well as suggest how this kind of modeling may be used to support the real-time control of multiple, uninhabited battlefield vehicles and other semi-autonomous systems.

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Predictive engineering models using the EPIC architecture for a high-performance task

Cited by 20 publications

References 9 publications

The GOMS family of user interface analysis techniques

The GOMS family of user interface analysis techniques

Achieving Closed‐Loop Control Simulation of Human‐Artefact Interaction: A Comparative Review

Using GOMS and Bayesian plan recognition to develop recognition models of operator behavior

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