David E. Kieras

An approach to the formal analysis of user complexity

A formal approach to analysing the user complexity of interactive systems or devices is described, based on theoretical results from cognitive psychology. The users knowledge of how to use a system to accomplish the various tasks is represented in a procedural notation that permits quantification of the amount and complexity of the knowledge required and the cognitive processing load involved in using a system. Making a system more usable can be accomplished by altering its design until the knowledge is adequately simplified. By representing the device behaviour formally as well, it is possible to simulate the user-device interaction to obtain rigorous measures of user complexity.

An overview of the EPIC architecture for cognition and performance with application to human-computer interaction

EPIC (Executive Process-Interactive Control) is a cognitive architecture especially suited for modeling human multimodal and multiple-task performance. The EPIC architecture includes peripheral sensory-motor processors surrounding a production-rule cognitive processor and is being used to construct precise computational models for a variety of human-computer interaction situations. We briefly describe some of these models to demonstrate how EPIC clarifies basic properties of human performance and provides usefully precise accounts of performance speed.

The GOMS family of user interface analysis techniques: comparison and contrast

Sine 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 severval GOMS analysis techniques that differ in appearance and form, underlying architectural assumptions, and predictive power. This article compares and contrasts four popular variantsof the GOMS family (the Keystroke-Level Model, the original GOMS formulation, NGOMSL, and CPM-GOMS) by applying them to a single task example.

The Role of a Mental Model in Learning to Operate a Device.

This report presents three studies concerned with learning how to operate a simple control panel device, and how this learning is affected by understanding a device model that describes the internal mechanism of the device. The first experiment compared two groups, one of which learned a set of operating procedures for the device by rote, and the other learned the device model before receiving the identical procedure training. The model group learned the procedures faster, retained them more accurately, executed them faster, and simplified inefficient procedures far more often, than the rote group. The second study demonstrated that the model group is able to infer the procedures much more easily than the rote group, which would lead to more rapid learning and better recall performance. The third study showed that the important content of the device model was the specific configuration of components and controls, and not the motivational aspects, component descriptions, or general principles. This specific information is what is logically required to infer the procedures. Thus, the benefits of having a device model depend on whether it supports direct and simple inference of the exact steps required to operate the device.

A Computational Theory of Executive Cognitive Processes and Multiple-Task Performance: Part 2. Accounts of Psychological Refractory-Period Phenomena

Abstract : Further simulations of multiple task performance have been conducted with computational models that are based on the Executive Process Interactive Control (EPIC) architecture for human information processing. These models account well for patterns of reaction times and psychological refractory period phenomena (delays of overt responses after short stimulus onset asynchronies) in a variety of laboratory paradigms and realistic situations. This supports the claim of the present theoretical framework that multiple task performance relies on adaptive executive control, which enables substantial amounts of temporal overlap among stimulus identification, response selection, and movement production processes for concurrent tasks. Such overlap is achieved through optimized task scheduling by flexible executive processes that satisfy prevailing instructions about task priorities and allocate limited capacity perceptual motor resources efficiently.

Using GOMS for user interface design and evaluation: which technique?

Since the seminal book, The Psychology of Human-Computer Interaction, the GOMS model has been one of the few widely known theoretical concepts in human-computer interaction. This concept has spawned much research to verify and extend the original work and has been used in real-world design and evaluation situations. This article synthesizes the previous work on GOMS to provide an integrated view of GOMS models and how they can be used in design. We briefly describe the major variants of GOMS that have matured sufficiently to be used in actual design. We then provide guidance to practitioners about which GOMS variant to use for different design situations. Finally, we present examples of the application of GOMS to practical design problems and then summarize the lessons learned.

Virtually Perfect Time Sharing in Dual-Task Performance: Uncorking the Central Cognitive Bottleneck

A fundamental issue for psychological science concerns the extent to which people can simultaneously perform two perceptual-motor tasks. Some theorists have hypothesized that such dual-task performance is severely and persistently constrained by a central cognitive “bottleneck,” whereas others have hypothesized that skilled procedural decision making and response selection for two or more tasks can proceed at the same time under adaptive executive control. The three experiments reported here support this latter hypothesis. Their results show that after relatively modest amounts of practice, at least some participants achieve virtually perfect time sharing in the dual-task performance of basic choice reaction tasks. The results also show that observed interference between tasks can be modulated by instructions about differential task priorities and personal preferences for daring (concurrent) or cautious (successive) scheduling of tasks. Given this outcome, future research should investigate exactly when and how such sophisticated skills in dual-task performance are acquired.

Towards a Practical GOMS Model Methodology for User Interface Design

Publisher Summary This chapter presents a practical GOMS model methodology for user interface design. The basic approach to user-interface design using the cognitive complexity approach would be that the iterative design process would be followed, but with the evaluation of a proposed design being done with simulation techniques rather than actual human user testing; only a final test of the design would require actual user testing. Additional user testing would be involved to develop aspects of the design, such as screen layout, that are not directly addressed by an analysis of the procedures entailed by the design. There are several problems in using the cognitive complexity approach as a design tool that have become clear from technology transfer. The chapter presents two critical problems: (1) the difficulty of constructing production rule simulation models, and (2) the difficulty of doing, in a standardized and reliable way, the detailed task analysis required to construct the representation of the procedural knowledge that the user should have to operate the system.

The role of a mental model in learning to operate a device

Abstract : This report presents two studies concerned with learning how to operate a simple control panel device, and how this learning is affected by understanding the internal structure of the device, which is a device model for the device. The first experiment compared two groups, one of which learned a set of operating procedures for the device by rote, and the other learned the device model before receiving the identical procedure training. The model group learned the procedures faster, and even after one week, retrained them better and executed them faster; a typical effect size is a 20% improvement. Furthermore, the model group could simplify, or make more efficient, the procedures far more often then the rote group. The second study examined the hypothesis that the improvement is due to the model group being able to infer the procedures, which would lead to more rapid learning and better recall performance. The same group manipulation was used, but subjects inferred the procedures rather than learning them, and thought out loud while doing so. The model group based their reasoning on direct inferences from the device model, and inferred the correct procedures in almost the minimum amount of time.

The acquisition and performance of text-editing skill: a cognitive complexity analysis

Kieras and Polson (1985) proposed an approach for making quantitative predictions on ease of learning and ease of use of a system, based on a production system version of the goals, operators, methods, and selection rules (GOMS) model of Card, Moran, and Newell (1983). This article describes the principles for constructing such models and obtaining predictions of learning and execution time. A production rule model for a simulated text editor is described in detail and is compared to experimental data on learning and performance. The model accounted well for both learning and execution time and for the details of the increase in speed with practice. The relationship between the performance model and the Keystroke-Level Model of Card et al. (1983) is discussed. The results provide strong support for the original proposal that production rule models can make quantitative predictions for both ease of learning and ease of use.