Abstract: "We present an analysis of an expert performing a highly interactive computer task. The analysis uses GOMS models, specifying the Goals, Operators, Methods, and Selection rules used by the expert; the GOMS models are implemented within an unified theory of cognition called Soar. Two models are presented, one with function-level operators which perform high- level functions in the domain, and one with keystroke-level operators which describe hand movements. For a segment of behavior in which the expert accomplished about 30 functions in about 30 seconds, the function-level model predicted the observed behavior well, while the keystroke-level model predicted only about half of the observed hand movements. These results, including th...
Since the seminal book, The Psychology of Human-Computer Interaction, the GOMS model has been one of...
The keystroke-level model (KLM) is the simplest model of the goals, operators, methods, and selectio...
Computer Go presents a challenging problem for machine learning agents. With the number of possible ...
Analyzing a task into Goals, Operators, Methods, and Selection rules (GOMS) is an established method...
The purpose of this research effort was to develop a practical computer-assisted aid for creating de...
The purpose of this research effort was to develop a practical computer-assisted aid for creating de...
A priori prediction of skilled human performance has the potential to be of great practical value bu...
Since the publication of The Psychology of Human-Computer Interaction, the GOMS model has been one o...
This article briefly discussed ways to overcome the limitations of existing GOMS (Goals, Operators, ...
The primary purpose of this work is to develop a methodology for designing human-error tolerant syst...
We describe a system, G2A, that produces ACT-R models from GOMS models containing hierarchical metho...
This paper is concerned with the problem of learning how to interact safely with complex automated s...
We report a way to build a series of GOMS-like cognitive user models representing a range of perform...
This research paper presents a feedback generation system using the Natural GOMS Language (NGOMSL) t...
GOMS model je široko poznati koncept koji se javlja u interakciji između računala i čovjeka. Od ini...
Since the seminal book, The Psychology of Human-Computer Interaction, the GOMS model has been one of...
The keystroke-level model (KLM) is the simplest model of the goals, operators, methods, and selectio...
Computer Go presents a challenging problem for machine learning agents. With the number of possible ...
Analyzing a task into Goals, Operators, Methods, and Selection rules (GOMS) is an established method...
The purpose of this research effort was to develop a practical computer-assisted aid for creating de...
The purpose of this research effort was to develop a practical computer-assisted aid for creating de...
A priori prediction of skilled human performance has the potential to be of great practical value bu...
Since the publication of The Psychology of Human-Computer Interaction, the GOMS model has been one o...
This article briefly discussed ways to overcome the limitations of existing GOMS (Goals, Operators, ...
The primary purpose of this work is to develop a methodology for designing human-error tolerant syst...
We describe a system, G2A, that produces ACT-R models from GOMS models containing hierarchical metho...
This paper is concerned with the problem of learning how to interact safely with complex automated s...
We report a way to build a series of GOMS-like cognitive user models representing a range of perform...
This research paper presents a feedback generation system using the Natural GOMS Language (NGOMSL) t...
GOMS model je široko poznati koncept koji se javlja u interakciji između računala i čovjeka. Od ini...
Since the seminal book, The Psychology of Human-Computer Interaction, the GOMS model has been one of...
The keystroke-level model (KLM) is the simplest model of the goals, operators, methods, and selectio...
Computer Go presents a challenging problem for machine learning agents. With the number of possible ...