@article{gray00csj,
title = {The nature and processing of errors in interactive behavior},
author = { Wayne D. Gray},
year = {2000},
date = {2000-01-01},
journal = {Cognitive Science},
volume = {24},
number = {2},
pages = {205--248},
abstract = {Understanding the nature of errors in a simple, rule-based task -- programming a VCR -- required analyzing the interactions among human cognition, the artifact, and the task. This analysis was guided by least-effort principles and yielded a control structure that combined a device-task rule-hierarchy with display-based difference-reduction. A model based on this analysis was used to trace action protocols collected from participants as they programmed a simulated VCR. Trials that ended without success (the show was not correctly programmed) were interrogated to yield insights regarding problems in acquiring the control structure. For successful trials (the show was correctly programmed), steps that the model would make were categorized as matches to the model; steps that the model would not make were violations of the model. The model was able to trace the vast majority of correct keystrokes and yielded a business-as-usual account of the detection and correction of errors. Violations of the model fell into one of two fundamental categories. The model provided insights into certain subcategories of errors; whereas, regularities within other subcategories of error suggested limitations to the model. Although errors were rare when compared to the total number of correct actions, they were important. Problems with just 4% of the keypresses would have prevented two-thirds of the shows from being successfully recorded. A misprogrammed show is a minor annoyance to the user. However, devices with the approximate complexity of a VCR are ubiquitous and have found their way into emergency rooms, airplane cockpits, power plants, and so on. Errors of ignorance may be reduced by training; however, errors in the routine performance of skilled users can only be reduced by design.},
keywords = {computational cognitive model, display-based difference-reduction, errors, HCI, interactive behavior, least-cost, principles of cognitive engineering},
pubstate = {published},
tppubtype = {article}
}
Understanding the nature of errors in a simple, rule-based task -- programming a VCR -- required analyzing the interactions among human cognition, the artifact, and the task. This analysis was guided by least-effort principles and yielded a control structure that combined a device-task rule-hierarchy with display-based difference-reduction. A model based on this analysis was used to trace action protocols collected from participants as they programmed a simulated VCR. Trials that ended without success (the show was not correctly programmed) were interrogated to yield insights regarding problems in acquiring the control structure. For successful trials (the show was correctly programmed), steps that the model would make were categorized as matches to the model; steps that the model would not make were violations of the model. The model was able to trace the vast majority of correct keystrokes and yielded a business-as-usual account of the detection and correction of errors. Violations of the model fell into one of two fundamental categories. The model provided insights into certain subcategories of errors; whereas, regularities within other subcategories of error suggested limitations to the model. Although errors were rare when compared to the total number of correct actions, they were important. Problems with just 4% of the keypresses would have prevented two-thirds of the shows from being successfully recorded. A misprogrammed show is a minor annoyance to the user. However, devices with the approximate complexity of a VCR are ubiquitous and have found their way into emergency rooms, airplane cockpits, power plants, and so on. Errors of ignorance may be reduced by training; however, errors in the routine performance of skilled users can only be reduced by design.
@inbook{gray18strugmann,
title = {The Essence of Interaction in Boundedly Complex, Dynamic Task Environments},
author = {Gray, Wayne D. and Destefano, Marc and Lindstedt, John K. and Sibert, Catherine and Sangster, Matthew-Donald D.},
editor = {Gluck, K. A. and Laird, J. E.},
isbn = {978-0-262-03882-9},
pages = {147-165},
publisher = {The MIT Press},
address = {Cambridge, Massachusetts},
chapter = {10},
series = {Strungmann Forum Reports},
abstract = {Studying the essence of interaction requires task environments in which changes may arise due to the nature of the environment or due to the actions of agents in that environment. In dynamic environments, the agent's choice to do nothing does not stop the task environment from changing. Likewise, making a decision in such environments does not mean that the best decision, based on current information, will remain ``best'' as the task environment changes. In this paper, we summarize work in progress which is bringing the tools of experimental psychology, machine learning, and advanced statistical analyses to bear on understanding the complexity of interactive performance in complex tasks involving single or multiple interactive agents in dynamic environments.},
keywords = {dynamic task environment, interactive behavior, Tetris},
pubstate = {published},
tppubtype = {inbook}
}
Studying the essence of interaction requires task environments in which changes may arise due to the nature of the environment or due to the actions of agents in that environment. In dynamic environments, the agent's choice to do nothing does not stop the task environment from changing. Likewise, making a decision in such environments does not mean that the best decision, based on current information, will remain ``best'' as the task environment changes. In this paper, we summarize work in progress which is bringing the tools of experimental psychology, machine learning, and advanced statistical analyses to bear on understanding the complexity of interactive performance in complex tasks involving single or multiple interactive agents in dynamic environments.
