Cargando…
Supporting dynamic change detection: using the right tool for the task
Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid chang...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5256471/ https://www.ncbi.nlm.nih.gov/pubmed/28180182 http://dx.doi.org/10.1186/s41235-016-0033-4 |
_version_ | 1782498720110084096 |
---|---|
author | Vallières, Benoît R. Hodgetts, Helen M. Vachon, François Tremblay, Sébastien |
author_facet | Vallières, Benoît R. Hodgetts, Helen M. Vachon, François Tremblay, Sébastien |
author_sort | Vallières, Benoît R. |
collection | PubMed |
description | Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one’s own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142–153, 2011; J. Exp. Psychol. Appl. 19:403–419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition. |
format | Online Article Text |
id | pubmed-5256471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-52564712017-02-06 Supporting dynamic change detection: using the right tool for the task Vallières, Benoît R. Hodgetts, Helen M. Vachon, François Tremblay, Sébastien Cogn Res Princ Implic Original Article Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one’s own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142–153, 2011; J. Exp. Psychol. Appl. 19:403–419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition. Springer International Publishing 2016-12-19 /pmc/articles/PMC5256471/ /pubmed/28180182 http://dx.doi.org/10.1186/s41235-016-0033-4 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Vallières, Benoît R. Hodgetts, Helen M. Vachon, François Tremblay, Sébastien Supporting dynamic change detection: using the right tool for the task |
title | Supporting dynamic change detection: using the right tool for the task |
title_full | Supporting dynamic change detection: using the right tool for the task |
title_fullStr | Supporting dynamic change detection: using the right tool for the task |
title_full_unstemmed | Supporting dynamic change detection: using the right tool for the task |
title_short | Supporting dynamic change detection: using the right tool for the task |
title_sort | supporting dynamic change detection: using the right tool for the task |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5256471/ https://www.ncbi.nlm.nih.gov/pubmed/28180182 http://dx.doi.org/10.1186/s41235-016-0033-4 |
work_keys_str_mv | AT vallieresbenoitr supportingdynamicchangedetectionusingtherighttoolforthetask AT hodgettshelenm supportingdynamicchangedetectionusingtherighttoolforthetask AT vachonfrancois supportingdynamicchangedetectionusingtherighttoolforthetask AT tremblaysebastien supportingdynamicchangedetectionusingtherighttoolforthetask |