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Supporting dynamic change detection: using the right tool for the task

Overview of attention for article published in Cognitive Research: Principles and Implications, December 2016
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Title
Supporting dynamic change detection: using the right tool for the task
Published in
Cognitive Research: Principles and Implications, December 2016
DOI 10.1186/s41235-016-0033-4
Pubmed ID
Authors

Benoît R. Vallières, Helen M. Hodgetts, François Vachon, Sébastien Tremblay

Abstract

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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 20%
Student > Doctoral Student 2 13%
Student > Master 2 13%
Student > Ph. D. Student 2 13%
Student > Bachelor 1 7%
Other 1 7%
Unknown 4 27%
Readers by discipline Count As %
Psychology 3 20%
Engineering 2 13%
Social Sciences 1 7%
Materials Science 1 7%
Neuroscience 1 7%
Other 0 0%
Unknown 7 47%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 May 2017.
All research outputs
#8,499,920
of 9,770,649 outputs
Outputs from Cognitive Research: Principles and Implications
#67
of 72 outputs
Outputs of similar age
#255,970
of 314,638 outputs
Outputs of similar age from Cognitive Research: Principles and Implications
#19
of 20 outputs
Altmetric has tracked 9,770,649 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 72 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 45.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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