Peripheral Notifications in Large Displays

Mairena, Aristides; Gutwin, Carl; Cockburn, Andy

doi:10.1145/3290605.3300870

Cited by 18 publications

(13 citation statements)

References 34 publications

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“…A one-way repeated measures ANOVA (RM-ANOVA) with a Greenhouse-Geisser correction found a significant main effect of location on response time (F1.17,10.54 = 9.48, p < 0.01). A Bonferroni posthoc comparison showed that the participants had a significantly faster response time for the center locations (M = 0.399s, SD = 0.086) compared to the right-side locations (M = 0.444s, SD = 0.172); this is similar to prior work, which has found lower detection accuracy for the right-side of the visual field [59]. There was no significant difference in response time between the center and leftside locations (M = 0.418s, SD = 0.138).…”

Section: Data Analysis and Resultssupporting

confidence: 86%

“…We decided to place the secondary information on the left-side because we did not want the quantity of the information to block the participant's view, and there was no significant difference in response time between the center and left-side locations. Also, people exhibit a leftward visual bias, known as pseudoneglect [60,61], which results in higher detection accuracy and faster motion processing for elements on the left when compared to the right [59,61,62]; even for computer screens [59]. Although pseudoneglect occurs in both righthanded and left-handed people, it is not evident in cultures that read right-to-left [63].…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

“…Another limitation for the first study was that the visual stimulus constantly changed, which could have prompted the participants to be more aware and closely monitor the stimulus. Prior work has found that motion has a high perception accuracy for peripheral visual notifications on computer screens (i.e., participants were able to quickly perceive the notifications), but it can also distract from the primary task [59,85]. Although the constant change may have prompted the participants, it does not detract from our main goal of comparing different types of stimuli.…”

Section: Limitations and Future Workmentioning

confidence: 96%

See 2 more Smart Citations

Designing Critical and Secondary Information in Augmented Reality Headsets for Situational Awareness

Woodward¹,

Smith²,

Wang³

et al. 2023

J. Engg. Res. & Sci.

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Augmented Reality (AR) headsets are being used in different contexts (e.g., the oil industry, healthcare, military); however, there is a lack of research and design recommendations on how information should be presented in the AR headset displays, especially for aiding users' situational awareness. We present two studies: one examining if existing findings on the perceptibility of three types of visual stimulus (color, text, shapes) can be applied to AR headsets for critical information, and one analyzing three different presentation styles (Display, Environment, Mixed Environment) for textual secondary information in AR headsets. Our study on secondary information is an extension of prior work. For critical information, we found that existing visual perception findings can be applied to AR headsets; there is a hierarchy of salient visual features. Understanding that we can utilize prior work on visual features helps in designing salient critical information for AR headset displays. For secondary information, we found that having the text in the Display and Environment presentation styles assisted in participants' perception and comprehension when compared to the Mixed Environment presentation style. Based on our results, we provide design recommendations on how to present critical and secondary information in AR headset displays to aid in users' situational awareness, which is essential in safety crucial domains such as the military.

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Section: Data Analysis and Resultssupporting

confidence: 86%

Section: Data Analysis and Resultsmentioning

confidence: 99%

Section: Limitations and Future Workmentioning

confidence: 96%

See 1 more Smart Citation

Designing Critical and Secondary Information in Augmented Reality Headsets for Situational Awareness

Woodward¹,

Smith²,

Wang³

et al. 2023

J. Engg. Res. & Sci.

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“…While previous work has evaluated a limited range of visual features 4,20 and has found them to be effective at guiding attention in scatterplot visualizations, 47,48 it is known that insufficient distinctiveness and background complexity can degrade results. 13,92,96…”

Section: Discussionmentioning

confidence: 99%

“…We chose visual variables that have been shown in previous research to pop out in a pre-attentive manner, that have been evaluated in previous visualization work 3,4,20,48 and that are available in commercial tools such as Tableau and visualization libraries such as D3.js, 65 VegaLite, 69 Plotly, and ggplot2. For all visual variables except shape, we created six levels of the effect to examine increasing difference between the target and the distractors – we call this “magnitude of difference.” Six levels were chosen to give us a wide range for each effect, while accounting for some effects that can be limited at either end of the scale (such as opacity), while others may not have such limits (such as size or motion).…”

Section: General Study Methodsmentioning

confidence: 99%

Which emphasis technique to use? Perception of emphasis techniques with varying distractors, backgrounds, and visualization types

Mairena

Gutwin

Cockburn

2021

Information Visualization

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Emphasis effects are visual changes that make data elements distinct from their surroundings. Designers may use computational saliency models to predict how a viewer’s attention will be guided by a specific effect; however, although saliency models provide a foundational understanding of emphasis perception, they only cover specific visual effects in abstract conditions. To address these limitations, we carried out crowdsourced studies that evaluate emphasis perception in a wider range of conditions than previously studied. We varied effect magnitude, distractor number and type, background, and visualization type, and measured the perceived emphasis of 12 visual effects. Our results show that there are perceptual commonalities of emphasis across a wide range of environments, but also that there are limitations on perceptibility for some effects, dependent on a visualization’s background or type. We developed a model of emphasis predictability based on simple scatterplots that can be extended to other viewing conditions. Our studies provide designers with new understanding of how viewers experience emphasis in realistic visualization settings.

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