Investigating Effects of Post-Selection Feedback for Acquiring Ultra-Small Targets on Touchscreen

Yu, Chun; Wen, Hongyi; Xiong, Wei; Bi, Xiaojun; Shi, Yuanchun

doi:10.1145/2858036.2858593

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…While the checklist task was unaffected, longer completion times were observed for the flight plan edit task, especially with visual feedback. This confirms findings from previous studies where multimodal feedback either showed no effect or slowed down data entry (Bender, 1999;Yu et al, 2016). One likely explanation for the different effects on the two tasks is that, for the flight plan entry task, participants waited for feedback on each entry before proceeding to the next because of the larger variety of possible errors, unlike the checklist task where they either succeeded or failed to check off an item.…”

Section: Discussionsupporting

confidence: 86%

Visual and auditory feedback to improve touchscreen usability in turbulence

Wan

Prinet

Sarter

2017

Proceedings of the Human Factors and Ergonomics Society Annual

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Touchscreens are being introduced to various mobile environments that are, at times, affected by vibrations and turbulence, such as modern car cockpits or flight decks of commercial and military aircraft. To assess and enhance the usability of touchscreens in these domains, this experiment examined the performance effects of turbulence on two flight-related tasks and the effectiveness of visual and auditory feedback for supporting error detection, fast completion times and multitasking. Nineteen pilots performed a flight plan entry and a checklist task in calm and turbulent conditions during manual flight and on autopilot. Results show that unaided performance suffers greatly in turbulence, both in terms of the number of errors and completion time. However, visual and auditory feedback both helped reduce these performance costs by improving error detection and multitasking. Participants preferred auditory feedback for text entry during manual flight and in turbulence. The findings from this study can inform the design and evaluation of touch screens for mobile environments, such as the flight deck, ambulances and surveillance operations.

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Section: Discussionsupporting

confidence: 86%

Visual and auditory feedback to improve touchscreen usability in turbulence

Wan

Prinet

Sarter

2017

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Examples of improving touch accuracy include using an offset from the finger contact point [11,45,48], dragging in a specific direction to confirm an intended target among many items [2,39,59], visualizing a contact point [53,60], applying machine learning [51] or probabilistic modeling [9], and correcting hand tremor effects by using motion sensors [44]. In addition to these techniques, researchers have sought to understand why finger touch is less accurate compared with other input modalities such as a mouse cursor.…”

Section: Improvements and Principles Of Finger-touch Accuracymentioning

confidence: 99%

Rethinking the Dual Gaussian Distribution Model for Predicting Touch Accuracy in On-screen-start Pointing Tasks

Yamanaka¹,

Usuba

2020

Proc. ACM Hum.-Comput. Interact.

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The dual Gaussian distribution hypothesis has been used to predict the success rate of target pointing on touchscreens. Bi and Zhai evaluated their success-rate prediction model in off-screen-start pointing tasks. However, we found that their prediction model could also be used for on-screen-start pointing tasks. We discuss the reasons why and empirically validate our hypothesis in a series of four experiments with various target sizes and distances. The prediction accuracy of Bi and Zhai's model was high in all of the experiments, with a 10-point absolute (or 14.9% relative) prediction error at worst. Also, we show that there is no clear benefit to integrating the target distance when predicting the endpoint variability and success rate.

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