A First Look at Identifying Strategies People Use in Making Predictions about Task Demand

Sublette, Michelle; Carswell, C. Melody; Seidelman, Will

doi:10.1177/1071181312561376

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…"MTurk" has been used to replicate results of in-person studies of expected control mappings (Sublette, Carswell, & Seidelman, 2012) as well as visualization designs (Heer & Bostock, 2010), so we used it as an inexpensive and convenient means of collecting data.…”

Section: Nonoccupant Expectanciesmentioning

confidence: 99%

Environmental Control Issues in a New Energy-Efficient Building

Lee¹,

Carswell²,

Seidelman³

et al. 2014

Ergonomics in Design: The Quarterly of Human Factors Applicatio

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The availability of adjustable lighting controls, with options other than “on” and “off,” have been associated with increased energy savings. In the present study, we describe the way customizable, networked lighting controls are actually used in a recently built research facility that received a formal designation for sustainable design (LEED Gold). In addition to usage patterns, we explored occupants’ knowledge of control operation as well as the expectancies of nonoccupants. We also identified a variety of features that, if redesigned in accordance with HF/E principles, might lead to reduced lighting usage.

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Section: Nonoccupant Expectanciesmentioning

confidence: 99%

Environmental Control Issues in a New Energy-Efficient Building

Lee¹,

Carswell²,

Seidelman³

et al. 2014

Ergonomics in Design: The Quarterly of Human Factors Applicatio

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show abstract

“…A second purpose of the present research was to determine whether crowdsourcing, such as from Amazon Mechanical Turk (MTurk) provides comparable data to that obtained in classroom or laboratory settings. Data from MTurk workers have been used in previous human factors research (Chien, Lewis, Hergeth, Semnani-Azad & Sycara, 2015;Heer & Bostock, 2010;Kelley & McLaughlin, 2012;Kittur, Chi & Suh, 2008;Lee, Carswell, Seidelman, & Sublette, 2014;Sublette, Carswell, & Seidelman, 2012), and may provide a more cost-effective, convenient, and diverse source for participants than may be found in a university classroom or laboratory (Gosling, Vazire, Srivastava, & John, 2004;Krantz & Dalal, 2000). There have been concerns about the reliability and validity of data collected from online participants (Gosling et al, 2004;Kraut et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Ratings of the Effectiveness of Warnings on Consumer Paint Stripper Products

Nemire¹

2016

Proceedings of the Human Factors and Ergonomics Society Annual

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Accidental inhalation of vapors from paint stripper products containing methylene chloride has resulted in injuries and deaths. Warnings on paint stripper products may play a role in reducing the number and severity of injuries resulting from using them. Warning labels consistent with Federal Hazardous Substances Act (FHSA) and American National Standards Institute (ANSI) Z535.4 formatting were evaluated by 159 university students and by 173 Amazon Mechanical Turk (MTurk) workers. Results indicated that both groups rated the ANSI-formatted warnings as more effective (more likely to be noticed, read, understood, and followed) than the FHSA-formatted warnings, providing support for both use of crowdsourcing as a reliable and valid source for study participants to evaluate warnings, as well as for updating FHSA labeling requirements to include evidence-based guidelines on warning effectiveness. Implications for future research are discussed.

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Further Explorations of the “White Space” Bias in Users’ Anticipation of Task Workload

Sublette

Carswell

Seidelman

et al. 2013

Proceedings of the Human Factors and Ergonomics Society Annual

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As more designers allow users to customize the look and feel of interfaces, users will be required to recognize the implications of their choices on their future performance, comfort, and enjoyment. Understanding the limits of people’s predictive capabilities may be an important component in identifying why people choose one product over another based on ease of use or why people have difficulties identifying tasks that can be performed together. The purpose of this study is to further explore users’ biases for utilizing the amount of white space in the stimulus as a predictor of task difficulty, to validate discrepancies between predicted task difficulty and performance outcomes found in previous research and the human factors literature, and to identify task-specific strategies that are used to anticipate task difficulty. The study uses the NASA Task Load Index (NASA-TLX) in prospective difficulty judgments for these three types of tasks: 1) a stimulus-response compatibility task, 2) a target acquisition task and 3) a perceptual search task. In general, participants predicted lower task demand for designs with more intervening white space. For the visual search task, these estimates of demand were consistent with participants’ actual performance reaction times. However, for the stove design and Fitts’ tasks participants rated tasks that were likely to result in more errors as less challenging suggesting that the type of task is an important factor in participants’ abilities to predict relative task difficulty.

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A First Look at Identifying Strategies People Use in Making Predictions about Task Demand

Cited by 3 publications

References 23 publications

Environmental Control Issues in a New Energy-Efficient Building

Environmental Control Issues in a New Energy-Efficient Building

Ratings of the Effectiveness of Warnings on Consumer Paint Stripper Products

Further Explorations of the “White Space” Bias in Users’ Anticipation of Task Workload

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