Cognitive modeling and task analysis: Basic processes and individual differences

1997 ◽  
Author(s):  
Phillip L. Ackerman ◽  
◽  
Ruth Kanfer
1991 ◽  
Vol 22 (4) ◽  
pp. 277-277 ◽  
Author(s):  
Sharon L. Wadle

Lack of training is only an excuse for not collaborating outside of the therapy room. With our present training, speech-language clinicians have many skills to share in the regular classroom setting. This training has provided skills in task analysis, a language focus, an appreciation and awareness of individual differences in learning, and motivational techniques.


1970 ◽  
Vol 2 (3) ◽  
pp. 423-428 ◽  
Author(s):  
Vernon C. Hall ◽  
Richard Salvi ◽  
Lydia Seggev ◽  
Edward Caldwell
Keyword(s):  

Author(s):  
Derek Brock ◽  
Deborah Hix ◽  
Lynn Dievendorf ◽  
J. Gregory Trafton

Software user interfaces that provide users with more than one device, such as a mouse and keyboard, for interactively performing tasks, are now commonplace. Concerns about how to represent individual differences in patterns of use and acquisition of skill in such interfaces led the authors to develop modifications to the standard format of the User Action Notation (UAN) that substantially augment the notation's expressive power. These extensions allow the reader of an interface specification to make meaningful comparisons between functionally equivalent interaction techniques and task performance strategies in interfaces supporting multiple input devices. Furthermore, they offer researchers a new methodology for analyzing the behavioral aspects of user interfaces. These modifications are documented and their benefits discussed.


Author(s):  
Александр Григорьевич Корченко ◽  
Бахытжан Сражатдинович Ахметов ◽  
Светлана Владимировна Казмирчук ◽  
Андрей Юрьевич Гололобов ◽  
Нургуль Абадуллаевна Сейлова

2021 ◽  
Vol 12 ◽  
Author(s):  
Christina Koessmeier ◽  
Oliver B. Büttner

Social media is a major source of distraction and thus can hinder users from successfully fulfilling certain tasks by tempting them to use social media instead. However, an understanding of why users get distracted by social media is still lacking. We examine the phenomenon of social media distraction by identifying reasons for, situations of, and strategies against social media distraction. The method adopted is a quantitative online survey (N = 329) with a demographically diverse sample. The results reveal two reasons for social media distraction: social (e.g., staying connected and being available) and task-related distraction (e.g., not wanting to pursue a task). We find individual differences in these reasons for distraction. For social distraction, affiliation motive and fear of missing out (FoMO) are significant predictors, while for task-related distraction, self-regulatory capabilities (self-control, problematic social media use) and FoMO are significant predictors. Additionally, typical distraction situations are non-interactive situations (e.g., watching movies, facing unpleasant tasks). Strategies used to reduce distractions mostly involved reducing external distractions (e.g., silencing the device). This paper contributes to the understanding of social media use by revealing insights into social media distraction from the user perspective.


Author(s):  
Elizabeth L. Fox ◽  
Joseph W. Houpt

The type and amount of task demands that humans must simultaneously process and respond to influences how efficient they are in completing the tasks. Capturing how and to what degree human efficiency changes in different task environments is crucial to inform an appropriate system design. An individual-based analytic approach is necessary to accurately capture performance changes and lend practical suggestions. We can provide designers with the amount and type of task demands that we expect a person to sustain adequate performance given their unique underlying cognitive properties. We develop a metric, multi-tasking throughput (MT), that provides the extent to which a person processes tasks more efficiently, the same, or less efficiently when required to complete several different types of tasks at once. This is a cognitive-based, standardized metric; meaning it yields the relative degree of change from a baseline model that is created to accommodate to unique individual differences, numbers of tasks, and task characteristics. We quantify MT by using transformations of RTs to predict the extent that external demands of multi-tasking exceeds what the cognitive system can accommodate to thereby hindering performance. We use a real world dual-task application to highlight the apparent differences in strategy and ability across individuals and alternative task environments.


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