Impact of individual differences, reward distribution, and task structure on productivity in a simulated work environment.

1973 ◽  
Vol 58 (3) ◽  
pp. 296-301 ◽  
Author(s):  
Alan G. Weinstein ◽  
Robert L. Holzbach
Keyword(s):  
Individual Differences ◽  
Work Environment ◽  
Task Structure ◽  
And Task

Learning from feedback: Effects of task information and task structure

2002 ◽  
Author(s):  
Ben R. Newell ◽  
Nicola J. Weston ◽  
Richard Tunney ◽  
David R. Shanks
Keyword(s):  
Task Structure ◽  
Feedback Effects ◽  
And Task

Division of labor

2018 ◽  
Author(s):  
Phanish Puranam
Keyword(s):  
Division Of Labor ◽  
Task Allocation ◽  
Task Structure ◽  
Heuristic Approaches ◽  
Object Based ◽  
The Creation ◽  
Task Division ◽  
And Task

Division of labor involves task division and task allocation. An extremely important consequence of task division and allocation is the creation of interdependence between agents. In fact, division of labor can be seen as a process that converts interdependence between tasks into interdependence between agents. While there are many ways in which the task structure can be chunked and divided among agents, two important heuristic approaches involve division of labor by activity vs. object. I show that a choice between these two forms of division of labor only arises when the task structure is non-decomposable, but the product itself is decomposable. When the choice arises, a key criterion for selection between activity vs. object-based division of labor is the gain from specialization relative to the gain from customization.

scholarly journals Effects of stimulus and task structure on temporal perceptual learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rannie Xu ◽  
Russell M. Church ◽  
Yuka Sasaki ◽  
Takeo Watanabe
Keyword(s):  
Temporal Discrimination ◽  
Discrimination Performance ◽  
Task Structure ◽  
Comparison Task ◽  
Interval Representation ◽  
Before And After ◽  
Temporal Intervals ◽  
Context Specific ◽  
Temporal Learning ◽  
And Task

AbstractOur ability to discriminate temporal intervals can be improved with practice. This learning is generally thought to reflect an enhancement in the representation of a trained interval, which leads to interval-specific improvements in temporal discrimination. In the present study, we asked whether temporal learning is further constrained by context-specific factors dictated through the trained stimulus and task structure. Two groups of participants were trained using a single-interval auditory discrimination task over 5 days. Training intervals were either one of eight predetermined values (FI group), or random from trial to trial (RI group). Before and after the training period, we measured discrimination performance using an untrained two-interval temporal comparison task. Our results revealed a selective improvement in the FI group, but not the RI group. However, this learning did not generalize between the trained and untrained tasks. These results highlight the sensitivity of TPL to stimulus and task structure, suggesting that mechanisms of temporal learning rely on processes beyond changes in interval representation.

The Effect of Processing Code, Response Modality and Task Difficulty on Dual Task Performance and Subjective Workload in a Manual System

1987 ◽  
Vol 31 (7) ◽  
pp. 847-851 ◽  
Author(s):  
Yili Liu ◽  
Christopher D. Wickens
Keyword(s):  
Task Performance ◽  
Dual Task ◽  
Task Difficulty ◽  
Task Structure ◽  
Time Sharing ◽  
Multiple Resources ◽  
Response Modality ◽  
Dual Task Performance ◽  
Processing Resources ◽  
And Task

We report here the first experiment of a series studying the effect of task structure and difficulty demand on time-sharing performance and workload in both automated and corresponding manual systems. The experimental task involves manual control time-shared with spatial and verbal decisions tasks of two levels of difficulty and two modes of response (voice or manual). The results provide strong evidence that tasks and processes competing for common processing resources are time shared less effectively and have higher workload than tasks competing for separate resources. Subjective measures and the structure of multiple resources are used in conjunction to predict dual task performance. The evidence comes from both single task and from dual task performance.

Extending the User Action Notation for Research in Individual Differences

1995 ◽  
Vol 39 (4) ◽  
pp. 253-257
Author(s):  
Derek Brock ◽  
Deborah Hix ◽  
Lynn Dievendorf ◽  
J. Gregory Trafton
Keyword(s):  
Individual Differences ◽  
User Interfaces ◽  
Expressive Power ◽  
Interaction Techniques ◽  
Standard Format ◽  
Input Devices ◽  
Interface Specification ◽  
Patterns Of Use ◽  
Multiple Input ◽  
And Task

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.

scholarly journals Why Are We Distracted by Social Media? Distraction Situations and Strategies, Reasons for Distraction, and Individual Differences

2021 ◽  
Vol 12 ◽  
Author(s):  
Christina Koessmeier ◽  
Oliver B. Büttner
Keyword(s):  
Social Media ◽  
Individual Differences ◽  
Online Survey ◽  
Media Use ◽  
Self Control ◽  
Social Media Use ◽  
User Perspective ◽  
Affiliation Motive ◽  
Problematic Social Media Use ◽  
And Task

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.

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