scholarly journals Evaluating instruction quality across narrative modality using measures of real-time cognitive load

2007 ◽  
Vol 76 (11-12) ◽  
pp. 850-855 ◽  
Author(s):  
Christelle Despont-Gros ◽  
Olivier Rutschmann ◽  
Antoine Geissbuhler ◽  
Christian Lovis

2021 ◽  
Author(s):  
Calum Hennings ◽  
Muneeb Ahmad ◽  
Katrin Lohan
Keyword(s):  

2016 ◽  
Vol 40 (5) ◽  
pp. 573-581 ◽  
Author(s):  
Ann L Edwards ◽  
Michael R Dawson ◽  
Jacqueline S Hebert ◽  
Craig Sherstan ◽  
Richard S Sutton ◽  
...  

Background: Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. Objectives: The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). Study design: Case series study. Methods: We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. Results: Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. Conclusion: Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects. Clinical relevance Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.


2016 ◽  
Vol 48 (4) ◽  
pp. 149-160
Author(s):  
Kamila Łucjan

Abstract Intense development of computer technology has taken place in the last several decades made it possible to cartographically present variability of phenomena in a dynamic way. As a result of using animation techniques in cartography there appeared new methods of presentation of changes, referred to as direct. Considering the character of the relation between display time and real time, two basic types of animated maps have been distinguished: temporal and non-temporal. Other criteria of classifying animation are the presence and level of interactivity and the technical criteria of production. Regardless of the applied classification, perception of the contents of animated maps is one of the main issues, since using animation leads to a significant cognitive load specific for dynamic methods. Fast sequence of data and its quick disappearance can result in omission of some information because in the case of animated maps there is a higher risk of exceeding perception potential of users than in the case of static maps. Higher efficiency of animated map perception can be achieved by applying methods of cognitive overload reduction determined through experimental research. The most important of them are: using control tools, directing attention with dynamically blinking lights, locating connected objects close to one another, using sound, adapting generalization level to the characteristics of moving images and accounting for the age and experience of map users. Among more sophisticated solutions are such elements as so-called decay and a combination of static and animated map features in the form of semi-static animations.


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