scholarly journals Do multiple tasks enhance dishonesty in tournament incentives environment?

2020 ◽  
Author(s):  
Nawaf Alotaibi
Keyword(s):  
Multiple Tasks

EXPRESS: Spatial-numerical associations from a novel paradigm support the Mental Number Line account

2021 ◽  
pp. 174702182110087
Author(s):  
Lauren Aulet ◽  
Sami R Yousif ◽  
Stella Lourenco
Keyword(s):  
Memory Task ◽  
Verbal Working Memory ◽  
Number Line ◽  
Mental Number Line ◽  
Task Demands ◽  
Numerical Magnitude ◽  
The Novel ◽  
Alternative Account ◽  
Subject Design ◽  
Multiple Tasks

Multiple tasks have been used to demonstrate the relation between numbers and space. The classic interpretation of these directional spatial-numerical associations (d-SNAs) is that they are the product of a mental number line (MNL), in which numerical magnitude is intrinsically associated with spatial position. The alternative account is that d-SNAs reflect task demands, such as explicit numerical judgments and/or categorical responses. In the novel ‘Where was The Number?’ task, no explicit numerical judgments were made. Participants were simply required to reproduce the location of a numeral within a rectangular space. Using a between-subject design, we found that numbers, but not letters, biased participants’ responses along the horizontal dimension, such that larger numbers were placed more rightward than smaller numbers, even when participants completed a concurrent verbal working memory task. These findings are consistent with the MNL account, such that numbers specifically are inherently left-to-right oriented in Western participants.

scholarly journals A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

2021 ◽  
Vol 11 (1) ◽  
pp. 363
Author(s):  
Juan Jesús Roldán-Gómez ◽  
Eduardo González-Gironda ◽  
Antonio Barrientos
Keyword(s):  
Augmented Reality ◽  
Access To Information ◽  
Team Leaders ◽  
Team Members ◽  
Multiple Tasks ◽  
Different Types ◽  
Complete Survey ◽  
Virtual And Augmented Reality ◽  
Robotic Technologies

Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry to apply different types of robots in the context of firefighting missions. Finally, all this information is used to propose a concept of operation for the comprehensive application of drone swarms in firefighting. The proposed system is a fleet of quadcopters that individually are only able to visit waypoints and use payloads, but collectively can perform tasks of surveillance, mapping, monitoring, etc. Three operator roles are defined, each one with different access to information and functions in the mission: mission commander, team leaders, and team members. These operators take advantage of virtual and augmented reality interfaces to intuitively get the information of the scenario and, in the case of the mission commander, control the drone swarm.

scholarly journals Lighting for pedestrians: Does multi-tasking affect the performance of typical pedestrian tasks?

2021 ◽  
pp. 147715352110026
Author(s):  
Y Mao ◽  
S Fotios
Keyword(s):  
Emotion Recognition ◽  
Recognition Task ◽  
Obstacle Detection ◽  
Detection Task ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Impaired Performance ◽  
Lighting Conditions ◽  
Multiple Tasks ◽  
Visual Tasks

Obstacle detection and facial emotion recognition are two critical visual tasks for pedestrians. In previous studies, the effect of changes in lighting was tested for these as individual tasks, where the task to be performed next in a sequence was known. In natural situations, a pedestrian is required to attend to multiple tasks, perhaps simultaneously, or at least does not know which of several possible tasks would next require their attention. This multi-tasking might impair performance on any one task and affect evaluation of optimal lighting conditions. In two experiments, obstacle detection and facial emotion recognition tasks were performed in parallel under different illuminances. Comparison of these results with previous studies, where these same tasks were performed individually, suggests that multi-tasking impaired performance on the peripheral detection task but not the on-axis facial emotion recognition task.

scholarly journals Designing optimizing procedures for task switching to ensure efficiency in the hospital laboratory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young-Jin Kim ◽  
Garam Kim ◽  
Sangil Kim ◽  
Dawoon Jung ◽  
Minwoo Park
Keyword(s):  
Task Switching ◽  
Compliance Rate ◽  
Turnaround Time ◽  
Optimal Time ◽  
Work Efficiency ◽  
Work Quality ◽  
Time Optimization ◽  
Sample Classification ◽  
Multiple Tasks ◽  
Changeover Times

AbstractThis study aims to improve the efficiency of task switching in hospital laboratories. In a laboratory, several medical technicians perform multiple tasks. Technicians are not aware of the marginal amount of time it takes to switch between tasks, and this accumulation of lost minutes can cause the technician to worry more about the remaining working time than work quality. They rush through their remaining tasks, thereby rendering their work less efficient. For time optimization, we identified work changeover times to help maintain the work quality in the laboratory while reducing the number of task switching instances. We used the turnaround time (TAT) compliance rate of emergency room samples as an indicator to evaluate laboratory performance and the number of task switching instances as an index of the task performer perspective (TPP). We experimented with a monitoring system that populates the time for sample classification according to the optimal time for task switching. Through the proposed methodology, we successfully reduced not only the instances of task switching by 10% but also the TAT non-compliance rate from 4.97 to 2.66%. Consequently, the introduction of new methodology has greatly increased work efficiency.

Optimally Scheduling Jobs with Multiple Tasks

2017 ◽  
Vol 45 (2) ◽  
pp. 36-38 ◽  
Author(s):  
Ziv Scully ◽  
Guy Blelloch ◽  
Mor Harchol-Balter ◽  
Alan Scheller-Wolf
Keyword(s):  
Multiple Tasks

scholarly journals Comparing continual task learning in minds and machines

2018 ◽  
Vol 115 (44) ◽  
pp. E10313-E10322 ◽  
Author(s):  
Timo Flesch ◽  
Jan Balaguer ◽  
Ronald Dekker ◽  
Hamed Nili ◽  
Christopher Summerfield
Keyword(s):  
Human Error ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Artificial Agents ◽  
Trial And Error ◽  
Stimulus Space ◽  
Cognitive Mechanisms ◽  
Error Training ◽  
Multiple Tasks ◽  
Continual Learning

Humans can learn to perform multiple tasks in succession over the lifespan (“continual” learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion. Analysis of human error patterns suggested that blocked training encouraged humans to form “factorized” representation that optimally segregated the tasks, especially for those individuals with a strong prior bias to represent the stimulus space in a well-structured way. By contrast, standard supervised deep neural networks trained on the same tasks suffered catastrophic forgetting under blocked training, due to representational interference in the deeper layers. However, augmenting deep networks with an unsupervised generative model that allowed it to first learn a good embedding of the stimulus space (similar to that observed in humans) reduced catastrophic forgetting under blocked training. Building artificial agents that first learn a model of the world may be one promising route to solving continual task performance in artificial intelligence research.

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