human performance
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2022 ◽  
Vol 11 (1) ◽  
pp. 1-42
Ruisen Liu ◽  
Manisha Natarajan ◽  
Matthew C. Gombolay

As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive. A robot’s ability to coordinate team activities improves based on its ability to infer and reason about the dynamic (i.e., the “learning curve”) and stochastic task performance of its human counterparts. We introduce a novel resource coordination algorithm that enables robots to schedule team activities by (1) actively characterizing the task performance of their human teammates and (2) ensuring the schedule is robust to temporal constraints given this characterization. We first validate our modeling assumptions via user study. From this user study, we create a data-driven prior distribution over human task performance for our virtual and physical evaluations of human-robot teaming. Second, we show that our methods are scalable and produce high-quality schedules. Third, we conduct a between-subjects experiment (n = 90) to assess the effects on a human-robot team of a robot scheduler actively exploring the humans’ task proficiency. Our results indicate that human-robot working alliance ( p\lt 0.001 ) and human performance ( p=0.00359 ) are maximized when the robot dedicates more time to exploring the capabilities of human teammates.

2022 ◽  
Akshay Vivek Jagadeesh ◽  
Justin Gardner

The human visual ability to recognize objects and scenes is widely thought to rely on representations in category-selective regions of visual cortex. These representations could support object vision by specifically representing objects, or, more simply, by representing complex visual features regardless of the particular spatial arrangement needed to constitute real world objects. That is, by representing visual textures. To discriminate between these hypotheses, we leveraged an image synthesis approach that, unlike previous methods, provides independent control over the complexity and spatial arrangement of visual features. We found that human observers could easily detect a natural object among synthetic images with similar complex features that were spatially scrambled. However, observer models built from BOLD responses from category-selective regions, as well as a model of macaque inferotemporal cortex and Imagenet-trained deep convolutional neural networks, were all unable to identify the real object. This inability was not due to a lack of signal-to-noise, as all of these observer models could predict human performance in image categorization tasks. How then might these texture-like representations in category-selective regions support object perception? An image-specific readout from category-selective cortex yielded a representation that was more selective for natural feature arrangement, showing that the information necessary for object discrimination is available. Thus, our results suggest that the role of human category-selective visual cortex is not to explicitly encode objects but rather to provide a basis set of texture-like features that can be infinitely reconfigured to flexibly learn and identify new object categories.

2022 ◽  
pp. 63-74
Paul Stretton

2022 ◽  
Vol 23 (1) ◽  
pp. 65-67
Lillian Su ◽  
Sapna R. Kudchadkar

2022 ◽  
pp. 524-537
Patrick R. Lowenthal ◽  
Gina Persichini ◽  
Quincy Conley ◽  
Michael Humphrey ◽  
Jessica Scheufler

Digital literacy is essential for individuals entering college and the workplace. Students with disabilities experience a greater challenge in acquiring the skills necessary to succeed. This chapter explores the disability digital divide, success factors for acquiring digital skills, and the implications of a digital literacy curriculum developed for special education classrooms in Idaho. It demonstrates how leveraging human performance improvement (HPI) models, incorporating universal design for learning (UDL) principles, and supporting classroom teachers resulted in a curriculum to help young people with disabilities to acquire the digital skills they need to be prepared for college and the workplace.

2022 ◽  
pp. 1783-1799
Marko Kesti ◽  
Aino-Inkeri Ylitalo ◽  
Hanna Vakkala

Digital disruption and continuous productivity improvement require more from people management, thus raising the bar for leadership competencies. International studies indicate that leadership competence gaps are large and traditional leadership training methods does not seem to solve this problem. This article's findings supports this situation. The authors will open the complexity behind organizational productivity development and present game theoretical architecture that simulates management behavior effects to human performance. New methods enable practice-based learning that enables formatting leaders' behavior so that it will create long-term success with continuous change. The authors will present gamified leadership training procedure and discuss the practical learning experiences from a management simulation game. The authors' study reveals challenges at interactive leadership skills, thus, it is argued, that there seems to be problems at the leadership mind-set. Therefore, more sophisticated learning methods and tools should be used.

The Building environment and the performance of its systems directly impact the experience and comfort of a building occupant. This POE study examines the relationship between building and human performance. LEED-rated building was selected as a case study to analyze its performance after being in operation. The occupants’ satisfaction was evaluated in terms of the thermal comfort and human use with the application of online questionnaire. The environmental impact was determined through various measurements including room temperature, relative humidity, air velocity, lighting levels and carbon emission. The outcomes of this study identify the building systems efficiencies as well as the systems in need of retrofit. The POE results can help building designers address user needs more effectively and fine-tune the systems to improve sustainability.

Yue Shu LIU

LANGUAGE NOTE | Document text in Chinese; abstract also in English. Human beings always try to transcend their limitations. Emerging technologies provide a set of powerful tools that promise to significantly improve human performance, stimulating the desire of some technical experts to transform the human body. Against this backdrop, superhumanism has come into being in today's society and is flourishing. Superhumanism has been criticized by some Chinese scholars on the basis of traditional Chinese thought. Their criticism of superhumanism is a difficult task that involves multi-level reflection on human nature, technology, and value. I argue that for the issue of superhumanism, theoretical innovation is more important than continuing to invoke traditional thought.

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