Explore the vast range of titles available.

Virtual environments are increasingly being used for training. It is not fully understood what elements of virtual environments have the most impact and how the virtual training is integrated by the brain on the sought-after skill transference to the real environment. In virtual training, we analyzed how the task level of abstraction modulates the brain activity and the subsequent ability to execute it in the real environment and how this learning generalizes to other tasks. The training of a task under a low level of abstraction should lead to a higher transfer of skills in similar tasks, but the generalization of learning would be compromised, whereas a higher level of abstraction facilitates generalization of learning to different tasks but compromising specific effectiveness. A total of 25 participants were trained and subsequently evaluated on a cognitive and a motor task following four training regimes, considering real vs. virtual training and low vs. high task abstraction. Performance scores, cognitive load, and electroencephalography signals were recorded. Transfer of knowledge was assessed by comparing performance scores in the virtual vs. real environment. The performance to transfer the trained skills showed higher scores in the same task under low abstraction, but the ability to generalize the trained skills was manifested by higher scores under high level of abstraction in agreement with our hypothesis. Spatiotemporal analysis of the electroencephalography revealed higher initial demands of brain resources which decreased as skills were acquired. Our results suggest that task abstraction during virtual training influences how skills are assimilated at the brain level and modulates its manifestation at the behavioral level. We expect this research to provide supporting evidence to improve the design of virtual training tasks.

This paper focuses on the analysis of Bebras Challenge tasks to find Informatics tasks that develop abstract thinking. Our study seeks to find which Bebras tasks develop abstraction and in what way. We analysed hundreds of tasks from the Czech contest to identify those tasks requiring participants to abstract directly or use abstract structures. Results show that an agreement among experts on stating which task is focused on abstraction is at a moderate level. We discovered that tasks focused on abstraction occur four to five times less frequently in sets of contest tasks than algorithmic tasks. Our findings proved that abstract tasks results compared with algorithmic ones did not differ in neither age nor gender group of contestants.

Robots that are capable of learning new tasks from humans need the ability to transform gathered abstract task knowledge into their own representation and dimensionality. New task knowledge that has been collected e.g. with Programming by Demonstration approaches by observing a human does not a-priori contain any robot-specific knowledge and actions, and is defined in the workspace of the human demonstrator. This article presents a new approach for mapping abstract human-centered task knowledge to a robot execution system based on the target system properties. Therefore the required background knowledge about the target system is examined and defined explicitly.

This chapter presents the core programming‐by‐demonstration (PbD) methods for task modeling at a trajectory level of task abstraction. It describes the most common methods used for task modeling at the low‐level of abstraction. Based on the level of task abstraction, the methods are categorized into high‐level task representation at the symbolic level of abstraction and low‐level task representation at the trajectory level of abstraction. Statistical methods have been widely used in robotics for representing the uncertain information about the state of the environment. The statistical frameworks represent the uncertainties of robot's perception and action via probability distributions, instead of using a single best guess about the state of the world. Regarding robot learning from observation of human demonstrations, the theory of statistical modeling has been exploited for representing the uncertainties of the acquired perceptual data.

This chapter contains sections titled: Introduction Why Parallel IR? Review of Previous Work Distribution Methods for Inverted File Data Tasks in Information Retrieval A Synthetic Model of Performance for Parallel Information Retrieval Empirical Examination of Synthetic Model Summary and Further Research Exercises References

This chapter explains how to adapt cognitive behavioural therapy (CBT) for children and young people. CBT can involve a complex set of skills to systematically identify, test, and challenge cognitions and processes. Similarly, an ability to “think about thinking” is involved in those approaches that require the child to observe, acknowledge, and accept cognitions and emotions. These processes involve a degree of cognitive maturity and sophistication and require an ability to engage in abstract tasks such as viewing events from different perspectives, and curiously observing thoughts and emotions. In order to engage in CBT, children need to be able to: access and communicate their thoughts; generate alternative attributions about events; identify and understand different emotions; and link thoughts, feelings, and situations. The chapter further considers common problems when undertaking CBT with children and also discusses how to handle these problems.