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As indicated by previous research, aging is associated with a decline in working memory (WM) functioning, related to alterations in fronto-parietal neural activations. At the same time, previous studies showed that WM training in older adults may improve the performance in the trained task (training effect), and more importantly, also in untrained WM tasks (transfer effects). However, neural correlates of these transfer effects that would improve understanding of its underlying mechanisms, have not been shown in older participants as yet. In this study, we investigated blood-oxygen-level-dependent (BOLD) signal changes during n-back performance and an untrained delayed recognition (Sternberg) task following 12 sessions (45 min each) of adaptive n-back training in older adults. The Sternberg task used in this study allowed to test for neural training effects independent of specific task affordances of the trained task and to separate maintenance from updating processes. Thirty-two healthy older participants (60-75 years) were assigned either to an n-back training or a no-contact control group. Before (t1) and after (t2) training/waiting period, both the n-back task and the Sternberg task were conducted while BOLD signal was measured using functional Magnetic Resonance Imaging (fMRI) in all participants. In addition, neuropsychological tests were performed outside the scanner. WM performance improved with training and behavioral transfer to tests measuring executive functions, processing speed, and fluid intelligence was found. In the training group, BOLD signal in the right lateral middle frontal gyrus/caudal superior frontal sulcus (Brodmann area, BA 6/8) decreased in both the trained n-back and the updating condition of the untrained Sternberg task at t2, compared to the control group. fMRI findings indicate a training-related increase in processing efficiency of WM networks, potentially related to the process of WM updating. Performance gains in untrained tasks suggest that transfer to other cognitive tasks remains possible in aging. (C) 2016 Elsevier Inc. All rights reserved.
Hintergrund: Im Rahmen des reformierten Psychotherapeutengesetzes wird eine starkere Praxisorientierung in der klinisch-psychologischen Lehre und in der Prufung psychotherapeutischer Kompetenzen verankert. Hierbei sollen Studierende durch die Interaktion mit standardisierten Patient*innen (SP) therapeutische Kompetenzen erwerben und demonstrieren. Fragestellung: Das Ziel des vorliegenden Beitrags ist es, eine evidenzbasierte Umsetzung dieser neuen Lehr- und Prufungsformate zu unterstutzen, indem bisherige Forschungsbefunde zum Einsatz von SP dargestellt und Bereiche, in denen weitere Forschung notwendig ist, aufgezeigt werden. Ergebnisse: Empirische Befunde zeigen, dass SP psychische Storungen authentisch darstellen konnen. Voraussetzung dafur sind beispielsweise die Auswahl geeigneter SP, detaillierte Rollenanleitungen, spezifisches Training, Feedback und Nachschulungen. Auch wenn einige Forschungsfragen, wie zur vergleichenden Wirksamkeit des Einsatzes von SP, noch unbeantwortet sind, lassen sich praktische Implikationen fur SP-Programme in Lehre, Prufung und Forschung ableiten, die in einem Ablaufschema dargestellt werden. Schlussfolgerungen: Der Einsatz von SP bietet gro ss es Potenzial fur die klinisch-psychologische Lehre und Ausbildungsforschung. Um den Einsatz von SP an anderen Standorten zu unterstutzen, werden Beispielmaterialien (z.B. Rollenanleitung) in den elektronischen Supplementen (siehe www.karger.com/doi/10.1159/000509249 fur alle Supplemente) zum Artikel zur Verfugung gestellt.
V-Edge
(2022)
As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: Instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays lower-bounded by propagation speed, edge servers deployed in close proximity to the user (e.g., at some base station) serve as proxy for the cloud. This is particularly interesting for upcoming machine-learning-based intelligent services, which require substantial computational and networking performance for continuous model training. However, this promising idea is hampered by the limited number of such edge servers. In this article, we discuss a way forward, namely the V-Edge concept. V-Edge helps bridge the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available. Thus, V-Edge acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks. We introduce the general V-Edge architecture, and we characterize some of the key research challenges to overcome in order to enable wide-spread and intelligent edge services.