M
Manuel Ninaus
Researcher at University of Innsbruck
Publications - 84
Citations - 2111
Manuel Ninaus is an academic researcher from University of Innsbruck. The author has contributed to research in topics: Computer science & Task (project management). The author has an hindex of 17, co-authored 64 publications receiving 1479 citations. Previous affiliations of Manuel Ninaus include Leibniz Institute for Neurobiology & University of Tübingen.
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An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games
Elizabeth Boyle,Thomas Hainey,Thomas Connolly,Grant Gray,Jeffrey Earp,Michela Ott,Theodore Lim,Manuel Ninaus,Cláudia Ribeiro,João Pereira +9 more
TL;DR: Future research on digital games would benefit from a systematic programme of experimental work, examining in detail which game features are most effective in promoting engagement and supporting learning.
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Learning to modulate one's own brain activity: the effect of spontaneous mental strategies
TL;DR: The results indicate that successful SMR based NF performance is associated with implicit learning mechanisms, and participants stating vivid reports on strategies to control their SMR probably overload cognitive resources, which might be counterproductive in terms of increasing SMR power.
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Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training
TL;DR: Preliminary results suggest that participants whose confidence in control over technical devices is high might consume additional cognitive resources, which higher effort in turn may interfere with brain states of relaxation as reflected in the SMR.
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Neural substrates of cognitive control under the belief of getting neurofeedback training
Manuel Ninaus,Silvia Erika Kober,Matthias Witte,Karl Koschutnig,Matthias Stangl,Christa Neuper,Guilherme Wood +6 more
TL;DR: Tasks such as those generally employed in NF training recruit the neuronal correlates of cognitive control even when only sham NF is presented, showing that the intention to control a moving bar is sufficient to engage a broad frontoparietal and cingulo-opercular network involved in cognitive control.
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Increased emotional engagement in game-based learning – A machine learning approach on facial emotion detection data
Manuel Ninaus,Manuel Ninaus,Simon Greipl,Kristian Kiili,Antero Lindstedt,Stefan Huber,Elise Klein,Hans-Otto Karnath,Korbinian Moeller,Korbinian Moeller +9 more
TL;DR: Using a machine learning approach on facial emotion detection data, it is substantiated that the emotionally engaging nature of games facilitates learning.