Debunking Differences between Younger and Older Adults Using a Collaborative Virtual Environment

Kothgassner, Oswald D.; Felnhofer, Anna; Hlavacs, Helmut; Beutl, Leon; Gomm, Jasmine; Hauk, Nathalie; Kastenhofer, E.; Kryspin-Exner, Ilse

doi:10.1007/978-3-642-41106-9_5

Cited by 24 publications

(50 citation statements)

References 20 publications

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“…This input device was chosen over a mouse or a keyboard device because it allowed participants to rest their arms comfortably on the chair and it permitted self-evident steering movements. A previous user study provides evidence to support the usefulness of the smartphone as an input device (Kothgassner, Felnhofer et al, 2013).…”

Section: Methodsmentioning

confidence: 94%

Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios

Felnhofer

Kothgassner

Schmidt

et al. 2015

International Journal of Human-Computer Studies

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286

162

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Section: Methodsmentioning

confidence: 94%

Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios

Felnhofer

Kothgassner

Schmidt

et al. 2015

International Journal of Human-Computer Studies

Self Cite

286

162

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“…To measure the participant's evaluation of the virtual environment the self-report questionnaire Technology Usage Inventory (TUI, [14]) was used. The TUI comprises the following four factors: (1) Ease of Use (three items referring to the perceived user-friendliness, e.g.…”

Section: Methodsmentioning

confidence: 99%

Why Does It Always Rain on Me? Influence of Gender and Environmental Factors on Usability, Technology Related Anxiety and Immersion in Virtual Environments

Schmidt

Kafka

Kothgassner

et al. 2013

Lecture Notes in Computer Science

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Abstract.Collaborative virtual environments and technical possibilities in general are still a growing and more and more important influence on everyday life. According to corresponding studies it seems that context conditions as well as individual factors such as gender play an important role in the experience of virtual environments (i.e. immersion and technology-related anxiety) and in the rating of the used technology (i.e. its perceived usefulness). Thus, the objective of the current study was to evaluate the above mentioned factors in a group of 14 women and 14 men using two different emotionally charged collaborative virtual environments. The corresponding results indicate that there are in fact gender differences: Women reported higher levels of technology-related anxiety and immersion. In general, participants in the rainy condition rated the usefulness of the virtual environment higher. Also, women in the rainy condition rated the perceived usefulness higher than women in the cheerful condition whereas in the group of men it was vice versa.

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“…Furthermore, the German version of the Positive and Negative Affect Scale (PANAS, [ 21 ]) was administered to assess the affective state of the participants. In order to investigate the usability of the device, participants had to complete the Technology Usage Inventory (TUI-II, [ 22 ]). On day 1, two subscales of this inventory were selected: (a) technology anxiety and (b) curiosity.…”

Section: Methodsmentioning

confidence: 99%

Tracking of Mental Workload with a Mobile EEG Sensor

Kutafina

Heiligers

Popović

et al. 2021

Sensors

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The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.

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Debunking Differences between Younger and Older Adults Using a Collaborative Virtual Environment

Cited by 24 publications

References 20 publications

Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios

Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios

Why Does It Always Rain on Me? Influence of Gender and Environmental Factors on Usability, Technology Related Anxiety and Immersion in Virtual Environments

Tracking of Mental Workload with a Mobile EEG Sensor

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