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Based on computerised behavioural tasks and neural measurements, previous research identified and examined the alerting, orienting, and executive “attention networks”. Advancements of virtual reality (VR) headsets offer new means to assess attentional networks and prior results obtained with an adapted version of the computerised Attention Network Test-Revised (ANT-R; Fan et al, 2009) implemented in VR (ANT-VR; Tekampe et al., 2023) confirmed their overall comparability. The current study utilised the high degree of experimental flexibility and control provided by VR headsets to investigate the effects of ecologically more valid stimuli and response feedback. In one experimental session,\nparticipants performed both the computerised ANT-R and the VR-adapted ANT-VR test. Overall, longer reaction times were observed for the VR test. Nevertheless, our results confirmed the similarity between tests of the observed network scores, and their interaction and integration effects. Some differences were observed as well. VR test delivery led to increased arousal, reduced disengaging costs as well as stronger and faster orienting. A validity effect was only observed for the ANT-R while previously reported location conflict was not observed for both variants. Participants reported good overall utilisation of the VR headset. These findings confirm that the ANT-VR offers a valid flexible and mobile test environment for controlled attention assessment\nand research outside the traditional laboratory or clinical settings."}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Social Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Attention"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Attentional Networks"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Virtual Reality"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Neuropsychological Assessment"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"preprint (PsyArXiv)"},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.31234/osf.io/tfc9g"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://doi.org/10.31234/osf.io/tfc9g"}}]},{"typeName":"notesText","multiple":false,"typeClass":"primitive","value":"In this repository, the data for the publication \"Attention networks and their interactions: From the lab to mobile virtual reality\" are included.\n\nFor the ANT-R, the data are saved in e-prime formats (.edat2 and .txt) with the following convention: ANT-R (ID).edat2/txt. Practice runs are named ANT-R_Practice (ID).edat2/txt. ID denotes participant number (1-50).\n\nFor the ANT-VR, the data are saved in json format with the following convention: ANT-VR (ID).json and ANT-VR_Practice (ID).json. ID denotes participant number (1-50).\n\nFor easier accessibility, the data are summarised in RawResults.csv showing the answers for quality of experience test, reaction times and accuracy score for each condition as well as computed contrasts according to Fan et al., 2009.\n\nColumn code\n\nQoE: Quality of Experience Test\nQoE_PQ: Picture Quality\nQoE_RQ: Responsiveness Quality\nQoE_TQ: Task accomplishment quality\nQoE_CQ: Comfort quality\nQoE_IQ: Immersive quality\nQoE_OQ: Overall Quality\n \n \nR: ANT-R PC Task\nVR: ANT-VR Virtual Reality Task\n \nRT: Reaction Time\nACC: Accuracy\n \nNC: NoCue\nDC: DoubleCue\nVC: ValidCue\nIVC: InvalidCue\n \nFC: Flanker Congruent\nLC: Location Congruent\nFIC: Flanker Incongruent\nLIC: Location Incongruent\n \n0: Cue-Target Interval 0 ms\n400: Cue-Target Interval 400 ms\n800: Cue-Target Interval 800 ms\n\n\n\nReferences\n\nFan, J., Gu, X., Guise, K. G., Liu, X., Fossella, J., Wang, H., & Posner, M. I.: Testing the behavioral interaction and integration of attentional networks. Brain and Cognition, 70 (2009), 209-220
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