This repository includes the behavioral data of [publication placeholder].

Abstract: Attention is a fundamental aspect of cognition. 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, participants 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 and research outside the traditional laboratory or clinical settings.

In this repository, the data for the publication "Attention networks and their interactions: From the lab to mobile virtual reality" are included.

For 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).

For 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).

For 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.

Column code

QoE: Quality of Experience Test QoE_PQ: Picture Quality QoE_RQ: Responsiveness Quality QoE_TQ: Task accomplishment quality QoE_CQ: Comfort quality QoE_IQ: Immersive quality QoE_OQ: Overall Quality     R: ANT-R PC Task VR: ANT-VR Virtual Reality Task   RT: Reaction Time ACC: Accuracy   NC: NoCue DC: DoubleCue VC: ValidCue IVC: InvalidCue   FC: Flanker Congruent LC: Location Congruent FIC: Flanker Incongruent LIC: Location Incongruent   0: Cue-Target Interval 0 ms 400: Cue-Target Interval 400 ms 800: Cue-Target Interval 800 ms

References

Fan, 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