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Record W4376959489 · doi:10.1038/s41598-023-34718-3

Adding virtual plants leads to higher cognitive performance and psychological well-being in virtual reality

2023· article· en· W4376959489 on OpenAlexaff
Fariba Mostajeran, Frank Steinicke, S. Reinhart, Wolfgang Stuerzlinger, Bernhard E. Riecke, Simone Kühn

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsSimon Fraser University
FundersUniversität HamburgBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftBundesministerium für Wirtschaft und EnergieEuropean Commission
KeywordsVirtual realityCognitionComputer scienceHuman–computer interactionCognitive psychologyApplied psychologyPsychologyNeuroscience

Abstract

fetched live from OpenAlex

Previous research has shown the positive effects of exposure to real and virtual nature. To investigate how such benefits might generalize to ever-more-prevalent virtual workplaces, we examined the effects of the absence or presence of virtual plants in an office environment in Virtual Reality (VR) on users' cognitive performance and psychological well-being. The results of our user study with 39 participants show that in the presence of virtual plants, participants performed significantly better in both short-term memory and creativity tasks. Furthermore, they reported higher psychological well-being scores, including positive affect and attentive coping, whilst reporting lower feelings of anger and aggression after exposure to virtual plants in VR. The virtual office with plants was also perceived as more restorative and induced a higher sense of presence. Overall, these results highlight how the presence of virtual plants in VR can have positive influences on users, and therefore, constitute important design considerations when developing future working and learning spaces.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.308
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations49
Published2023
Admission routes1
Has abstractyes

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