MétaCan
Menu
Back to cohort
Record W2886293624 · doi:10.11159/mhci18.103

Where to Take a Rest: An Attention Restoration Theory Perspective

2018· article· en· W2886293624 on OpenAlexvenueno aff
Kyungmi Chung, Jin Young Park

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2018
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsnot available
FundersNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningSungkyunkwan UniversityNational Research Foundation
KeywordsPerspective (graphical)Rest (music)Computer scienceCognitive sciencePsychologyArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

The purpose of the present study is to test the following research questions: RQ1.How does individuals' presence experience during exposure to 360° virtual nature differently affect the subjective evaluation of perceived restorativeness?RQ2.How does individuals' presence experience during exposure to 360° virtual nature differently affect involuntary attention restoration as indicated by the modulation of auditory Mismatch Negativity (aMMN) and P3a amplitude?;RQ3.Will the 'subjective' self-reported and 'objective' event-related potential (ERP) responses be congruent?Based on attention restoration theory [1], nature can allow depleted directed attention to rest and restore by drawing involuntary attention that requires no efforts.According to previous ERP studies on the during-effect of meditation, long-term mediators showed significantly reduced aMMN [2] and P3a [3] amplitudes than non-mediators.Assuming that the differences in individual's presence level would boost this meditation-like effect [4] and the aMMN would be attention-independent [5], we hypothesized that people with a high level of presence experience would have significantly increased perceived restorativeness scale (PRS) scores and reduced P3a responses to the natural virtual environment (VE) than those with a low level of presence experience.A total of forty healthy volunteers (22 males), aged 19 to 36 years (M = 23.78,SE = .56),were enrolled in this experiment.The 360° nature video was chosen from Google YouTube and then edited (Size: 4K, 3840 × 1920 pixels; Running time: 5 m 53 s).While actively viewing the video with LG VR glasses compatible with a LG G5 smartphone on, participants were guided to concentrate only on the give visual task and to ignore task-irrelevant incoming sounds via headphones.A passive auditory oddball paradigm was designed to last for 5 m 30 s and composed of 750-Hz standard (180 trials; 75 dB) and 1000-Hz deviant (60 trials; 75 dB) tones and the inter-stimulus-interval randomly presented between 1000 ms and 1300 ms.After the video ended, all participants were asked to fill out the PRS [6] and presence questionnaires [7,8].An independent samples t-test was performed to compare the PRS scores and MMN/P3a complex amplitudes between the two groups divided by a median split on the presence scale: (1) low-presence group [If PQ =< 84, N = 21; M = 72.90,SE = 2.39] and (2) high-presence group [If 84 < PQ, N = 19; M = 97.63,SE = 2.02].It was found that when viewing the natural VE, the high presence group (M = 110.84,SE = 5.59) showed significantly higher PRS scores than the low presence group (M = 92.57,SE = 4.83), t (38) = -2.487,p < .05),but both aMMN [t (38) = .808,NS] and P3a [t (38) = .930,NS] amplitudes were not significantly lower in the high presence group.In conclusion, individual's presence experience failed to strengthen the restorative effect of exposure to virtual nature, and their self-reported and ERP responses to virtual nature experience appeared to be incongruent.These findings might be explained by the possibility of unsatisfied full sensorial richness or satisfied presence sensation in the 360° VE.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.005
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Electrical Engineering and Computer Systems and ScienceSame topicIdentity, Memory, and TherapyFrench-language works237,207