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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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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