VisualEars: How an immersive art exhibit impacts mood during the COVID-19 pandemic
Bibliographic record
Abstract
This paper explores the positive impact of viewing a virtual art exhibit on mood during the COVID-19 Pandemic. During global lockdowns, depression, anxiety, and the burden of other mental illnesses have increased even among prior psychiatrically healthy individuals. Art and music-based interventions have shown to be effective clinical interventions in individuals with mental illness. The VisualEars project explored whether a virtual activity involving vision and auditory stimuli could improve positive and negative affect. Eight musical pieces were selected, and 28 visual artists from around the world visualized two musical pieces. A total of 56 works of art were created and hung in eight 3D virtual rooms. Visitors were randomly selected to either view the art exhibit without music (non-immersive) or view the art exhibit while listening to music (immersive). Visitors were asked to complete a positive and negative affect schedule (PANAS) in three languages (English, French, and Farsi) pre and post their virtual visit. A total of 160 participants completed baseline PANAS, 58 of which completed the follow-up PANAS. Linear mixed-effects models found that older participants had lower negative affect scores overall ( b = −0.3, p = 0.003), while male participants had lower positive affect scores overall ( b = −0.27, p = 0.02). Following the virtual exhibit participants of both conditions had higher positive ( b = 0.17, p = 0.03), and lower negative affect scores ( b = −0.19, p = 0.007). We found that the virtual art exhibit increased positive affect and decreased negative affect in participants, suggesting an overall improvement in mood attributable to the virtual exhibit. This suggests that virtual exhibits may serve as a beneficial and accessible intervention to improve mood during a pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".