Exploring fundamentals of immersive environment setups on food sensory perception in space contexts
Bibliographic record
Abstract
Research suggests that space travel alters sensory perception, however, it is not yet clear what individual factors affect this perception. Taylor et al. (2020) emphasised the importance of tailoring strategies to enhance palatability and intake based on individual differences. This study aims to evaluate how an immersive space-like environment, created using screens, influences food odour perception and emotional responses. Specifically, it explores the setup of immersive screen studies to assess sensory perception and affective responses over time, considering factors such as lighting conditions in dark versus bright rooms. 29 participants were involved in a crossover design experiment in which they watched a 20-min video of a rocket launch, accompanied by environmental sounds at 70 dB, following NASA's International Space Station noise constraints. The participants were randomly assigned to evaluate the video in either a dark or bright room. The rocket launch video was chosen for its emotional impact, as it can induce awe and self-transcendent experiences, like the “Overview Effect” experienced by astronauts. Participants assessed the intensity of three food odours (vanilla, citrus, and almond) at four time points: just after takeoff, and at 5, 10, and 15 min. Measurements included liking (9-point hedonic scale), intensity (Labeled Magnitude Scale), and emotional responses (using 39 terms from the EsSense Profile). Results showed that for vanilla and almond, odour liking remained consistent over time, regardless of lighting conditions. However, for citrus, liking increased over time in the dark room. An inverse relationship between positive and negative emotions throughout the immersion period was observed, highlighting the importance of time in evoking emotional responses. Emotions during testing with the immersive screens were generally positive, such as feelings of ‘calm’, suggesting that the methodology may not be entirely suitable for simulating the more cluttered and isolated environment of a space shuttle.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".