Moving On Up: Investigating the Embodied Metaphor of Verticality and its Effect on Overconfidence
Bibliographic record
Abstract
As investment in Virtual Reality (VR) continues, we sought to better understand the unique value proposition this technology has to offer by testing the impact of a virtually embodied metaphor on self-evaluation. In both experiments, participants completed multiple trivia rounds after experiencing different levels of verticality. We investigated whether the embodied metaphor of UP = better would then affect their overconfidence, measured as the difference between how well they estimated their own performance to be and how well they actually scored. In Experiment 1, we compared this effect between three different mediums: mental imagery, video, and VR, hypothesizing that the ascending VR condition would yield greater overconfidence scores. We speculated that VR, by engaging the body to a greater extent than the other two mediums, provides a mechanism through which the full effect of an embodied metaphor can activate. Our results did not support this hypothesis: we found no statistically significant difference in overconfidence scores between mediums in Experiment 1. However, Experiment 1 results did support our predictions that people perceive themselves to be more embodied in VR than they would be watching a video or imagining a scene. In a follow-up study with only mental imagery, Experiment 2, we found no main effect for contextual cues given during the experiment. There was a significant difference between ascending and descending conditions, however, counter to what we predicted: participants had higher overconfidence scores in the descending conditions. We discuss issues in the field of embodied metaphor research and suggest alternative routes for investigating metaphors in VR. In light of the growing interest in employing VR as a research tool, we discuss Experiment 1 methodologies, highlighting the advantages and disadvantages of conducting experimental research in VR.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".