Vection lies in the brain of the beholder: EEG parameters as an objective measurement of vection
Bibliographic record
Abstract
CURRENT TRENDS IN VECTION RESEARCHVection is typically defined as the subjective experience of self-motion in the absence of physical movement through space (Dichgans and Brandt, 1978;Hettinger et al., 2014).Vection is a common phenomenon in simulated environments such as driving/flight simulators, virtual reality (VR) interfaces, or video games (Riecke, 2011).Although vection was first described more than a century ago, the scientific interest in vection has recently been growing (see Frontiers in Psychology, 2015, The Future of Vection for a special issue devoted entirely to vection).Part of the motivation driving the need to better understand the mechanisms underlying vection comes from the fact that quantifying or characterizing vection may be critical to understanding its role in a variety of research approaches, theoretical assumptions, and applications.For example, outstanding questions include: (1) Is vection necessary to equate the behaviors elicited in simulated environments with those elicited in comparable real world environments?(2) Must a user experience true vection to optimize "transfer of training" effects across a variety of applications (e.g., transferability of driver training within a simulator to real world outcomes)?If vection elicits perceptions and behaviors under certain circumstances that are comparable to those elicited during real physical movements through space, the application of vectioninducing techniques could have wide-ranging implications.The biggest challenge in addressing these questions is that there are no well-validated, objective measures that can reliably identify or characterize the experience of vection.Instead, vection is typically measured using nonstandardized subjective rating scales that differ widely from study to study.The need for objective measurements is important for several reasons.For instance, the absence of objective and standardized measurements dramatically hampers the ability to compare across studies and verify the reproducibility of results.Further, any conclusions drawn from vection research (theoretical or applied) are based on a series of assumptions all hinging on the interpretation of qualitative data.In a recent review paper, Palmisano et al. (2015) emphasized the need for objective measurements of vection.In this paper we highlight EEG as a promising technique.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".