Do you believe in haptics?: Balance and the placebo effect
Bibliographic record
Abstract
Studies reveal that haptic input (touching lightly on a surface) can improve a person's balance (Afzal et al., 2015). However, it has been argued that it is impossible to separate the effects of psychological and social forces from the effect of a treatment itself (Crum et al., 2017). We examined whether a person's mindset could be altered through descriptive norms to create a placebo effect that would enhance the effect of haptic input on balance. Drawing on the placebo literature (Carvalho et al., 2016), we examined whether providing individuals with a normative message about the positive effects of haptic input would improve balance over the effects associated with haptics alone. Adult participants were randomly assigned to either a control (n = 36) or message condition (n = 33). Participants performed two quiet tandem-stance balance tests on a portable force plate separated by a 60-second rest period. On the first trial, all participants balanced without touch and on the second with touch. Prior to the second trial, those in the message condition were told that many others improved their balance with touch while the control group received no message. The DV was the mean total path traveled by the centre of pressure (COP). Controlling for the COP (first trial), ANCOVA results revealed COP on trial two differed significantly by condition (p = .004). Those receiving the message exhibited better balance. These results suggest that creating a placebo effect about the positive effects of haptic input may help improve balance.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".