Are placebo effects a perceptual illusion? Placebo effects on performance within the Bayesian Brain
Bibliographic record
Abstract
This chapter draws upon ideas within cognitive neuroscience to describe a conceptual account of how placebo effects may emerge and affect sporting performance. By viewing our brains as prediction-generators and hypothesis-testers, we may be able to understand how we use the noisy and uncertain information that we gather from our senses to form our perceptions, shape how we feel and determine how we act within the world around us. This chapter discusses how this is possible, based on a probabilistic combination of our brain’s prior expectations and sensory evidence to infer the most likely cause of the sensory information we receive. Expanding upon this idea, this chapter describes how placebo effects may emerge as a result of a change in the balance of this inferential process. This chapter describes initial evidence applying this conceptual framework to widely studied placebo effects, such as placebo-induced pain relief (i.e., hypoalgesia), before concluding with an evaluation of the possible implications for the study of placebo effects in sport and exercise. It is contended that placebo interventions may hold a particularly important role in uncovering the mechanisms underlying the development of fatigue, providing invaluable insight given its relevance to effective physical performance.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".