Pain Expectation Effects Are Predicted by Emotion Rather Than Precision
Bibliographic record
Abstract
Pain is a universal subjective experience that is influenced not only by the objective intensity of the sensory stimulus but also by multiple internal factors, such as expectations and emotions (Melzack and Casey, 1968). Unravelling the specific mechanisms through which these factors influence the pain experience may help us better understand how to predict, treat, and eventually prevent pain. However, the neural underpinnings of pain and its modulation by internal factors remain poorly understood. The ability of expectations to affect the pain experience is illustrated by the placebo and nocebo effects, which refer, respectively, to decreased pain resulting from positive expectations toward a treatment (e.g., pain cream) and increased pain resulting from negative expectations toward a procedure (e.g., an injection). Previous research has shown that the amount of pain one experiences when receiving an electric shock is influenced not only by stimulus characteristics, such as the intensity of the shock, but also by expectations (Hoskin et al., 2019). The effect of these expectations varies depending on how precise the expectations are thought to be. Thus, if one's expectations about the painfulness of an upcoming electric shock were more consistent (i.e., the participant always expected the same level of painfulness) in the past, their expectation about the painfulness had a greater influence on their actual pain experience. Accordingly, subjects who had more inconsistent expectations (i.e., the participant expected varying levels of painfulness) had reduced expectation influence on pain perception (Hoskin et al., 2019). This effect is consistent with a Bayesian model that proposes that the degree to which expectations influence perception is determined by the consistency or precision of the expectations (Hoskin et al., 2019). Other research demonstrated that the modulation of expectation effects by the preciseness of those expectations can also be … Correspondence should be addressed to Chloe L. Cheung at ccheu252{at}uwo.ca.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".