Perceptual inference in chronic pain an investigation into the economy of action hypothesis
Bibliographic record
Abstract
The experience of chronic pain critically alters one's ability to interact with their environment. One fundamental issue that has received little attention, however, is whether chronic pain disrupts how one perceives their environment in the first place. The Economy of Action hypothesis purports that the environment is spatially scaled according to the ability of the observer. Under this hypothesis it has been proposed that the perception of the world is different between those with and without chronic pain. Such a possibility has profound implications for the investigation and treatment of pain. The present investigation tested the application of this hypothesis to a heterogenous chronic pain population. Individuals with chronic pain (36; 27F) and matched pain-free controls were recruited. Each participant was required to judge the distance to a series of target cones, to which they were to subsequently walk. In addition, at each distance, participants used Numerical Rating Scales to indicate their perceived effort and perceived pain associated with the distance presented. Our findings do not support the Economy of Action hypothesis: there were no significant differences in distance estimates between the chronic pain and pain-free groups (F1,60=0.927; P=0.340). In addition, we found no predictive relationship in the chronic pain group between anticipated pain and estimated distance (F1,154=0.122, P=0.727), nor anticipated effort (1.171, P=0.281) and estimated distance (F1,154=1.171, P=0.281). The application of the Economy of Action hypothesis and the notion of spatial perceptual scaling as a means to assess and treat the experience of chronic pain are not supported by the results of this study.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".