Modeling the Action–Perception Loop and its role in Phantom Limb Pain using Active Inference
Bibliographic record
Abstract
Abstract Phantom limb pain is among the most prevalent and distressing consequences of limb amputation. Theories regarding its underlying mechanisms remain disputed, contributing to challenges in effectively treating the pain. In recent years, mathematical models grounded in the Bayesian inference framework have been used to describe various aspects of pain perception. However, pain is not only passively inferred but actively shaped through interactions with the environment—a dimension that classical Bayesian approaches typically do not capture. Because amputation disrupts both sensory input related to the limb and the ability to perform actions, a model incorporating both sensory and active components of pain may provide new insight into the mechanisms underlying phantom limb pain. To this end, we developed a model within the active inference framework, which extends Bayesian inference to include action selection. The model provides a conceptual account of how loss of limb control, ambiguity in sensory input pertaining to limb position, residual noxious input, and pre-amputation pain may contribute to the emergence and persistence of phantom limb pain. Furthermore, it offers insight into the possible mechanisms underlying common interventions and may help account for their variable efficacy across individuals. Author summary Phantom limb pain is a condition where pain is perceived as arising from a limb that is no longer present. Despite being one of the most prevalent and distressing consequences of limb amputation, theories regarding the underlying mechanism of phantom limb pain remain disputed. Here, we present a mathematical model that investigates possible mechanisms underlying this complex pain condition. Using the active inference framework, which combines sensory perception and action selection processes, the model provides a conceptual account of how four distinct factors – loss of control of the limb, ambiguity in sensory input pertaining to limb position, residual activity in afferent nociceptive neurons, and pre-amputation pain – may contribute to the emergence and persistence of phantom limb pain following amputation. Furthermore, our model offers insight into the possible mechanisms underlying common interventions and may help explain why their efficacy varies across individuals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".