Modeling the Action-Perception loop and its role in phantom limb pain using active inference

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.

Authors

Institutions

Publication Details

Journal
Journal of Theoretical Biology
Published
2026-11-01
DOI
https://doi.org/10.1016/j.jtbi.2026.112634
Primary Topic
Pain Management and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling the Action-Perception loop and its role in phantom limb pain using active inference

Malin Ramne, Torbjörn Lundh, Jon W. Sensinger
Journal of Theoretical Biology
Pain Management and Treatment
article

Modeling the Action-Perception loop and its role in phantom limb pain using active inference

Malin Ramne, Torbjörn Lundh, Jon W. Sensinger
article en

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.

Journal of Theoretical Biology
University of New Brunswick (CA), Institute of Biomedical Engineering (CA), Chalmers University of Technology (SE), University of Gothenburg (SE)
Good health and well-being
Openalex Percentile: Top 100%
Pain Management and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon