Bayesian Model of Pain

Conceptualization of pain perception as inference over hidden nociceptive causes, from Eckert et al. 2022

Neighborhood — ranked by edge-count

framework

Introspective Exploration Component
extends
The novel framework introduced in the paper: an HMM-based pain-belief signal integrated into the reward function to drive exploration

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Bayesian Model Selectionconcept0.798
Choosing among candidate models based on model evidence.
Normal Pain Perception Modelframework0.796
HMM parameterization representing healthy pain perception with informative transitions and emissions
Chronic Pain Perception Modelframework0.791
HMM parameterization with sticky transitions and ambiguous emissions representing maladaptive pain perception
Bayesian Mechanicsframework0.786
Bayesian Model Expansionmethod0.786
Adding new states or parameters to the generative model if it increases model evidence, enabling concept learning.
Bayesian Model-Based Reinforcement Learningframework0.783
RL variant that maintains beliefs over environment model; compared to active inference using Thompson sampling.
Bayesian model evidenceconcept0.779
The probability of sensory data under a generative model; negative log evidence is bounded by free energy.
Bayesian Model Averagingconcept0.769
Predictions formed by averaging over policy-specific beliefs, weighted by policy probabilities.