Perceptual Learning

Process of inferring causes of sensory information; unified with value learning as integral aspects of free energy minimization.

Neighborhood — ranked by edge-count

paper

framework

Free Energy Principle
associated_with
A foundational variational principle from statistical physics that formalizes how self-organizing systems maintain structural integrity and adapt to their environment by minimizing free energy—a mathematical bound on surprise or prediction error. Originally developed by Karl Friston, the framework unifies action, perception, and learning as processes of active inference, where systems both update internal models of the world and act upon it to reduce the divergence between predictions and observations.

claim

concept

Helmholtz's Neural Energy Principle
associated_with
Historical idea statistically reformulated to furnish model of perceptual inference and learning.
Hierarchical Models of Sensory Generation
supports
Enable brain to construct dynamic, context-sensitive prior expectations; foundational to perceptual inference scheme.

event

Perceptual inference and learning (lecture June 1, 2008)
about
Third lecture integrating action, perception, and learning under the free-energy principle.

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.

Learningconcept0.848
Inference of parameters encoding contingencies of the world (e.g., likelihood matrix A) at slower timescale than perception.
perceptual learning is, literally, an integral part of value learningquote0.839
Key assertion that perceptual and value learning are inseparable.
perceptual inferenceconcept0.833
The process of inferring causes of sensory inputs, a key aspect of the free-energy minimization scheme.
Perceptual Fieldconcept0.829
Area in space and time an agent can survey to find alternative paths to a goal; increases with collective size.
Physical learningframework0.826
Framework for solving inverse problems in which physical systems autonomously adapt their parameters in response to stimuli through local learning rules, without requiring computational design or explicit cost functions
Epistemic Learningconcept0.815
Learning model parameters through curious, uncertainty-reducing behavior; reducing ignorance about contingencies
Unsupervised Learningmethod0.810
Learning that builds a low-dimensional model of input data without error signals or rewards; Hebbian learning is an example.
Associative Learningconcept0.805
Sentience criterion; capacity occurs even in gene regulatory networks and non-neural morphogenetic agents.