finding

active

finding:internal-subsystem-dynamics-significantly-predict-external-subsystem-motion-via-canonical-variates-analysis-2-distributed-p-0-00052

Internal subsystem dynamics significantly predict external subsystem motion via canonical variates analysis (χ²-distributed, p=0.00052).

Empirical validation from primordial soup that internal states encode information about hidden environmental states.

Source paper

extracted_from

Life as we know it

(2013) · Karl Friston

Neighborhood — ranked by edge-count

Hypotheses (1)

hypothesis

Any system minimizing free energy will appear to engage in implicit Bayesian inference of hidden external causes.
supports
Predicts that internal states encode posterior beliefs about external world through gradient descent on free energy.

Communities (2)

community

Causal emergence in biological systems
members_of
Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Causal emergence via information-geometric coarse-graining
members_of
Framework using effective information (EI) and NIS+ to automatically discover macro-scale dynamics from micro-level data, validated on fMRI, Conway's Game of Life, and SIR models.

Methods (1)

method

Canonical Variates Analysis
implements
Statistical method assessing linear mapping between internal functional patterns and external structural motion.

Related by similarity (8)

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.

Internal states significantly predicted motion of external subsystems; best prediction for the farthest subsystem (magenta circle, Fig 4d).finding0.844
Result of canonical variates analysis showing statistical dependency between internal states and external motion.
Five out of 82 external subsystems had χ² values above the maximum null distribution, p=0.00052.finding0.772
Statistical significance of the prediction after time-flip control.
These dynamics are consistent with predictive processing since active inference prescribes dynamics that minimise prediction error.concept0.767
Connection between active inference neuronal dynamics and predictive processing theory.
NIS+ learns macro-dynamics matching ground-truth SIR dynamics from noisy micro-level data.finding0.756
Experimental result from Yang et al. (2023) reported in the survey.
We hypothesize that representation geometry drives model behavior — the geometric structure of internal representations causally shapes what models do externally.hypothesis0.749
The causal hypothesis motivating the use of causality (intervention) as the lens connecting representation and behavior geometry.
If internal states encode a probability density over external states, then it should be possible to predict external states from internal states.hypothesis0.744
The testable hypothesis driving the active inference analysis in the simulation.
Can external observers infer the anatomical goal state of a developing system by observing stress patterns?question0.741
LLMs can compute meaningful functions over perturbations to their internal states, establishing introspection as a real but layer-dependent phenomenonclaim0.739
Primary positive claim of the paper, grounded in strength comparison and localization results