finding

active

finding:in-aomic-id1000-movie-watching-fmri-data-nis-finds-a-one-dimensional-macro-state-representing-100-dimensional-micro-states

In AOMIC ID1000 movie-watching fMRI data, NIS+ finds a one-dimensional macro-state representing 100-dimensional micro-states.

Real brain imaging result suggesting a compressed emergent representation.

Source paper

extracted_from

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

(2023) · Bing Yuan · Jiang Zhang · Aobo Lyu · Jiayun Wu +5

Neighborhood — ranked by edge-count

Claims (1)

claim

The NIS and NIS+ frameworks provide effective solutions for causal emergence identification from data.
supports
Central claim of the machine-learning section, summarizing the contribution.

Communities (3)

community

Causal emergence in biological systems
members_of
Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Causal emergence in learning agents
members_of
Uses effective information (EI) and coarse-graining to link causal emergence with RL and biological learning.
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.

Findings (1)

finding

In AOMIC PIOP2 resting-state fMRI data, NIS+ finds a seven-dimensional macro-state with widely distributed attributions.
associated_with
Contrast to movie-watching condition, showing context-dependent emergence.

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.

NIS+ automatically discovers two-group macro-states in Boid model simulations matching the two boid groups.finding0.816
Yang et al. (2023) experiment on emergent herding behavior.
NIS+ learns macro-dynamics matching ground-truth SIR dynamics from noisy micro-level data.finding0.795
Experimental result from Yang et al. (2023) reported in the survey.
NIS+ captures emergent static/dynamic patterns such as 'gliders' in Conway's Game of Life within the latent space.finding0.770
Yang et al. (2023) demonstration of emergent pattern recognition.
PMI computed from color cooccurrences in CIFAR-10 images yields a perceptual color representation closely matching both CIELAB space and language model embeddings (SimCSE, RoBERTa)finding0.743
Validates theoretical PMI convergence claim on real data
fMRI showed increased right hemisphere anterior superior temporal gyrus activity for insight solutions, with EEG showing a gamma-band burst in the same region beginning 0.3s prior to insight (Jung-Beeman et al., 2004).finding0.742
Neural correlate of insight used to support prediction about early neural activity following structure learning
Novel place cell metric (largest connected component firing mass ratio) successfully distinguishes TEM-t memory neurons (place cells) from RNN neurons (grid cells)finding0.741
Methodological validation result confirming the place-cell metric separates cell types in TEM-t.
µ is an applicative homomorphism: µ(pure a) = pure a and µ(imf <*> imx) = µ imf <*> µ imx.claim0.739
Result for Image applicative specification.
2D projections of activations show clearly separable clusters for F0-F2 and A1 at layer 25, but increasingly entangled activations for F4-F5 and A2-A3.finding0.738
Visual geometric evidence for the fundamental entanglement of true/false activations in harder tasks.