claim

active

claim:biological-and-artificial-agents-share-causal-emergence-as-an-axis-of-learning-and-reorganization

Biological and artificial agents share causal emergence as an axis of learning and reorganization.

Interpretive assertion bridging Levin's biological cognition work with artificial RL; extends 'minds at all scales' thesis.

Source paper

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The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents

(2026) · Federico Pigozzi · Michael Levin

Neighborhood — ranked by edge-count

Hypotheses (1)

hypothesis

Successful RL agents exhibit causal emergence that predicts final reward early in training and aligns representational dynamics with reward improvement.
extends
Central finding: causal emergence serves as a previously undisclosed axis of neural representation reorganization in learning agents.

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 in learning and adaptation
members_of
Framework measuring how coarse-grained causal structure increases during learning across biological and artificial agents, using effective information and interventional methods.

Frameworks (1)

framework

Diverse Intelligence
associated_with
Research program studying intelligence at multiple scales and substrates; proposed as relevant to implications of mnemonic improvisation.

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.

Biological agents increase causal emergence after learning new memories.claim0.892
Prior empirical observation from biological systems; motivates investigation in artificial agents.
Causal emergence alignment with learning is a shared axis comparing biological and artificial creatures.claim0.886
Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.
Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.claim0.862
Authors' interpretive assertion that the observed alignment reveals a novel organizing principle of neural representation dynamics.
How causally emergent are artificial agents compared to biological ones?question0.855
Core motivating question addressed by the empirical RL study; identified as major knowledge gap.
successful agents exhibited causal emergence that was consistently predictive of final reward early in training and whose representational dynamics aligned with reward improvement in most tasks.quote0.834
Load-bearing summary of the main empirical finding that anchors the Causally Emergent Alignment Hypothesis.
Causal emergence provides new perspectives for causal representation learning, interpreting latent variables as emergent causalities.claim0.817
Cross-fertilization claim made in discussion.
Causal emergence can enable causal interventions to create better RL agents.claim0.816
Assertion that understanding causal emergence may lead to methods for manipulating agent representations to improve performance.
Biological networks exhibit the lowest EI among real networks and show the most significant causal emergence after coarse-graining.finding0.799
Finding from Klein & Hoel (2020) on real network analysis.