question
active
question:how-causally-emergent-are-artificial-agents-compared-to-biological-onesHow causally emergent are artificial agents compared to biological ones?
Core motivating question addressed by the empirical RL study; identified as major knowledge gap.
Source paper
extracted_from(2026) · Federico Pigozzi · Michael Levin
Neighborhood — ranked by edge-count
Papers (1)
paper
Findings (1)
finding
- Empirical result: CE measurements correlate with and predict learning performance in RL agents.
Claims (1)
claim
- Prior empirical observation from biological systems; motivates investigation in artificial agents.
Concepts (2)
concept
- Causal EmergenceaboutCore concept: degree to which an agent exerts unique predictive power on its future; key to cognition at all scales.
- Artificial agentsaboutSynthetic agents (here RL-trained neural networks) whose causal emergence was previously largely unknown; the paper addresses this gap.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Biological and artificial agents share causal emergence as an axis of learning and reorganization.claim0.855Interpretive assertion bridging Levin's biological cognition work with artificial RL; extends 'minds at all scales' thesis.
- Core definition from §1.
- Philosophical debate discussed in §5.2.
- Assertion that understanding causal emergence may lead to methods for manipulating agent representations to improve performance.
- Authors' interpretive assertion that the observed alignment reveals a novel organizing principle of neural representation dynamics.
- Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.
- Open problem stated in §5.4.
- Claim by Comolatti & Hoel (2022) endorsed by this survey.