ETIs are described by conditions necessary for deep learning / non-decomposable function learning

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• ETIs are the evolutionary equivalent of deep learning: (i) required functional relationships encode non-decomposable functions, (ii) these are enacted by basal cognition mechanisms, and (iii) conditions for deep model induction predict ETI occurrence.hypothesis0.898
  Overarching three-part hypothesis stated in introduction
• The conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions (deep model induction)claim0.822
  Core interpretive claim of the paper connecting ETIs to connectionist learning
• Deep Learning as ETI Equivalentclaim0.818
• H2: The conditions necessary for the induction of deep models, familiar in connectionist models of learning and cognition, are predictive of the conditions necessary for an ETI to occur.hypothesis0.812
  Second hypothesis linking learning theory directly to evolutionary transitions
• Conditions necessary to evolve new individuality level are described by conditions for learning non-decomposable functions (deep model induction)hypothesis0.807
• Shallow interaction structures cannot compute non-linearly separable functions; depth (hidden layers) is necessary for ETI-relevant individualityclaim0.763
  Assertion that deep organization is mandatory, based on connectionist theory
• Conventional evolutionary framework cannot explain adaptations in systems that are not already evolutionary units, creating a chicken-and-egg problem for ETIsclaim0.761
  Central critique of existing theory, motivating the connectionist alternative
• A connectionist approach resolves the chicken-and-egg problem of ETIs by focusing on the changing organisation of relationships between lower-level units, not unit frequenciesclaim0.761
  The paper's proposed solution