hypothesis

active

hypothesis: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

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.

Overarching three-part hypothesis stated in introduction

Source paper

extracted_from

Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality

(2022) · Watson, Richard A. · Levin, Michael · Buckley, Christopher L.

Neighborhood — ranked by edge-count

Claims (1)

claim

Before a transition, the higher-level unit of selection does not exist, yet complex adaptations creating that unit must evolve through bottom-up selection on lower-level units.
supports
Central theoretical puzzle in ETI research: explains why existing frameworks struggle with ETI explanation.

Concepts (2)

concept

Deep Learning
supports
Learning hierarchical representations of non-decomposable functions; proposed as formal equivalent to ETI process.
Non-decomposable (Non-linearly Separable) Functions
gates
Mathematical formalism from connectionism where output depends on context-dependent interaction of inputs; proposed as formal requirement for ETIs.

Frameworks (1)

framework

Evolutionary Connectionism
supports
Proposed framework translating connectionist learning principles into natural selection domain to explain ETIs.

Questions (1)

question

How can selection explain adaptations at system level before the higher-level unit exists as an evolutionary unit?
gates
Fundamental challenge: conventional evolutionary theory cannot explain system-level features required for ETI without presupposing the higher-level unit.

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.

ETIs are described by conditions necessary for deep learning / non-decomposable function learninghypothesis0.898
Deep Learning as ETI Equivalentclaim0.845
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.hypothesis0.825
Second hypothesis linking learning theory directly to evolutionary transitions
The conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions (deep model induction)claim0.802
Core interpretive claim of the paper connecting ETIs to connectionist learning
Conditions necessary to evolve new individuality level are described by conditions for learning non-decomposable functions (deep model induction)hypothesis0.798
Conventional evolutionary framework cannot explain adaptations in systems that are not already evolutionary units, creating a chicken-and-egg problem for ETIsclaim0.797
Central critique of existing theory, motivating the connectionist alternative
Evolutionary transitions in individuality constitute a form of deep model induction.claim0.788
Links ETIs to the learning of hierarchical representations.
Evolutionary Transitions in Individuality (ETI)framework0.782
Theory explaining how new levels of biological organization and individuality emerge through transitions in collective intelligence and problem-solving rescaling.