hypothesis

active

hypothesis: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

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.

Second hypothesis linking learning theory directly to evolutionary transitions

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

The conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions (deep model induction)
supports
Core interpretive claim of the paper connecting ETIs to connectionist learning

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 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.hypothesis0.825
Overarching three-part hypothesis stated in introduction
Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.825
Central claim about the power of connectionism.
ETIs are described by conditions necessary for deep learning / non-decomposable function learninghypothesis0.812
The ability to represent relationships (e.g. correlations or associations) among variables, rather than a system of independent variables, is essential for any connectionist model of cognition.claim0.806
Necessary condition for connectionist cognition.
Deep Model Induction Equivalence Hypothesis H2framework0.805
H1: Individuality requires a dynamical process (development), mediating the plastic expression of components in the context of one another, with the specific form of computing a collective character that is a non-linearly separable function of (embryonic) particle characters, with the effect of coordinating reproduction based on this collective character.hypothesis0.788
Main hypothesis about the architecture of individuality
Conditions necessary to evolve new individuality level are described by conditions for learning non-decomposable functions (deep model induction)hypothesis0.788
Evolutionary transitions in individuality constitute a form of deep model induction.claim0.787
Links ETIs to the learning of hierarchical representations.