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.

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• 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.claim0.846
  Central claim about the power of connectionism.
• Connectionist Models Of Cognitionframework0.835
• Connectionist Models of Cognition and Learningframework0.829
  Models where intelligence arises from organisation of connections between simple processing units, used as basis for evolutionary connectionism
• These relationships are cognitive architectures regardless of the substrate in which they are implemented.claim0.820
  Asserts that organismic identity is fundamentally a cognitive structure.
• Can Connectionist Models Of Individual Cognition And Learningquestion0.811
• 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.hypothesis0.806
  Second hypothesis linking learning theory directly to evolutionary transitions
• The application of connectionist models also extends into the domain of evolutionary systems, where the connections of a network are changed by variation and selection.claim0.801
  Formal equivalence between evolutionary variation/selection and connectionist learning.
• Object models in which time, versioning, causality, etc., are significant are probably far better modelled by considering the time component as another key rather than an intrinsic property of the underlying model.claim0.794
  Claim that orthogonal dimensions like time should be explicit keys in the associative model.