Connectionist models can exhibit learning bottom-up, without centralised control or an external teacher, and without any performance feedback applied at the system level.

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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.claim0.869
  Central claim about the power of connectionism.
• Connectionist Models of Cognition and Learningframework0.835
  Models where intelligence arises from organisation of connections between simple processing units, used as basis for evolutionary connectionism
• Can Connectionist Models Of Individual Cognition And Learningquestion0.803
• Connectionist Models Of Cognitionframework0.798
• Connectionist learning principles apply equally to evolutionary systems where variation and selection alter network organization, instantiating distributed learning at evolutionary timescales.hypothesis0.797
• 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.claim0.792
  Necessary condition for connectionist cognition.
• How are model spaces constructed and explored in the absence of a full model (bottom-up structure learning)?question0.786
  Research gap explicitly identified: the paper uses top-down BMR from a full model, avoiding this challenge
• 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.claim0.785
  Formal equivalence between evolutionary variation/selection and connectionist learning.