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.

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• Connectionist Models of Cognition and Learningframework0.885
  Models where intelligence arises from organisation of connections between simple processing units, used as basis for evolutionary connectionism
• Connectionist models can exhibit learning bottom-up, without centralised control or an external teacher, and without any performance feedback applied at the system level.claim0.869
  Key property of distributed unsupervised learning.
• Connectionist Models Of Cognitionframework0.858
• Connectionist learning principles apply equally to evolutionary systems where variation and selection alter network organization, instantiating distributed learning at evolutionary timescales.hypothesis0.855
• Can Connectionist Models Of Individual Cognition And Learningquestion0.852
• 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.claim0.846
  Necessary condition for connectionist cognition.
• 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
• Neural networks and physical systems with emergent collective computational abilities (Hopfield, 1982)concept0.815
  Original Hopfield network paper; the attractor dynamics in TEM memory retrieval are a continuous version of this.