If sub-neural chemical dynamics are crucial for cognition, then neural models ignoring them will fail to capture key cognitive phenomena.

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• The huge chemical complexity within each synapse suggests that neural models of cognition that refer only to changing weights of synaptic connections and ignore sub-neural chemistry are probably ignoring some of the most important explanatory mechanisms in brains.claim0.891
  Sloman's critique of mainstream neural network theories.
• Investigating the causal substructure of neural representations is necessary to avoid misidentifying data structures of simpler representations as abstract conceptsclaim0.802
  Motivated by the finding that lexical entailment decomposes into word identities.
• 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.claim0.797
  Central claim about the power of connectionism.
• 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.claim0.786
  Necessary condition for connectionist cognition.
• Cognition and learning are substrate-independent; principles familiar in artificial neural networks can be implemented by any network of signals with suitable weighted inputs.claim0.785
  Central thesis enabling unification of neural, developmental, ecological, and social networks as instances of collective intelligence.
• A purely cognitive particle has no representational basis on which to distinguish self-generated from environment-generated observations; metacognitive architecture is the prerequisite for the separation priorclaim0.782
  Developmental account of why and when sigma emerges during ontogeny
• We hypothesize that hallucinated rationales in 1B-models result from lack of necessary vision context; incorporating vision features should reduce hallucination and improve rationale quality.hypothesis0.782
  Predictive hypothesis driving the investigation in Section 3.3; supported by experimental evidence.
• Large parts of the human brain are irrelevant or only weakly relevant to awareness and moral status, suggesting engineering could produce more efficient minds at different scalesclaim0.781
  Supports the mind-scale dimension by noting human brain inefficiency relative to morally relevant properties