I think there are two self-organizing learning networks. One is the neural network and the other is the bit network. The human and the computer. The beauty of connectivism is its explanation of how these two networks connect such that two humans can share neural networks.
Unfortunately humans do not directly connect neurons, nor do we connect neurons to bits. Our communication with other humans and with computers is mediated by language. And imho language is an arbitrary system of symbols i.e. words, gestures, voice, and images.
It is (unfortunate that) these symbols which make up our conceptual network. Even if one where to deny a symbolic system in connectivism, he/she would still be forced to use language to deny that. I think it would be more practical to admit the limitations of our languages and try to approximate the "if" neurons of one person can directly communicate with the neurons of another.
What had bothered me about the current state of connectivist explanation of concepts is the selection of what nodes should represent. In the neural level it is explicit. In the social level it is natural to identify an actor as the node. The human is after all a world in himself/herself. But in the conceptual level, made up of words, it is not so clear. I think a lot of what is represented as nodes should be represented as ego networks or component networks.
This could be illustrated in Chemistry. If we consider salt for instance. Salt is not represented as a node but a network of basically Sodium and Chlorine, connected by bonds (please correct me if I'm wrong because I'm not a chemist). I think we misrepresent a lot of concepts like salt as nodes in our conceptual layers, and have not gotten around to identifying the basic elements of a learning conceptual network that are simple enough to have no meaning when standing by themselves. Resulting in a salty network.
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