Networks and the Problem of Panpsychism
Networks and the Problem of Panpsychism
A crucial deficiency of network models is their inability to modally identify or recognize the inputs they receive. To begin solving this problem, we ask to what extent current models resemble non-living systems. If we find neural network models trustworthy at first glance, but recognize strong analogies with other complex systems found throughout nature, we must consider whether these models are underconstrained or simply inaccurate. If not, we may have to accept panpsychism, holding that mind-like properties are found to be widespread throughout nature. When network models are compared with a system that appears utterly nonconscious-a group of rocks bathing in sunlight-it does not appear to be trivial to pinpoint why this inanimate system should be denied any neural-network like (or cognitive) capacity. But should this lead us to defend panpsychism? Considering the limitations of network models, various other approaches are scrutinized, such as computational functionalism, global workspace theory and information-theoretical frameworks. These are found to face the same problem as encountered before: a failure to attribute meaning or content to the information they process. But rather than defending panpsychism, these considerations lead us to conclude that current theories are still underconstrained.
Keywords: Global workspace, Information theory, Meaning, Microconsciousness, Panpsychism, Semantic network, Symbol-grounding problem, Turing test, Wakefulness
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