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The Experience-Dependent Dynamics of Human Consciousness

Birgitta Dresp

Open Journal of Philosophy 2018 DOI: 10.4236/ojpp.2018.82010 (opens in new tab)

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AI-extracted from the abstract
Characteristics Theoretical or philosophical paper Peer reviewed
Citations 2
Key points Argues that human consciousness is epigenetically determined and experience- and context-dependent, changing unpredictably over time, so a successful artificial intelligence emulation is unlikely. Any such code would need to simulate temporal signal reverberation, de-correlate spatial from temporal signals, make implicit processes explicit, and use dynamically adaptive computations; no known machine or neural network learning approach has this potential.

Abstract

By reviewing most of the neurobiology of consciousness, this article highlights some major reasons why a successful emulation of the dynamics of human consciousness by artificial intelligence is unlikely. The analysis provided leads to conclude that human consciousness is epigenetically determined, experience, and context-dependent at the individual level. It is subject to changes in time that are essentially unpredictable. If cracking the code to human consciousness were possible, the result would most likely have to consist of a temporal pattern code simulating long-distance signal reverberation and de-correlation of all spatial signal contents from temporal signals. In the light of the massive evidence for complex interactions between implicit (non-conscious) and explicit (conscious) contents of representation, the code would have to be capable of making implicit (non-conscious) processes explicit. It would have to be capable of a progressively less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure identical to that of the human brain, from the synaptic level to that of higher cognitive functions. The code’s activation thresholds would depend on specific temporal signal coincidence probabilities, vary considerably with time and across individual experience data, and would therefore require dynamically adaptive computations capable of emulating the properties of individual human experience. No known machine or neural network learning approach has such potential.

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