Wong’s article mentioned from a paleontological perspective that population size may have been fundamental in accelerating symbolic thought which possibly helped our ancestors to externalize clan/tribe membership or identity. This underlying logic in prehistoric human communication shows that it was predisposed for social lubrication. This in turn developed the human appetite to communicate better with each other, and also effectively “game” the system by nurturing a talent to manipulate and persuade other humans in a competitive sense. The theory also explains rationally, the current technological race in which we as a human collective (by which I mean the big tech entities) are primarily focused on getting increasingly better at generating more efficient algorithms so that their machines can work faster than others.
If “that symbolic thought does not come innately built in, but develops by internalizing the symbolic process that underlies language. So species that have not acquired the ability to communicate symbolically cannot have acquired the ability to think this way either” (Deacon), then the billions of lines of code (as symbolic representation) that generate the virtual realities we encounter on a daily basis today must definitely mimic a procedural network in the human brain, albeit a limited part of it. If this does happen to be the case, what more can we do with computers upon a fully mature understanding of the origins of human cognition and its influence of our instinctual craving for symbolic reference?
Most interesting to me was that Donald dubs our books, movies and codes (normatively and rather simplistically labelled cultural artifacts) “distributed” cognitive networks of the human mind that can prove to be a rich material of study for cognitive scientists. It is my emerging opinion that machines will never reach human level intelligence. Their “languages” simply do not have the capacity for an “explanation”. As a result, it becomes almost impossible to program the element of curiosity into machines, which is a large part of thinking like a human being. The level of intelligence exhibited by the most advanced machines today attribute to a very limited method or “network” by which human cognition functions. To reach a point of singularity comes with it a rather cumbersome prerequisite to first clearly understand the early workings of the human mind and how much of it has been hijacked by symbolic representations that we created a long time ago more plausibly on instinct, than by method.
On a non-symbolic note, new evolutionary brain science research purports that the human brain grew in power or intelligence not because of its increasing size, but because we began cooking with fire, which freed up a lot of our time that was previously spent on grazing and foraging. This finding leads one to comfortably speculate that upon the preponderance of the projected omniscient, general or “strong” Artificial Intelligence, when most of our jobs are rendered obsolete by automation, we are going to find ourselves in more productive leisure cultivating a further exponentiation of the human brain’s cognitive prowess, rather than a collective numbing of the human mind.
1. Kate Wong, “The Morning of the Modern Mind: Symbolic Culture.” Scientific American 292, no. 6 (June 2005): 86-95.
2. Terrence W. Deacon, The Symbolic Species: The Co-evolution of Language and the Brain. New York, NY: W. W. Norton & Company, 1998.
3. Merlin Donald, “Evolutionary Origins of the Social Brain,” from Social Brain Matters: Stances on the Neurobiology of Social Cognition, ed. Oscar Vilarroya, et al. Amsterdam: Rodophi, 2007.
4. Suzana Herculano-Houzel and Katrina Fonseca-Azevedo, “Metabolic constraint imposes tradeoff between body size and number of brain neurons in evolution”. Rio de Janeiro, Brazil, 2012.