Implications of Confabulation Theory

Confabulation theory has a variety of implications. A few examples are discussed here.

Since all of cognition is "categorical" (i.e., based upon the symbol sets of the thalamocortical modules), the total number of modules, and the number of symbols in each of those modules, provides a reasonable estimate for the "descriptive power" of a brain. A trout may have only a few tens of modules, each with a few hundred symbols. A raven might have hundreds of modules, each with many hundreds of symbols. A human probably has thousands of modules, each with thousands to hundreds of thousands of symbols. Similarly, the total number of knowledge links that an animal possesses gives a crude quantification of how "smart" that animal is (although, clearly, the distribution of those knowledge links also matters: idiot savants may have huge numbers of knowledge links).

The experiments of Chap. 6 imply that the average human possesses billions of items of knowledge, of which the majority are often obtained in childhood. Some humans may possess tens, or perhaps even hundreds, of billions of items of knowledge. Clearly, since there are only about 32 million seconds in a year, the average rate of knowledge acquisition often exceeds one item per second and might sometimes exceed 100 items per second. It is thus not surprising that we need to sleep a third of the time in order to catch up with evaluating and selectively solidifying each day's new cognitive knowledge links (i.e., implement cognitive learning control decision-making for recently established, and intrinsically rapidly fading, temporary knowledge links - which is probably the main activity of sleep).

Humans (and animals in general) are almost certainly much "smarter" than has been generally appreciated. Assuming such findings are confirmed, fields as diverse as psychology, education, philosophy, psychiatry, medicine (both human and veterinary), law, and theology will need to be extensively overhauled.

With one relatively small exception, the axonal connectivity between the tha-lamocortical modules in the human brain seems to roughly resemble that of other great apes. That one exception is the modules of the human language faculty - which seem to connect widely to modules in many other faculties. In this sense, language is the hub of human cognition. It seems likely that this (along with having a brain which is, overall, over three times larger) can explain some of the commanding power of human thought in comparison with that of other apes. As we learn more about cetaceans, it may well be that some of them (and perhaps other species as well, such as jays, ravens, and parrots) also have this language hub cognitive architecture characteristic to some degree.

The near-term implications of confabulation theory for neuroscience are uncertain. Neuroscience is dominated by bottom-up thinking and by "methods." To succeed, neuroscientists must often spend the decade after completing their Ph.D. developing their own effective experimental methods. The subset of aspirants who successfully complete this process must then, in general, inaugurate and manage a large lab that quickly acquires enormous built-in inertia. After completing this arduous initiation at about age 40, few of these newly established neuroscientists are going to be interested in abandoning, or significantly altering, their research direction in order to begin to follow up on the hypotheses of confabulation theory. Thus, integration of confabulation theory into neuroscience is likely to be largely confined to new investigators who decide to pursue experimental exploration of confabulation theory's neuroscience implications (probably mainly using human subjects carrying out controlled thought processes while being monitored by brain activity scanners with greatly improved spatial and temporal resolution). Assuming this established social pattern continues to hold, it seems unlikely that confabulation neuroscience can join the mainstream of the subject until the next decade.

Notwithstanding the above, members of the small community of mathematical neuroscientists may soon realize that, given the hard constraints provided by confabulation theory, it may be possible to tackle large-scale understanding of brain function. For example, it may be possible within a few years to build an integrated functional mathematical model of cerebral cortex, thalamus, basal ganglia, subthalamus, red nucleus, substantia nigra, hippocampus, amygdala, hypothalamus, spinal cord, locus coeruleus, pons, and cerebellum. This model may well answer most of the large questions of neuroscience that remain after confabulation theory.

A large-scale human brain modeling project of this sort will surely require a widely knowledgeable and exceptionally well educated team of hundreds of mathematical neurobiologists and computer scientists operating as willing and compliant subordinates under the hierarchical command of a master genius. The usual "herd of cats" sort of scientific research program would probably not work effectively in this instance. I personally know at least five people who could each probably successfully lead such an effort. Such an integrated brain modeling project is, in my opinion, one of the most important tasks that the human species should now carry out. It will be expensive (probably exceeding $200,000,000 per year for a decade; along with another $400,000,000 for a proper building to house the project and the budget for the required equipment). A single, open, international project of this type would seem ideal. However, given the potential economic and national security implications, multiple projects of this type seem more likely. With respect to these practical implications of confabulation theory, I leave it to you, the reader, to form your own opinion as you absorb the book's content.

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