Knowledge valuation network
This text is part of: "I would never send my kids to school" by Piotr Wozniak (2017)
Knowledge valuations
All granular pieces of knowledge processed by the brain are instantly evaluated for their relevance, coherence, and value. We instantly know if information is understandable and useful. We also often instantly notice when it is inconsistent, incoherent or irrelevant.
Unusual and surprising bits of knowledge are highly valued, however, the probability isn't the best reflection of value from the brain's point of view. There are highly unlikely events of low significance (e.g. asteroid strike in a remote planetary system), and likely events that change one's life (e.g. the answer to "Will you marry me?").
Knowledge valuation relies primarily on the applicability of knowledge in achieving personal goals.
The emotional brain and the rational brain
Knowledge valuation network is an evaluation system based on a resultant of emotional and rational valuations of knowledge. In literature, it may be referred to broadly as neural valuation circuitry, which is not necessarily knowledge-specific.
In the valuation network, emotional valuations will connect information with rewards in the primitive brain centers responsible for hunger, thirst, sex drive, etc. Rational valuations will be knowledge-based. An example of a pure emotional valuation comes from an answer to "Where is the nearest fast food shop?". Knowledge-based valuations may be more complex and highly networked, i.e. dependent on a network of subvaluations. Answer to "Which book is best for my exam?" is evaluated through one's goals that include passing an exam leading to getting a degree affecting one's job prospects, and contributing to lifetime goals. Emotional and rational valuations segregate anatomically. The emotional valuations come from what has metaphorically been described as older portions of the triune brain: reptilian and paleommamalian structures. For example, a specific stimulus processed by the thalamus may send separate signals to the amygdala for an emotional evaluation, and to the neocortex for a rational valuation. The emotional brain is philogenetically older. Personality and education determine if rational valuations can control or override emotional valuations.
Decision tree in fast thinking
Knowledge valuation network is the network of memory connections that determine the value of an individual piece of knowledge. If learning is interpreted as a task, valuation network will determine the perceived task value (see: Problem valuation network).
In computational terms, knowledge valuation network can be compared to a decision tree. Goals and emotions determine core values at the root of the tree. Semantic connections between pieces of knowledge can be interpreted as fractional value transfer from goals to details. A well-organized semantic network of well-consolidated and well-chosen knowledge will need mere milliseconds to make expert decisions. This is what Kahneman calls automatic fast thinking (if you are interested in tough problems that require slow problem solving, see How to solve any problem?). The same kind of processes, that underlie decision making or problem solving, participate in knowledge valuation. Like many expert decisions, the valuation is fast and it is often running with low participation of conscious intentionality. In short, we sometimes die to know things without fully being able to explain why. This process is hardly under our own control, let alone the control of the teacher at school. For efficient learning, valuations must be high.
Figure: Xefer.com is a tool that helps understand knowledge as a network. It relies on semantic links between Wikipedia articles
Valuation network in education
The brain builds a valuation network in the course of learning over years and decades. Through optimization in sleep and via forgetting, the network is polished and smoothed up for efficient operation. This makes it easy to take valuation shortcuts. A student choosing a book may no longer see his exam in the full context of his whole life. He might have developed a quick shortcut: "In the next 3 months, all I want to do is to pass geology".
Knowledge valuation network is highly specialized and very different from individual to individual. The balance between reason and emotions will differ. The balance between goals will differ. The valuation network will shape differently in the mind of a criminal, and differently in the mind of a researcher with lofty goals based on the good of mankind.
The picture shows exemplary valuations that are determined by personal interests in cancer and fasting:
.png)
Figure: Exemplary hypothetical concept activations and valuations upon encountering a declarative statement "In fasting, the NK cells learned to use fatty acids as fuel instead of glucose, which is typically their primary energy source. This really optimizes their anti-cancer response because the tumor microenvironment contains a high concentration of lipids, and now they’re able enter the tumor and survive better".
Here is a set of concept maps activations that results from reading the passage. Colors indicate concept connections that form concept maps that represent individual statements:
- I employ fasting (time-restricted feeding) (light green)
- I believe in health effects of fasting (brown)
- Health is essential for productivity (pink)
- Productivity serves IVS (intrinsically valuable state) (dark brown)
- Fasting does metabolic training on NK cells (as suggested in the passage) (purple)
- NK cells are important in combating cancer (this is prior knowledge reinforced by the passage, which is represented in total by light blue)
- Cancer is my main longevity risk (e.g. due to my family history) (black)
- Longevity serves IVS (intrinsically valuable state) (dark blue)
The highly branched concept map responsible for conveying the newly acquired knowledge from the passage is presented in the pinkish circular area. The essential value concepts are located on the right on the white background. They focus on self and the main life goals (incl. longevity, productivity, etc.). Red arrows show how concepts impart value on other concepts in knowledge valuation network. The value of a piece of knowledge is imparted by associations with goal of goals (IVS). IVS imparts value on health, longevity and productivity, which in turn make fighting cancer important, while the news reveals that fasting trains NK cells to improve natural fight against cancer
Valuation network in development
The development of the network will depend on the personality, lifetime experience, and the environment. Childhood trauma or personality characteristics, e.g. impulsivity, may increase chances of developing a criminal mindset. Some traumatic events in early life may favor developing biased networks based on single-minded obsessions (see: falsity vector). The environment and the available knowledge will determine passions, interests, goals, and network subvaluations (see: conceptualization).
All strategies that promote healthy brain development will also promote rich, highly individualized, and efficient valuation network. Those will underlie a sparkling learn drive. All educators agree that we want to help kids have a good grip on their emotional life and build smart, creative, and knowledgeable brains.
The chief problem of educational systems is a cookie-cutter approach in which all kids are fed the same knowledge in an industrial fashion with little respect to the key component of efficient learning: the learn drive. Learn drive is a perfect computational device that matches the current status of the semantic network representing knowledge in the brain with current input produced by the knowledge valuation network in response to information available in the environment. If the kid insists that he must see that YouTube video, his own brain is the best authority. All interference will affect future independence and creativity.
While a lecturing teacher may spend 45 minutes to feed a child with a long string of symbols that produce low valuations, and negligible memories, the same kid, with access to Google, within 3-5 minutes, will identify pieces of information with high valuations, and easy coding for lifetime retention (for an opposing view see: The morbid myth of Digital Dementia). For kids well trained in the process, the efficiency of knowledge acquisition may be an order of magnitude higher in self-learning. When I say "order of magnitude", I am just being cautious and conservative. I do not want to run into accusations of hyperbole. I included a couple of examples of specific comparisons in this text elsewhere (e.g. 13 years of school in a month or 1600% acceleration of learning during vacation).
Where I speak of golden nuggets of knowledge, Peter Thiel speaks of the power law: a small set of core skills honed to perfection can produce power returns.
Knowledge valuation in the brain
The research into the actual anatomical implementation of the knowledge valuation network in the brain is of paramount importance for the understanding of the human mind. It is essential for prevention of depression and addictions. Knowledge valuation underlies efficient learning, creativity, and problem solving.
Good learning is pleasurable. Rewards of food, sex, or drugs tend to saturate. Happy learning does not have this property. It is easy to avoid unhappy learning. This is done instinctively via the learn drive. This is why learning is of supreme hedonic importance. It can literally lift societies to a new happier level.
Orbitofrontal cortex
The networked nature of knowledge valuation is indicative of the use of cortical resources. Indeed, most of researchers seem to lean to the belief that the entire system of valuations might be centered in the orbitofrontal cortex (OFC) with the level of abstraction increasing towards anterior areas. There are many models and hypotheses on how individual subsystems affect valuations (e.g. common currency, common scaling, somatic marker, appraisal-by-content, multiple component, cognitive-motivational interface, parallel appraisal, locationist vs. constructionist models, etc.). In the common currency model, all valuations from all sub-systems (hedonic substrates) are integrated and provide the ultimate signal of "wanting" or "liking". For example, (1) knowledge-based valuations from medial OFC (mOFC) might combine with (2) reward anticipation from the nucleus accumbens (NA), and (3) food appraisal messages from the insula to affect decision-making in the choice of a restaurant for the next meal.
Common currency model
OFC is a fantastic research area due to the convergence of many lines of human interests: drug addictions, ahedonia, learned helplessness, obsessive compulsive disorder, etc. The common currency model seems to indicate that the high associated with explosive creativity or explosive learn drive is neurochemically and neuroanatomically comparable to the high produced by low doses of cocaine.
There is a lively dispute on whether all rewards are translated into a reward signal that converges on the same type of neurons, or if they retain the origin of their character. I think the discussion is redundant as specificity can be conferred by individual concept map activations, while the ultimate valuation generated by a single output can constitute the common currency. In all valuations, we need to have a convergence due to the existence of a single answer corresponding with a single concept map activation. Some OFC neurons seem to specifically encode high-level value.
In knowledge valuation and in decision-making, we need a single boss. Redundancy can be used to restore the valuation system, but there is no escape from a concept neuron decision. We cannot have two decision makers that would make a hand with a fork stab an itching eye during a dinner even though competing neural forces make such a scenario possible due to a computing error.
Emergence of knowledge valuations
Building up the valuation network may occur via the interaction of individual concept maps. For example, if the exam concept is valued because of the job prospects concept, they may be coactivated, and the valuation of the employment concept may confer a valuation on the exam-related concept map. The degree of the activation and the associated concept valuations may determine the ultimate appraisal. Myelin concentration may increase in pathways targeting the ventral striatum, which may be one of the ways to explain how the learn drive can be boosted with learning (or suppressed with coercive learning a school). The role of the orbitofrontal cortex in determining valuations might be similar to the role of the hippocampus in establishing long-term memories. Those highly connected regions of the cortex may play a role of a switchboard that connect areas of interest only to relinquish the role of a matchmaker while the linked concept maps (or centers) develop their own wiring for fast connectivity (e.g. in sleep). With new wiring, highly valued concept might affect pleasure centers with no mediation from the OFC. This way, some concept cells (e.g. associated with one's favorite actor) could generate pleasant valuations by sheer solo activation.
Harms of reversal learning
In case of a negative school conditioning, we may associate irrelevant contexts (e.g. colors of items in SuperMemo) with low valuations. In this scenario, the concept of a white item, or the coactivation of the concept of item and color, will suppress valuation by providing a strong negative input. Outwardly it looks like a cut-off signal that blocks valuations (perhaps in the lateral OFC). In such contexts, association of concepts would still be possible, and short-term retrieval might be likely, however, low valuation would prevent consolidation of memory (e.g. by blocking the transfer to long-term cortical storage)(see: How school turns off memory). Reprogramming reward (e.g. swapping template color in SuperMemo) could occur in reversal learning. We know that animals with OFC injury are impaired at reversal learning (Mishkin 1972), which adds to the evidence for the anatomical location of the supreme valuation networks. If we keep overriding valuation signals, we might end up with the war of the networks, which is my hypothetical claim on the origins of learned helplessness induced by schooling. School coercion might be seen as a form of perpetual reversal learning that will wear on network plasticity leading to long-term adverse effects on the ability to evaluate rewards in decision-making. In that light, human memory might be seen as an EPROM with limited number of erase cycles. If long-term learning is seen as a buildup of the synaptic substrate that is then pruned in the process of stabilization (which in turn reduces synaptogenesis), reversal learning might lead to an unresponsive system in which learning is no longer possible.
Goals vs. habits
The knowledge valuation network is central in healthy free learning. In contrast, passive schooling leads to learned helplessness. Coercion converts goal-oriented behaviors into the acceptance of passive habits (as opposed to healthy habits honed in the pursuit of goals). The output from the knowledge valuation network is suppressed by lower valuations in the system (i.e. lower activation of concept maps of interest). This naturally leads to a less joyful state of mind. When learn drive withers, when curiosity dies, life becomes a series of habits executed with little reward (see: 50 bad habits learned at school).
Knowledge valuation that affects the course of life
I have my own striking example of the power of the valuation network in confrontation with the education system:
In 1985, I computed the approximate function of optimum intervals for knowledge review needed for developing long-term memories. This was the birth of spaced repetition. Originally, the function was applicable using a pen and paper. Within a few months, I realized the system was extremely powerful. I knew that I could double its power with the use of a computer. However, I did not know anyone who could write learning software based on my math. In those days, the entire population of programmers in Poland was made of old timers doing Fortran or Cobol on mainframes, or a growing mass of amateur enthusiasts working with microcomputers such as ZX 81, Commodore 64, or ZX Spectrum. I decided to write the program myself. I had no programming skills though. I was a student of computer science, and I asked my teachers for help. However, our only course of programming was the assembly language of Datapoint. Those skills were great for playing with registers and coming up with 11*11=121. I wanted to learn something more useful for programming SuperMemo. My school kept demanding that I learn to compute the resistance of an electronic circuit, or learn symbolic integration. My knowledge valuation network produced a simple output: programming skills would lead to SuperMemo, which would lead to faster learning (in all fields, incl. electronics or calculus). I was determined to learn programming. My school was determined to stop me (by loading other compulsory courses). In desperation, I enrolled in the University of Economics in Poznan, which had a course of algorithmic languages. The course focused on Pascal. I had to do my normal load of classes and do my Pascal in extra time. That course was nice, but we did all learning in theory, and on paper. There were very few PCs at Polish universities in those days (1986) and most practical applications ran on mainframes called Odra (produced for Soviet block in Poland as of 1960). When I finally got my first computer: ZX Spectrum (Jan 4, 1986), I could finally start learning to program real computers. Before my computer arrived, I started writing my first program. I wrote it on paper! It was a program for organizing my day (sort of Plan in SuperMemo). Not much later, I was able to learn Pascal too. First I had to reduce the bad impact of school and cut the load of classes. I struck a deal with my teacher of electronic circuits. I would do some high-pass filter calculations for him, and this would be a chance to improve my Pascal skills. The program took many hours to write and was a monumental waste of time. It was a perfect example of bad learning. I hardly understood how my own program worked. However, it was still better than just learning diagrams. For my programming skills, that learning was good, and I improved a lot.
It is hard to express it in words to those who do not know programming, but the difference of knowledge valuations between university courses and doing one's own programming is comparable to the size difference between the plum and the Jupiter. While my colleagues suffered through boring lectures in electronics and metrology, I could make my start. I would learn nothing at school. I would learn a bit in my extracurricular course of Pascal. However, only the practical knowledge backed up by passion and clear goals mattered. By December 1987, my effort culminated in writing the first version of SuperMemo, which totally changed the course of my life. Open mind of my supervisor Dr Zbigniew Kierzowski let me devote my whole Master's Thesis to the subject of SuperMemo. Happy 80th birthday Professor Kierzkowski! It was pretty unusual for a student to make his own determination on that scale, and then compound it with the fact that the thesis was written in English (less than a decade later, Polish parliament tried to make such efforts illegal). This involved a big administrative and tactical battle back in 1989.
My school almost destroyed SuperMemo, i.e. the major source of my present joy. There was no malice involved. Most of my college teachers were fantastic people. It was the system that was designed to squeeze students through a rigid curriculum rather than give them space for creative expression that is the best basis of educationMy school was actively trying to block me from accomplishing the most important thing that underlay my entire professional life and future. If I was a bit more compliant, more conformist, more prone to social pressures, I would be a "better" student, invest more time in the theory of electronic circuits, calculus, metrology, and abstract algebra. As a result, this article would have never been written. This site would not exist.
I would not trade my present life for any other type of career in research or industry. I survived the denial attack by providing resistance based on strong knowledge valuation network.