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== The main problem in education ==

The main problem with regards to education is the belief that learning may cause displeasure, and that this displeasure should be [[The grind is the glory|endured]] to achieve more learning.

There are countless educators who believe that school should be like work: it is unpleasant but it just needs to be done. In this chapter, I will explain that the opposite is true: 

{{Important note|'''Good learning is inherently pleasurable''', and without pleasure there is no good learning.}}

The displeasure myth is so prevalent that even good teachers believe that pain is part of learning.

In this chapter, I show that the pleasure of learning is wired into the brain, and how we systematically destroy this [[Education counteracts evolution|gift of evolution]] at the cost of mankind's health, learning, creativity, and ultimately future.

The main problem of education is also one of the main problems of society. By destroying the pleasure of learning we are contributing powerfully to the destruction of the pleasure of living. We have built an education system that sets millions of people up for a life of unhappiness.

Chances are you are skeptical of my words, as the myth of unpleasant learning is a potent side effect of [[schooling]]. Therefore this chapter is an attempt to convince you. And all that is necessary to abolish this myth is an understanding of the simple mechanism by which new knowledge is encoded in the brain.

== Learn drive and entropy ==

The concept of entropy is helpful in understanding why most kids do not learn much at school.

You may recall from your physics class that entropy is a measure of disorder, and that the second law of thermodynamics states that the entropy of an isolated system never decreases. This is the type of sexy law of physics that we tend to remember for life. It is [[Applicability|universally applicable]].

There is a sister concept in information theory called [https://en.wikipedia.org/wiki/Entropy_(information_theory) Shannon entropy]. It can be understood as the average value of information transmitted by a source. For example, take a channel that is continually transmitting a string of identical letters into infinity (e.g. a string of As: "AAAAAA..."). It is entirely predictable and carries an entropy of zero. We do not learn from such a channel.

[https://en.wikipedia.org/wiki/Claude_Shannon Claude Shannon] proposed the concept of information entropy in 1948. Soon after, scientists were hypothesizing as to whether the entropy of an information channel may have a powerful impact on how the brain perceives the value of the channel. In 1957, [[Music and entropy|Meyer hypothesized]] that the entropy of music determines the perception of its beauty. He concluded that a higher entropy may result in subjective tension, which correlates with more meaningful musical moments.

Meyer's thinking was [[Impact_of_syncopation_on_the_pleasure_of_music|later refined]] to better understand the perception of music and information in general. There is more to music than just information. This is visible through the phenomena of a song being entertaining and fun for many playbacks. But this is rarely the case with books.

Music is a universal message. If you were given a choice of a radio channel, you would quickly tune out from noisy static and you would also not be too excited about zero entropy silence. However, most people will respond positively to a regular beat of a drum. As long as it wasn't being drummed on broken glass, which we are wired to dislike, we would find a radio channel with a regular drumbeat more interesting than a silent one. This will naturally last only for a while until the drumbeat itself becomes boring and too predictable.

Today, we can finally test the response of the brain to information entropy. Neuroimaging shows that the [https://www.ncbi.nlm.nih.gov/pubmed/15896570 anterior hippocampus responds to the entropy of a visual stream], and similar findings have been confirmed for the [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403290/ ventral striatum]. Therefore we are now certain that the brain responds to information entropy. The entropy sensor is important in scanning the environment for learning opportunities. This is the prelude to the reward that underlies the [[learn drive]].

== Prior knowledge in information seeking ==

We need to distinguish between information and meaning. Entropy is not a good measure of the latter. The measure of meaning must involve the brain itself in addition to the information channel metric. Prior knowledge is essential in learning. Imagine that in your search for an interesting channel on the radio you find a news service. If the service is delivered in Thai and you do not speak Thai, you will prefer a service delivered in English. In information sense, news channels may have the same entropy, yet your prior knowledge will make you opt for the English channel. While the Thai channel delivers a stream of sounds, the English channel delivers a stream of [[Abstract knowledge|concepts]]. Without understanding the knowledge of the recipient, information entropy tells us little. We cannot determine a signal-to-noise ratio.

Every listener will have his or her own preferred level of information entropy. For most music lovers, the regular beat of a disco or techno will be somewhat more interesting than the isolated beat of a drum. This type of music carries a higher average level of information. For a more sophisticated listener a bit of syncopation will be welcome. However, syncopation requires a degree of prior learning. Those with lesser knowledge of music may get confused with increased rhythmic complexity. If there is too much information in the beat it [[Impact of syncopation on the pleasure of music|may no longer be possible to dance to the music]]. To an average ear, the genius of Wynton Marsalis may be hard to perceive. Top shelf jazz music is reserved for only a small fraction of highly educated listeners, as for most of the population, as the complexity increases, the music slowly disintegrates into the direction of radio static.

== Entropy detectors in the brain ==

The brain cannot effectively detect the entropy of the signal hitting the retina or the eardrum. Like pixels of a monitor, retinal cells are not aware of what they display. If the detector, such as the hippocampus, is to light up in response to entropy, it must operate on the inputs from the entorhinal cortex (i.e. the input to the hippocampus itself). Those inputs will present the signal after a high degree of processing. Instead of pixels, it may present a concept. A high entropy signal at the sensory inputs will lose most of its noise component early in the process of neural selection, completion, and [[generalization]]. The signal-to-noise ratio will determine how much information is lost. The bigger the noise, the bigger the loss. The smarter we are, the more selective this processing will be and the more information will be lost at that stage. That's good. We become blind to detail. Pattern recognition will act like a deterministic function, which by definition, results in a drop in entropy. Complex patterns may become simple concepts. Those concepts will provide the actual input to the detector, e.g. the hippocampus.

Note that the visual stream produced in experiments that prove the [https://www.ncbi.nlm.nih.gov/pubmed/15896570 response of the hippocampus to signal entropy] has a highly [[Abstract knowledge|symbolic nature]]. As such, the stream will lose far less information in processing. That highly simplified and [[generalization|conceptualized]] message will be scanned for surprisal and provide guidance to the entire [[learn drive]] system. This is why, in this case, the hippocampus appears to be responding to input entropy.

The above reasoning explains why both low and high entropy sensory signals can be uninteresting. After a degree of processing, a high entropy signal may lose all its noise and deliver a low entropy input to the hippocampus. We then observe the illusion of an "optimum entropy" level at sensory input. We need a new concept, '''[[learntropy]]''', that will help us accurately determine the attractiveness of the signal. Learntropy needs to take into account the high degree of processing of information before it can activate reward centers in the brain. Learntropy is discussed [[#Learntropy|later]] in this text.

== Speed of information processing ==

An under-appreciated factor in sensory information scanning is the speed of information processing in the brain.

For every piece of music, there is a tolerable playback range where the beauty of the music is appreciated. A high speed playback can be annoying and the music may become hard to decode, as the high speed goes beyond our processing power. The same piece of music slowed down can quickly lose its appeal. The same happens in speech delivery or in classroom lecturing. For the same information and the same entropy level, we may accomplish highly different levels of signal attractiveness. There is always an optimum speed of delivery and that speed depends on all other factors that power the [[learn drive]], incl. prior knowledge. As such, speed of delivery is highly individual.

I like to listen to lectures at 1.4x speed. I use 1.3x for more ambitious pieces. I never speed up [https://en.wikipedia.org/wiki/Fareed_Zakaria_GPS Fareed Zakaria] though, but rather relish every piece of information in this show. Students in a classroom lecture do not have a speed-up or slow-down button. Even the pause button, if available, is hard to hit as it may annoy other students.

In schools, all too often, the speed of delivery surpasses student's processing capacity. This results in negligible learning and high stress. There is no time to [[Futility of schooling|enjoy the landscapes in the window of a high-speed train]]. At MIT they call it ''"drinking from a firehose"''.

== Probability vs. knowledge ==

Low probability events carry more information. Average information determines entropy. Prior knowledge determines the perception of an information channel's entropy.

If you happen to tune in to radio news and you hear that "''Janet Jackson has delivered a baby''", your degree of interest will depend on the probability of the event. If you have no idea who Janet Jackson is, this is a high probability event. If some [http://www.theworldcounts.com/stories/How-Many-Babies-Are-Born-Each-Day 350,000 women deliver babies every single day], this is no longer news and is not new or interesting. The first death of a soldier in a war makes news, but when deaths incrase into the thousands, young lives become just a statistic.

If you happen to know Janet Jackson or like her music, the probability of a baby delivery drops dramatically to the level of "''once in a lifetime''" (for Janet). This can make you become interested. However, if you recall Janet as a beautiful girl from some ancient sitcom, her baby delivery may go into the category of "''Impossible!''". If you realize Janet is 50 years old, and you know about menopause, you may instantly become morbidly curious about her case. Your prior knowledge determines how you respond to the message. There is no optimum entropy level for a channel. There is only an optimum entropy level that fits a specific brain. At this point you may see that we need to introduce a new derived concept, which we will [[#Learntropy|later]] call '''learntropy'''. Learntropy will determine the attractiveness of a given channel for a given brain.

If you love Janet-like gossip, the channel rich in that gossip will provide the right level of surprisal for you. It will provide the learntropy match. If you lack knowledge or your priorities differ, you will tune out. Your learning priorities will also determine your level of knowledge in particular areas and your response to any particular channel and its information entropy.

== Predictability and surprisal ==

Probability and complexity are not the only components in information perception. We seem to look for a balance between predictability and surprise. I like funk. In this type of music, the bassline is often highly predictable with the [[Impact of syncopation on the pleasure of music|optimum dose of syncopation]]. It makes it easy to synchronize the body motion with the rhythm. However, funk would not be interesting if it did not carry surprise. This is where the sophisticated jazz riffs tickle the neural system responsible for the detection of surprisal. In addition, after decades of learning, there is a whole database of signals that my brain responds to. There may be that one backup singer voice that I recognize and like. My brain is ready for funk.

I love Ken Robinson lectures on creativity. In one way, they are highly predictable. I totally agree with Robinson, so you can say that Robinson feeds my confirmation bias. This is pleasurable. When people agree with us, we like to say "''great minds think alike''". But if Robinson just kept repeating the same dry mantras on how schools kill creativity, he would lose his appeal. Entropy can be interpreted as the average expected [https://en.wikipedia.org/wiki/Self-information surprisal]. Robinson's delivery carries a great deal of nice surprises. He may paint the same models in a different and unusually creative way. As a result, '''the brain receives new information, produces a generalization, and confirms the existing models'''. Generalizations derived from new contexts increase [[knowledge coherence]]. This a very pleasing type of complementarity in a message based on a known model.

Robinson lectures find a good balance between predictability and surprise.

The most pleasing information channels will keep delivering surprises that confirm existing models and arm them in new semantic twigs on which new knowledge can be built. A surprise that destroys existing models may not be pleasing at first, but may lead to a highly pleasing revolution in thinking.

Metaphorically, you can imagine this as the information channel massaging your tree of knowledge and adding new branches like a potter who adds [[jigsaw puzzle metaphor|new layers of clay]] to his perfectly shaped creation.

== Detecting surprisal ==

Human [[learn drive]] is based on detecting [https://en.wikipedia.org/wiki/Self-information surprisal]. We have known that for ages. All models of human and machine learning involve that concept under different names. [https://en.wikipedia.org/wiki/Jean_Piaget Piaget] wrote about [https://en.wikipedia.org/wiki/Jean_Piaget#Schema schemata] that fall into disequilibrium under the impact of surprisal. In his models of the neocortex, [https://en.wikipedia.org/wiki/Jeff_Hawkins Jeff Hawkins] speaks of prediction errors that underlie learning and intelligence. I like to speak of [[model]]s, their elaboration (when new information fits the model), contradiction (when new information requires changes to the model), and [[generalization]] (when [[forgetting]] and [[memory optimization]] sculpt out new quality from the model).

For the reward of learning, a new surprising piece of information needs to fit pre-existing knowledge ([[model]]s, schemata, predictions, or so). For the reward to be delivered, neural processing is necessary. Information on the input needs to be processed, and compared with information stored in the brain. One of the chief processors of input information in the brain is the hippocampus. It is the brain's information switchboard that is able to compare the input with prior knowledge.

Measuring the entropy of the visual stream is not necessarily a reliable indicator of the pleasing power of the information channel. All information streamed to the hippocampus undergoes a high degree of processing. A stream of pixels representing a beautiful beach will be processed into a series of shapes and textures. Those in turn will model palms, sand, and the sea. This highly compressed simple information will determine the original response to the information input. 

Scanning for information in the environment is equivalent to scanning for scents of food. The scent is enticing, but only the actual feeding is a true reward. This is why entropy scanning does not need to be rewarding. All it needs to do is to lead to a reward. The anterior hippocampus responds to entropy, as noticed earlier, however experimental design made sure that the entropy refers to the combination of simple shapes that do not lose much information during input processing. Instead of speaking of signal entropy, we should rather focus on the input entropy at the information comparator such as the hippocampus. It is not the retinal pixels that matter, but the shape of the palm as represented on the comparator input. For the comparator, the high entropy pattern of grayness or static noise will not differ from whiteness or silence. They will all bring the same entropy on input: zero. This is why I used the term [[learntropy]] to accurately refer to the attractiveness of the information channel.

The anterior hippocampus that responds to signal entropy is famous for the discovery of the Halle Berry neuron (see [http://phys.org/news/2005-06-single-cell-recognition-halle-berry-neuron.html more]). Using electrodes implanted in a consenting epilepsy patient, researchers were able to pinpoint a single neuron consistently responding to images of Halle Berry in various contexts. The same neuron would also respond to Halle Berry's name. At the same time, posterior hippocampus might respond less consistently to Jennifer Aniston (perhaps an indication of a preceding layer of neural processing).

Most of us have no idea how Halle Berry smells and her smell might not be unique enough to activate Halle Berry neuron in the hippocampus, however, even the smell signal can get there fast via just a few synapses in the olfactory bulb, olfactory tubercle, piniform cortex, and the entorhinal cortex (see picture). However, if one could hear the sound of Halle's voice, it might meet the sound signal in the olfactory tubercle, contribute to recognition, and result in the subsequent activation of the Halle neuron in the hippocampus or further down in the neocortex.

[[File:Olfactory system anatomy.jpg|692px|center|Olfactory system anatomy]]
{{:File:Olfactory system anatomy.jpg}}

Does it all mean that Halle resides permanently in the patient's hippocampus? Due to the association of the hippocampus with formation of new memories, we may rather think that Halle shows up in hippocampal neurons as a result of the recognition. Her permanent place in the heart of the patient is likely situated further downstream in the neocortex. We now know that in the process of memory consolidation, knowledge engrams [http://www.jneurosci.org/content/29/32/10087.full move from the hippocampus to the neocortex]. We are also pretty sure that this process is happening [[memory optimization in sleep|in sleep]]. It is in the neocortex that we should look for concept neurons representing Halle or one's grandmother. This last possibility gave rise to a hypothetical type of neuron called '''the [[grandmother cell]]'''.

In monkeys, researchers could identify [[grandmother cell]]s in the visual cortex that respond to faces. There we might find cells that more consistently fire up in contact with Halle's image. However, the concept of Halle might still reside elsewhere and be activated, among others, by visual cortex cells upon noticing Halle.

Another activation route might come from hearing Halle's name on the news. The entire recognition process would be orchestrated by the entorhinal cortex and the hippocampus while the ultimate Halle neuron would light up somewhere in the layers of the neocortex.

For information rich signal to generate a reward, there must be a low probability event detected on input and encoded via association as new knowledge in the cortex. Where anterior hippocampus would respond to the entropy, the [https://www.ncbi.nlm.nih.gov/pubmed/15896570 activity of the extensive bilateral thalamo-cortical network would be modulated by the surprise factor]. There we shall search for the roots of the pleasure of learning. There are also other comparator centers that might be involved depending on the type of the message. The amygdala has also been found to likely produce rewards when detecting novel visual signals. The same amygdala neurons that respond to rewarding visual stimuli may respond to novel visual stimuli. [[Amygdala may be involved in rewarding novel input|Rolls hypothesized that this may implement the reward of novelty via the amygdala]].

We know that the hippocampus connects directly with the nucleus accumbens (the brain pleasure center). This connection might be used in two contexts:
# the anticipation of pleasure and
# the ultimate reward.
The anticipation would follow the detection of a high [[learntropy]] signal and would result in active pursuit of high value messages. Detecting a message by the hippocampus might then simultaneously send associative learning messages to the neocortex and the reward signal to the pleasure center. That would spell the moment of learning something new!

== The wow factor ==

In the summer of 1977, while looking for extraterrestrial intelligence, SETI researchers discovered an unusual radio signal coming from [https://en.wikipedia.org/wiki/Sagittarius_(constellation) Sagittarius]. In the bland low-level noise of cosmic space the signal was highly unlikely. Low probability marks high surprisal. Astronomer Jerry Ehman circled 6 letters corresponding with the signal on a printout and mark it with "Wow!".

<div class="center">
[[File:Wow signal.jpg|500px|A scan of a color copy of the original computer printout, taken several years after the 1977 arrival of the Wow! signal]]
{{:File:Wow signal.jpg}}
</div>

"Wow!" is how the brain responds to a sudden discovery. The moment is highly pleasurable. The entire purpose of the [[learn drive]] is to look for wow factors in the environment. These are the most valuable nuggets of knowledge that complement what is currently known: the current model of reality. The pleasure of [[incremental reading]] comes from the condensed power of wows streamed into the student's brain.

Thus far, we have seen the impact of entropy, surprisal, predictability, and current knowledge on learning. In this case, the mere probability of the signal does not fully explain its power. It is the interpretation that stands behind it (see: [[Knowledge valuation network]]). At the moment of making his note, Ehman could sense the enormity of its implications. This had been the most powerful evidence thus far and ever since for the existence of intelligence other than human intelligence. If the same signal represented detecting sardines in the ocean, there would be no "wow!". Not even in the Arctic. 

The reliability of the information channel is important. If the error rate is high, the [[learn drive]] may weaken. When Penzias and Wilson discovered cosmic microwave background radiation in 1964, there was no "wow!". Perplexed researchers went on to remove pigeon droppings from their radio antenna. Pigeon droppings received a priority in their explanation of the mysterious noise. In 1978, for their discovery, Penzias and Wilson received a Nobel Prize.

When a scientist makes a discovery, he may exclaim "''Eureka!''" and punch the air. A neural network somewhere in his brain has produced a [[generalization]] that results in sending a reward signal. This propagates further and makes an old man jump around the lab like a child.

The same happens early in life. A toddler in an empty room will scan the environment for low probability components like colorful objects, new toys, etc. When a toddler experiments with a spoon dropping off the table, she is like a little scientist. However, when the brain makes a [[generalization]] "''all falling spoons make noise''", she is rewarded too. She may celebrate in the exactly same way as the happy scientist, independent of the age. A big smile is the first clear sign.

The same happy thing occurs to a lesser degree in all forms of learning controlled by the [[learn drive]]. It does not matter if we learn about a celebrity or the chemical composition of a rock. Things are interesting because they reward the brain through the learn drive mechanism.

A creative process will also produce rewards. An association deemed useful is rewarding. An association that leads to a solution to a difficult problem is even more rewarding. Clearly there is a gradation of rewards. The system can quantify the probability of information, association, or a solution. The lower the probability, the higher the reward.

== Knowledge valuation network ==
{{:Knowledge valuation network}}

== Learntropy ==

There are many factors that affect how messages and information channels are perceived and valued by the brain. In preceding sections we have noticed that the brain does not respond just to entropy. There are many factors that modulate the impact of entropy or surprisal of individual messages. Those factors include: encoding, speed of delivery, pre-processing (e.g. generalization, completion, recognition, etc.), prior knowledge (incl. valuation, emotional valence, channel reliability, etc.), optimum level (affected by speed of processing), and more.

The complexity of the process calls for a better concept that can encapsulate all those nuances. I suggest the use of the term learntropy to describe the attractiveness of an educational channel or signal from the point of view of an individual brain in a specific context.

{{Important note|'''Learntropy is the attractiveness of any educative signal as determined by the [[learn drive]] system.'''}}

Lectures can be boring or attractive. Learntropy expresses their attractiveness from the point of view of an individual.

While entropy has a precise mathematical definition, learntropy would probably best be measured by the response of the reward system to the act of learning from the analyzed signal. As much as entropy depends on the probability of individual messages, learntropy will depend on the rewarding power of these messages (pictures, sounds, sentences, etc). That rewarding power will be associated with probability, but the valuation will largely depend on the [[knowledge valuation network]].

For good learning there is a reward. However, there is also bad learning. There is a [[decoding failure penalty]]. If a student makes an effort to decode a message and fails, he is penalized. This is how frustration is born. This is how the dislike of learning begins. If learntropy is low, reward is little, penalty is high, and the net result may be negative. If we take negative reward signals into account, learntropy could actually assume negative values. A boring lecture could carry negative learntropy. It will result in suppressing the [[learn drive]].

High knowledge valuations contribute to high [[learntropy]], which in turn is necessary for attention and semantic slotting in of knowledge for long-term retention. In a powerful feedback loop, '''learntropy enhances the [[learn drive]], which underlies valuations that determine learntropy'''. This feedback loop is kept in check by forgetting, learned helplessness, aging, injury, and the sheer availability of mental resources. With rational learning and lifestyle management, esp. with respect to the [[natural creativity cycle]], the equilibrium can be maintained at the high [[learn drive]] level for decades.

== Signal timing vs. learntropy ==

The degree of reward obtained from individual messages in the learning stream will determine the level of signal learntropy. A lecture on a boring topic will carry low learntropy. Surfing the net for titbits of information needed to solve a specific problem will carry high learntropy.

Unlike [https://en.wikipedia.org/wiki/Entropy_(information_theory) Shannon entropy] that is based on averages, learntropy will be more of a trailing average where recent messages will carry a higher weight than messages delivered earlier in time. In addition, learntropy is rooted in rules that govern the [[two component model of memory|consolidation of memory]], incl. the [[spacing effect]].

The learntropy of a boring lecture will shoot up once a golden nugget of knowledge fills an important gap in understanding. The increase in learntropy will be proportional to the expression of the [[stability]] of the memory trace determining knowledge valuation (incl. descending traces in the [[knowledge valuation network]]). The impact of a golden nugget will wane in time. The cumulative effect of those happy discoveries will determine the level of learntropy at any given time (e.g. during a lecture).

The above shows that educators can influence learntropy, enhance the [[learn drive]], and enhance long-term learning outcomes. Feeding passive knowledge is a bad strategy. Providing answers should be selective and should favor high importance abstract and universal questions. Free explorations of self-directed learning are the best formula for lifelong sustainable [[learn drive]] and lifelong learning.

All forms of schooling tend to suppress the [[learn drive]]. As a result, many adults may find it difficult to internalize the message on the importance of learntropy in learning. However, in the modern world, nearly everyone is faced with the need to solve a minor technical or health problem on their own. The problem may be as simple as a trivial change to setup in Facebook options. The harder it is to find the solution to a problem, the greater the reward in finding answers. The harder it is to find answers, the more persistent and extensive the search and exploration. Those feelings should be familiar to everyone. However, suppression of the [[learn drive]] always results in lesser knowledge, lower self-esteem, and all explorations might come to an end earlier. In other words, those who lost their creative drive at school, or later in life, will give up earlier, or perhaps never even try. In that sense, all technical problems and glitches that come with computers, Internet, technology, etc. have some positive side effect of stimulating the vestiges of the lost [[learn drive]] even in the most passive individuals. The only requirement is that those quests need to end with a degree of success. Otherwise, the opposite may happen. The penalty signal may lead to conditioning a withdrawal from exploration.

You can quickly answer this instant quiz about your own [[learn drive]]. If you face a minor problem in life, do you seek a human expert or you rely on Google? If your car fails, or you computer crashes, or you get injured, or you got a stomach ache, where do you go?

== Learntropy and learn drive ==

In a process similar to [[Forgetting curve|forgetting]], the impact of [[learntropy]] reward will decline exponentially over time. Like in [[spaced repetition]] review, the new reward will bring back learntropy to a high level. Like in a [[spacing effect]], longer breaks may result in the same message being more rewarding.

There is a major difference between the reward signal determining learntropy and the consolidation signal determining recall in learning: once you learn something, repeated review in short spaces of time is pointless, once you drive recall probability to 100%, you can let time pass before the next review. The upper limit on learntropy might be hard to reach. If you love a lecture, with some twists of facts or delivery, you can love it more. If you remember a singular memory, you cannot remember it better by tricks employed in a short space of time. You can reformulate the memory using mnemonic techniques and affect its durability, but once the probability of recall is 100%, the best thing to do for the memory might be to leave it unused for a while or employ it in varying context, which may essentially lead to developing new memories that will form redundant connections to the original singular memory.

Extinction of learntropy occurs via lack of reward signal. Extinction of [[learn drive]] is a matter of [[forgetting]] (incl. forgetting through brain cell loss).

Learntropy will be additive over individual messages with exponential decline and diminishing returns. By optimizing the timing of rewarding messages, we can drive learntropy high and make learning become one of the most pleasurable activities on the par with rewards of food, sex, drugs, etc. If you are skeptical, recall obsessive videogamers who can literally starve while playing nights. [[Videogames]] can highjack the [[learn drive]] and combine it with the reward of gambling. Rewards of gambling might also be governed by similar rules of decline and boost as learntropy, however, they are subject to [[variable reward]] which can lead to addiction. It is important to distinguish between the pleasure of learning and harmful addictions (see: [[Addiction to learning]]).

Learntropy will determine the [[learn drive]], but both will be sustained with different rules. Learn drive is knowledge dependent, and as such will be subject to [[spaced repetition]]. As [[Concept network|knowledge is a network]], speaking of optimum stimulation of [[learn drive]] is probably pointless. To maximize learn drive, we should engage in lifelong learning, respect [[natural creativity cycle]], and take care of the brain health (i.e. health in general).

== Optimum information delivery ==

In schooling, we might envisage a lecture delivered at optimum [[learntropy]] level, in which a student keeps saying "''wow! wow!''". She keeps taking down notes as fast as humanly possible. More often though, the lecture will buzz a high entropy signal or ooze boredom. Its learntropy will be low or even negative.

If optimum learntropy levels depend on the student, how can a teacher optimally deliver knowledge to a classroom? Sometimes universal delivery is impossible. In other cases, it is difficult enough to require genius teaching skills. For most teachers, lecture delivery keeps most kids bored or frustrated.

In lecture delivery, a lucky few may get most of the message. For a fraction of the gifted, the lecture may carry nothing new. For them it is boring. For other kids, message complexity goes above their comprehension level. In such cases, the lecture can be frustrating if they try to decode it. A lecture on string theory might be comparable to a noise of randomly shuffled English words. Lecturing is an exercise in timewasting. Nobel Prize winner [[Carl Wieman]] compared it to [[Wieman: Lectures make no sense|blood-letting]].

To avoid the frustration of negative learntropy, students will tune out like you tuned out from that Thai channel I mentioned [[#Prior knowledge in information seeking|earlier]]. Children will ignore the static noise coming from the teacher and tune in to other channels that carry more appropriate levels of learntropy (e.g. Facebook on a phone under the desk). Even if their comprehension is good, the knowledge delivered may not complement their current knowledge. If it does not generate [[Knowledge valuation network|high-quality high-value]] generalization, it will be considered obvious or irrelevant.

Low learntropy, even if occurring occasionally, conditions the student to tune out. After a while, students will develop a filter that will turn a teacher into a silent radio channel carrying zero entropy and zero learntropy. Improvements to lecture quality will become futile. The teacher disappears!

In a classroom setting, a student will often not be able to zero in on a better signal. The same signal is dished out to all students and they all may get equally bored. In contrast, Googling for good keywords can bombard the brain with perfectly timed low probability messages that will [[Knowledge crystallization|fit the current knowledge tree like a jigsaw puzzle]]. Google is a very cheap and efficient generator of "wow!".

In [[incremental learning]], the learntropy scanner will pick best channels, prioritize those and employ perfect timing for maximizing semantic connectivity and memory consolidation. This should make it easy to understand why '''I am extremely happy, I will never ever be forced to sit in a school bench!''' I love learning too much!

All the above examples illustrate how intricate the interaction between the signal and the brain is in recognizing things worth learning. The reward of learning is the best known indicator of learning quality. When students are happy, we are on the right track. When schools are the place of misery, we are failing on a societal scale.

{{Important note|'''The only reliable detector of knowledge complementarity and [[coherence]] are the neural networks of the [[learn drive]] system. This is why knowledge cannot be prepackaged and imposed on students.'''}}

This is explained using a [[Knowledge crystallization|crystallization metaphor]]. The neural details of the reward system follow in the section: [[#Learning rewards|Learning rewards]].

== Gripping lectures ==

We love learning, but we usually hate to be taught. Those feelings correlate with creativity, which can probably be explained by the fact that creative elaboration is essential for [[pattern completion]] that underlies comprehension.

In learning, we decide what to investigate. The [[learntropy]] evaluation strictly depends on the status of the brain and current memory activations. In teaching, knowledge is dished out independent of what we think of it. Many students list boring subjects as their number one reason for disliking school. Not bullying, stress, or early waking. Excruciating boredom! I write about the astronomical difference between self-directed learning and learning at school [[Learning history: school vs. self-directed learning|here]]. It is all about the [[learn drive]]!

I am amazed with how many resources are wasted on research that looks for ways to keep kids interested during lectures, while it should be obvious that lectures are just a poor educational tool. Eye contact analysis? Engagement analysis? Efforts to quantify passion? All kids are equipped with natural [[learn drive]] and our priority should be to ensure we do not destroy that drive. [[Schools_suppress_the_learn_drive|Force-feeding knowledge]] is the prime destroyer of the [[learn drive]]. In addition, there are many socioeconomic factors that prevent a great chunk of kids to thrive even in the best circumstances. Some kids will never show passion for learning. In most cases, it is not their fault. Only a tiny fraction are limited by disabilities, health, and less fortunate genetic endowments. The exponential decay in the [[learn drive]] with age is caused primarily by compulsory schooling. Passive lecturing is a huge contributor to that process.

Naturally, there are lectures that work. [[Khan Academy]] is jam-packed with good examples. Even a spoken lecture with no slides can work. A TED talk on YouTube can be fun. It can satisfy the [[learn drive]]. [https://en.wikipedia.org/wiki/Massive_open_online_course MOOCs] are founded on the principle that one rock-star teacher is better than thousands of rank-and-file teachers repeating the same mantra. You can learn a lot even if you are just a passive listener. There are conditions though: you need to be intensely curious about the subject, or you need to love the speaker, or both. '''There is only one sure mechanism for ensuring the lecture is interesting: you need to choose it on your own!''' This is just one more aspect of the need for self-directed learning.

In addition to choice, in lecturing, you definitely need a pause button in case you need to take a toilet break, or quiet the hunger pangs. Nothing can ruin a lecture as effectively as a bursting bladder. Last but not least, most lectures could benefit from Netflix's Skip Intro feature.

Naturally, the lecture will work best if you enhance it with your own creative thinking or even quick research. This is why pausing for a minute, or for a day might be essential for learning efficiency. Against the claims of some psychiatrists, creative breaks and a wandering mind have nothing to do with [[ADHD]]. As long as they are remotely relevant, they are hallmarks of great learning.

I use two methods for consuming lectures incrementally. My first method is to listen and exercise. Exercise improves focus. Good focus reduces the need for a pause, however, it also reduces the creative aspect of learning. For subjects of highest priority, I use [[incremental video]] where I can pause and resume multiple times. I can even keep the most important lecture extracts for future review. However, even incremental video isn't the best approach to learning. It cannot compete in speed and volume with [[incremental reading]]. Sometimes it makes better sense to employ incremental reading and process the lecture transcript than to listen to the lecture itself. This is particularly visible in fact-rich lecturing.

I choose my video materials mostly on the basis of speakers who I just love to listen to. In the context of this article, I know you would love Ken Robinson lectures! Go and see: [[Robinson: Schools kill creativity]]!

== Learning rewards ==

The pleasure of learning might be one of the most satisfying possible pleasures. As opposed to eating or having sex, the pleasure of learning does not terminate with the act. The pleasure of learning is sustainable and wanes slowly only with the overload of networks involved in learning. It can be reset back to the baseline with sleep. The pleasure of learning has been shown to involve the same mechanisms as the [[Biederman model|pleasure of heroin or cocaine]]. Unlike feeding or sex, pleasurable learning can fill most of the waking time. In that sense, the pleasures of learning, creativity, problem solving, and productivity might be great tools in stoic hedonic therapy. Whereas the need for food is easily satisfied in a healthy individual, the need for learning may never end. The [[learn drive]] depends on the status of current knowledge and this status can be manipulated with learning itself.

{{Important note|'''All people with mood swings should consider [[Learning and depression|learning as therapy]].'''}}

=== Learn drive reward ===

I have mentioned a couple of examples of how the [[learn drive]] leads to a reward signal in the brain. We know that low probability information can be rewarding. So can a generalization that contributes new knowledge. A snippet of information that leads to a great goal of understanding is highly valued. A [[jigsaw puzzle metaphor|missing piece in a jigsaw puzzle]] carries a great reward. One obscure word, once decoded, can make a whole long text switch from a tangle of sentences into a clear line of reasoning.

Confirming a model via a generalization or laying foundations for a new better model both feel great. In addition, all model confirmations associated with strong emotions can lead to euphoria: "''My team is the best in the world!''", or "''Yes! My newborn is healthy indeed!''", or "''Yeah! I knew that hard work will earn me that promotion!''". However, when discussing the [[learn drive]], I would like to filter out that extra emotional layer that may obscure the picture. We need to remember that learning is pleasurable independent of whether it brings rewards from employing the knowledge.

The ''Aha!'', ''Wow!'' or ''Eureka!'' of discovery is the purest and ultimate prize in learning. It does not need to entail further reward in accolades or praise from others. Here, the knowledge is its own reward.

The common denominator of this reward is the encoding of new highly-valued information in memory.

{{Important note|'''The learn drive reward comes from [[knowledge valuation network|high-value knowledge]] ready for long-term storage.'''}}

In our quest to understand reality, while the total amount of information stored in the brain increases, the entropy of stored knowledge drops. '''With learning and modeling, it takes less and less effort to understand the complexity of the world.'''

=== Evolution of the learn drive ===

Scientists say that smart animals play more. I say that it is even more interesting to note that species that play more are smarter. I hypothesize that the '''[[learn drive]] may have been the trigger factor in the explosion of the human brain size'''. It is not that birds or mammals faced a change in environment that required more thinking. It is not that humans suddenly faced extinction had they not blown up the size of their cortex. It may have been the emergence of the [[learn drive]] that suddenly allowed better usage of the expensive increase in the number of brain cells. Before there was the [[learn drive]], adding brain size might leave an animal with an extra head weight to carry and an extra set of cells to feed. Without the [[learn drive]], the extra brain space might remain unused and likely undergo wasteful atrophy. If schooling attempts to override the [[learn drive]], it will contribute to the disuse of that evolutionary advantage. It will contribute to society that is less smart and less creative.

If we plot the brain size over the timeline of human evolution, we can see a powerful upswing around 2 million years ago. Paleoanthropologists tend to attribute that swing to better brain nutrients in the diet, [https://en.wikipedia.org/wiki/Catching_Fire:_How_Cooking_Made_Us_Human cooking], and the like.

If the hypothesis on the emergence of the [[learn drive]] is correct, ''Homo habilis'' would be a candidate for the starting point of the breakthrough. This could point to the transition from a simple procedural play drive of birds and mammals towards a more sophisticated declarative learn drive that ultimately leads us to building abstract models of reality, which underlie human intelligence. ''Homo habilis'' has also been hypothesized to lead to the emergence of [[Homo habilis: the emergence of childhood|childhood dominated by brain growth]] (from weaning to an average of 7 years old).

The late arrival of the [[learn drive]] in evolution would suggest that it is not a simple property emergent in neural networks (see: [[Biederman model]]). Otherwise it might easily show up in fish or earlier. The [[learn drive]] requires a dedicated set of neural structures that are able to send a reward signal at the point of detecting an incremental contribution to a coherent structure of declarative knowledge. This signal and the underlying structure might differ in procedural learning and declarative learning. It might also differ for different classes of sensory input.

=== Procedural learning reward ===

I hypothesized about [http://www.super-memory.com/english/ol/ol_files/refinement_circuitry_in_stochastic_learning.jpg circuits that might run procedural learning] back in the 1980s. In my [http://super-memory.com/english/ol.htm Master's Thesis], out of ignorance, I used my own term [http://www.super-memory.com/english/ol/ol_memory.htm "''stochastic learning''"]. I had no idea that two decades earlier, back in 1969, David Marr proposed a theoretical model of the cerebellar cortex that fit my own thinking. In the new millennium, [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2805361/ there is a lot of data to confirm the model].

The idea of a procedural learning circuit is very simple. Imagine you ride a bicycle. You apply your conscious mind to learn individual moves needed to mount the bike and to then continue pedalling. However, once you are on the way, the procedural learning system makes sure you can execute all moves automatically with minimum neural effort without participation of conscious supervision or minimal supervision over a set of [https://en.wikipedia.org/wiki/Command_neuron command neurons]. Procedural learning will determine your [https://en.wikipedia.org/wiki/Motor_program motor program]. This procedural learning system will make minor random adjustments to the sequence of signals sent to the motor system (hence the name "''stochastic learning''"). You can view those random changes as procedural creativity. Each time your bike loses balance, a penalty signal will be sent from the error-detecting network to cancel proposed corrections. That penalty signal will play the role of a teaching signal for the motor program.

During sleep, memories will be reorganized to eliminate the need for conscious input, simplified, optimized, and garbage signals that have a low contribution to the skill will be rejected. With each kilometer cycled, the sequence of signals will be perfected by trial and error. With each bout of sleep the wrinkles will get smoother. Riding a bike will become a pleasure. That pleasure seems to peek in the transition from clumsy conscious rider to a natural.

In a similar fashion, with each sentence typed on the computer, you will strike fewer typos. Do you know where ")" is on the keyboard? How about "}"? The more fluent you are in typing, the more likely you are to forget this detail. When the conscious control of motor sequences is taken away, declarative knowledge of the position of ")" on the keyboard may be thrown away as "garbage". It is no longer needed.

=== Declarative learning reward ===

Things are a bit more complex in explaining the declarative [[learn drive]]. There is a definite reward to [[declarative memory|declarative learning]]. Some things are just interesting, and finding out the truth is pleasing. At the neural level, the brain will scan inputs and neural activations to look for areas of high [[learntropy]] with the maximum delivery of new knowledge matching the current status of memory. Any meaningful message of low probability will be deemed more attractive. A bright fractal pattern will be deemed beautiful. A gray randomness of colors will be deemed boring. The same will occur in the case of a more complex visual message. A vibrant forest is beautiful. The same forest may seem unattractive in winter, in draught, or under the impact of environmental pollution. [https://en.wikipedia.org/wiki/Steven_Pinker Steven Pinker] remarked that we are attracted to images that ooze vitality. I disagree. The attraction is much wider. We may be equally well attracted to a deathly volcano or a frozen landscape of Antarctica. We love environments, signals, messages, or brain activations that can express complex information using [[abstract knowledge|simple models]]. The picture of a beautiful beach can be represented by a couple of simple shapes and textures.

Entropy of information is related to the compressability of data. Signal processing begins on the input. The retina performs a 100-fold compression of the visual input signal. The visual cortex receives simple representations of shapes and relationships. The hippocampus receives that information in an episodic context. Those signals may end up changing the status of a single synapse in the neocortical long-term memory storage.

The [[learn drive]] is based on seeking effective ways of representing knowledge in neural networks. [[Learn drive]], [[memory optimization in sleep]], and [[forgetting]] are essential to maximize compressibility, [[abstractness]], [[applicability]], and performance. This is how the brain makes sure that we can see a complex world using simple representations. That's the core of [[human intelligence]]. If artificial intelligence researchers could equip robots with a human-like [[learn drive]], given sufficient memory, their learning capacity might be inexhaustible.

=== Reward centers in learning ===

In 2014, researchers reported that the [[Curiosity_improves_learning| activity in the nucleus accumbens was increased in the state of "high curiosity"]]. They have also demonstrated what we have always known: this state improved memory performance. In addition, that improved performance spilled onto incidental learning, i.e. learning that would not spark curiosity on its own. This research was widely reported in media with a wrong interpretation: "''curiosity primes the brain for better memory''". For example, Scientific American headlined "''Neuroimaging reveals how the brain’s reward and memory pathways prime inquiring minds for knowledge''". The paper itself suggested the need for ''"stimulating curiosity"''.

As reward centers can be involved in the anticipation of pleasure, we should rather see the results of the research as an indicator that the [[learn drive]] is associated with pleasure. It is the learn drive that causes learning. It is learning that is pleasurable. The headline should be "''Neuroimaging confirms that efficient learning is pleasurable''". In other words, the sequence is not "drive -> pleasure -> learning", but "drive -> learning -> pleasure".

Instead of speaking of the need to "stimulate curiosity", which should rather speak of the need to "develop the [[learn drive]]". The key difference is in perceiving stimulation as quick-fix approach that might be used in a classroom as opposed to a long-term process that takes months and years. An advertising campaing may use cheap tricks to stimulate our curiosity, while a lifelong passion is a formula for insatiable and unwaning [[learn drive]], which is a perfect warranty for unceasing learning.

It is true that the state of curiosity will improve attention and this will improve overall learning, however, this should not ever be used as a classroom strategy. Gamification of learning makes sense only if rewards come from target learning, not from learning that surrounds the target. Many learning programs for children use bright colors, unusual sounds or smiling faces to attract attention to induce learning. However, once habituation sets it, this form or artificial gamification stops being effective. Moreover, incidental knowledge does not last. Any effort to employ curiosity to spark incidental learning is non-specific and inefficient. Equally well we might hope that pharmacological intervention, e.g. with Ritalin, could improve learning. Instead, learning must be its own reward. 

The nucleus accumbens and the ventral tegmental area are involved in pleasure, in anticipation of pleasure, and in signal evaluation. The signals from the [[knowledge valuation network]] converge into those areas in both their [[Brain_centers_involved_in_valuation_of_anticipated_outcomes:_nucleus_accumbens_and_VTA|motivational and affective valence]]. Dopamine is involved in the anticipation of pleasure. As dopamine is involved in attention, anticipation of pleasure alone would lead to improved learning due to a better focus on the source of information that is expected to deliver the pleasure.

If you are unconvinced, think of how much you hate your news channel when they do their tricks to pique your interest, and then say "''find out after the break''". You can get even more livid when they ruin it all with "''Breaking News!''". Anticipation can lead to frustration too. Only actual learning provides the reward. Only actual learning reward makes sense from the point of view of evolution. We do not want to reward an animal for the mere sight of food.

The buzz in the nucleus accumbens can be a direct expression of pleasure or might also indicate the state of pleasure seeking. In the end, the actual interpretation does not matter for the ultimate conclusion: '''boredom and displeasure are the enemies of learning'''.

For efficient learning in which new knowledge complements current knowledge, we need to follow the [[learn drive]]. In simple terms, this means that the pleasure of learning is desirable in education. We should never learn in the state of displeasure (cf. [[#Desirable difficulty|Desirable difficulty]]). Painful learning comes from the brain letting the student know that, in information theoretic sense, the new knowledge does not fit! It will be rejected. Pleasure is a good guide!

From the above neural reasoning we derive the obvious, '''the best warranty of efficient learning is to let students learn on their own and follow their own passions.'''

== Biederman model  ==

{{:Biederman model}}

== Desirable difficulty ==

{{:Desirable difficulty}}

== Addiction to learning ==

{{:Addiction to learning}}

== Displeasure of learning ==

When I claim that all learning is pleasurable, I hear a chorus of voices like "''I had to go through an awfully stressful exam that gave me lots of good knowledge for life''". Those voices confuse the pleasure of good learning with the displeasure of factors that turn learning into a horror for many students. Those horror factors are bad teachers, harsh parents, deadlines, stress, bad sleep, awful textbooks, excess volume, and more.

I hear that without deadlines or school-imposed goals, the learning would be replaced with videogames, novels, TV, hobbies, sports, etc. This might be true for many reasons. Some of those activities may carry pleasures unrelated to learning. However, they will also be beneficial for reasons of learning or exercise. A well-rounded student should be free to slow down, allocate his time for fun learning and other fun activities. Slow progress might bring more benefit. 

There is no way the equation of learning could produce unhappiness in the wake of good learning. The blame will always be elsewhere. All negatives should be studied and eliminated.

In the ultimate account, even if there is a displeasure related to exams, certificates and duties, this displeasure should be imposed on the student by herself.

{{Important note|
'''Pleasurable learning can be buried in displeasure caused by stress, bad people, bad schools, bad textbooks, and more'''.}}

== Learning and procrastination ==

If learning is the most sustainable form of pleasure, why do half of the students procrastinate? This is nearly a triple of the figure for the general population.

The answer is simple and important: students procrastinate because as much as good learning is a pleasure, bad learning is highly unpleasant. Most of assignments at school or even college carry a great deal of mismatch with the needs of the [[learn drive]]. This kind of learning is ineffective and unpleasant. Those kids will often play computer games in the evening claiming they ''need to rest their brains''. I doubt their brains are at rest. They actually do jobs that they find pleasurable. A great deal of that pleasure comes from new learning. Unfortunately, there are no credits at school for good gaming, so the sinusoidal cycle of chores-and-fun begins on the next day or even the same day with homework.

I never stop being amazed how many students call themselves lazy. At the same time they can do many heroic feats of physical of mental work as long as these are enjoyable or serve their own goals. Even those with thousands of memorized items in SuperMemo often give themselves low conscientiousness scores. Goals of learning can be hazy, but even if they are crystal clear, poor match between the input and prior knowledge can result in significant displeasure. If [[learntropy]] is low, assignments can be boring. If it is negative, they will be repulsive.

The battle between high goal valuations and negative rewards of bad learning will result in procrastination. Procrastinators often call themselves lazy even if they are nothing but.

If you think you are lazy about learning, you need to re-evaluate your materials and your methodology. Even simple violations of the [[natural creativity cycle]] can kill the fun of learning.

== Learning and depression ==
Learning is a sustainable and non-addictive form of pleasure with hardly any side effects other than cost in time. In addition, good learning tends to absorb the mind, and promote more learning by boosting the [[learn drive]]. This means that learning should be employable as therapy in depression.
{{:Learning and depression}}

== Optimization of education: Global or Local? ==
Is there a risk in using pleasure as a guiding light in education?
{{:Optimization of education}}
Optimization of education must adhere to the '''[[#fundamental law of learning|fundamental law of learning]]''' (next).

== Fundamental law of learning ==

{{:Fundamental law of learning}}

== Summary: Pleasure of learning ==

* human brain naturally tunes in to "interesting information" in the environment
* learning and discovering new things is rewarding
* many educators subscribe to the dangerous myth that learning may cause displeasure and still be effective
* surprisal is highly valued in new knowledge acquisition
* predictability and surprisal may both add to attractiveness of the information channel
* attractiveness of the information channel depends on the prior knowledge
* information delivered to the brain must account for prior knowledge. This factor makes universal delivery, e.g. via lecturing, very difficult
* attractiveness of the information channel depends on the speed of delivery and the speed of processing
* the speed and complexity of information delivery in learning must to tailored to individual needs
* the encoding of a new high value associative memory occurs simultaneously with sending a signal to reward centers in the brain
* failed tailoring of information channels in schooling leads to lack of reward
* learning provides a unique type of sustainable pleasure that may have therapeutic value
* for systemic reasons, schooling usually fails to tune in to child interests
* unrewarding nature of schooling is the chief cause of near-universal dislike of "learning" at school
* by destroying the pleasure of learning we contribute to creating an unhappy society
* the fundamental law of declarative learning states: '''No pleasure, no learning!'''