Question Everything

The SOTU (Again) and the Dumbing Down of the Polity

What Did We Hear? What is the Reality?

In spite of an aversion to doing political analysis, or seeming to, I've started using the State of the Union Address to remark on the incredible disconnect between the political milieu in the US (and by extension most of the rest of the world) and biophysical reality. Last year I offered my suggestions for what the SOTU should contain if Obama were smart, wise, and knowledgeable as well as honest with the citizenry. In a post-address follow-up I noted that, of course, he didn't mention any of the items I had suggested.

This year I've decided to wait and see what he has to say first and then do a comparison between the promises he makes and the biophysical reality that has so far been pretty much on track from my prior assertions. My real topic is not the SOTU and how far off track the president and congress are from really addressing the real problems facing us. But my topic now is a follow-on to my last blog on the effects of commodification of higher education — the dumbing down of higher education to accommodate the larger masses of people entering colleges with lesser intelligences. The same basic phenomenon is taking place in our polity. One name provides a poster child for the effect — Sarah Palin. But there are many from both the right and left.

When You Wish Upon A Star

When I was a kid I loved Walt Disney and a visit to DisneyLand was a dream. When I was a youngster I thrived on Pinocchio, Peter Pan, and Cinderella; wishing for what you wanted made it come true if only you believed and were a good person. When I finally did get to DisneyLand I was in my early thirties. I enjoyed the ‘E’ rides and the nostalgia. But I had long since learned that wishing didn't make things so. And it was plain for all to see that the ‘magic kingdom’ was really a lot of glitz and technology that made it seem as if wishes could come true. If you were still a child, the place might actually have seemed real.

I don't know if Mr. Obama ever made it to DisneyLand, to see that all that fantasy was truly make believe. I imagine he and his family have been to one of the Disney theme parks. But apparently he still believes in wishing upon a star.

Obama has convinced me, with this speech, that he has no real grasp of physical or even political reality. And I can't say I'm surprised. When Obama got elected I started writing a series of blogs on the theme that Obama's presidency presented the acid test of whether a nominally smart person could really make the kinds of changes not that we could just believe in, but changes that answered the necessities of biophysical reality, e.g. post-peak oil. Everything he has done since then has simply provided evidence that he couldn't do the right things. Either he just doesn't understand the problems, or he is just another political animal that has rationalized his avoiding the truth by a ‘need’ to get reelected.

Obama told the world the kinds of things people wanted to hear. He told both ideologies things that he supposed represents his political practicality. He got applause from either group at certain points where he appealed to their beliefs. Talking tough about government spending got positive responses from the right while talking about investments in education got positive responses from the left. Was he triangulating? If he thought that is what he was doing he is still wishing. The political reality of the US today is that there really is no middle ground. There is no territory where compromise will work. The next two years with Republican control of the House will tell the story. It will provide the evidence, but I am going to go out on a limb and suggest that even so, Obama will not learn from his mistakes. [I base this on his recent appointments to fill Chief of Staff and various economic adviser positions.]

Telling the world that we can solve energy problems with clean technology continues to be patently untrue, and as time goes by more and more energy experts are coming to this conclusion. But everybody, right and left, want desperately to belive that ‘clean coal’ is a feasible future. Many on the right, while denying the reality of human-caused climate change, nevertheless see coal as a way to reduce our dependence on foreign oil (same thing about natural gas). The left see it as the climate savior and also subscribe to the energy independence meme. There would appear to be a political middle ground that Obama believes he can work toward. The tragedy is that there is no such thing as clean energy sources that are truly renewable and fossil fuel-based approaches are doomed to depletion. There is no middle ground because biophysical reality preclude it as a viable political space.

I am tempted to go on in this vein, but frankly I grow weary of making the same observations over and over again, seeing our political system devolve into chaos. Rather I want to ask the larger question of why is our polity in such sad shape. Why are the citizens, politician, leaders, and media so poorly informed about reality that they could spend so much time and energy playing at this game? Why do the majority of people continue to wish upon a star? Has no one noticed that DisneyLand is fake reality?

My questioning has led me to a possible resolution that is, in fact, a follow-on to my last blog regarding the problem with commodifying higher education.

A Few Loose Ends RE: Intelligence

In my last blog a few readers got hung up on the characterization of intelligence, especially the reference to IQ and the notion that this capacity follows a bell-shaped (Gaussian) curve of distribution in the general population. The hang up, I think, centers around the idea, that I put forth, that there is a native capacity for problem solving intelligence that is required in order for one to tackle more intellectual material as has been found in higher education historically. We spent no small amount of time discussing (or haggling) over the nature/nurture debate and the efficacy of the egalitarian premise that every one should get a baccalaureate (and e-mails outnumbered comments two to one!). If you read that piece, you may recall that I asserted that this social dictum has resulted in the watering down of the intellectual challenges that had been the hallmark of higher education (dumbing down, as it is known in higher ed circles). The results have been disastrous for education in general. One mechanism that I didn't mention, but now comes to mind, is the fact that the overall dumbing down applies to schools of education as much as to any other professional school, such that the graduates, who will turn around and teach K-12, suffer the same lack of critical thinking and knowledge.

In my mind, intelligence is just a surrogate for a much deeper competency of mentation that really is at issue when it comes to understanding very complex problems and working on solutions. What I think I should have spent some time discussing is intellect, rather than intelligence per se. Let me take a moment to correct that.

In my working papers on sapience (the brain basis of wisdom) I discuss the relationship of the three generally acknowledged cognitive functions, intelligence, creativity, and affect with a fourth function that has been more recently studied as wisdom (see: Sternberg, R. (ed), Wisdom: Its Nature, Origins, and Development, Cambridge University Press, 1990). My perspective on these functions (or what some psychologists call mental constructs) is to relate them to actual brain functions and even map them to specific brain regions or combinations of regions. Thus I put emphasis on the fourth function not as the construct wisdom, but the underlying brain capacity to process high-order judgments and have suggested the seat of this capacity is in Brodmann area 10 in the prefrontal cortex (see “Is Brodmann Area 10 the Key to Human Evolution?”). Please see those papers for more details. The relationship between them may be represented in a Venn diagram showing each as a domain of processing.

 

Figure 1. The relationships between sapience, intelligence, creativity, and affect are represented as a Venn diagram. All of these domains of processing interact with one another to some degree or another. This is represented by the intersections. The relative size of the ovals (not to scale in any real sense) represent the dominance of these domains in the mind life of an average individual.

 

As depicted in Fig. 1, sapience tends to be rather small compared with intelligence and affect. Creativity is smallish. On an individual basis you will find the relative dominance of any of these domains varies considerably. However, I have conjectured that because sapience is the newest domain of processing to be accreted onto the evolving brain, it is generally small in comparison to the others. Most people do not have great wisdom when they mature, nor do they show tremendous judgment in complex social matters. What is interesting in this representation is the intersection area of all four ovals. This represents the areas of interaction (communications between brain regions processing the various domains). For example the affect domain is largely, though not exclusively, mapped to the limbic system and brain stem. Note that it overlaps intelligence and this is indicative of the kind of influence emotional responses may have over purely rational thought (which actually doesn't exist, but lets give intelligence the benefit of the doubt for the moment). This kind of relationship has been explored by Antonio Damasio (“Descartes' Error”) so the reader is directed to that work for more details.

The intersection of real interest, however, is where all four domains overlap (Figure 2). When intelligence, creativity, a little bit of affect (motivation), and sapience interact and cooperate with one another we find the intellect at work. The word intellect is often conflated with intelligence and even shows up this way in some references (e.g. the Wikipedia page for intellect redirects to intelligence; I'll have to try and fix that!) It is understandable how this conflation occurs since intelligence, which is the part of the mind which is responsible for directed search for patterns that match and rational choice, plays a part in intellectual endeavor. However, our choices, or decisions are not just guided by following pattern matching rules (e.g. this pattern is more like pattern A than pattern B so choose A as a match). We also use judgment. That is, we use tacit knowledge to steer our decision processing in directions that pertain to a larger and older context. Each situation that requires our decisions is not necessarily unique, but has attributes that correspond with a generalized form of the situation. As we gain life experience we encode many similar situations (a form of classification is at work) in tacit models that then can be called upon from subconscious levels to provide what we often call intuitions. The better we have learned from life, the more veridical and complete our tacit models are, the better and more accurate judgments we make. People with this ability are called wise.

 

Figure 2. Intellect is the processing sub domain that integrates sapience, intelligence, creativity, and, to some degree, motivation from the affective domains. The intersection between these four domains provides the basis for intellectual competency.

 

The intellect, however, is much more than mere problem solving capacity. The intellect is as much involved with decisions about what needs to be learned and how to learn it. It is the center for critical thinking, being able to detect differences between patterns (generated by intelligence) claimed to be the same or similar and generating questions (queries) to explore and explicate those differences. The latter depends on the strength of creativity. And it is all motivated by a strong desire to understand, as in how the world works. The strength of the intellect critically depends on the strength of the three major higher cognitive components. Weakness in any of these three create weakness in the whole intellectual capacity. Moreover, there needs to be a proper balance of the three. Of course a strong affective motivation is needed as well, but the keenness of the intellect (what we sometimes call sharpness) is critically dependent on the three higher capacities. Too much intelligence in relation to creativity or sapience and we have a person who is very rule-driven, probably very organized in life matters, Too much creativity and we have a person who is insufficiently disciplined, generating many more marginal ideas than practical or usable ones. I am not concerned about too much sapience, however. If anything, I think the norm is too little good judgment. Even when intelligence and creativity are well proportioned relative to one another, an insufficient level of sapience diminishes the intellect considerably. And this, of course, is what I think is the problem. Keen intellect and sapience (as demonstrated by increased wisdom later in life) are correlated. Some of the smartest people I have met or read about may still not have a developed intellect. They are good at solving local-scale problems, even very complex ones. But they do not see those problems and solutions as part of a larger context. They do not add the lessons learned to their storehouse of tacit knowledge. They tend to retain what they learned in a context-specific form, unable to transfer the principles to other contexts.

When I used intelligence as the relevant variable in the last post I really should have used intellect. Higher education is an intellectual exercise, or should be in my opinion. Learning and memorizing job skills is a highly context-dependent form of learning that doesn't necessarily engage the intellect. Many educators familiar with Bloom's Taxonomy of learning have commented on how our education system has devolved away from the highest level (the taxonomy can also be seen as a hierarchy) where ‘Evaluation’ (#6 in the cognitive domain) has been left out. It is far easier to teach ‘knowledge’ in Bloom's language, than to teach people to think with keen intellects.

And, much as I hate to say it (I can see the complaints coming in now) there is a reason for this. Put simply the vast majority of people really don't have the brain equipment, properly balanced, as suggested above, to have keen intellects. It is intellectual ability that determines the ultimate value of a higher education. As my previous blog indicated, we have trapped ourselves into dumbing down higher education to accommodate the less keen intellects, thus devaluing higher education for all.

This is not to say that every individual is not capable of some level of intellectual capability. They are, for the most part. Rather it is basically the same argument about the normal distribution of intelligence itself, only now complicated by the balance between three cognitive domains as well as absolute magnitude of those domains in any one individual. I suppose this is why I focused on intelligence rather than try to get into the complexities of intellect, but that was a mistake that I hope to have now corrected. It doesn't make the debate any easier, perhaps. There will be those who earnestly believe that an adequate environment could improve any intellectual ability. They would be right up to the point of recognizing that there is every bit as much of a genetic potential argument that limits the amount of intellectual capacity just as it limits intelligence. A bad environment may stunt whatever intellect is in the genes, but a great environment can't push the intellect beyond what is inherent. Some people have more than others. And a few have much more than most. Those are the ones who should pursue higher education to learn to apply their intellect to important problems for the benefit of the rest of society.

Moreover, it is the higher intellects who, in the best of all possible worlds, would be governing our societies. Now who is wishing upon a star???

Dumbing Down the Polity

The same phenomenon can be claimed in the political process. An easy first approximation is to note that the citizenry is the product of the dumbed down schooling, so they do not, as a result, have even moderate critical thinking skills.

How do you explain a phenomenon like Sarah Palin? Or Michelle Bachman? Or Joseph Liberman? Or George W. Bush? It's kind of a trick question. The left-leaning reader will probably answer that it is obvious, I have listed Republicans, or Tea Partiers, or turn-coats. They will blame it on the obviously (to them) flawed ideologies that motivate the party faithful. But sorry to disappoint the lefties. The same question applies to Rod Blagojevich, Nancy Pelosi, Harry Reid, and, now, Barak Obama! Oh, and don't forget Bill Clinton!

Each of these people named, in their own way, and in slightly different ways, are actually pretty clever. They have learned to maneuver the political mazes of their respective parties and the national or state political stages to achieve leadership positions. In other words, they are actually intelligent and creative. Even Mr. Bush is smart enough to adopt a Texas drawl and good ol' boy demeanor to compensate for his very clear lack of intellect. And he is perhaps the best example of the answer to my question. The citizenry that have put these people into positions of power wanted them there. They thought that they would make good leaders. On what basis?

Sound bites. That is it. A twenty-second ad on TV is all that is needed for the majority of Americans to make up their minds about who they want. It doesn't even really matter what they claim as their positions. In fact these days most elections are determined by which candidate had the best smear ads against rivals!

Now think about the leadership records of these examples. Who among them has actually demonstrated keen intellect? I would give Clinton higher marks than most for being able to parse statements and questions and give back a razor-sharp answer. But I would draw the line at, “... it depends on what the definition of ‘is’ is.”! Caught (literally) with his pants down, he resorted to the height of foolishness. Of course the House of Representatives (mostly Republicans) were acting foolishly to pursue impeachment proceedings over Bill's foolish act (see Halpern, D., 2002, “Sex, Lies, and Audiotapes: The Clinton-Lewinsky Scandal”, in Sternberg, R. (ed). Why Smart People Can Be So Stupid, Yale University Press, pp106-123, 2002)

Meanwhile all of the shenanigans that pass as political intrigue are nothing more than a show with the actors jostling for recognition. The media make money off of this and the public eats it up. But the fact remains that the higher echelons of governance leadership are being taken over by increasingly less capable individual who have virtually no intellectual power even if they are socially clever. The last politician of national repute that appeared to have the intellectual horsepower to understand the problems we face was Al Gore, in my opinion. He, at least, got the global warming problem right, long before it became a real political issue.

The American brand of democracy, a representative-based, tricameral form, started out fairly well by most accounts. Just as universities once, long ago, were reserved mostly for the intellectually elite (yes I know the moneyed elite too, but nothing is ever perfect), the governance of the nascent USA was seen as being the responsibility of a few individuals (yes I know all white men - you're going to miss the point if you keep thinking of the politically-correct negatives!). And those individuals were selected from among those who spent a great deal of time thinking about the issues of governance. Moreover, they were selected by a public that also thought about the issues. As Thomas Jefferson said, “Whenever the people are well-informed they can be trusted with their own government”, and also “An informed citizenry is the only true repository of the public will.”

I would be the first to admit that a lot has changed since Jefferson's time. Perhaps chief among them is the sheer complexity and volume of issues that the modern world presents us. Who has time to become adequately informed, and judging by how few senators and congress persons read the evidence documents before voting to give George W. Bush unlimited war powers against Iraq I would say no one has the time. Clearly Obama doesn't have time to read more broadly about economics or peak oil so even he is uninformed.

Wishing For a Solution!

Is there a solution to this conundrum? We have an uninformed citizenry, an uniformed and un-intellectual governance leadership, and we have monumental problems facing us. Yes, there is a solution, at least a feasible solution. But it is only feasible from the standpoint that excludes the democratic process! There are a few people who do have the intellects that would allow them to address the systemic nature of problems and find workable solutions. The only problem is that though they might be workable, the spoiled, uninformed, and un-intellectual (indeed anti-intellectual) public would not be able to handle them because they involve real and deep sacrifices. Ergo, the sufficiently intellectual individuals could only operate in what amounts to an autocratic system. Does this sound familiar? Of course, it is Plato's notion of the philosopher king, or Nietzsche's vision of the übermensch. Both ideas have been deemed unworkable by sociologists and political scientists. And so there will never be a concerted effort to find and ensconce such individuals into the high seats of governance. There will be no council of wise elders for us.

Instead, here is what will eventually happen. I predict it because this is what always happens in these circumstances. Crisis will erupt. The people will panic and turn to whoever seems to be the strongest savior. Might will prevail and to secure that position of power the real intellectuals will be smoked out and killed. This is an age old story for a species that is low in sapience and high in fear. It will come to that here in the good ol' US of A. Never happen here? Just watch.

Sapience is necessary and it must be in balance with intelligence and creativity in order that the average person be able to be intellectual. But high sapience is not what our species possesses. At least not at present. The end will come and the destruction will ensue. Strong arms will win in the short run, but wisdom will prevail in the long run. There will still be remote pockets of wise people who saw it coming and prepared. The world of climate calamities and diminished energy resources will select against the merely clever because it will take significant wisdom to take in the whole systemic nature of the world. Well, at least that is my wish upon a star!

Is there a role for elitism in higher education (again)?

Does Everyone Need a Baccalaureate Degree?

The American society (and followed it seems by most OECD and some developing countries) has decided that a four year baccalaureate degree is the standard for education. Once, not that long ago, the standard was a high school diploma. I will recount some of the factors that contributed to the evolution of this assumption, but the main question will be: Is this right? Is a baccalaureate degree the level of education to which everyone should aspire?

Because there are real, observable problems that have attended this evolution. Most people have heard of the grade inflation problem. That problem is associated with performance measures for colleges and teaching that include retention (keeping most students in classes and progressing toward the degree in a reasonable amount of time, e.g. a four-year degree in no more than five calendar years), student evaluations of courses and teachers, and several other more subtle pressures that basically push teachers and college administrators to fudge when it comes to quality issues (rigor). Moreover, competition for students forces colleges (and this also means universities) to spend money on peripherals that do not directly affect student learning achievement such as sports complexes. You would be hard pressed to look at a modern university/college budget these days and be able to link the larger share of expenditures directly to the core mission of education. Even the research component of universities/colleges, while nominally supporting teaching (keeping the professors current in their fields), actually results in a diminishment of time spent by those researcher/teachers in the classroom. The only sustainable model for funding classes is to have huge service course sections that are taught by TAs (grad students) to obtain income to support the smaller advanced subject classes (upper division and graduate). In sum, the modern American university/college is in deep trouble from trying to be all things to all people. It is ending up being almost useless to most.

Higher education was not always considered as the basic standard for education. It was the gold standard with graduate school being the platinum standard. It was, for lack of a better word, elite. But that was back when that word did not carry such a negative connotation as it seems to today. The world is full of examples of elitism that still performs some kind of positive social function (see populism for an explanation of why elitism has a negative connotation). For most of history elites were an essential part of social organization. Even today, most Brits (and some other European citizens) still think having a class of royalty is a nifty idea in spite of the fact that there doesn't seem to be any logical reason for it at all (economics may eventually force the disbanding of the royal families but not logical reason). There are managers of companies. Not everyone can be the president of an organization. We recognize that there are special skills, talents, and knowledge that attend doing jobs that entail coordinating others in an organization. Or, as in the case of the queen, providing a proud figurehead for national egotistic purposes.

Why then, have we come to believe that there should be no such thing as an exclusive college-educated elite? We did believe that there should be such a class not more than fifty years ago. This figure shows what the general beliefs about general intelligence and school-level potential for a population that was normally distributed (the bell curve) for intelligence.

 

 

The standard evidence for this case was the results of intelligence quotient (IQ) testing which showed that there was, indeed, a normal distribution of intelligence capacity in the general population. The mean value (the peak of a normal distribution) was given the index value of 100, meaning that half of the population had higher IQs while the other half had lower IQs. The bulk of the population have intelligence measures close to the mean. Only a smaller number have much higher intelligence measures and only a very small number have substantially higher measures. These corresponded with the notions of baccalaureate-level capability (as a terminal degree), post-baccalaureate potential, and doctorate-level potential.

In spite of a rather large body of evidence showing success in various levels of school and intelligence, as measured by a variety of methods, that corresponds generally with the above model, the attitude today has shifted considerably. This figure shows a rough approximation of the change in beliefs about the level of schooling achievable regardless of intelligence measures.

 

 

Notice that the areas of potential for college and graduate schooling have shifted considerably to the left. The new baccalaureate potential now covers much of what had been high school and trade school potential. The intelligence distribution hasn't changed. The curve has not become skewed to the right somehow. People are no more intelligent today than they were in 1940. What has happened is that the definition of school potential has shifted to the left. And to what effect?

Shifting Attitudes

Elitism or the existence of an intellectual elite based on native intelligence capacity and accomplishment in rigorous higher education has lost its luster in our society.

The basic problem seems to be that there has been a growing attitude of anti-intellectualism in the USA, in particular. Many more people don't like the idea that someone else is smarter than they are (and I often wonder if there might not be a deeper connection with the growing sentiment of entitlement in this country). What is incredible about this sentiment is that it can co-exist with the notion that we need really smart people to design and build stuff that we like to use. I suppose one might describe this as a love-hate relationship. But the fuzzy thinking that it entails allows many to believe that everyone could be smarter if they had the chance to go to college, ignoring any biological facts about potential for intelligence.

Simultaneous (probably co-evolutionary) with the growth of anti-intellectualism we have the competition among organizations who want the very best and brightest employees. I first became aware of this back in the 1960s when I first attended college as an undergraduate. The general story going around was that a college degree — any degree — indicated an ability to think and learn which employers were starting to realize was important in an increasingly technology-based economy. Ergo, in a competition between prospective employees for a job, the one(s) that had earned degrees would have the advantage. Seems perfectly reasonable. But the idea was still based on the notion that college graduates were among the thinking elite. Thus hiring them would give the companies that did a competitive advantage that was protected. Once it became clear (say for example to high school advisors) that there was a high demand for college-degreed employees it was an easy move to start preferentially recommending college to high school students. Coupled with studies that indicated a correlation between level of education and expected pay levels, the conclusion was inevitable: everyone should go for the college degree.

Finally the desire to have more college graduates was a governmental policy in the same vein as every family should be able to own their own home. In the post-WWII years GIs were able to get low interest loans to go to college or technical schools as an attempt to support the growing demand for college-educated and skilled workers.

Thus in the post-war era there started a flood of students seeking college degrees. College became the new populist standard for education. Universities and colleges all over the country expanded. The demand for college professors caused an increase in the number of PhD granting institutions. Growth in higher education is, as it is in industry and commerce, intoxicating. It is self-reinforcing. A little growth begets a greater desire to grow more. And to justify such growth everyone started telling the story that just about anyone who wanted to go to college could succeed.

But, if we are expanding higher education to incorporate a larger proportion of the population (as noted above) then it would easily explain why we have to resort to grade inflation among other coping mechanisms to make college-level work accessible to more students. It would explain the rapid conversion of many areas of skills that were traditionally blue-collar type jobs into “professions” that require four years of in-depth education in order to qualify them as baccalaureate-worthy. We are witnessing the rapid conversion of many technician-level trades into BA or BS degrees and departments opened in even major universities in order to accommodate growth in demand for college-level education. Trade schools are looked down on as not worthy.

I could go on for a long time along this path. Indeed there are a growing number of books devoted to the subject of what is wrong with higher education institutions that do just that. And very many analysts have identified what I have called the ‘commodification’ of education as a root cause of the decline in quality. That decline, in turn, is what is seen as causal in the decline of competency for graduates. The explanation is simple. The curriculum is targeted at the middle of the class — the average student. And the average intelligence of the college population is declining so the rigor of the subjects has to be softened so that the average student can handle it. You also don't want to drop too many from the bottom because that would look bad on your retention numbers. Compounding and reinforcing this trend, the average intelligence of newly minted PhDs is likely to be going down as well. Certainly the softening of rigor in the lower degree programs is migrating into the doctorate level as well, meaning that many of the newly graduated PhDs going into teaching are quite happy with softer subject contents since it is possible that they themselves would be unable to handle the more rigorous content.

And here is the core problem. Standards have declined to keep volumes up. Now people who manage to graduate are, themselves, much less motivated and comprehending of the rig our and their responsibilities in being a professional. And they misbehave! Too many incompetent or uncaring people are getting jobs that should go to those with a stronger sense of moral duty as well as higher levels of intelligence. But the whole economy is growing (or was) so fast that demand for anybody who could breathe and got a BA in business could end up being a division boss. And even the brighter graduates in those positions are not necessarily better suited. Remember: We watered down the rig our so that the average student could get through. The brighter students suffered without necessarily being aware of what was happening. If they noticed anything at all it would have been how bored they generally felt with many classes.

There has been a general slackening of the emphasis on professionalism, which includes ethical considerations and responsibilities to society. In its place we are seeing the rise of accountability measures to be taken and used as sticks to get teachers and their schools to perform. This is just now gaining traction in public higher education with legislators and business leaders pushing the agenda, but parents swallowing the rhetoric as well. It is already rampant in K-12 education, it is the ‘law of the land’ — No Child Left Behind, Race to the Top! And, unsurprising to those of us who remember what professionalism was really about, it is having exactly the opposite effects than desired. Its main mechanism for measuring effectiveness is the standardize test on standardized curriculum. The latter is not completely objectionable, at least less so for K-12, but the former is abhorrent. No one yet has made a convincing argument that these tests actually measure anything worthwhile beyond a student's capacity for rote memorization and test taking abilities. Most people have heard about the teaching-to-the-test phenomenon that accompanies the uses of these tests. I have been a witness to the agonies over this very thing in the State of Washington (see this Wikipedia article on the WASL but beware that there are questions of bias in that article).

Some Ideological Premises

Basically I find that there are a number of premises that are accepted blindly when talking about the problems in higher education. Many of these are also accepted as true for high school and the reason that high schools are failing (in some sense much worse than colleges). Let's take a look at some of these and see how they contribute to bad conclusions and decisions with respect to, say, education reform.

The first is based on ignoring the possibility that not all people have equal IQs (or general intelligence levels). We assume that all children and young adults can learn the same curriculum if we but organize it and deliver it in such a way that they can absorb it, thus transferring the responsibility for learning from the students to the teachers. And transferring the blame when learning doesn't appear to be happening to the teachers. But the anti-elitism that prevails dulls our ability to recognize that there really are brighter and duller people when it comes to learnability and (self)motivation. I reassert that much of this attitude has been strengthened by seeing so much abuse of power by those nominal elites who, having gotten their education and high paying positions (due to loosening the standards) turn around and screw up. Who wouldn't start to hate intellectuals if they represent a crowd that takes advantage of their positions at the expense of others? A good coping mechanism for cognitive dissonance over such a problem is to assert that we can all become elites. Or, rather, we can all become highly educated — that we are all equally endowed intelligence-wise.

This sentiment is reinforced by another ideological premise, that higher education leads to higher pay! If you want higher paying jobs you need more education. On the face of it this is true. If you correlate the incomes of more educated folks you find that they do, on average, have higher incomes. But, and this is a big but, you have to recognize that just because there is a correlation doesn't translate into a causal relation that asserts, in effect, that if everyone got more education they would ALL have higher incomes! Higher than what? The average income? This kind of thinking is so incredibly fallacious that it astounds me that any thinking person would accept this premise. Yet most do. If you, dear reader, grasp this argument you can now understand why I claim that even very smart people can be unusually foolish (lack sapience). What I suspect drives society to accept this premise is that every parent thinks only about their own children's future and they want the most for those children. That is certainly natural. But given the force of the higher ed = more pay meme, those parents sell their kids the idea that they should only want to go to college and not, for example, want to be a trades person. What often comes as a surprise to many people is finding out that most plumbers (blue collar) make more money than most lower level managers (white collar) with business degrees. But even if they were aware of this, how many parents would suggest to their kids to become a plumber? Those same parents will quite happily pound on their children to do well in school so they can get into college and get really good jobs when they graduate.

But leave it to our education reformers to suggest fixes (work arounds) for the failings that occur when we accept these premises as true. The first work around is to claim that all students can learn a standardized curriculum. There is a tiny bit of justification for this assumption, but only, it turns out, for very young students. This harkens back to the days when most children who got any education were expected to learn to read, write, and do arithmetic. This was the standardized curriculum. How many of you learned to read via the Dick and Jane books? Later, as the industrial revolution and its application to farming freed more high school-aged students from needing to go to work after grade school, the idea was extended to high school education but with more elaboration on the basics (e.g. learning geometry and algebra, poetry and prose, etc.). College was still viewed as a place where just the most elite (learners) would go to learn about the world through liberal studies. Some very elite would go on to graduate school and learn the law, medicine or some other specialty. But it was still generally believed that a standard curriculum could be applied to high school education, preparing all graduates for their lives in the more complex industrial society. That, of course, is when the trouble began. States started requiring all children of school age to attend through at least some minimal grade, at first middle school and now high school. It is, in fact, against the law for children in these age groups to not attend without some special conditions (e.g. home schooling). But almost as soon as society adopted, without question, the premise that all children could learn and could learn a standardized curriculum the trouble started. High schools soon found a need to broaden their definition of what a standardized curriculum entailed because there were simply too many students in that age bracket who could give a rip about world history or poetry. So shop and home economics were invented! [as an aside the most valuable class I took in high school outside of the science classes was typing!]

At each stage in the development of our industrial society the breadth and depth of what was considered relevant and necessary knowledge was expanded. This was partially in response to the complexification of society as the industrial revolution proceeded and was followed by the information revolution. The amount of knowledge needed to do even clerical or blue-collar work expanded such that not even a four-year high school education seemed adequate. The explosion of professions further mandated that more than high school knowledge would be needed by professionals. Business schools are a good example. The first objective of a business education is to familiarize students with the principles of how a business is run, what the mechanics are, and develop skills in managing these. The only real model that we had for education beyond high school was college. So business schools as colleges within universities became the rage. No one really stopped to ask if a huge volume of graduates from these schools was really necessary for the business world, as opposed, say, to a modest number of accountants. It seemed, and still apparently seems, the unexamined answer is YES. But I dare you to go ask some mid-level manager, ten years out from getting a business degree, how much of what they were taught has actually made much of a difference for them.

Attempts were made to produce two-year degrees modeled after the first two years of a baccalaureate degree. These were called associate degrees and were relegated to a new, and presumably cheaper version of college called variously community or “junior” colleges. Many trade schools, catering to skill training in the trades, upgraded their offerings to look more like college classes. They now go by the name technical college. One way to make the education offered in these schools affordable for state support is to not pay faculty as much as their counterparts in universities. There are a number of repercussions that fall from this, many have been documented in the literature. Let's just say the adage, “You get what you pay for” has some traction here. Another factor is what amounts to an amplified dumbing-down of subject matter since these colleges are generally open to all citizens regardless of their high school performance. Between cost-cutting measures and open access policies, community colleges too often find themselves providing something less than an equivalent to the first two years of a four-year schooling. Having witnessed the fates of way too many transfer students, coming from a community college expecting to take up at the junior level of classes in the four-year college, and seeing how many of them have to take remediation courses, or retake subjects because what they had learned was not sufficient, I can attest to the fallacy of thinking that an associates degree is actually, and always, equivalent to the first two years of a baccalaureate. The increased cost of providing remediation courses in the four-year schools puts further pressures on those institutions. But we are so committed to the higher education for all principle that we grin and bear it.

Another premise generally accepted without reflection is that schooling can be accomplished most effectively following the assembly line model of moving products through in lock step.

Once more the sheer volumes of students forced through the (public) education system mandated finding some efficient method to crank out graduates. The age-related grade system provides this kind of staged development process. Assuming that all children of a given age can learn the same things, or are ready to learn the curriculum that their age-peers seem to be able to learn, we bunch kids up by age group and work hard to push them through to the next stage (grade).

This model weakens in college where multiple ages might be found in the various class levels. However the belief in a standardized curriculum for professional and science degrees runs as strongly as ever. Curriculum is largely dictated by disciplinary areas. Departments roughly approximate the kinds of knowledge and skills found in the practice of their disciplines. Courses are taught in a rough sequence from fundamentals and survey of the field (freshman) to courses delving into subspecialties that collectively define the overall subject (sophomore and junior) to courses that go into great depth in selected subspecialties (senior electives). This all seems quite reasonable for many fields. The only real problem with it is that the courses are often presented in disparate fashion. Necessary interrelations may not be made explicit so at times the field can appear to be a disjointed assemblage of interesting topics. The survey courses are meant to provide a grand overview that students can use to form a conceptual map of the subject territory. But those courses are generally taught at the freshman level when younger students have not yet really grasped the method for forming such maps on their own. They will tend to even forget that a subject taught in a junior level course was introduced and placed into context in a freshman level introductory/subject survey course. Ergo they end up graduating still not comprehending the system of knowledge they just acquired!

Can Any of This Be Fixed?

In some ways the problems I've just covered are going to go away, but not because we, in higher education, fix them. They will go away because public higher education is going to implode. It will certainly go through radical retrenchment as state revenues continue their downward spiral and legislatures continue to cut budgets. The administrations of these institutions will respond by cutting programs and downsizing others, meanwhile generally protecting their own jobs. The number of students entering whatever universities will go down drastically as tuitions rise to try and boost revenue. Unless the universities mindfully attend to the quality of the students who do get in (instead of selling seats to the highest bidders) this will not help the educational outcomes very much. Indeed it may make matters worse.

There may, however, be another way forward. It involves radical cost cutting of peripheral activities, especially administrative fussing around.

We begin with a recognition of what the core mission of the university is (always has been and, one hopes, will be in the future). It is not, surprising to some, putting on great football games. Over the years the university has taken its name root (universe) a little too seriously. It has tried to become all things to all people (as described above). The proliferation of collateral departments with their plethora of mid-level managers has been the dominant cost driver. Growth in terms of enrollments and programs has become the operative paradigm and in order to grow you have to out-compete your rivals. You can't necessarily do that with pure academics, so you resort to fancy athletic facilities and other amenities of “student life”.

As this evolution has proceeded university governance has also morphed into a business management model as opposed to a faculty deliberation and consensus model. Essentially, in competing for resources to maintain growth, the institution becomes a capitalist enterprise even if it is nominally non-profit. To manage a business has required so-called professional management (did you know that you can get a PhD in educational institutional administration?) The old governance model, in which various senior faculty rotated through department chair, dean, and provost jobs fell apart as more and more of the administrators morphed into professional chairs, deans, and provosts. Rather than step down at the end of a period of service, these now-professional administrators would either stay put or get caught up in the rat race of promotion by looking for a higher position at another school.

University governance shifted from the old senate/president/regents working in concert model to the faculty senate becoming more of an advisory role with the president and regents looking more and more like business executives and boards of Directors. Indeed, members of the boards of regents were taken more and more from the business world rather than the ranks of emeritus professors and civil leaders. This is all part of that larger pattern of turning schools into training facilities for workers.

The fix would be to go back to the old model, letting faculty take the roles of deans and provosts, if for no other reason than to reverse the cost appreciation trend. Professional managers seem to have a need to expand their little empires. Temporary servants have no incentive to do so. But it would be nice if we did it because we were going back to the core mission. We, in higher education, need to re-imagine the university in its ancient form. It is there to provide growth of minds, not training for jobs. We don't even know what kinds of jobs will be available or needed by society twenty years out. So why would you design academic programs to train students for jobs that might just well be obsolete (or disappear)? I'm betting the bulk of work that will need to be done in the future will be food production and that we will be right back to the agrarian society within the next fifty years (provided we don't destroy ourselves by reacting badly to the decline of energy available). And only a smaller fraction of the population will ever have the mental capacity for the kind of growth of mind higher education can provide. We will return to the days when only some category of elite will attend college and succeed, We'll get there not because we, as a society, made a decision to do so. It will be because we will not have the wealth resources to invest in years of additional education for the masses. The real question will be, will it be an intellectual elite or will it be a wealth-hoarding elite. I'm betting the latter won't really be able to survive for long in the future. So eventually...

Energy Costs and the Economy

Why Do Prices Go Up?

I've hammered on this issue many times, but it seems that it needs even more exposure now. Oil price has been hovering around the $90 - $95 price range for several months now. That range isn't really hard since there are many grades of oil (light sweet to tar sands gunk) that have a range of prices based on the desirability, a function of how easy it is to refine the oil to high demand products like gasoline and diesel fuels. I haven't seen anything resembling a weighed average price, by type and volume say, but you can see the general trend by tracking prices of any of several sources. For example Brent Blend (North Sea source) was running in the high $90s a few days ago. Tapis crude (from Malaysia and traded in Singapore for Asian markets) was running over $100 on that same day. And West Texas Intermediate (USA, most often quoted benchmark price) was running just over $90. Here is the problem. The price of oil is a major cost factor in everything else in the economy.

Way back in March of 2008, when the price of WTI went over $100 I wrote What's wrong with this picture? in which I dissected the cost accounting of a supply chain from a basic material extraction (from natural resources) through intermediate processing and component forming, up through final product production. What I was trying to show is that while energy costs recorded for each enterprise in the chain were relatively small in dollars, that the fact is every stage along the way energy inputs were necessary to get to a final product. In point of fact all work must have energy inputs, and thus all inputs to production ultimately resolve to energy. This includes labor, materials, and overhead, which allocates costs to historical expenditures for physical plant assets as well as current operating expenses. Extractive industries are generally very energy intensive even when the energy is that of human labor (e.g. miners).

The energy costs (in joules or BTUs if you prefer) accrue at every stage. In this blog I want to explicate this aspect just a bit more and examine the way in which rising costs of fossil fuels propagate throughout the economy and contribute to inflation, i.e., the rising costs of all products, not just, say, gasoline. But, insidiously, inflation is not the only impact we will see from rising energy costs. These are just a natural reflection of a simple biophysical economic fact. The supply of net energy to do all useful work is already in decline. This is because the amount of energy that is consumed in the extraction of fossil fuels has been steadily increasing while the rate of production (esp. of oil) has been decreasing. We have already passed the peak of net energy, so we are able to do less real work (as opposed, say to fantasy financial services work done on Wall Street and by gambling bankers - not all bankers, just the greedy ones). It appears now that we have passed the global peak of extraction of conventional oil, and we have certainly reached the point where new production is only for high energy cost oils (e.g. from deep water drilling or tar sands). Readers would do well by reading postings in The Oil Drum and The Energy Bulletin for starters. You will find other blog pointers in my blog roll to the left. All of these are examining the peak oil phenomenon and exploring the implications for macroeconomics.

The graph below, supplied by Wikipedia (have you made your donation yet?), shows the general trend of WTI prices from 1996 to 2008. As can be seen in the graph, oil prices basically increased by five times in that period. Eventually it reached a top of $147 and some change and then tumbled back down to $40+ when the demand for oil plummeted and triggered the 2009 recession. For a good overview of the oil production vs. price history see Gail Tverberg's “Will 2011 be a rerun of 2008?” on The Oil Drum.

 

Graph 1. WTI price trends 1996 to 2008 (1994 is a typo - source Wikipedia.

 

Clearly the price signal has been on a more or less steady climb for the decade. There is no oil embargo to explain this, only the twin effects of peaking production and falling energy return on energy investments (EROI) which translates into higher costs of production.

But the really bad news is that oil is an input to everything else in the economy, either directly or indirectly. Its cost increases are most readily and quickly felt in the transportation sector. But oil is also used as chemical feedstock for plastics and other derived products. It is simply everywhere in one molecular form or another. As I said transportation systems rely very heavily on oil in the form of gasoline and especially diesel. The latter is a major input to the extraction industries, even for coal (I've been trying to assess the magnitude of this relationship as it might have a great impact on coal production costs).

One of the most sensitive sectors that gets impacted almost immediately is food production. Food is the necessary energy input to human labor. Though the energy content of food is derived from sunlight, the organization and operations of food production — the modern industrial agricultural operation — runs on diesel and electricity (much of which is produced by coal-fired plants). Artificial fertilizers are absolutely mandatory in order to maintain high yields per acre and that is necessary to feed the urban populations. Fertilizer components, fixed nitrogen, phosphates, and potash (potassium) have to be manufactured or mined and shipped to the point of production, then the product shipped to the point of use. It has been estimated by various energetics analysts that one calorie of sunlight in food costs ten calories of fossil fuel to produce and deliver.

Inflation is the increase in asset (or services) costs over time. There are two real causes of inflation. The first is the biophysical increase in costs due to shrinking availability of net energy, which leads to less real work and fewer assets being produced. The dollar costs of what does get produced goes up (as does the price of the energy inputs). The second is due to not adjusting the currency value in accordance with the shrinkage of real assets. This would be done by reducing the amount of currency in circulation. But, of course, this is exactly the opposite of what the US Fed is doing (see quantitative easing). Way back in 2008 I wrote about the dual problem that we are about to face, contraction along with inflation (not just stagflation but Contractflation).

The Biophysical Economy — i.e., Reality

Real wealth ultimately comes down to two biophysical stocks: physical assets and biomass! The former means all of the physical objects, including the ones representing knowledge assets like bits in a computer file. The latter includes the totality of human beings, their pets, and their non-food plants (e.g. ornamental plants). Of these, of course, the total mass of humanity is what we are primarily concerned with. For a more complete explanation of the assets/biomass breakdown see my tutorial, “Work, Exergy, the Economy, Money, and Wealth”.

Figure 1 greatly simplifies the overall picture found in Figure 3 of the above tutorial. Here I have just included the two stocks (actually as active processes, oval shapes in the systems diagramming lexicon) and the sources of material and energy that enable their construction (including biological reproduction) and maintenance.

 

Figure 1. A condensed macroeconomic view of reality. All of humanity is represented by the biomass subsystem. There are two main streams of flows from the natural world that support the continued creation of biomass. The first is the parallel extractions of energy, one in the form of extrasomatic energy. Fossil fuels make up the largest single part of this energy flow (> 80%). Hydroelectricity and nuclear make up most of the rest with very small contributions from wood and other vegetable matter and even smaller contributions from high-tech, so-called, renewables like wind and solar. The second energy extraction is from solar energy to food production. This is made possible on large scales by the supplement of fossil fuel energy from the first energy extraction (red arrows). The second flow is material extracted from nature and formed into usable assets (which includes more energy extraction equipment!) Humans provide energy flow feedback to all of the other processes in the form of labor (including intellectual labor).

 

The key concept in all of this is the flow of energy, assumed to be in the proper form after extracting raw energy and converting it to the forms that drive our work engines (e.g. food or gasoline or electricity, etc.). Energy is the prime input to every single activity in the economy.

Figure 2 depicts the situation which I called contraction. As energy sources diminish, post peak oil, combined with the rapidly declining energy return on energy investment (i.e. higher costs for extracting energy) there is a necessary shrinkage of net energy available to do the useful work in the economy. Ergo, both biomass and assets shrink since their production rates and maintenance are curtailed.

 

Figure 2. This figure shows the situation of the biophysical economy subsequent to the increase in energy costs to extract energy (larger red arrow feeding back into the energy extraction process) and the shrinkage or depletion of the fossil fuel energy resources (e.g. sometime after peak oil). The net energy available to run the rest of the economy is shrunk so that asset production as well as biomass production (after shrinkage in food production) must decline in response. Worse yet, the reduction of net energy flows means losing the ability to maintain current stocks of biomass (increased death rate) and assets.

 

This is what is staring us in the face right now. We have reached, by all reasonable indications, the peak of oil production in total barrels pumped. We seem to be on what is called a bumpy plateau rather than a definitive peak owing to the response of the economy (contraction or recession) that lowers demand for energy and thus slows the pumping rate temporarily. As the economy has seemed to pick up growth momentum (don't try to sell that to those whose jobs went missing or lost their homes to foreclosure of course) the speculation of higher demand and a non-ability to actually increase production over what the likely peak number was appears to be elevating the futures price for oil and thus we find ourselves back at the 2008 situation once again.

There is every likelihood that the $85+ price level is putting a tremendous drag on every global economy, even, or especially the Chinese and Indian economies. If the price of oil stays at that level for much longer, say two more months, I would not be surprised to see us in what economists call a double dip recession, but where the next low will be much lower than that of 2009. As the oil further depletes there is nothing but upward pressure on prices even as recessions seem to temporarily cause a decline in short-term prices. The daily, even weekly ups and downs of oil prices are just market jitters — noise. The long term trend (as in Graph 1) is terribly clear.

Compounding this issue is the fact that new oil finds have been getting smaller and smaller as far as field size goes. And they have been happening in more remote, and hence more costly, locations like deep water. Or the oil is actually not found in pools but embedded in shales and tar sands which require extremely expensive extraction methods and technologies. So the likelihood of bringing more capacity on line as a result of higher prices is getting smaller and smaller over time. The net payoff of investments in new oil production is approaching zero. The marginal returns are already in decline.

There is no solution to the fundamental problem. There will be contraction even if we were to somehow find the resources to invest massively in alternative energy projects. The latter simply cannot scale up to meet the same level of demand as is currently met by fossil fuels. If the rate of depletion of the latter is high, then it becomes unlikely that we would even be able to muster that investment since it means diverting a substantial amount of energy from even necessary asset production/maintenance (forget about discretionary spending). The Chinese government, with their more autocratic abilities, seem to be trying this now. They are investing more energy into alternative sources, but as they do they are having to substantially increase their imports of oil and even coal. Not that many years ago we told stories about how much coal China and the US had to provide energy for perhaps several centuries. But that was just a story and not a very good one at that.

I have been writing a lot about the need to ramp up (from the grass roots) our approaches to food security by learning and practicing permaculture. Up until now it has been as much an exercise in intellectual musing about the application of systems science to localized living. I have been exploring some possibilities, but frankly have found the potential sources of financing not ready to hear the message. Now I wonder if I'm too late for myself and family. Given the rates of energy reduction I am witnessing I would say it is now time to act. If you have the ability to go full bore into starting a permaculture community now is the time to make it happen. Choose your site wisely. And good luck.

Brain Complexity at Multiple Scales

It would be fair to say that I am absolutely enthralled by the workings of the brain.

For me, the brain represents the epitome of complexity. Everything that we humans are, everything we do, all of our feelings, our thoughts (thought itself), our behaviors, all are due to the workings of that marvelous roughly kilogram and a half of gelatinous living mass. Human behavior is infinitely complex in details. Unpredictable, often. Yet there are universal aspects to behavior that should resolve down to how the brain works.

If we have any hope whatsoever of understanding ourselves as a species, and appreciating our strengths and weaknesses as a collective society as well as individuals, we need to understand what is going on in our brains. Progress is being made. Whether it will produce adequate results in time to formulate plans and policies regarding how to mitigate our species' weaknesses before we wipe ourselves out is another question. Nevertheless, our neurosciences are delving deeply into the workings of the brain and we are learning. What stands out the most, right now, is the sheer complexity of how a bunch of living cells can, ultimately, produce human behavior.

For most of human history, we have largely assumed that our mental strengths, namely problem solving intelligence and creativity, can and have overcome any weaknesses. The latter have been relegated to our emotional side. We have assumed that our ‘darker side’ is a left-over remnant of our animal evolution (or if you are of a religious bent, perhaps it is a result of the free will thing). The popular (both in philosophical and vernacular realms) view that human intelligence can subdue our animal spirits and this is what leads to civilizations based on the rule of law has dominated all of our thinking about what we humans are, how to explain our history, and what our destiny will be, especially if we work hard with our rational minds to overcome our emotional minds. The creation of the fictional character Spock, on the television series Star Trek is perhaps the best example of this self-image zeitgeist. Spock, from the planet Vulcan, was a being of pure logic (except that he was half human and the ‘lesser’ qualities would sneak out from time to time to make the plot interesting). As such he could be counted on to think through any conundrum and save the day. The idea that humans might, one day, evolve to have this mental capability was, and still is, appealing since we can easily point to all of our failings arising from the negative emotions.

But evolutionary neuroscience and psychology are showing us more and more that this notion of a perfectly logical being is not only not likely, it is not even desirable. Emotions and affective states in general are a necessary function of the brain. They are essential to most forms of decision making for the simple reason that most decisions are made under considerable uncertainty. And emotional responses to situations that are similar to other prior encountered situations that had an emotionally meaningful outcome can be very helpful in making rapid assessments and decisions (see Antonio Damasio's “Descartes' Error” for a full explanation).

Evolution found another way to balance emotional influence over intelligence (decision making) in the form of sapience (see my working paper series on sapience and wisdom, here), the capacity to override or modulate emotional responses and bring tacit knowledge to bear on decisions; what we call judgment. Sapience is the brain basis for gaining wisdom over one's life. As one experiences the vagaries of life one gains tacit knowledge (subconscious knowledge that can be applicable to a large variety of situations). In more mature adults this produces the various behavioral attributes we recognize as wisdom. The capacity for sapience is the newest evolutionary step in making humans the creatures they are. It, like affect, shapes intelligent decision making, but based on knowledge acquired during one's life, not evolutionarily hard coded into the limbic brain. Unfortunately, as the newest brain faculty, it is also the weakest (not as influential as either affect or the logic of intelligent/creative problem solving.) Presumably our social organization with its dependence on cooperation for group survival would eventually lead to stronger capacities in this area. Sapience is a brain function that could undergo further evolutionary strengthening given the chance. Indeed, there is some growing evidence that the there is a reasonably wide range of sapience strength in the population already. Just as there is variation in intelligence and creativity, the same seems to be true for sapience as well. A great deal more work in neuroscience and psychology will be needed to clarify this situation and some of that, particularly in the area of judgment capacity, is already underway.

Meanwhile one can only look at the complexity of the brain and marvel at the profound nature of mind. In this post I want to view that complexity from a hierarchical scale perspective. That is, by examining some of the issues of complexity at various scales of organization we can begin to see how the overall functioning of the brain is the result of complex processes operating at all of these scales and the interactions between them.

Complexity

It must be the height of audaciousness for we humans to actually believe we can understand how the brain works! And yet it often comes as a surprise to many non-neuroscientists just how much we do understand about the brain's workings. Any complete understanding is still far off, but there are glimmers of hope. For one thing we have a reasonably good handle on the scope of complexity involved in brain functioning. By this I mean the way in which the brain is a complex system comprised of complex subsystems, which, in turn are comprised of complex sub-subsystems. The state of neuroscience, viewed from a macro-level, shows a hierarchy of complexity from the molecular functions within synaptic junctions (and associated glial cells) through the workings of whole neurons, through circuits of neuronal assemblies, all the way up to the functioning of whole brains comprised of complexly intercommunicating modular regions.

There are still many details not yet understood, such as exact wiring diagrams for circuits and regions. We still have a long way to go to delineate the intricacies of all of the neuromodulator molecules and their effects on various kinds of neuron types. Even the zoo of neuron types is probably not complete. But nevertheless the broad outlines of some very powerful principles appear to be taking shape that allow us to produce better hypotheses leading to better experiments and yielding more useful information per unit time.

In this somewhat lengthy blog, I would like to provide a descriptive story of the levels of complexity of the brain from what is generally now accepted knowledge in neurobiology. However, interwoven within this story I will have to include my own speculations/observations that might help tie the various thread levels together. The reason I'm jumping into this fray is that I am not a neurobiologist with a reputation to protect, who studies a specific level of phenomenon from a reductionist perspective. I am, on the other hand, a dedicated scholar of all things brain. I have been deeply involved in tracking developments at all levels of brain complexity for many years. And I have no particular bias regarding any particular aspect of brain science save my intense interest in sapience. When I go on a speculative tangent, I will try to flag it as such so you, dear (patient) reader, will be able to judge the story accordingly.

Figure 1, below, shows a schematic diagram of brain organizational complexity so far as it is understood today. There are multiple different dimensions of complexity and here only three are shown. The first dimension of complexity in the figure could be called levels of organization, from the synapse through to functional neuronal clusters such as nuclei (globular-like clusters) and cortices (sheet-like clusters). These levels are dependent on those below as indicated in the concentric ovals. Synaptic complexity is, itself, dependent on chemical processing complexity at a lower level than shown. The second dimension could be called functional identity; various brain regions process different kinds of information even if the underlying mechanisms for doing so are the same as delineated in the first kind of complexity. Finally, the third dimension of complexity is indicated as the inter-module wiring organization or schema that produces the whole brain activity and the animal's behavior. All of this is shown as against another dimension, that of genetic and developmental complexity, which will not be covered in this blog.

 

 

Figure 1. A schematic representation of three dimensions of complexity in the functioning brain, against the background of genetic and developmental complexity.

 

Other dimensions of complexity, to some degree, map onto these dimensions rather naturally. For example the temporal dimension roughly maps onto the levels of complexity with the fastest time scale represented by events in the synaptic junctions (chemical reactions). However, it turns out that longer-term processes also impact the synapses as is the case for the development of long term memory traces (see below). Longer temporal scales are involved in neuronal activities, and longer still scales in circuit activities.

Another important dimension of complexity not addressed in the schematic is genetics and particularly the role of evolution and development on the shaping of the phenotypic form. To some degree these can be seen in the various functional modules, their sizes and relative processing powers being due to both genes (evolution) and development influences (e.g. epigenetic factors).

I will attempt to tie these into the story as they seem fitting. But this story will largely revolve around the diagram as a kind of backbone on which to hang the flesh, as it were.

There have been a spate of predictions from the artificial intelligence crowd over the years about when the computational processing power of computers would overtake the capacity of the brain (c.f. “Mind Children by Hans Moravec). Relying on the so-called Moore's Law phenomenon as an indicator of processing power, and a very overly simplistic estimate of neuronal processing power (in megaflops per second — a wholly unjustified comparison), these futurists estimated that we would soon be able to build computers that exceeded the human brain's abilities. Aside from the fact that they started with almost no real understanding of just what sorts of algorithms they might need to emulate to get that kind of ability, they seriously and grossly underestimated the informational complexity of just what is going on in the brain in the first place. If one were to hazard a guess at the processing capacity of the brain, they should start by looking at the complexity of just a synapse all by itself. Neural network modelers are infamous for trying to simplify the synapse down to a simple scalar variable (the synaptic weight) and a linear learning equation that would change the weight according to simplistic interpretations of Hebb's rule. In fact the complex chemical processing taking place at the synapse is extremely difficult to emulate in our very fastest computers. The best we can do is approximate some functional form of what it is doing to form and maintain memory traces. The real thing is wildly complex. Just ask any neurobiologist.

The Synapse

Synapses are the junctions between neurons. First note that there are actually two major categories of synapses, electrical and chemical. Actually both have chemical and electrical phenomena going on so that distinction is a little confusing. Chemical synapses probably ought to be called semi-discrete or pulsed synapses since they operate on the basis of pulses of electrochemical discharges of neurotransmitters. Electrical synapses might better be called continuous or gradient synapses since they communicate information more like an analog device. The problem with either of these descriptors is that they don't quite own up to what the phenomenon actually entails. For example, in the case of the pulsed synapse, the release of neurotransmitter chemicals into the synaptic gap leads to an analog build up. If the pulses come in rapid succession, there is something like a gradient response in the post-synaptic membrane. Nature, it seems, could not make up her mind in terms of the discrete signal vs. analog and so chose to create two versions of a mixture between the two. It could be that discrete-like pulsed signals are more reliable over long distance communications channels (the axons of the neurons - all of the electrical synapses are between near neighbor cells). But analog (continuously fluctuating) signals are needed for reliable storage and integration of state information.

Figure 2 shows a (still oversimplified) diagram of a typical chemical synapse. An axon or ‘cable’ from another neuron, or a sensory end organ, conducts a pulsed signal called an action potential. This is an electrochemical wave that travels down the axon (always away from the origin) and terminates in a synaptic bud. The latter contains packets of neurotransmitters of various kinds depending on the type of neurons involved. There are many different kinds of neurotransmitters and many more kinds of neuro-active molecules (also called neuromodulators). Some neurotransmitters stimulate the receiving neuron to fire, others inhibit it. Right here we find many sub-dimensions of complexity. The dynamics of post-synaptic membrane behaviors are highly variable and dependent first on the kinds of neurotransmitters and modulators found in the vicinity of the post-synaptic membrane.

 

Figure 2. The synapse is an incredibly complex, dynamic actor in the whole panoply of neural processing. Here chemical and electrical forces interact to produce cell to cell signaling with signal processing, such a noise filtering, and even noise introduction for some purposes. The synapse is the site of memory trace encoding in that the receptivity of the post-synaptic membrane increases under certain signal and associated conditions. See text for details.

 

Synapses can form just about anywhere on the receiving neuron's body, but in most instances form where an axon comes in contact with a dendritic spine (as in the figure). On the arrival of an action potential (1) the pre-synaptic boutton (bulge) releases the packet contents into the gap between pre- and post-synaptic membranes, where they rapidly diffuse across and couple with receptor sites on the post-synaptic membrane (2). These receptors are tied to protein channels that open (or close depending on which neurotransmitters) allowing the influx of sodium ions (3) into the interior of the post-synaptic compartment. This sudden influx of positive ions changes the electro-potential across the membrane which can cause various voltage-sensitive channels to open as well (4). There are actually a number of other ions that may cross the membrane in response to a signal, but the important aspect here is that the electro-potential is changed dramatically. This depolarization wave can travel down the dendritic spine toward the cell body (shown as an arrow from #3 to a wave form traveling toward the receiving neuron's main body). When I get to the neuron level of complexity I will further elucidate what happens then. For now it is only important to recognize this and certain chemical changes in the immediate post-synaptic compartment as real-time responses to the incoming action potential signal.

One major real-time effect is for calcium ions (Ca⁺⁺) to enter the post-synaptic compartment (5). This starts a series or cascade of chemical events that operate on a slower time scale (6) than the real-time events and depend on the accumulation of calcium ions over time. The ‘intensity’ of a pulsed neural signal is encoded in the frequency of action potential arrivals. The more frequently that they arrive at the synapse, the more calcium ions build up (and are slow to be removed) and thus stimulate several different second messenger effects (7 and 8). One effect (7) reinforces the sensitivity of the post-synaptic compartment as a form of short-term trace of the preceding action potential stream called short-term potentiation. This simply means that the synapse becomes more likely to fire a depolarization event even with weaker (less frequent) subsequent inputs until the cell manages to sequester or remove the accumulated calcium. Effectively the calcium buildup acts as a kind of leaky integrator or capacitor storing a short-term memory of recent events (see this Wikipedia article for more details). At the end of a long chain of second messenger events and in correlation with other chemical conditions brought on by either the activities of nearby synapses or by neuromodulators in the extra cellular matrix, chemical signals are sent to the protein construction mechanisms (called ribosomes - 9) and back to the cell body and to the nucleus (10). The former would appear to reinforce instructions to keep producing proteins needed to keep the channel concentration up to snuff. The latter is thought to be activating genes in the nucleus to increase the production of messenger RNA (somehow tagged to be delivered to the synaptic compartments that sent for it!) that will up the concentration of channels in the post-synaptic membrane (11, 12, and 13). These membranes undergo structural changes that are very long lasting and constitute longer-term strengthening of the memory trace. Synapses, so strengthened, are capable to generating huge depolarizations even with weak incoming signals, and even after long periods of quiescence.

Synaptic dynamics and the encoding of memory traces in synaptic strength in several different time domains is the basis for memory phenomena at the circuit level as I will discuss below. But as you can see, already we are dealing with immense complexity (chemical and temporal) and we haven't even considered what is going on in the rest of the neuron. We'll now consider a bit of that.

The Neuron

The first thing to understand is that there are many kinds of neurons in the brain (Figure 3). I can barely do justice to the variety and what is known of their different functions. Here I will just consider a single type of neuron, one that is common in the cortical structures discussed later. That is the pyramidal cell (see upper left corner of the figure below). This kind of neuron, of which there are sub-types depending on what cortical area one is looking at, appears to act as a major integrator of diverse convergent signals from both local and distant neurons.

 

Figure 3. Various neuron types found in the brain. Image from Consortium on Cognitive Science Instruction (image)

 

Almost all of these types of neurons derive from a base-type and they all have many features in common insofar as their functioning for receiving and sending signals. Figure 4 shows what we might call a typical arrangement for neurons in their role as communications devices.

 

Figure 4. Typical neurons receive input signals (action potentials) in the dendrites or on the cell body and send signals down the axon toward other neurons or end effectors (like muscles). A fatty sheath (myelin) insulates the axon much as a plastic coating insulates an electric wire but also helps to speed the signal in those axons that are so equipped. Not all axons have sheaths, however. Generally only long distance axons (from one brain region to another) are sheathed. The bundles of long-distance axons form what is known as the white matter in the brain. Neuron bodies form the grey matter. Image courtesy Tutorbene (image).

 

Neurons are said to ‘fire’ each time an action potential is sent down the axon. Whether or not a neuron fires depends on a complex integration of all of the incoming signals received in the dendrites and cell body synaptic junctions. At the root of the axon (see slightly darkened region of the cell body above where the axon emerges) a ‘threshold’ function determines if the sum excitation from the inputs should, at a given instance in time, generate an output action potential. If the inputs are weak, or out of synchrony, the neuron can fail to fire or fire very sparsely (low frequency). On the other hand, if the inputs are strong and closely aligned in the temporal domain, the neuron can fire vigorously (high frequency). Thus, the neuron is a relay, a filter, and an amplifier depending on the total input activity. Basically the dendritic processes and synapses provide a temporal integration over all incoming signals. The neuron's dendritic tree structure and its body membrane provide a spatial integration over all incoming signals. This is how it is possible for even a single neuron to act as a pattern recognizer.

The dynamics of neuronal firing is based on all of the previously mentioned complexities of synaptic dynamics plus those resulting from the complexities of the cell itself. Synapses that strengthen due to high levels of activity at critical moments when other inputs have excited a particular cell (or even just a particular branch of a dendrite!) produce a memory trace through the neuron. If those synapses have a tendency to excite the cell body they can produce an output signal in the cell whenever their source cells are excited. It is the case that a single neuron may participate in many different memory traces, acting as a convergence zone for different memory features that constitute a cause for activating whatever other cells the axon runs to.

Neurons participate in networks in which each neuron acts as a little integrating computer of sorts. Many neurons can innervate a single neuron, which, in turn, can activate many other neurons. The dendrites pull in signals from all over, both local (nearby) neurons and distant ones. The axons generally branch out and run to many other neurons, again, both local and distant. The possibilities are literally infinite, especially since we now know that neurons are forever forming new connections even while breaking off old unused ones. The brain is continually reforming as new memory traces are built from new experiences. At the same time it reinforces memory traces that have proved to be useful in living experience. But each kind of neuron is a complex processing unit in itself. We are just beginning to unravel some of the mysteries of various sub-functions that the different types of neurons perform, their dynamics, and their interactions with one another.

Interested readers may find more information about synaptic and neuronal processing in my paper on simulating these, “Toward a Theory of Learning and Representing Causal Inferences in Neural Networks”.

The Local Network

Neurons work together in complex networks to process spatio-temporal patterns of inputs (say from the senses), associate these patterns with context (other patterns) as well as with the internal state of the organism (drives, emotions, ideas, etc.) to produce meaningful output (e.g. behaviors). In the basic sense, meaningful output means producing behaviors that support the fitness of the beast in question. It turns out that many researchers have demonstrated how neural networks and their activities produce meaningful behavior in various animal models. While this is still preliminary there are many really convincing demonstrations of how neurons (including primitive brains), working together in specifically organized networks, produce fit behaviors.

One of the most primitive networks that evolved for the purposes of movement control is called a ‘central pattern generator’ (CPG). This is an arrangement of neurons that mutually excite and inhibit dynamically in such a way as to produce an undulating (not-quite sinusoidal, but cyclical) wave when innervating opposed muscle groups. I, along with my colleague Paul Fisher, explored this phenomenon when developing a search control strategy for our robot MAVRIC . Our published paper, “Foraging Search at the Edge of Chaos” provides a detailed description of simple CPGs as found in nature and the one we simulated (using the simulated neuronal and synaptic dynamics described above) to produce the ‘drunken sailor walk’ search pattern described in that paper. CPGs of various kinds are responsible for most of the kinds of dynamic muscle coordinations needed, for example, to generate varying gaits in running. The same circuit can respond to different input signals by changing average frequency and amplitude of the wave pattern output. This is an example of nature's phenomenal way to provide multiple functionality out of single components.

CPGs generally do not involve a great deal of learning. They are basically multi-phasic oscillators that switch modes depending on variations in input (through synapses from other circuits) signal. Those inputs, however, can be subject to long-term modification due to learning, i.e. memory traces encoded in the synaptic chain in other circuits. The above paper on Causal Inference also explains the basic neuronal unit of networks that learn associations between signals that are hard wired to convey semantic information (e.g. the smell of food) and signals that come from incidentally firing circuits. If the latter consistently fire a short time prior to the firing of the semantic signal, then a longer-term association is encoded between them, such that the incidental signal (called a conditioned stimulus in the psychology literature) may be sufficient, by itself, to cause the receiving neuron to fire, leading eventually to a motor response (the conditioned response).

Another wonderful example of a local circuit network that preprocesses sensory information is found in the retina of the eye. Here a variety of neuron types receive signals from the rods and cones (light detectors) and process information about intensity changes between neighbors (including the timing) such that they generate signals conveying information about direction of motion of objects in the visual field. Indeed the networks in the retina supply a fair amount of information that is then sent to the brain via many fewer axonal processes than one might have thought necessary. The eye produces something like primitive meaning extraction from the visual world that saves communications costs in getting signals to the brain.

Nuclei and Cortical Modules

There are two basic kinds of structures into which neural circuits are built. One is essentially a globular-like cluster called a nucleus. These nuclei are not just aggregates of undifferentiated cells or homogeneously distributed neurons. They have internal structure and may be comprised of many kinds of neurons. But they are generally more primitive in that they usually perform fairly specific functions, such as acting as distribution relays for incoming sensory data. Or they may process the data to extract meaningful spatio-temporal patterns, and if they find such patterns to signal the presence and location of objects in the environment that match those patterns. The patterns to be recognized are hard coded into the neural circuits with very little if any learning taking place. These nuclei simply respond to the presence of a specified pattern in the data stream from the sensors and activate or operate to select an evolutionarily determined response. For many of these nuclei the main output is to secrete neuromodulators that may have both a neural signaling and endocrine signaling purpose. The latter have various impacts on the physiology of the body. For example the perception of a threat may trigger a fight or flight response that includes both neuromuscular priming and prepping the body for elevated metabolism to support fighting or fleeing.

Figure 5 shows a schematic of this primitive brain architecture. This brain is designed to be a purely stimulus-response processor, having evolved from even more primitive neural networks in worms and such.

 

Figure 5. The earliest brains developed a consistent architecture with sensory processing feeding into several general pattern recognizing modules that were hard coded by evolution to ‘perceive’ the environment. This primitive central nervous system matched sensory data with pre-programmed reactions (affective action selection) that were then processed into motor programs for output response. Sensory inputs included body sensations that could modify the response under certain conditions. The motor programs sent signals down the spinal cord to the muscle pair groups that moved the limbs and tail (e.g. swimming fish).

 

Even though this kind of brain does not learn in the conventional sense, it still possesses short-term and even intermediate-term synaptic potentiation for encoding temporal traces in the circuits. It has a very primitive capacity for short-term memories so that responses that need to play out over a little longer period can do so. It would do no good to detect a threat, start to flee, and then quickly ‘forget’ the presence of danger. The beast would stop running as soon as the threat pattern was out of perception. Thus some form of memory is available in even these primitive brains, through the mechanisms described for synapses above.

Around 250 million years ago there was a major revolution in brain architecture with the development of cortical structures. These are multi-layered sheets of neurons of many different kinds that have small-scale local networks (near neighbors talking to one another) and some long-distance communications between small clusters located far away in the sheet.

 

Figure 6. In cortical structures one can find local clusters of tightly communicating neurons of different kinds. In this figure the red neurons send inhibitory feedback to the grey (e.g. pyramidal) cells and the blue (helper) cells. Pointed arrows are excitatory synapses and circle termini represent inhibitory synapses. Local clusters are wired to regulate the activity of the cluster. Pyramidal cells are most likely doing the bulk of long-term learning (engram encoding) whereas the other neurons are providing dynamic control, e.g. to prevent runaway excitation in closed loop feedback. Long distant communications is generally excitatory and allows the development of associations between clusters.

 

The main revolutionary development had to do with the cortices being organized in layers and small local clusters. The sheet arrangement enables the construction of elaborate maps, where clusters can act like positional locations of discrete percepts and concepts. Each cluster receives input from a different layer in the cortex that is receiving signals from a sensory or post-sensory processing module, such as a nucleus in the lower brain. Between the horizontal layers and the vertical clusters (e.g. cortical columns) cortical tissue appears to be well suited to encode more complex patterns than was possible in the more primitive brain. Moreover, the patterns can be learned from on-going experience rather than be hard coded into the tissues. This means that late reptiles, early mammals a birds, were able to learn important patterns in their lifetimes, meaning that they were more adaptable to changes in the environment that could never have been anticipated through evolution.

Cortices probably evolved from nuclei, some of which have layered architectures (like concentric layers in an onion). Figure 7 shows a schematic representation of an early cortical brain. Note that the old brain is still there, with some minor modifications, it is still doing its job. But now the newer cortex is added atop the old brain and receives neural as well as endocrinic (neuromodulator) signals from the older. The newer brain adds additional motor control (esp. learned behavior patterns) to the whole organism, which is relayed down through the older brain with its established interface to the musculature.

 

Figure 7. A major leap in the evolution of brains involved the development of cortical structures that could encode long-term memory traces of patterns and relations. The paleocortex evolved in later reptiles, birds, and early mammals. Cortices are sheets of cells in which local circuits (e.g. cortical columns) can encode object patterns (learned objects) and long-distant communications between these circuits provide ways to encode relations between objects. The cortical structures provided the first real environment learning capabilities.

 

The new brain is comprised of the old brain and a new cortical structure (called the paleocortex since it was evolved before the newer neocortex). The old brain still does its jobs and influences the newer cortex in doing its job. Initially this is just learning patterns and associations that help modulate the older affective pattern-response matching. To some degree the newer abilities had to regulate or modulate the older capacities in order to produce more nuanced responses to patterns that the old brain might have misclassified. But the newer brain did not yet have the more elaborate model building capabilities that would come with the evolution of the neocortex in latter mammals.

The Neocortical Brain, the Prefrontal Cortex and Executive Control

The final major revolution in brain architecture comes with the advent of a yet newer layer of cortical tissue called the neocortex. Like its predecessor it is comprised of additional layers and is internally organized to represent many more perceptual and/or conceptual details. It is also capable of forming transitory connections between clusters (now representing wholly formed concepts) to do ‘what-if’ analysis. That is, it can form hypothetical connections between dynamically represented concepts just to see what happens. This is the origin of creativity and invention. It is also the basis for the development of rational decision processing. Maps of situation concepts, decision nodes, and learned outcomes from experience can now be represented and used to guide future decisions under similar sets of conditions. This applies to learned decision paths (e.g. in expertise) and possible decision paths (models).

In order for this new capability to work, however, it needed a much more elaborate form of executive control over the forming and testing of new circuits. The motor system (or rather the premotor cortex that had evolved to do a primitive form of planning for multiple behavioral options that could be chosen as needed in highly volatile environments) gave rise to the prefrontal cortical regions in the primate brains. There we see a final convergence zone for every kind of neural signal from the most primitive parts of the brain as well as the later evolved parts. This region is responsible for thinking, but also for melding affective signals and sapient signals (judgment) with on-going rational decision processing. The latter is probably under the direct control of the premotor area (dorso-frontal area) and acts not too differently from a computer churning away through a graph search algorithm (though the analogy is not really that good - effectively the brain progressively activates concentric rings of possible clusters representing decision points. Those clusters that have strong damping either from the limbic system or from the sapience system are pruned from the decision tree. Those that have strong positive (excitation) from either system will be preferred such that the nodes they are subsequently connected to will be activated. Mutual cross inhibition of nodes further reduces the number of parallel paths as decision options are eliminated. Thinking through a problem requires both conscious directional control (such as always checking the direction against a priori desired results) and subconscious affective/judgment steering control.

 

Figure 8. The neocortex adds another level of complexity to the established paleocortical brain. The neocortex can encode much more detail and many more patterns than could the paleocortex. Moreover, the neocortex allowed the development of circuits that could be used to build models of the dynamics of the world as learned through experience. Much of this is encoded tacitly and can be brought to recall/usage by special executive control circuits evolved in the prefrontal cortex.

 

As fantastical as all of this sounds it actually represents a straightforward development of hierarchical levels of complexity based on the lower levels described above. The capacity to rapidly and tentatively form excitatory connections between cell clusters ultimately depends on the same factors that control neural wiring development (say in early embryonic development) and the dynamics of synapses able to locally modify their efficacy and maintain it for long periods of time relative to the life of the animal. These are inherent in the most primitive animal neurons. It is just minor modifications here and there in the genetic program that produces neurons and circuit forms that have been successfully selected throughout the course of animal evolution that result in the high degree of complexity we see in the human brain today.

The last step in hominid evolution involved the enlargement and capabilities of the prefrontal patch of tissue known as Brodmann area 10 (BA10). It is the patch right behind the eyebrows. It appears to have made a rapid expansion about the same time that we think humans developed language and learned to control fire. It is known to be involved in many judgment functions as well as social interactions. It is also known to have a more advanced form of communication with other parts of the brain through Von Economo (spindle) cells. Finally it is highly connected (recurrently) with every other part of the prefrontal cortex, suggesting that it is truly an ultimate convergence zone for control signals.

I posit BA10 is the seat of both consciousness (self-, other-, and of awareness) and sapience. Returning to Spock from Star Trek, it would be as if a being with only a cortical brain were possible. But this isn't the case. Our brains (and here I refer to animals in general) first evolved to monitor our bodies and our environment, to match the needs of the body and its safety with the situations found in the environment. That monitoring and control function still exists and is still carried out by the limbic and lower brain areas. But what we do know about the development of sapience is that it has high speed connections back to the limbic system that help to regulate or dampen the limbic responses. Perhaps in most humans these connections or the control competencies of BA10 are so weak that the limbic system still has undue sway over our actions. But there is some control. Rather than someone like a pure Vulcan with no limbic urges at all, perhaps the model is of a level of sapience that learns to be exceptional in down modulating emotions so that judgment can provide the better influence over intelligence and creativity. Such a capability would necessarily involve much longer range and longer time scale thinking since it would not be motivated by immediate gratification needs associated with limbic-driven decisions.

As with all but a few (r)evolutionary developments in the brain the changes have been based on simple modifications at one scale of complexity that gave rise to new competencies and even new scales of complexity. I suspect we will find the evolution of BA10 was based on some pretty minor tweaking at the genetic level dealing with the developmental control over the prefrontal cortex (you know the old story about how little difference there is between chimpanzee DNA and our own!). But because it was built on an incredible hierarchy of complexity the resulting ubercomplexification was nothing short of spectacular. I believe very strongly that with a little help, or the right evolutionary circumstances, there is more to achieve.