Category: Philosophy

I dwell in Possibility– – Emily Dickinson

So What?

Why learn about the world with no immediate practical application?

I’ve said that there is value in writing to learn and photographing to see. What’s the value of learning? Of seeing?

Knowing how to bake bread or bake beans is clearly useful and there’s no question of practicality. Science, even at its most exploratory, seems useful as long as it promises more powerful manipulation of nature. Sometimes the possibility of science is obvious, as in understanding the role of an enzyme in energy metabolism to affect cellular function. Even when the connection is unclear, learning more seems to have potential value even if the present result is impractical. All information has value and there are no dead ends, only detours. Learning broadly is often necessary preparation for learning more narrowly and usefully

Is philosophy of mind of any use? Is it as useful as neuroscience itself? Might thinking about the nature of the mind at least contribute to the usefulness of information about brain structure and function? Why explore the relationship between mind and brain? Why worry about the apparent contradictions between deterministic physical models and subjective free will?

If I can’t tell whether the world is real or an illusion, does it matter? Is the mind made of ectoplasm attached to the brain by a neural-spiritual interface? These questions have been around for centuries. Every year we learn more about the brain. Do we know any more about the mind? Is what we’ve learned potentially useful?

I’d like to convince you that understanding how mind is generated from brain is a useful way to improve brain function. For me, this is a fundamental reason why brain science is important. Learning about the brain should be a path to deciding better.

Learning From Experience, Teaching the Brain

In the *Consolations of Philosophy“, Alain DeBotton writes, ”In their different ways, art and philosophy help us, in Schopenhauer’s words, to turn pain into knowledge." We know what art is and we know that art helps us learn to see. Philosophy, in the broadest meaning of love of knowledge is a similar direct path from experience to knowledge.

Ignoring the question and pretending that knowledge and brain are independent domains is to miss an opportunity to understand what it means to “know” and therefore try to improve the everyday use of knowledge.

So what have you learned personally from your years of mental experience? TYou’ve made good, profitable decisions about the world. You’ve made mistakes of course. Better yet, how often have you thought that you were right, absolutely sure you were right, and later learned that the true state of the world was not at all what you thought?

The stock market serves as a wonderful lab for training the mind to decide better if approached mindfully. There’s profit in correctly identifying an undervalued stock that subsequently rises in value. On the other hand, buying into hype and choosing a company close to failure is exactly the kind of pain that Schopenhauer was referring to as leading to knowledge. What is it that has improved from years of experience in the market that we call “knowledge”? Is it mind that has better judgement now? Is it the brain that can now choose more accurately under conditions of uncertainty?

The Brain Makes Maps

We are beginning, just beginning I think, to understand how knowledge is stored and retrieved in the brain. The insights go back to the beginnings of brain physiology, when recordings of single neurons in awake, behaving animals began to be possible. It was obvious from the start that you and I don’t perceive the world directly and whole, but broken down into very small elements. Our retinas are the light sensing neural arrays at the back of the eye. Like the individual pixels that make up the sensor array in a camera, each photoreceptor senses the light from a small part of the visual scene. The whole picture is represented, but its been deconstructed into a mosaic in which each element has been disconnected from every other element.

Somehow that array of light intensity is reconstructed into a sensory impression that we experience subjectively as seeing the world. What’s reconstructed is more than just a visual sensory impression, the seen world has meaning. Its as if there are little call outs from the objects- blue book, time, moving fly making that buzzing noise, so annoying …

The way in which sensory input is organized into coherent perception remains one of the fundamental questions in neuroscience. In the visual system, the brain starts abstracting local features like color form and edges from the map of intensities sensed at the eye. These features are mapped from visual space into brain maps, creating a neural representation of features in the scene. At higher levels, features become maps of objects with meaning like books and flies. The maps of words for these objects are separate, but can be called on when the fly or the book needs to be mentioned.

The brain is a set of maps, spatially orgainized, each representing different sensory streams or, on the action side, control of different parts of the bodies and movement of them through space. To catch a fly requires the map of visual space containing the fly to be registered with the map of arm and hand movement. The connections and coordinating systems to do all of that are known. In fact, simpler versions of them can be studied in frogs who need project a sticky tongue out into visual space for fly catching activities. Lunch in this case.

It’s a small conceptual step to suggest that valuation of stock, reading another person’s motivation, and understanding calculus are all brain maps of various types. They are simplified representations that model aspects of the real world. The maps are not strictly spatial, but reflect our models of how the physical world is laid out and how it can be manipulated. As simple models, they are not perfectly accurate just as a geographical map is not the terrain itself but rather a useful representation for navigation.

The Mind Mapped

Learning is the act of making better brain maps. The more accurate the model of the world is in the brain, the better it will navigate the world itself. Misconceptions, inaccuracies and the unknown are all bad or missing parts of the map that will make decisions more prone to error. A fully accurate and comprehensive map isn’t ever possible. By their very nature, maps are restricted representations of the world. The world itself is too big and complex to deal with directly.

The exploration of the relationship between mind and brain is for me an effort to create a more accurate map of deciding. We feel like we are creatures split in two. Our ethereal minds seem to inhabit and be constrained by physical bodies. A more accurate map would show just the brain working away and our subjective mental experience as a view into what that brain is doing. It becomes easier to discard distinctions like “rational decision making” and “intuition” when the underlying brain structure and function is the map of mind.

I got used to things going pretty well, going well enough. It was a matter of standing on a steady stone, moving to one higher or broader when the opportunity presented.

I told myself to appreciate them as platforms, thinking of these situations as being good places to be. I learned at the beginning that not to decide is to decide and not to do is to do.

I’ve made some observations that may have some value, but mostly I’ve read the words of others. Let’s see if I’ve gotten it right.

“You’re lost inside your houses
And there’s no time to find you now
Well your walls are turning
Your towers are burning
Gonna leave you here
And try to get down to the sea somehow”

Rock Me On the Water
-Jackson Browne

“Truth is what works.”

-William James

On my daily run today, Jackson Browne’s Rock Me On the Water came up on the Genius Playlist. I connected emotionally with the song as I almost always do, feeling that yearning for the transcendent truth that brings joy, identifying with the seeker on his journey to understanding, seeking the peace that lies beyond the mundane world.

William James was attacked during his lifetime and in subsequent decades by philosophers who felt he was destroying the search for truth by making it completely relative. For him, truth was a construct of the mind based on theories and mental models. Truth is a quality of thought based on how well what we think resembles the external world. He called this Pragmatism. We can approximate an accurate view of the world, but never reach ultimate truth.

The materialist philosophers who attacked him believed there’s a transcendent, absolute truth in the world that can discovered through observation and experimentation. How could it be, they asked, that one person’s truth could differ from someone else’s? How could I see one truth today and a different one tomorrow? Truth must be an absolute quality of statements. What is not True is False. The world of philosophy moved toward logic and proof and away from James’ view of world as a construct viewed by mind via brain processes.

I began to appreciate James’ views after reading contemporary cognitive science and philosophers of mind. Once one begins understanding that our brains function by creating models of the external world, this psychological definition of truth becomes most relevant. We build a mental model of the three diminutional space around us. We hear music and make sense of tone spatially, thinking of notes as being high or low, moving quickly or slowly. We think of crime epidemics as infectious disease or honesty as clean behavior in metaphorical models where one model provides meaning to a different model.

Where does that leave Jackson Browne’s romantic search for (capital T) Truth? Is there any reason to “get down to the sea”? Is it entirely an illusion to seek a non-scientific transcendent truth?

I submit that the poet is talking metaphorically about looking beyond the commonplace mental models that we use when “we’re lost inside our houses”. It is the mission of the poet to find the sea and come back and tell us about the journey and what it’s like to experience that joyous song.

Certainly the purpose of the spiritual search for truth is personal gain and fulfillment. Enlightenment is the clearest, most “right” view of the world possible. Reaching full human potential is a personal goal. One becomes a poet by returning from the sea and singing the song, inspiring others to join the joyous song.

Being particularly lucky or unlucky is sure to interfere with good decision making. It’s hard to tell whether you’re succeeding because of a confluence of favorable effects due to chance or due to your exceptional brilliance. One’s internal model of self probably plays a role in how events are interpreted. Are you special or just really, really lucky?

If success came through chance, the model predicts that the future is going to look more average, for good or bad. It may or may not result in less risk taking. I can take risk knowing that the outcome is largely not in my control. I may win or lose; it’s knowing the odds of success that’s important.

On the other hand, if you’ve climbed to the top based on your merit then the internal model predicts continued success beyond the average, never regressing to the mean.

Case in point? Barack Obama. From Andrew Gelman writing at Frum Forum

More to the point, I don’t think that in January, 2009, Obama had any feeling he was in trouble.  For one thing, he’d spent the previous two years beating the odds and winning the presidency.  (Yes, a Democrat was favored in the general election, but Obama was only one of several Democrats running.)  As I and others have discussed many times, successful politicians have beaten the odds and so it is natural for them to be overconfident about future success.

Losing the 2010 midterms may have been a wakeup call, but then again it’s easy to construct a narrative where we claim responsibility for good outcomes and blame chance or other outside causes for the bad outcomes.

Purely from a statistical point of view, expect success to be followed by failure more often than not. In the end, we’re all average.

The whole is greater than the sum of the parts.

This is the essence of a complex adaptive system. Any system that is straightforward enough to be a simple adding up of the effects of each part really isn’t worth contemplating as a system. It’s a collection of independent agents. A stack of checkers of different thicknesses are such a linear system. Stack them up and the height simply adds up in a linear way.

Once the components start acting on each other and themselves, behavior becomes complex and increasingly difficult to predict based on knowledge of the components and their connections. This is not due to ignorance. Collection of more and more data doesn’t help at all. There is some aspect of the whole that is not just the linear addition of the parts.

Once a system is made of connected components that have inputs and outputs, that are processing information, its behavior can become difficult to predict with precision. It could be a mechanical system like a thermostat connected to a heating system, a computer program with subroutines, nerve cells connected in brain circuits, stock traders in a market, the atmosphere are all complex adaptive systems. The mechanical and computer level examples are the most useful for study because they are clearly in the mechanistic, Newtonian, deterministic world and yet their future state cannot be known.

The difference between an additive system and a complex system is in the relationships. Negative and positive feedback create unexpected behaviors in the system. Small effects in one component produce large effects elsewhere because of the nature of the connections which are not simply proportional but non-linear instead.

We’re surrounded by complex systems.Arguably simple linear systems are exceptions and may be idealized simple models rather than real functioning systems out in the world. As thinkers, we study simple systems or simplify the complex into idealized simple systems because they are easy to deal with in a deterministic and reductionistic manner.

We are ignorant of the state of the past and the future. That creates uncertainty. Because of complexity, even if we had perfect knowledge, we’d still be unable to know the future.

In 1961, Edward Lorenz discovered chaos in the clockwork universe.

Lorenz was running a computer simulation of the atmosphere to help forecast the weather. He wanted to rerun just part of one sequence, so instead of starting at the beginning, he started the run in the middle. In order to start in the middle, he used the output of the program at its midpoint as a new starting point, expecting to get the same result in half the time.

Unexpectedly, even though he was using a computer, following strict deterministic rules, the second run started with the same values as before but produced a completely different result. It was as if uncertainty and variability had some how crept into the orderly, deterministic world of his computer program.

As it turned out, the numbers used from the middle of the run were not quite the same as the ones used when the program was at that same point the first time because of rounding or truncation errors. The resulting theory, Chaos Theory, described how for certain kinds of computer programs, small changes in initial conditions could result in large changes later on. These systems change over time where each condition leads to the next. This dependence on initial conditions has been immortalized as “the butterfly effect” where a small change in initial conditions- the wind from a butterfly’s wings in China, can have a large effect later on- rain in New York.

This sensitivity to the exact values of parameters in the present makes it very hard to know future values in the future. As its been formalized mathematically, chaos theory applies to “dynamical system” which simply is a system that changes over time according to some rule. The system starts in some initial state at the beginning. For our purposes, think of it as now, time zero. Rules are applied and the system changes to its new state- wind is blowing, temperature is changing, etc based on rules applied to the initial state of the atmosphere. The rules are applied to the new state to produce the next state, etc.

Chaos may not have been the best word to describe this principle though. To me it suggests complete unpredictability. Most real or mathematically interesting dynamical systems don’t blow up like that into complete unpredictability. Using the weather, for example, even if the butterfly or small differences in ocean surface temperature makes it impossible to know whether the temperature in TImes Square in New York will be 34 degrees or 37 degrees on February 7th, either one is a likely value to be found in the system at that time in that place. Measuring a temperature of 95 degrees F in New York in February is impossible or nearly so.

Dynamical systems like the weather often show recurrent behavior, returning to similar but non-identical states over and over as the rules are applied. Following the values over time describes a path that wanders over values, returning after some time to the same neighborhood. Not exactly the same place because it started in a slightly different place than the last time around, but in the same neighborhood. Just unpredictably in the same neighborhood.

This returns us to the distinction between knowing the future and predicting it. The future state of chaotic system can be known because small changes in initial conditions result in large changes in result. But those large changes recur in a predictable range of values. A chaotic system can be predicted even though its future state can’t be known. When it comes to the TImes Square temperature, climate data tells us what range the chaotic values move within from season’s cycle to season’s cycle. In drug development, the chaotic system of taking a pill every day and a measuring drug levels in the blood allows prediction as the range of likely values, but because initial conditions change and cause large, unpredictable effects one can’t know in advance whether today’s measure will be high or low. Its almost never the average, it varies around the average.

It’s important to see how important prediction is for making decisions when the future is unknown. Because the uncertain future is orderly we actually know a lot about it; we don’t know it in all of its particulars. We must make decisions knowing what range of possibilities the future can assume. Chaos Theory suggests that this kind of uncertainty is in the very nature of the world because of the behavior of dynamical systems, any rules dictate how a system changes over time.

I’m indebted to Glen Alleman for pointing out that John Maynard Keynes wrote a book on probability, A Treatise on Probability which starts with the Bayesian view of probability as belief and moves on to explain how Frequentist concepts fit into the Bayesian world view.

Herding Cats: Books for Project Managers: “Each paragraph in the book provides insight like this. Two paragraphs later is the core of the current ‘black swan’ of probabilistic management. There is a distinction between part of our rational belief which is certain and that part which is only probable. The key here is there are degrees of rational belief and if we fail to understand, and more  important, fail to ‘plan’ in the presence of these degrees, then were are taking on more risk and not knowing it. This is a core issue in the financial crisis and managing projects in the presence of uncertainty. “

This relationship between belief and probability is an important basis for decision making, forming the bedrock of what I see as the American philosophy of Pragmatism, Its a bottom up point of view that is rooted in experience and practicality. William James, who codified this point of view as “Pragmatism” famously said, “Truth is what works”.

Exploring a bit of this Keynes book already and knowing that Keynes was influenced by his Cambridge associations with G.E. Moore who similarly took this bottom up, individual belief based approach most famously in Principia Ethica. So perhaps this Cambridge-Bloomsbury connection makes this really Anglo-American philosophy.

There was a time when our search for truth as a culture led us into periods of severe doubt and Continental philosophies like Existentialism and Deconstructionism. These were times of great shifts in values and cultural upheaval. Arguments from first principles were swept aside by feelings of being without roots in a world without intrinsic meaning.

For James, Moore and Keynes, there’s a grounding in the pragmatic idea that there is a real world out there that we can know and predict however imperfectly. Decisions based on our beliefs have consequences so we had better work on refining those beliefs and improve our decision making.

Perhaps we’re ready for a return to a more practical Anglo-American philosophy based on experience, culture, belief and the scientific approach to finding meaning in the world. At least I know I am.

Steven Strogatz has been one of the leading figures in the mathematics of biological systems. While synchronization of independent elements is the thread that brings his book  Sync together, it’s all in the context of the new systems view of biology.

His process is to frame a question about complex systems and then look for answers by running computer simulations of the process. When order emerges in the simulation, he and colleagues try to discern the mathematics underlying the order. You see these connections are so complicated that one can’t predict their behavior by inspection and reason. In general, its easier to recreate aspects of them in a simple model in order to understand how they behave.

This is a very basic demonstration of emergent behavior of a system. The behavior of the larger system can’t be predicted by understanding the behavior of the components and their interconnections. Even more interesting is how small changes to the indivual units or their connection strength can radically change the emergent bahavior of the system. Once you have a simple working model, deeper understanding of the possible states of the system can be gained by looking at behavior over a wide range of assumptions and conditions. Here Strogatz is interested in how synchronous activity emerges in networks.

These simplified systems aren’t real, but are useful tools. Just as a map is not the terrain, a system model is not the system itself. The model is useful only if it predicts the behavior of the real system. Just as a map is only useful if it can predict the proper route through the landscape. This is the iterative nature of science and a reflection of William James’ Pragmatism. Truth is what works.

As a scientist, I gained a bit of insight into why it’s easy to manipulate the state of some biological systems. I spent many years in the lab studying mechanisms of cell death. I could never understand why so many investigators were able to find so many different ways to halt the process once it had been set in motion by an experimental perturbation. Surely all of these processes couldn’t be independently responsible for the cell death? If they were independent, then blocking just one wouldn’t help cells survive. Other, unaffected processes would carry out the deed.

The many interactions within a cell place some events at nodes that have broader effects. The other day, an accident on a highway here in Baltimore managed to tie up much of the traffic north of the city. There were cascading events as traffic was shunted first here and then there by blockage and congestion in one key pathway after another. Similarly, cell processes or cell state can be shifted from one state to another by strategic triggers.

Tools like network maps and simulations promise to provide a means for understanding complexity that won’t yield to simple cause and effect diagrams. Strogatz ends the book with some contemplation of how consciousness arises from the network of neural connections. It may be that syncronization across the cerebral cortex is responsible for the binding of shape and color of visual objects or the binding of object and word.

Of course, its this idea of mind and meaning as the emergent effect of complex systems that has interested me for some time now. As a neurobiologist, calling meaning an emergent quality of brain is a neat way to bridge the material with the immaterial worlds.

How do you abandon the illusion of control and embrace uncertainty?

We could be less anxious and less stressed if we gave up trying to control the uncertain future. Living in the now, choosing actions that increase the chances of a favorable state of affairs can be challenging and stimulating.

The uncertainty we live with every moment is hard to grasp. In fact, we tend to simplify the world down to something less complex and more manageable. But the simplicity and gain in control are illusions. So we stress about what will really happen.

Uncertainty is lack of predictability. Predictable situations are comfortable because we know what’s almost certainly going to happen. Surprises are just that- unexpected and unanticipated.

We know we can’t predict the future in part because  our knowledge of the world is generally incomplete. Since we don’t know what’s around the corner or what other people are thinking, that part of the world can’t be predicted.

Even with perfect knowledge, the world itself would remain unpredictable. The result of any particular choice is uncertain because it can result in a wide range of outcomes. We never know whether we made the right decision even in retrospect because we only know what happened after that decision was made. Speculating about what a different decision would have brought about is not very useful, because again the outcome can only be guessed at and never known. There are two many complex interactions and unintended consequences. Once the choice is made, the result is largely out of our control.

Interestingly, giving up the illusion of control is a core belief in religious and spiritual movements. Recognizing a greater power like God or Fate that controls the world is a great aid in abandoning illusions of determining outcomes. I don’t think spirituality is necessary to adopt an attitude of embracing uncertainty, uncertainty arises naturally in the world. But the attitude of humility gained by recognizing a power greater than ourselves seems to help in this embrace of uncertainty. Adopting this mental stance in making decisions provides reward in the  releases from the fear of unanticipated outcomes. It also provides a space for God or Fate to act even in a seeming clockwork world of cause and effect.

After all, the only truth certain is that the world is uncertain and truth can’t be fully known.