Category Archives: Science

Paranormal Investigations

When I was a kid my friends and I did lots of camping. We’d sit around the campfire late into the night, talking. Without fail, my friend John would capture our interest with some really engaging story. It would go on and on, getting wilder and wilder until we’d all eventually realize we’d been had. He was just messing with us again, having fun seeing just how gullible we could be. And somehow we all fell for it at least once on every trip.

In the 1970’s author and anthropology student Carlos Castaneda wrote a series of books detailing his tutelage under the a mystic Yaqui Indian shaman named don Juan Matus. The first books were fascinating and compelling. But as the books progressed, they became increasingly more fantastic. Eventually these supposedly true accounts escalated into complete and utter fantasy. Despite this, or because of it, hundreds of thousands of people reportedly made trips to into the desert in hopes of finding this fictional don Juan Matus. In fact, Castaneda was awarded a doctoral degree based on this obviously fictional writing.

Castaneda never admitted that his stories were made-up. We once had “mentalist” Yuri Geller who refused to admit that his fork-bending trick was only just a trick. We have long had horror films that purport to be “based on actual events.” These sort of claims were once only amusing. But now these kind of paranormal con jobs have escalated, like one of John’s campfire stories, to a ridiculous and frankly embarrassing and even dangerous level in our society. This kind of storytelling has become normalized in the prolific genre of “paranormal investigations” reality television shows.

We need to say – enough already.

Sadly, we see dozens of these shows on networks that call themselves “Discovery” or “Learning” or “History” or (most gallingly) “Science.” There are hundreds of shows and series on YouTube and elsewhere that purport to investigate the paranormal. These shows do us no service. In fact they are highly corrosive to our intellectual fabric, both individually and socially.

They all follow the same basic formula. They find some “unexplained” situation. They bring in experts to legitimize their investigations. They interview people about how they feel apprehensive or fearful about whatever it is. They spend a lot of time setting up “scientific” equipment and flashing shots of needles on gauges jumping around. They speculate about a wide range of possible explanations, most of them implausibly fantastic. They use a lot of suggestive language, horror-film style cinematography, and cuts to scary produced clips. And they end up determining that while they can’t say anything for sure but they can say that there is indeed something very mysterious going on.

These shows do tremendous harm. They legitimize the paranormal and trivialize real science. They turn the tools and trappings of science into cheap carnival show props.

Some of these shows are better than others. They do conclude that the flicker on a video is merely a reflection. But in the process, in order to produce an engaging show, they entertain all sorts of crazy nonsense as legitimately plausible explanations. In doing so, they suggest that while it may not have been the cause in this particular case, aliens or ghosts might be legitimately be considered as possible causes in other cases. By entertaining those possibilities as legitimate, they legitimize crazy ideas.

There would be a way to do this responsibly. These shows could investigate unexplained reports and dispense with all the paranormal theatrics and refuse to even consider paranormal explanations. They could provide actual explanations rather than merely open the door to paranormal ones.

MythBusters proved that a show that sticks to reality can be entertaining.

I am not sure what is worse, that this is the quality of diet that we are fed, or that we as a society lap it up and find it so addictively delicious.

A Healthy Model of Equality

Thomas Jefferson prominently enshrined the phrase “all men are created equal” in our Declaration of Independence. This phrase has ever since embodied perhaps the single most important and enduring foundation of the American experiment (see here).

Certainly all people of good-will respect and value this “immortal declaration.” And certainly no one limits their interpretation to the literal meaning of the phrase. For if children quickly and demonstrably became unequal, the idea of equality at creation would lose any practical or useful meaning whatsoever. So we generally accept that “created equal” also implies that we remain equal throughout our lives, independent of what we do or do not accomplish in life.

But this must be much more than a mere rhetorical or theoretical equality. It must extend far beyond a mere begrudging recognition that all people have the right to basic human rights and dignity. It must be a practical working belief that operates at the real functional interpersonal level which allows us to work together in this human project as equal partners.

Indeed, without a sincere and unqualified recognition of the equality of all individuals, our social fabric cannot endure. It is not possible to have a fair and just society if we feel, even deep down, that some are deserving and others are not; that some are superior merely by virtue of their social status or race or gender or even by their level of accomplishment in life. To allow for such fundamental bases of inequality is to travel down the road toward slavery and subjugation and exploitation and ultimately into the abyss of social disfunction.

Yet, moving beyond a mere allowance of certain inalienable rights to a true respect for each individuals capabilities and worth is not easy. In fact that is a huge understatement. For in our everyday life in every social interaction we see that people are simply not equal. It is laughably obvious that in fact we are not equal by wide margins. Some folks are brilliant, others stupid. Some sane, others insane. Some gifted, others inept. Some strong, others puny. Some have lived honorable lives, others lives of ignobility.

The truth is, we cannot help but observe glaringly wide disparities on any measure of worth you care to assess.

So how can we truly hold the ideal of equality alongside the reality of inequality harmoniously in our minds? How can we sincerely believe in equality without lying to ourselves about the reality? And how can we acknowledge the reality without lying to ourselves about our belief in the ideal?

This requires some rationalization. Rationalization is not a bad thing. We all have to find some coherent model for reconciling contradictory ideas. Therefore, we all must find some kind of understanding that allows a recognition of equality to thrive, fully and harmoniously in our individual brains and in our collective psyche, alongside the reality of inequality.

You may already have your own rationalization that works well for you. But here’s how I rationalize it. It’s not perfect, but no model can be. It has long worked pretty well for me.

  1. Excluding physical or chemical debilitation, a human’s total capacity to think is neurologically dependent upon their physical brain capacity.
  2. All human brains are the same size, or close enough as the differences do not matter. Therefore our total brain “power” is essentially the same and all of it is used in some manner.
  3. Brains exhibit a wide spectrum of capabilities. Think of it as an impracticably wide bar chart. Each bar is a narrow trait, like perhaps “math,” or “kindness,” or “neuromuscular control,” but much finer grained than those.
  4. Everyone’s bar chart is a unique. It is a signature of who they are. Everyone has some high bars and some low bars. But the total area under the bars adds up to the same total power.
  5. Some bars are particularly valued by society at any given time, some are measured on an SAT exam and some are not. Some make you a business tycoon, some a starving artist. But although some signatures may be seen as more important to society, or lead to greater success, all are equal and all are valuable to society.

So, in my rationalization all people are truly equal. True, some may be less appreciated or less helpful in a given situation, but all are nevertheless worthy of true respect in my mind for their unique strengths. There is no contradiction whatsoever with the observed differences between individuals. Aspiration and reality are fully reconciled.

This model has helped me to reconcile equality with differences. It has in fact helped me appreciate equality by virtue of our differences. It has helped me to feel proud of my own personal strengths while simultaneously humble about my weaknesses and while still being as worthy and as flawed as anyone overall. It has helped me recognize that being smart or skilled in one area does not make anyone particularly smart or skilled in another. That has helped me apply a healthy level of skepticism to opinions put forth by “smart” people in areas outside their proven expertise and to allow that otherwise uninformed people can offer valuable insights in others. It has helped me understand that traits like “smart” or “sane” are not simple binaries but complex and nuanced and somewhat arbitrary. We are all smart in some things and delusional others (see here). It has also helped me to value undervalued traits and to recognize that disrespecting people for one very low bar of their chart does not mean you disrespect them in totality and that respect overall does not require you to respect every trait.

And further, we should value the undervalued signatures in our society more than we do. It is our failure and our loss if we do not identify and utilize whatever unique strengths each individual has. There are no useless skillsets, only underutilized and underappreciated skillsets.

I think these rationalizations have led me in a healthy direction. Maybe this model will help you come to a more healthy and helpful view of equality as well.

The Impending Doom of Written Language

Sci Fi and Fantasy are often lumped together, but they are very distinct literary forms. The core difference is not simply whether the subject matter is dragons or space ships, but whether the subject matter is plausible or not. Whether it could become reality. Dragons could be Sci Fi if originating in a plausible manner and if they adhere to the laws of chemistry and physics. Conversely, a space ship becomes fantasy if it jumps through time and performs “science” feats what would consume fantastically implausible amounts of energy. Lots of Sci Fi fans are actually consumers of fantasy every bit as unrealistic as Lord of the Rings.

Really good Sci Fi is not merely plausible, but likely, even predictive. Great Sci Fi is unavoidable, or more aptly inescapable, given our current trajectory.

But even mind-boggling Sci Fi can often reflect a disappointing lack of imagination.

Take for example the obligatory transparent computer screen that we see in every Sci Fi show. Or even the bigger budget full-on 3-D holographic computer interfaces that provide eye-candy in every major feature nowadays. These look cool, but are probably pretty unimaginative. Plausible and likely, but crude interim technologies at best.

Take for example my own short Sci Fi story Glitch Death (see here). In it, I envision a future in which direct brain interfaces allow people to use computers to “replace” the reality around them with perceptual themes. In that future, we skip quickly past archaic holographic technology and beam our perceptions directly into the brain.

But even that only touches the surface. For example, why would a future direct-to-brain technology be limited to flashing words across our visual field and allowing us to hit “virtual buttons” floating in mid-air? To explain my thoughts on this, let’s digress and talk about math for a moment.

Today we have entered a time where math hardly matters anymore. Oh yes, we must of course understand the concepts of math. We must understand addition, division, and even the concepts of integrals and derivatives and more complex algorithms. But we don’t need to learn or know how to compute them. Not really. We have computers to handle the actual manipulative mechanics of numbers. Most of us don’t really need to learn the mechanics of math anymore, even if we use it everyday.

We are already well on the way there with language as well. We have devices that “fix” all of our spelling and formatting automatically. We don’t actually have to produce typographically correct written text. All we need to do is to communicate the words sufficiently for a computer to understand, interpret, correct, and standardize. We are at the verge of being able, like math, to simply communicate concepts, but not worry about the mechanics of language construction and composition.

So, back to my Sci Fi vision of the future of direct-to-brain interfaces and their likely ramifications. Interfaces like the one envisioned in Glitch Death would soon make written language, and perhaps much of verbal language, prohibitively cumbersome and obsolete. Why shoot words across our visual field, forcing us to read, comprehend, process, and assimilate? Why indeed when the computer could instead stimulate the underlying processed and interpreted symbols directly at their ultimate target destination in our brain. We wouldn’t need to actually read anything. We would simply suddenly know it.

In this situation, we would not need written material to be stored in libraries in any human recognizable language. It would be more efficiently housed in computer storage in a language-independent format that is most closely compatible with and efficiently transferrable into the native storage of the same concepts in the human brain.

In this future, all of which is directly in our path of travel assuming we survive our own follies, we deal at basic symbolic levels and tedious processes of math and language become largely offloaded. Forget tools to translate human languages. We will be able to simply discard them for a symbolic language that essentially transforms us into telepathic creatures. And in this form of telepathy, we don’t hear words in our head. We just transmit ideas and thoughts and understanding and experiences with the aid of our computer interfaces. The closest depiction in popular Sci Fi is perhaps the implantation of memories in the 1990 film “Total Recall.”

A real fascinating unknown to me is, how would humans process and interact without language? Do we require at least an internal language, internal dialogue, to function? I have always wanted to be a subject in an experiment to be made to forget all language, say by hypnosis or drugs, and to experience functioning without it. Like a dog might process the world. Technology may inevitably force that experiment upon us on a huge social scale.

It’s not true that “A sufficiently advanced technology is indistinguishable from magic.” Magic would defy the fundamental restrictions of physics and chemistry. That’s how we’d know the difference. A telepathic future facilitated by direct-to-brain computer interface is Science Fiction, not Fantasy.

The Greatest Failure of Science

Before I call out the biggest, most egregious failure of science, let me pay science some due credit. Science routinely accomplishes miracles that make Biblical miracles seem laughably mundane and trivial by comparison. Water into wine? Science has turned air into food. Virgin birth? A routine medical procedure. Angels on the head of a pin? Engineers can fit upwards of 250 million transistors in that space. Healing a leper? Bah, medicine has eradicated leprosy. Raising the dead? Clear, zap, next. Create life? Been there, done that. It’s not even newsworthy anymore.

And let’s compare the record of science to the much vaunted omniscience of God himself. Science has figured out the universe in sufficient detail to reduce it to practically one small Standard Equation. It turns out to actually be kind of trivial, some would say. Like God, we can not only listen in on every person on the planet, but no mystery of the universe is hidden to us. We have looked back in time to the first tick of the cosmic clock, down inside atoms to quarks themselves, and up to view objects at very edge of our “incomprehensively” large universe.

Science routinely makes the most “unimaginable” predictions about the universe that are shortly after proven to be true. Everything from Special Relativity to the Higgs Boson to Dark Matter to Gravity Waves and so many other phenomena. Nothing is too rare or too subtle or too complex to escape science for long.

Take the neutrino as just one representative example among so many others. These subatomic particles were hypothesized in 1931 by Wolfgang Pauli. They are so tiny that they cannot be said to have any size at all. They have virtually no mass and are essentially unaffected by anything. Even gravity has only an infinitesimal effect on neutrinos. They move at nearly the speed of light and pass right through the densest matter as if it were not there at all. It seems impossible that humans could ever actually observe anything so tiny and elusive.

Yet, in 1956 scientists at the University of California at Irvine detected neutrinos. Today we routinely observe neutrinos using gigantic detectors like the IceCube Neutrino Observatory at the South Pole. Similarly we now routinely observe what are essentially infinitesimally tiny vibrations in time-space itself using gravity wave detectors like the LIGO Observatory.

The point is, when talking about anything and everything from infinitesimally small neutrinos to massive gravitational waves spread so infinitesimally thin as to encompass galaxies, science can find it. If it exists, no matter how well hidden, not matter how rare, no matter how deeply buried in noise, no matter how negligible it may be… if it exists it will be found.

Which brings us to the greatest failure of science.

Given the astounding (astounding is far too weak a word) success of science in predicting and then detecting the effects of even the most unimaginably weak forces at work in the world around us, it is baffling that it has failed so miserably to detect any evidence of the almighty hand of God at work.

I mean, we know that God is the most powerful force in the universe, that God is constantly at work shaping and acting upon our world. We know that God responds to prayers and intervenes in ways both subtle and miraculous. So how is it that science has never been able to detect His influence? Not even in the smallest possible way?

Even if one adopts that view that God restricts himself rigorously to the role of “prime mover,” how is it that science has found nothing, not one neutrino-scale effect which points back to, let alone requires, divine influence?

It is mind-boggling when you think about it. I can certainly think of no possible explanation for this complete and utter failure of science to find any shred of evidence to support the existence of God when so many of us are certain that He is the most powerful force at work in the universe!

Can you?

Three Major Flaws in your Thinking

BrainwavesEEGToday I’d like to point out three severe and consequential flaws in your thinking. I know, I know, you’re wondering how I could possibly presume that you have major flaws in your thinking. Well, I can safely presume so because these flaws are so innate that it is a statistical certainty that you exhibit them much the time. I suffer from them myself, we all do.

Our first flaw arises from our assumption that human thinking must be internally consistent; that there must necessarily be some logical consistency to our thinking and our actions. This is reinforced by our own perception that whatever our neural networks tell us, no matter how internally inconsistent, nevertheless seems totally logical to us. But the reality is that our human neural networks can accommodate any level of inconsistency. We learn whatever “training facts,” good or bad, that are presented to us sufficiently often. Our brains have no inherent internal consistency checks beyond the approval and rejection patterns they are taught. For example, training in science can improve these check patterns,  whereas training in religion necessarily weakens them. But nothing inherently prevents bad facts and connections from getting introduced into our networks. (Note that the flexibility of our neural networks to accommodate literally anything <was> an evolutionary advantage for us.)

Our second flaw is that we have an amazing ability to rationalize whatever random facts we are sufficiently exposed to so as to make them seem totally logical and consistent to us. We can maintain unquestioning certainty in any proposition A, but at the same time be perfectly comfortable with proposition B, even if B is in total opposition with and incompatible with proposition A. We easily rationalize some explanation to create the illusion of internal consistency and dismiss any inconsistencies. If our network is repeatedly exposed to the belief that aliens are waiting to pick us up after we die, that idea gradually becomes more and more reasonable to us, until eventually we are ready to drink poison. At each point in the deepening of those network pathways, we easily rationalize away any logical or empirical inconsistency. We observe extreme examples of this in clinical cases but such rationalization affects all our thinking. (Note that our ability to rationalize incoherent ideas so as to seem perfectly coherent to us was an evolutionary necessity to deal with the problems produced by flaw #1.) 

The third flaw is that we get fooled by our perception of and need to attribute intent and volition to our thoughts and actions. We imagine that we decide things consciously when the truth is that most everything we think and do is largely the instantaneous unconscious output of our uniquely individual neural network pathways. We don’t so much arrive at a decision as we rationalize a post-facto explanation after we realize what we just thought or did. Our consciousness is like the General who follows the army wherever it goes, and tells himself he is in charge. We feel drawn to a Match date. Afterwards when we are asked what attracted us to that person, so we come up something like her eyes or his laugh. But the truth is that our attraction was so automatic and so complex and so deeply buried, that we really have no idea. Still, we feel compelled to come with some explanation to reassure us that we made a reasoned conscious decision. (Certainly our illusion of control is a fundamental element of what we perceive as our consciousness.)

So these are our three core flaws. First, our brains can learn any set of random facts and cannot help but accept those “facts” as undeniable and obvious truths. Second, we can and do rationalize whatever our neural network tells us, however crazy and nonsensical, so as to make us feel OK enough about ourselves to at least allow us to function in the world. And thirdly, when we ascribe post-facto rationalizations to explain our neural network conclusions, we mistakenly believe that the rationalizations came first. Believing otherwise conflicts unacceptably with our need to feel in control of our thoughts and actions.

I submit that understanding these flaws is incredibly important. Truly incorporating an understanding of these flaws into your analysis of new information shifts the paradigm dramatically. It opens up powerful new insights into understanding people better, promotes more constructive evaluation of their thoughts and actions, and reveals more effective options for working with or influencing them.

On the other hand, failure to consider these inherent flaws misdirects and undermines all of our interpersonal and social interactions. It causes tremendous frustration, misunderstanding, and counterproductive interactions.

I am going to give some more concrete examples of how ignoring these flaws causes problems and how integrating them into your thinking opens up new possibilities. But before I do that, I have to digress a bit and emphasize that we are the worst judge of our own thoughts and conclusions. By definition, whatever our neural network thinks is what seems inescapably logical and true to us. Therefore, our first thought must always be, am I the one whose neural network is flawed here? Sometimes we can recognize this in ourselves, sometimes we might accept it when others point it out, but most of the time it is exceedingly difficult for us to recognize let alone correct our own network programming. When our networks change, it is usually a process of which we are largely unaware, and happens through repeated exposure to different training facts.

But just because we cannot fully trust our own thinking doesn’t mean we should question everything we think. We simply cannot and should not question every idea we have learned. We have learned the Earth is spherical. We shouldn’t feel so insecure as to question that, or be intellectually bullied into entertaining new flat Earth theories to prove our open-mindedness or scientific integrity. Knowing when to maintain ones confidence in our knowledge and when to question it, is of course incredibly challenging.

And this does not mean we are all equally flawed or that we cannot improve. The measure is how well our individual networks comport with objective reality and sound reason. Some of our networks have more fact-based programming than others. Eliminating bad programming is not hopeless. It is possible, even irresistible when it happens. Our neural networks are quite malleable given new training facts good or bad. My neural network once told me that any young bald tattooed male was a neo-Nazi, that any slovenly guy wearing bagging jeans below his butt was a thug, and any metro guy sporting a bushy Khomeini beard was an insecure, over-compensating douchebag. Repeated exposure to facts to the contrary have reprogrammed my neural network on at least two of those.

OK, back on point now. Below are some examples of comments we might say or hear in conversation, along with some analysis and interpretation based on an awareness of our three flaws. I use the variable <topic> to allow you to fill in the blank with practically anything. It can be something unquestionably true, like <climate change is real>, or <god is a fantasy>, or <Trump is a moron>. Alternatively, if you believe obvious nonsense like <climate change is a hoax>, or <god is real>, or <Trump is the greatest President ever>, using those examples can still help just as much to improve your comfort level and relations with the other side.

I don’t understand how Jack can believe <topic>. He is so smart!

We often hear this sort of perplexed sentiment. How can so many smart people believe such stupid things? Well, remember flaw #1. Our brains can be both smart and stupid at the same time, and usually are. There are no smart or stupid brains, there are only factually-trained neural network patterns and speciously trained neural network patterns. Some folks have more quality programming, but that doesn’t prevent bad programming from sneaking in. There should be no surprise to find that otherwise smart people often believe some very stupid things.

Jill must be crazy if she believes <topic>.

Just like no one is completely smart, no one is completely crazy. Jill may have some crazy ideas that exist perfectly well along side a lot of mostly sane ideas. Everyone has some crazy programming and we only consider them insane when the level of crazy passes some socially acceptable threshold.

I believe Ben when he says <topic> is true because he won a Nobel Prize.

A common variant of the previous sentiments. Ben may have won a Nobel Prize, he may teach at Harvard, and may pen opinion pieces for the New York Times, so therefore we should give him the benefit of the doubt when we listen to his opinions. However, we should also be cognizant of the fact that he may still be totally bonkers on any particular idea. Conversely, just because someone is generally bonkers, we should be skeptical of anything they say but still be open to the possibility that they may be reasoning more clearly than most on any particular issue. This is why we consider “argument by authority” to be a form of specious argument.

It makes me so mad that Jerry claims that <topic> is real!

Don’t get too mad. Jerry kinda can’t help it. His neural network training has resulted in a network that clearly tells him that <topic> must obviously be absolutely true. Too much Fox News, religious exposure, or relentless brainwashing will do that to anyone, even you.

How can Bonnie actually claim that she supports <topic> when she denies <topic>???

First, recall flaw #1. Bonnie can believe any number of incompatible things without any problem at all. And further, flaw #2 allows her to rationalize a perfectly compelling reason to excuse any inconsistency.

Clyde believes in <topic> so he’ll never support <topic>.

Not true. Remember our flaws again. Clyde’s neural network can in fact accommodate one topic without changing the other one, and still rationalize them perfectly well. All it takes is exposure to the appropriate “training facts.” In fact, consistent with flaw #3, after his network programming changes, Clyde will maintain that he consciously arrived at that new conclusion through careful study and the application of rigorous logic.

Sonny is conducting a survey to understand why voters support <topic>.

Social scientists in particular should be more cognizant of this one. How often do we go to great efforts to ask people why they believe something or why they did something. But remember flaw #3. Mostly what they will report to you is simply their rationalization based on flaw #2. It may not, and usually doesn’t, have anything to do with their extremely complex neural network programming. That is why “subjective” studies designed to learn how to satisfy people usually fail to produce results that actually do influence them. Sonny should look for more objective measures for insight and predictive value.

Cher should support <topic> because it is factually supported and logically sound!

Appeals to evidence and logic often fail because peoples’ neural network has already been trained to accept other “evidence” and to rationalize away contrary logic. It should be no surprise that they reject your evidence and conclusions and it doesn’t accomplish anything to expect Cher to see it, let alone berate or belittle her when she does not.

And that brings us to the big reveal of this article…

There is a fourth flaw that is far worse than the other three we have discussed so far. And that is the flaw that most of us suffer from when we fail to integrate an deep awareness of flaws 1-3 into our thinking. We may not be able to completely control or eliminate flaws 1-3, but we can correct flaw number 4!

This discussion may have left you feeling helpless to understand, let alone influence, our truth-agnostic neural networks. But it also presents opportunities. These insights suggest two powerful approaches.

The first approach is more long-term. We must gradually retrain flawed neural networks. This can be accomplished through education, marketing, advertising, example-setting, and social awareness campaigns to name a few. None of these efforts need to be direct, nor do they require any buy-in by the target audience. The reality of network training is that it is largely unconscious, involuntary, and automatic. If our neural networks are exposed to sufficient nonsense, they will gradually find that nonsense more and more reasonable. But the encouraging realization is that reprogramming works just as well – or better – for sound propositions. And to be clear, this can happen quite rapidly. Look at how quickly huge numbers of neural networks have moved on a wide range of influence campaigns from the latest fashion or music craze to tobacco reduction to interracial relationships.

The second approach can be instantaneous. Rather than attempt to reprogram neural networks, you force them to jump through an alternate pathway to a different conclusion. This can happen with just a tiny and seemingly unrelated change in the inputs, and the result is analogous to suddenly shifting from the clear perception of a witch-silhouette, to that of a vase. Your network paths have not changed, yet one moment you conclude that you clearly see a witch, and the next it becomes equally obvious that it is actually a vase. For example, when Karl Rove changed the name of legislation, he didn’t try to modify people’s neural network programming, he merely changed an input to trigger a very different output result.

I hope these observations have given you a new lens through which you can observe, interpret, and influence human behavior in uniquely new and more productive ways. If you keep them in mind, you will find that they inform much of what you hear, think, and say.

Don’t Believe your Eyes

eyesToday I wanted to talk about perceptions. Not our feelings, but what we actually see, feel, smell, hear, and taste. That is, the “objective” inputs that drive our feelings. Should we really “only believe our eyes?

I think not.

In my book (see here) I talk about how we should be skeptical of our own memories and perceptions. Our memories are not recordings. They are docudrama recreations drawing upon various stock footage to put together a satisfying re-imagining. We remember going to the beach as a child. But in “recalling” details of that experience, we draw upon fragments from various sources to fill it in. The “slant” of that recreation is strongly dependent upon our current attitudes and biases. Our re-imagined, and often very distorted memory then reinforces what we believe to be a “vivid” recollection next time we recall it. Over time our “clear” memory can drift farther and farther from reality like a memory version of the “phone game.”

I contend that our brains work similarly with regard to our senses. We don’t see what we think we see. Our perceptions are filtered through our complex neural networks. It is a matched, filtered, processed, censored, and often highly biased version that we actually see, hear, or feel.

We know that our subconscious both filters out much of the information it receives, and adds in additional information as needed to create a sensible perception. I always favor a neural network model of brain function. As it relates to perception, our neural network receives a set of sensory data. It matches that data against known patterns and picks the closest match. It then presents our consciousness with a picture – not of the original data – but of that best-fit match. It leaves out “extraneous” information and may add in missing information to complete that expected picture. That is, we do not actually see, hear, smell, or taste a thing directly. We see, hear, smell, or taste a satisfying recreation that our network presents to us.

This should not be controversial, because we experience it all the time. Based on sparse information, we “see” fine detail in a low resolution computer icon that objectively is not there. We fail to see the gorilla inserted into the background because it is out of place. We are certain we see a witch or a vase in a silhouette, depending on our bias or our expectations at that moment.

But though this should be evident, we still do not take this imprecision seriously enough in evaluating the objectivity of our own memories or perceptions. We still mostly put near-absolute faith in our memories, and are generally even more certain of our perceptions. We believe that what we perceive is absolutely objective. Clearly, it is not.

In essence, what we believe we objectively recall, see, hear, or touch is not the thing itself, but a massaged recreation of our neural network match. The version we perceive can often be wrong in very important ways. Our perceptions are only as reliable as our neural networks. And some neural networks can be more compromised than others. We can recall or even perceive radically crazy things if our neural network has been trained to do so. I campaign against belief-based thinking of all sort because it seriously compromises these critical neural networks in crazy ways.

Even more unrecognized are the ways that this phenomenon is largely ignored as it impacts scientific research. Scientists often give far too much credence to reports of perceptions, often in extremely subtle ways.

As a simple illustration, consider how we often mock wine connoisseurs who claim to taste differences in wines but cannot pick these out in blinded studies. However, consider the confounding impact of their (and our) neural networks in even this simple case. When experiencing a wine, all the associated data is fed into the drinker’s neural network. It makes a match and then presents that match to the consciousness. Therefore, if the network does not “see” one critical factor, say color, it matches to white, not red, and presents and entirely different taste pattern the the drinker, ignoring some “extraneous” flavors and adding some other “missing” ones.

These same kinds of neural network matching errors can, and I have to assume often do, confound even more rigorous scientific studies. And they are further confounded by the fact that these mismatches are typically temporary. With every new set of data, our neural networks adjust themselves, the weightings change, to yield different results. The effect of a drug or placebo, for example, may change over time. If scientists see this, they typically look exclusively for other physiological causes. But it may be a neural network correction.

That is why I always admonish my readers to stick with inputs that will strengthen your neural networks toward sound objectivity rather than allow them to be weighted toward the rationalization of, and perception of, beliefs and nonsense. But since none of us can ever have perfect networks, or even know how accurate ours performs in any given match, we all need a healthy amount of skepticism, even with regard to our own memories and perceptions.

I further urge scientists to at least consider the impact of neural network pre-processing on your studies, and to develop methodologies to explicitly detect and correct for such biases.

 

Humans are Inexplicable

brainWhether it be in science or business or politics or popular culture, we expend an inordinate amount of time and effort trying to figure out why people do whatever people are doing. We seem to have more analysts than actors, all desperately trying to explain what motivates people, either by asking them directly or by making inferences about them. For the most part, this is not merely a colossal waste of time and effort and money in itself, but it stimulates even greater wastes of time and effort and money chasing wildly incomplete or erroneous conclusions about why we do what we do.

Asking people why they did what they did, or why they are doing what they are doing, or why they are going to do what they are going to do, generally yields useless and misleading information. It is not clear that people actually have distinct reasons they can recognize let alone articulate. It is quite likely in fact that most of the decisions we make are made unconsciously based upon a myriad of complex neural network associations. These associations need not be rational. These connections don’t need to be internally consistent to each other or related to the actual outcome in any way. But in our post-rationalizations and post-analyses we impose some logic to our decisions to make them feel sensible. Therefore, the reasons we come up with are almost completely made-up at every level to sound rational or at least sane to ourselves and to those we are communicating to.

The truth is, we can’t usually hope to understand our own incredibly complex neural networks, let alone the neural networks of others. Yes, sometimes we can identify a strong neural network association driving a behavior, but most determinative associations are far too diffuse across a huge number of seemingly unrelated associations.

The situation gets infinitely worse when we are trying to analyze and explain group behaviors. Most of our shared group behaviors emerge from the weak-interactions between all of our individual neural networks. The complexity of these interactions is virtually unfathomable. The challenge of understanding why a group does what it does collectively, let alone figuring out how to influence their behavior, is fantastic.

If you ask a bird why it is flying in a complex swirling pattern along with a million other birds, it will probably give you some reason, like “we are looking for food,” but in fact it is probably largely unaware that it is even flying in any particular pattern at all.

So why point all this out? Do we give up? Does this imply that a rational civilization is impossible, that all introspection or external analysis is folly?

Quite the contrary, we must continue to struggle to understand ourselves and truly appreciating our complexity is part of that effort. To do so we must abandon the constraints of logic that we impose upon our individual and group rationalizations and appreciate that we are driven by neural networks that are susceptible to all manner of illogical programming. We must take any self-reporting with the same skepticism we would to the statement “I am perfectly sane.” We should be careful of imposing our own flawed rationality upon the flawed rationality of others. Analysts should not assume undue rationality in explaining behaviors. And finally, we must appreciate that group behaviors can have little or no apparent relationship to any of the wants, needs, or expressed opinions of those individuals within that group.

In advanced AI neural networks, we humans cannot hope to understand why the computer has made a decision. Its decision is based upon far too many subtle factors for humans to recognize or articulate. But if all of the facts programmed in to the computer are accurate, we can probably trust the judgement of the computer.

Similarly with humans, it may be that our naive approach of asking or inferring reasons for feelings and behaviors and then trying to respond to each of those rationales is incredibly ineffective. It may be that the only thing that would truly improve individual and thus emergent thinking are more sanely programmed neural networks, ones that are not fundamentally flawed so as to comfortably rationalize religious and other specious thinking at the most basic level (see here). We must focus on basic fact-based thinking in our educational system and in our culture on the assumption that more logically and factually-trained human neural networks will yield more rational and effective individual and emergent behaviors.

 

Religion in Public Schools

The teaching of religion in public schools is a topic that stimulates a great deal of honest debate on all sides of the issue. Should religion be taught at all? And if so, what religions? Even well-meaning atheists might feel that religion should be taught, as long as all religions – and atheistic perspectives as well – are taught equally and fairly without bias.

That sounds laudable and enlightened in theory. However, many plans that sound great in theory inevitably turn out to be disastrous when put into practice. Teaching religion in public schools is one such example.

I have personal experience with this. While serving in the Peace Corps in South Africa, I worked for their Department of Education. The South African Constitution requires that all religions be treated equally. In order to comply with the spirit of their Constitution, the Department of Education has adopted a policy that all religions should be taught fairly and equally in the public schools.

Sounds great right? The trouble is that teachers, particularly rural teachers, do not know all religions and do not care to know all religions – let alone teach them fairly. At the point where lofty policies touch the students. all that this accomplishes is to give teachers cover to preach and proselytize their own religious views in the classroom and to misrepresent and disparage all other religions – and atheism is demonized most of all.

The problem of state sanctioned religious instruction is not merely a matter of the recruiting and training and monitoring of teachers. False even-handedness spills over into teaching materials as well. Science texts typically enumerate a long list of native creation myths as legitimate. In at least one science text, after describing the monkey myth, and the milk myth, and many others, it concluded with what was almost an obligatory footnote that said “and some scientists believe that the world was created by natural means and human beings evolved.”

This sort of false balance, not unlike giving equal deference to climate change deniers, is an almost inevitable consequence of a misguided and ill-fated attempt to be fair and inclusive with regard to the teaching of religion.

I came away from my experience in South Africa more convinced than ever that our American system of simply keeping religion out of our public schools is on balance the best, most practical system of fairness. There is no shortage of alternate venues where people can preach and teach religion as much as they wish. Therefore, there is no compelling need being met by including religion in public schools, that warrants the certain risk of abuse and unintended consequences.

Assiduously keeping religion out of our public schools is in fact the more fair, the more enlightened, and the more realistic policy position.

Our Amazing Yet Deeply Flawed Neural Networks

NeuralNetwork

Back in the 1980’s when I did early work applying Neural Network technology to paint formulation chemistry, that experience gave me fascinating insights into how our brains operate. A computer neural network is a mathematically complex program that does a simple thing. It takes a set of training “facts” and an associated set of “results,” and it learns how they connect by essentially computing lines of varying weights connecting them. Once the network has learned how to connect these training facts to the outputs, it can take any new set of inputs and predict the outcome or it can predict the best set of inputs to produce a desired outcome.

Our brains do essentially the same thing. We are exposed to “facts” and their associated outcomes every moment of every day. As these new “training sets” arrive, our biological neural network connections are physically weighted. Some become stronger, others weaker. The more often we observe a connection, the stronger that neural connection becomes. At some point it becomes so strong that it becomes undeniably obvious “common sense” to us. Unreinforced connections, like memories, become so weak they are eventually forgotten.

Note that this happens whether we know it or not and whether we want it to happen or not. We cannot NOT learn facts. We learn language as children just by overhearing it, whether we intend to learn it or not. Our neural network training does not require conscious effort and cannot be “ignored” by us. If we hear a “fact” often enough, it keeps getting weighted heavier until it eventually becomes “undeniably obvious” to us.

Pretty amazing right? It is. But here is one crucial limitation. Neither computer or biological neural networks have any intrinsic way of knowing if a training fact is valid or complete nonsense. They judge truthiness based only upon their weighting. If we tell a neural network that two plus two equals five, it will accept that as a fact and faithfully report five with complete certainty as the answer every time it is asked. Likewise, if we connect spilling salt with something bad happening to us later, that becomes a fact to our neural network of which we feel absolutely certain.

This flaw wasn’t too much of a problem during most of our evolution as we were mostly exposed to real, true facts of nature and the environment. It only becomes an issue when we are exposed to abstract symbolic “facts” which can be utter fantasy. Today, however, most of what is important to our survival are not “natural” facts that can be validated by science. They are conceptual ideas which can be repeated and reinforced in our neural networks without any physical validation. Take the idea of a god as one perfect example. We hear that god exists so often that our “proof of god” pathways strengthen to the point that we see proof everywhere and god’s existence becomes intuitively undeniable to us.

This situation is exacerbated by another related mental ability of ours… rationalization. Since a neural network can happily accommodate any “nonsense” facts, regardless of how contradictory they may be, our brains have to be very good at rationalizing away any logical discrepancies between them. If two strong network connections logically contradict each other, our brains excel and fabricating some reason, some rationale to explain how that can be. When exposed to contradictory input, we feel disoriented until we rationalize it somehow. Without that ability, we would be paralyzed and unable to function.

This ability of ours to rationalize anything is so powerful that even brain lesion patients who believe they only have half of a body will quickly rationalize away any reason you give them, any evidence you show them, that proves they are wrong. Rationalization allows us to continue to function, even when our neural networks have been trained with dramatically nonsensical facts. Further, once a neural network fact becomes strong enough, it can no longer be easily modified even by contradictory perceptions, because it filters and distorts subsequent perceptions to accommodate it. It can no longer be easily modified by even our memories as our memories are recreated in accordance with those connections every time we recreate them.

As one example to put all this together, when I worked in the Peace Corps in South Africa a group of high school principals warned me to stay indoors after dark because of the witches that roam about. I asked some questions, like have you ever personally seen a witch? No, was the answer, but many others whom we trust have told us about them. What do they look like, I asked. Well they look almost like goats with horns in the darkness. In fact, if you catch one they will transform into a goat to avoid capture.

Here you clearly see how otherwise smart people can be absolutely sure that their nonsensical “facts” and rationalizations are perfectly reasonable. What you probably don’t see is the equally nonsensical rationalizations of your own beliefs in god and souls and angels or other bizarre delusions.

So our neural networks are always being modified, regardless of how smart we are, whether we want them to or not, whether we know they are or not, and those training facts can be absolutely crazy. But our only measure of how crazy they are is our own neural network weighting which tells us that whatever are the strongest connections must be the most true. Further, our perceptions and memories are modified to remain in alignment with that programming and we can fabricate any rationalization needed to explain how our belief in even the most outlandish idea is really quite rational.

In humans early days, we could live with these inherent imperfections. They actually helped us survive. But the problems that face us today are mostly in the realm of concepts, symbols, ideas, and highly complex abstractions. There is little clear and immediate feedback in the natural world to moderate bad ideas. Therefore, the quality of our answers to those problems and challenges is entirely dependent upon the quality of our basic neural network programming.

The scientific method is a proven way to help ensure that our conclusions align with reality, but science can only be applied to empirically falsifiable questions. Science can’t help much with most of the important issues that threaten modern society like should we own guns or should Donald Trump be President. Our flawed neural networks can make some of us feel certain about such questions, but how can we be certain that our certainty is not based on bad training facts?

First, always try to surround yourself by “true and valid” training facts as much as possible. Religious beliefs, New Age ideas, fake news, and partisan rationalizations all fall under the category of “bad” training facts. Regardless of how much you know they are nonsense, if you are exposed to them you will get more and more comfortable with them. Eventually you will come around to believing them no matter how smart you think you are, it’s simply a physical process like the results of eating too much fat.

Second, the fact that exposing ourselves to nonsense is so dangerous gives us hope as well. While it’s true that deep network connections, beliefs, are difficult to change, it is a fallacy to think they cannot change. Indoctrination works, brainwashing works, marketing works. Repetition and isolation from alternative viewpoints, as practiced by Fox News, works. So we CAN change minds, no matter how deeply impervious they may seem, for the better as easily as for the worse. Education helps. Good information helps.

There is a method called Feldenkrais which can be practiced to become aware of our patterns of muscle movement, and to then strip out “bad” or “unnecessary” neural network programming to improve atheletic efficiency and performance. I maintain that our brains work in essentially the same way as the neural networks that coordinate our complex movements. As in Feldenkrais, we can slow down, examine each tiny mental step, become keenly aware of our thinking patterns, identify flaws, and correct them. If we try.

Third, rely upon the scientific method wherever you can. Science, where applicable, gives us a proven method to bypass our flawed network programming and compromised perceptions to arrive at the truth of a question.

Fourth, learn to quickly recognize fallacies of logic. This can help you to identify bad rationalizations in yourself as well as others. Recognizing flawed rationalizations can help you to identify bad neural programming. In my book Belief in Science and the Science of Belief, I discuss logical fallacies in some detail as well a going deeper into all of the ideas summarized here.

Finally, just be ever cognizant and self-aware of the fact that whatever seems obvious and intuitive to you may in fact be incorrect, inconsistent, or even simply crazy. Having humility and self-awareness of how our amazing yet deeply flawed neural networks function helps us to remain vigilant for our limitations and skeptical of our own compromised intuitions and rationalizations.

Technology Empowers Our Humanity

CustomerSupportNot that may years ago, read/write CD/ROM drives were essential and a good one was quite expensive. I once paid top dollar to get a top rated drive from Toshiba. It never worked. I called Toshiba dozens of times over 6 months trying to get it working. It would take an hour to get past hold, read off serial numbers and customer info, fax in receipts, explain the problem all over again, to get transferred and repeat it all, to get disconnected, go through it all yet again, only to be told to clean the drive, to call Microsoft, to contact Intel, to reinstall Windows, to buy higher quality disks, to change bios settings, or buy a new connection cable.

In the end, it turned out that this was a known issue with the drive, but Toshiba had a policy not to admit to any such issues. Instead, they intentionally made me jump onerous technical support hurdles and run off on expensive and time-consuming wild goose chases for six months before they finally admitted as much. Most people gave up well before that, but I was on a mission. Nevertheless, in the end I tossed the drive in the garbage.

Everyone has their customer support horror stories. Not that long ago, such infuriating experiences were the norm, not the exception. I had many similar experiences with Sony in particular and resolved never to buy anything from them ever again.

But today customer support has transformed dramatically. Today, wonderful customer support is the norm, not the exception.

AT&T exemplifies this welcome new normal for customer service. The hotspot on my mobile phone quit working. Although I knew it was not an issue with AT&T because it worked on my wife’s phone, I went to their site, hit chat, immediately got a wonderful representative named Stephanie who happily helped me reset my phone, 5 minutes later my hotspot was working!

That’s great customer service. And it’s not just huge companies that are putting the service back in customer service. My garage door light started blinking in a regular pattern as if indicating some error. I called Guardian Garage Doors and immediately got a wonderful guy on the phone. He heard my issue and asked me to text him a video. I did so and after a short hold said their engineers didn’t know what the problem was but wanted me to send it in so they could diagnose it. He offered to rush out a replacement. But minutes later he called back and suggested I try replacing my LED bulb. I did so even though it seemed silly, LED’s don’t do that. But apparently they do. That fixed it!

This is nothing remotely like the bad old days of Toshiba and Sony era customer “support.” The kind of great customer support we often see today is greatly facilitated by technology. It is enabled by the Internet, by chat technology, by searchable knowledge bases, by intelligent call routing systems, and by interconnected global workforces.

But while these technologies are incredibly empowering, real people and attitudes are still essential to great customer support. Technology doesn’t make representatives so pleasantly informal yet professional in demeanor. Technology doesn’t ensure that customer service departments are staffed to connect quickly and to stay on as long as it takes to resolve an issue. It takes sensible management to not interrogate you to prove your identity, ownership, and warranty. It is an explicit choice to authorize representatives to own issues even if they are not directly responsible. And it is their conscious decision to admit to issues candidly rather than reflexively conceal and deny them beyond all rationality.

So, while I often bash private sector corporations, I must give credit where credit is due. Some things do get better. Customer service stands in direct contradiction to widespread fears of a cold and impersonal technology-dominated future. It shows us that technology, properly implemented, can make our lives and our interactions not only more efficient and satisfying, but at the same time more friendly, more personal, more sensible, and yes, more human as well.