Saying Consciousness Is a Wave Function Collapse Is Like Saying Your Mind Is a Square Root

Anesthesiologist Stuart Hameroff has recently written an article with the title "Consciousness Is the Collapse of the Wave Function."  The article is a rambling mishmash of physics, chemistry and neuroscience that completely fails to provide anything resembling a credible notion for how a brain could produce awareness. 

Hameroff discusses a theory advanced about 20 years ago by him  and physicist Roger Penrose, what is called the Orchestrated Objective Reduction theory. The theory has been basically ignored by the scientific world, and it rather seems that almost no one seems to believe in it other than Penrose and Hameroff. The theory claims that consciousness is produced by some tiny units called microtubules. 

Microtubules are units inside neurons, and each neuron has many of them. The function of microtubules is known: they serve to provide structural support for a neuron, and also help transport chemicals. Claiming that they also provide consciousness rather reminds me of that classic Saturday Night Live sketch in which someone claims that his floor wax is also a dessert topping.   

Hameroff makes the untrue claim that something has taken place to lend credibility to this theory. He states this:

"Penrose suggested that wavefunctions collapse spontaneously and in the process give rise to consciousness. Despite the strangeness of this hypothesis, recent experimental results suggest that such a process takes place within microtubules in the brain."

What are these recent experimental results? The article does not tell us. The article has no reference or link to any scientific paper. It merely has a link to a Wikipedia.org article on something called superradiance, an article saying nothing about the mind or consciousness.  Hameroff's claim that experimental results support his theory is untrue, and his failure to cite or link to such results suggests his statement is groundless.  

The article is a classic example of using what I call the Mixture Method, a method I describe in my post "The Mixture Method Works Wonders When Selling Speculation as Science." The method consists of mixing up speculations with either scientific facts or mathematics or a combination of the two, usually in a way so that the speculative parts are a relatively small part of the paper or article. The goal is to kind of give a scientific flavor or a scientific sound to some claim that is speculative. Often the scientific facts cited are irrelevant to the speculations made, or have only a tangential relation to them.  

A long section of Hameroff's article suggests that he is mainly just spouting irrelevant facts to give some scientific sound to his speculation. Beginning with his sentence "light is the part of the electromagnetic spectrum that can be seen by the eyes of humans and animals – visible light," and ending with his probably incorrect sentence "These micelles somehow developed into functional cells, and then multi-cellular organisms, long before genes," we have more than 500 words mentioning things that are all irrelevant to the claim made in his title, and all irrelevant to the issue of how people have consciousness. The discussion is a very jumbled hodgepodge of scientific facts and irrelevant observations, bouncing all over the place, going from the early universe to facts of chemistry to speculations about the origin of life to mention of mystical experiences. Irrelevant scientific facts are being cited at great length, to help give some scientific sound to some speculation that is metaphysical. 

When you're following the mixture method, it helps if one of the scientific topics mentioned is an extremely obscure topic. That way you can mention some deep-sounding scientific topic, and people will probably fail to notice how such a mention is irrelevant to your speculation. The deep scientific topic mentioned is the collapse of the wave function.  The wave function is some mathematical concept that comes up in the abstruse field of quantum mechanics.  Supposedly a wave function "collapses" when a measurement is made of a particle such as a photon or an electron. 

That has nothing to do with consciousness, and nothing to do with minds.  When people try to drag the collapse of the wave function into a discussion of the origin of consciousness, what goes on seems to go like this:

(1) The hi-tech type of physicist measurement occurring when wave functions collapse is stretched a hundred-fold, into the more general idea of "observation."

(2) "Observation" is then conflated with "consciousness." But observation is not consciousness. Security cameras observe things, and they are not conscious. And unconscious automated equipment can measure things. 

Using the term "it is becoming apparent" for something for which there is zero evidence, Hameroff states this: "It is becoming apparent that consciousness may occur in single brain neurons extending upward into networks of neurons, but also downward and deeper, to terahertz quantum optical processes, e.g. 'superradiance' in microtubules, and further still to fundamental spacetime geometry (Figure 1)." The figure 1 is a diagram that very strangely shows us  columns consisting of (1) a neuron; (2) microtubules in a neuron; (3) hypothetical bumps in spacetime.  What is this superradicance being talked about? Penrose refers us to a wikipedia.org article on that topic, but the article makes no mention of biology or the brain. It merely mentions high-energy physics sources of superradiance such as hot gases, and astrophysical sources of superradiance.  

The paper "Superradiance -- The 2020 Edition" is a 261-page physics paper on the topic of superradiance. The paper makes no mention of the brain, no mention of neurons, and no mention of cells, but it does talk a lot about black holes.  It seems Hameroff has no business mentioning superradiance to try to support the claim that consciousness is produced by microtubules. 

Here's a reality check: it's dark as the dark side of Pluto inside brains and inside microtubules.  A scientific article gives us the truth about radiance from cells:

"Cifra and colleagues cultured millions of yeast cells in a light-tight chamber. The signal detected using photomultiplier tubes tends to be extremely weak: A photon emitted every 15 minutes per cell..Cifra is cautious about concluding whether these ultraweak emissions—he prefers the term 'biological auto-luminescence'—play a significant role in biological signaling, or if they are simply by-products...From a theory standpoint, Cifra says, the signals are simply too low to be used for communication."

Even if superradiance were to be occurring in microtubules, it would do nothing to show that microtubules or brains produce consciousness. Radiance and superradiance are physics phenomena, not mind phenomena.  While people refer to "light of the mind" or "the light of consciousness," or a "mental illumination," they are merely being metaphorical.  Light is no more consciousness than heat is consciousness. 

Hameroff states, "I agree that consciousness is fundamental, and concur with Roger Penrose that it involves self-collapse of the quantum wavefunction, a rippling in the fine scale structure of the universe." It seems that Hameroff is bouncing around between three different ideas:

(1) the idea that consciousness is produced by microtubules;

(2) the idea consciousness is the collapse of a wave function;

(3) The idea that consciousness is produced by a rippling in the fine scale structure of the universe. 

These are three different ideas, all groundless.  Which one does Hameroff believe in? Apparently all three, as if he can't make up his mind. It sounds like Hameroff can't get his story straight.  

According to mainstream understanding of quantum mechanics, a wave function collapse occurs only upon observation or measurement. There are no observations or measurements occurring within microtubules.  So in the context of mainstream quantum mechanics, talking about wave function collapses within microtubules doesn't make sense.  Of course, you can always play around with unorthodox theories of quantum mechanics, but doing that has produced some of the silliest statements of modern scientists, such as Hugh Everett's bat-bleep-crazy theory of some infinity of parallel universes. 

Penrose and Hameroff found it necessary to become quantum theory heretics, by postulating the speculative idea that wave functions spontaneously collapse (instead of only occurring during measurement or observation).  Maybe their thinking was rather like this: wave function collapses as we now understand them occur only with conscious observers, so if wave functions spontaneously collapse, they would produce conscious observers.  No, they would not. If such spontaneous wave function collapses occurred, it would merely mean we need to revise the current prevailing idea that wave function collapses only occur with observation.  Similarly, the fact that car crashes only occur with drivers gives you not the slightest warrant for thinking that spontaneous car crashes not involving observers (like two unmanned parking lot cars colliding) would somehow conjure up the sudden appearance of car drivers.   

Thinkers such as Hameroff try to suggest that there can be quantum effects in the brain, and that the brain can act like a quantum computer.  Such insinuations are futile in explaining human awareness.  Computers can compute, but they are not aware, and have no consciouness. You can't compute your way to consciousness. Also, it is completely erroneous to think that you can show a brain could compute by showing some brain tissue acts like computer hardware. Computing inside a computer requires not just hardware but also computer sofware. Brains have nothing like the software in computers that enables computation. 

I can give some advice for people trying to make some progress in understanding the human mind:

(1) Don't just study microtubules, but study the entire brain, studying at great length exceptional case histories of minds that performed well even with very little brain, and studying at great length the topic of neural shortfalls, all the ways in which the brain fails to have the physical characteristics we would expect it have if it were the source of our minds and the storage place of our memories. You can get such information by reading the posts at this blog. 

(2) Don't study quantum mechanics or high-energy physics when trying to clarify the human mind, but do make a very thorough study of human mental phenomena, including a long and thorough study of the evidence for psi and observational reports of seemingly paranormal and currently inexplicable human experiences such as near-death experiences, hypnotic phenomena and out-of-body experiences.  

(3) Do not become a fan of any theory that takes the futile, dead-end approach of merely trying to explain "consciousness" (a bloodless,  stripped-down term suitable for describing the mind of an ant), and recognize the necessity for explaining the full range of human mental phenomena, including memory, thinking, personality, belief, understanding, self-hood, creativity, and the many well-observed and carefully documented anomalous mental phenomena that our professors should be paying very close attention to but senselessly ignore. 

A mind is so much more than just "consciousness"

EEG Studies Fail to Provide Robust Evidence That Brains Think or Retrieve Memories

To try to provide evidence for their claims that memories are stored in the brain and that brains produce mental phenomena such as thinking and imagination, neuroscientists look for what they call neural correlates of mental activity. A neural correlate of a mental activity would be some sign that a brain acts differently or looks differently when someone does a particular type of mental activity such as thinking or recalling. 

The most common way that a neuroscientist will look for a neural correlate of a mental activity is to image someone's brain while he is doing some mental activity, typically using an fMRI scanner.  In my post "The Brain Shows No Sign of Working Harder During Thinking or Recall," I discussed the failure of such studies to provide robust evidence that brains produce thinking or recall.  The following are tips for analyzing such studies:

(1) Search for the phrase "percent signal change" to quickly find out how much of a difference was found during some mental activity. A large fraction of all fMRI-based neural correlate studies will use such a phrase. 

(2) Find out the sample size used, and whether a sample size calculation was used to determine whether the sample size was adequate. The vast majority of fMRI-based neural correlate studies fail to provide a sample size calculation, and the vast majority of such studies use way-too-small study group sizes, so small that they are not reliable evidence for anything. 

What you will typically find is that such studies will show only extremely small changes in brain activity, involving changes of smaller than one half of one percent.  Such variations of only about  one part in 200 or smaller are no robust evidence that brains produce thinking or produce recall. We would expect to get variations of such a size given random moment-to-moment fluctuations in brains, variations that would occur even if a person was not thinking and not recalling anything. And the fact that the vast majority of fMRI-based neural correlate studies use way-too-small study group sizes means that such studies are not robust evidence of anything.  As discussed here, a recent large study was announced with a headline of "Brain studies show thousands of participants are needed for accurate results," but a typical fMRI-based neural correlate study will not even use dozens of participants. 

But there is an entirely different way in which neuroscientists can look for neural correlates of memory recall and thinking. Rather than using big fMRI machines to scan the brain, a neuroscientist can hook up brains to electroencephalography machines (EEG machines) that read electrical activity of the brain.  To produce such readings, many different electrodes will be attached to the heads of subjects who are being tested. The output is not an image of the brain, but a reading showing lines that go up and down.  A neuroscientist can study such lines, looking for some neural correlate of thinking or recall that shows up as a difference in a wiggly line. 

Scientists studying such EEG outputs are looking for what they call an event-related potential or ERP.  In theory an ERP is some EEG pattern that might be repeated whenever some mental event occurs such as recognition or recall or concentration. In the literature an ERP is typically described as some blip occurring over less than a second. Figure 5 of the paper here gives us a "heat map" of various claimed ERP effects relating to cognition. The claimed effects have various names listed on the right side of the heat-map, names such as N400 and CDA (standing for contralateral delay activity). 

What typically goes on is a cherry-picking affair.  A neuroscientist will typically use a type of EEG device with 128 electrodes, each of which is attached to a different part of the head.  After the device records neural activity,  there will be 128 different readings, each from a different part of the head.  Each reading will be some long wavy line.  Imagine a paper scroll about three inches high and 100 meters long, with a wavy line stretching from beginning to end, and you'll have a rough idea of the output from any electrode.  Neuroscientists will not typically show us some graph showing the statistical average of all of these lines. Instead, they will be free to choose any group of electrodes, to try to show some correlation effect.  

Imagine you are a neuroscientist. Did you fail to get any correlation effect from averaging the outputs from electrodes 98, 99, 100 and 101? Then you can just keep playing around with electrode combinations until you get something that looks like an effect. For example, maybe you'll get something that looks like an effect if you average the results of electrodes 34, 35, 37 and 38.  If the studies were properly designed, using a pre-registration in which an exact methods description was published before data gathering, such dubious "slice and dice until you get a desired result" techniques would not be possible. But we almost never see any such pre-registration in these EEG neural correlate studies. Also, there's no rule that you cannot cherry-pick two or three electrodes that were not adjacent. 

So, for example, in the study here in Figure 3 we have a diagram showing two graphs of nice-looking ERP effects. The caption tells us the first graph is from electrode 65, and that the second graph is from electrode 91. But Figure 2 shows that 128 electrodes were used, and that electrode 65 is on the other side of the head, nowhere close to electrode 91.  Our authors have apparently cherry-picked the results from 128 electrodes, looking for the results that would best show the desired effect.  

A scientific paper about the shortfalls of studies looking for these ERP effects tells us the following:

"An example of this issue is described in a recent paper by Luck and Gaspelin (2017), who demonstrated how 'researcher degrees of freedom' could influence statistical analysis of ERP data. ERP recordings typically employ dozens of electrodes and result in hundreds of time points, which results in an almost unlimited variety of possible data analysis approaches, and, consequently, in the probability of a false significant finding approaching certainty."

Many such studies have been done, but they have failed to produce any robust evidence that human brains produce memory recall or thinking. Let us look at some of these studies, and the results that have been claimed. I will use the "heat map" in Figure 5 of the paper here to select the best-reported claimed ERP effects for cognitive activity. According to that figure, the best-reported ERP effects relating to cognition are:

(1) A CDA or contralateral delay effect having something to do with memory;

(2) an FN400/N400 effect (also called an "ERP old/new effect) having something to do with recognition;

(3) an N170 effect having something to do with categorization;

(4) a P100 effect  (also called a P1 effect) having something to do with attention.

It is claimed that an "ERP old/new effect" (apparently the same or similar to an FN400/N400 effect) is some EEG sign of recognition.  Looking at the  papers attempting to show this effect, we see nothing that looks very impressive. The claim is that when you have people look at some list of words that includes words they were asked to memorize and words they were not asked to memorize,  that for only about a fifth of a second some type of brain wave looks slightly different when that wave is read from the parietal region of the brain. 

No robust evidence has been provided for such an effect, because the study group sizes used in the studies claiming such an effect are too small. Even if such a fraction of a second effect was observed, it can be explained without assuming that a memory has been retrieved from the brain.  When somebody recognizes something, there can be a kind of "aha" effect in which muscular responses differ very slightly.  For example, after recognizing a face in the crowd, a person's facial expressions can be different than when encountering a stranger, with the difference lasting only an instant. Such a difference could easily be the explanation for some marginal fraction-of-a-second difference showing up in a reading of brain waves. 

In one paper I read claiming to get this fraction of a second "ERP old/new effect," the instructions were for subjects to click an "Old" button if they recognized a word, and a "New" button if they did not. The instructions stated that the "Old" button should only be clicked if the subject was sure he had seen the word before. With such instructions, there easily could be a kind of momentary pausing effect when people thought they recognized a word, during which they were wondering whether they were sure about seeing the word before.  Such a muscular pausing could be the cause of this fleeting "ERP old/new effect," with the effect having nothing to do with a difference in brain activity during recognition. 

This "ERP old/new effect is apparently the same (or involves or is related to) something called the N400 response. A paper described it like this:

"The N400 is a negative-going wave peaking at about 400 ms, whose amplitude is larger after presentation of a stimulus whose probability of occurrence is low within its semantic context (Kutas & Federmeier, 2011). For example 'He spread the warm bread with socks' would elicit a larger N400 than 'He spread the warm bread with butter” (Kutas & Hillyard, 1980).' "

This is another alleged neural correlate of cognitive activity that can easily be explained purely by muscle activity having nothing to do with the mind. The person presented with some crazy sentence may have a different muscle response, perhaps a look of bemusement on his face, or a kind of "huh?" look on his face.  Since the reported N400 response only involves a fraction of a second difference, we can't tell whether evidence is being picked up of brains thinking, or merely evidence of a tiny-bit different muscle response. 

A meta-analysis of studies about this claimed N400 response tells us that the average number of subjects used is only about 15.  Is such a sample size large enough? It is not, judging from the paper here. That paper is devoted to estimating how large a study group size would be needed to detect a particular ERP effect, one similar to the claimed N400 response and the claimed "ERP old/new effect." The paper tells us that to get a fairly good 80% statistical power would require at least "30– 50 clean trials with a sample of 25 subjects." 

There's another claimed ERP effect called the contralateral delay effect or CDA. The effect is claimed to occur as a fraction-of-a-second blip when people are shown screens having colored circles  or colored squared, and asked to identify whether a later screen matches the previous screen. Figure 1 of the paper here shows the type of screens shown.  The visual below shows the kind of screens shown, and how long the inputs were shown.

After taking EEG recording of brain waves of people during such an activity, scientists have claimed that there is some distinctive blip that shows up (lasting only a fraction of a second), something they call a contralateral delay effect or CDA. It has been claimed that such an effect is a correlate of working memory.  But since the alleged effect is extremely short-lived, it provides no evidence that brains store memories. What is showing up could simply be related to vision or to some color persistence effect by which a perceived color will hang around in the mind or brain for a second or two. 

It is well-known that there is something called an "afterimage," in which you can see something after you stopped looking at it.  For example, the web page here has a photo of Amy Whitehouse that is strangely colored. Look at the dot at the center of the photo for 30 seconds, and then look to the blank white area to the right of the photo. You will then see a ghostly afterimage of Amy Whitehouse. Whatever that type of effect is, it isn't memory.  It's just a "lingering of perception" thing.  The claimed CDA effect may merely be picking up that type of short-term thing, not something related to a brain storage or retrieval of memories. 

The N170 effect is some ERP effect supposedly produced when someone is shown a picture of a face. Referring to a mere fraction of a second, the wikipedia.org article on the effect claims that this alleged effects only lasts "130-200 msec after stimulus presentation." Figure 1 of the paper here has a diagram similar to the schematic diagram below, with the black line representing the response from seeing faces, and the gray line representing the response from seeing objects that are not faces.

This meta-analysis tells us that most of the faces used in studies of the N170 effect have involved emotional faces. The faces shown usually had expressions such as fear, disgust or joy. You can easily explain the fraction-of-a-second blip shown without imagining that viewing faces involves some recognition activity by the brain, and that all that is being picked up is a slight physiological response in regard to emotional stimulus. Studies of the N170 effect do not rule out some scenario like this:

(1) You see a face with an emotional expression, and your mind or soul (not your brain) recognizes the emotion. 

(2) Seeing emotion on someone's face produces a slight physiological response, which shows up as a fleeting blip in brain waves. 

The P100 effect (also called a P1 effect) is also some claimed small-fraction-of-a-second effect supposedly occurring for about 50 milliseconds when a person engages in visual selective attention, such as looking at only the left part of a screen. Eye muscles behave differently when you focus on only one side of a screen. Since such an instantaneous effect can easily be explained in terms of muscle activity involving the eyes, it provides no good evidence that brains are producing mental attention.  

Nothing we have discussed provides any good evidence that brains produce thinking, that brains store memories, or that brains retrieve memories. What kind of test can we imagine that would be a good test of such claims? The test might go something like this:

(1) Subjects wearing EEG electrodes on their head would be asked to look at photos displayed on a computer screen, with each photo shown for five seconds.  Most of the photos would be photos of people who were not famous and could not be recognized. One third of the photos would be photos of famous people with neutral expressions, none of whom were scary or threatening.  A computer program would assure a random shuffling of the photos. 

(2) Subjects would be asked to remain motionless and expressionless. Subjects would be told to simply say in their mind (without speech)  "Go" if they recognized the face, and "No" if they did not. 

(3) Attempts would be made from reading brain waves to determine whether there was any correlation between the perception of recognized faces and the perception of faces that were not recognized. 

Such a test would fail. No robust evidence would be found for a neural correlate of recognition. 

I used the "heat map" in Figure 5 of the paper here to select the best-reported claimed ERP effects for cognitive activity. It is interesting what is not reported in that heat map. According to the map it seems:

(1) There are no strong ERP/EEG effects for learning. 

(2) There are no strong ERP/EEG effects for decision making. 

(3) There are no strong ERP/EEG effects for prediction. 

(4) There are no strong ERP/EEG effects for executive function. 

(5) There are no strong ERP/EEG effects for perception.

(5) There are no strong ERP/EEG effects for speech.

Overall, EEG studies fail to provide robust evidence that thinking or decisions or memory retrieval or memory storage occurs because of the brain. The shape-seeking scientists eagerly looking for these slight, fleeting blip effects in EEG lines can be compared to people eagerly scanning the clouds looking for shapes that resemble animals, to back up some belief that the ghosts of dead animals live in the sky. 

The sample sizes used in these EEG/ERP studies are generally way too small to provide a robust evidence for a real effect. The headline of a news release of an important recent study is "Brain studies show thousands of participants are needed for accurate results." But these EEG/ERP studies typically involve only about 15 subjects per experiment.  A huge defect calling into question the reliability of all such studies is that the researcher is free to scan the results from 120 electrodes, and cherry-pick the output from whatever few electrodes he finds most shows some sub-second effect that is being eagerly sought, doing additional cherry picking that involves looking for some one-second slice of time in which the effect will show the most. This is a recipe for "conjuring phantoms." Given such complete freedom to scan data looking for some fleeting blip in wavy lines, it is easy to find almost any imaginary effect you might be hoping to find. In general, the fleeting ERP blips that are found can be explained as brain involvement in muscle activity and physiological activity, without postulating that brains are the source of thinking and memory. 

Principles of a Post-Materialist Science

What we can call the Age of Materialist Science has given us a scientific academia landscape with the following dysfunctional features:

• People trained as scientists are coerced into accepting or paying lip-service to doubtful belief tenets such as the nonexistence of souls and spirits, the neural origin of all mental effects, and the accidental origin of all biological innovations, contrary to a vast number of facts and observations.

• Much of what is called science consists of the belief dogmas of a belief community, often ideas contradicted by much observational evidence, such as beliefs that human mental phenomena are purely brain effects.

• Scientists act like conformist members of a belief community, afraid to challenge belief dogmas that have become kind of sacred tenets in their community.

• Scientists are effectively encouraged to ignore a vast body of relevant observations of the paranormal, observations conflicting with the materialist belief tenets of the scientist's belief community.

• Much of the activity of scientists consists of doing poorly designed experiments trying to provide evidence for various beliefs prevailing in the scientist belief community.

• A system of peer review exists by which anomalous observations and contrarian analysis and heterodox viewpoints can be prevented from being published.

• Smug achievement legends are constantly repeated, even when they make no sense, such as the claim that a nineteenth century scientist did something to explain protein molecules and super-complex cells that he knew nothing about. 

• Overconfidence, hubris and knowledge overestimation is systematically encouraged, along with absurd claims that scientists “pretty much understand” things that are a thousand miles over their head.

The diagram below contrasts principles of good science with the current tendencies of scientists in universities:

The diagram below gives us a rough sketch of what we have in the Age of Materialist Science:

Someday this very dysfunctional system may be replaced by something better. Below are some very rough thoughts about some of the principles that a reformed, post-materialist science might follow.

• Do not put any previous scientist on any kind of pedestal, or reverently attach special value to his thoughts and theories.

• Recognize the strong possibility of an observer getting novel and currently inexplicable observational results, and instead of ignoring such results, direct funding and attention to follow up on them.

• Make federally funded scientific research freely available to all, rather than hiding scientific work behind paywalls or in expensive journals that make the taxpayer pay again for research he already funded.

• Value all observations by previous careful observers, not dismissing such observations with excuses such as saying people believed the wrong things when the observations were made.

• Discourage inflexible and one-sided printed science textbooks, replacing them with electronic works that allow readers to add links and comments that draw attention to errors and omissions in the works, and draw attention to conflicting evidence and contrary viewpoints.

• Recognize that humans do not understand the deep mysteries of the origin of life, the origin of large organisms and the origin of minds, and be less generous in funding scientists spending most of their careers trying to bolster previous boasts claiming explanations for  such things, while encouraging a critical analysis of their work.

• Do not ignore or dismiss repeatedly reported observational phenomena with a claim that the thing cannot be happening because it is impossible.

• Recognize that science is only one of quite a few important ways of reaching truth, acknowledging the equal importance of other paths such as logic, mathematics, history, scholarship and direct personal experience. 

• Revise psychology textbooks and biology textbooks so that they discuss at length hard-to-explain or inexplicable human observations and anomalous experiences, rather than censoring and suppressing such observational reports. 

• Follow the principle that when reliable observers frequently report specific kinds of observations of the  inexplicable and anomalous, such observations should be given more attention rather than less attention.

• Recognize the high tendency of social structures such as universities and colleges to create conformist belief communities that may have a negative impact on scientific progress, and give rise to speech customs and belief traditions that masquerade as well-established science.

• Create a credential system where anyone who passes a very hard 3-hour test on some scientific subject can be certified as an expert on that topic, even if he has not passed through the conformity-creating system of colleges and universities.

• Create and fund alternative structures and organizations for learning and research separate from universities and colleges that have been so infected by conformist belief traditions, not as a replacement but as a rival and an alternative.

• Respect the observations of people who are not professional scientists, rather than having some snobbish elitist attitude that observations count for little unless they are made by professors or near-professors.

• Revamp the current university and college science instruction system, which suffers from an authoritarian teacher/follower model not varying much from the teaching model of two thousand years ago, replacing it with some model encouraging more dissent and discussion of alternative viewpoints and evidence conflicting with predominant assumptions.

• Revamp or rethink the "one-to-many" model of collegiate classroom instruction which tends too strongly to produce a meek acceptance of ideology or dubious claims passed on by authorities.

• Replace in many university departments the current habit of giving someone with a master's degree a PhD based only on some very narrow research on one tiny topic, and make extremely broad multi-subject cross-discipline study the thing that gets you the PhD. 

• Federally fund independent scientists with worthy research proposals, even those working outside of the university system.

• Have a large fraction of all federally funded scientific research approved by persons outside of academia, to help prevent "echo chamber maintenance" effects whereby some scientist belief community with "purse string" control keeps funding research designed to support its cherished beliefs.

• Create a system in which public comments can appear at the end of every online science paper, allowing the public to dispute poorly designed papers, discuss conflicting evidence, and dispute papers making claims not justified by anything in the paper.

• To discourage studies in which researchers feel free to slice and dice data in innumerable ways until they find something "statistically significant," create a pre-registration system for experimental studies, in which papers must only report on whether the results supported a previously announced hypothesis and whether the data collection and data analysis procedures followed matched a detailed experimental plan published before any data was collected. Also have a red-flag system whereby the reader is warned of the failure of an experimental paper to follow such a standard.

• Create sample-size calculation conventions and study-group-size standards that limit the proliferation of misleading experimental studies in which false alarms (easily explainable by chance effects) are heralded as scientific discoveries.  

• Stop acting as if unintuitive principles not suggested by common experience (such as "everything must be explained by matter and energy" or "there cannot be mysterious unseen influences") should be preferred over intuitive principles suggested by common experience (such as "accidents don't produce complex inventions" and "where there's lot of smoke, there's probably fire," which is a good rule-of-thumb in dealing with whether  observational reports suggest some underlying reality).  

• Stop promoting scientists based on the number of papers they have published, and encourage alternate promotion criteria such as the number of times a scientist has published a paper judged to follow a "Best Practices" standard. 

• End the current secretive peer-review system that acts as an ideological filter preventing dissenting viewpoints, reports of conflicting evidence, and novel observational reports, replacing it with a “let scientists stumble but flag their stumbles” system that will encourage public comments about any mistakes in a scientific paper, along with also a quality grading system whereby inferior papers can be low-graded.

• Stop making dubious claims of a scientific consensus that are not established by secret ballots of scientists, and create some system for secret balloting of scientists that will clarify how much they agree on opinions, a system that always offers a variety of belief answers including “I don't know.”

• Make a large fraction of all scientific funding go to studies that will be guaranteed publication studies in which publication is assured even if a null result is found.

• Make a small fraction of all scientific funding go to groups trying to disprove or falsify prevailing ideas and assumptions.

• Reform the speech and writing habits of scientists, to discourage the continuation of misleading speech practices and misleading visuals that are shockingly common in scientific literature.

• Stop referring to speculative unobserved things such as dark matter, dark energy and accidental macroevolution as "science," and accurately refer to them as "scientist speculations." 

• Reform current profit structures that reward bad science and bad scholarship that ignores important relevant evidence, and create novel profit structures that reward best-practices science and scholarship that takes into account all relevant evidence.

The current profit structure is extremely dysfunctional

Why the "A Memory Is Stored Throughout the Brain" Idea Makes Things Much Worse

No matter what form such an idea takes, the idea that the human brain stores memories creates the most gigantic difficulties, difficulties so bad that we should reject all claims that memories are stored in brains.  Let us look at two different forms of such an idea, and look at some of the difficulties that each form creates. 

The most common form of the idea that brains store memories is the idea that a memory is stored in one particular little spot of the brain, with each memory being stored in a different tiny spot.  Below are some (but not all) of the huge problems that such an idea creates:

(1) The spot selection problem. While computers have operating system algorithms for choosing a random storage spot,  a brain would seem to have no method or capability of choosing one small little storage spot for a memory to be placed. So if, for example,  we imagine that a brain placed a memory in storage spot number 263,432 out of 250,000 storage spots, we have the problem: why would that particular spot have been used to store the memory, and not some other spot? 

(2) The writing and encoding problem.  Once some spot had been selected for a memory to be written, something learned would have to be translated into neural states or synapse states and then written. There is no credible theory of how learned information or episodic memories could be translated into neural states or synapse states. There is no known mechanism in the brain for writing information. A computer has an operating system with formally designed encoding protocols such as the ASCII protocol and a protocol for converting decimal numbers into binary numbers. A brain has no such thing. A computer has a read-write head for writing information. The brain has no such thing. 

(3) The navigation problem.  Humans routinely display the ability to instantly recall learned information, given a name, date or image. So, for example, if you say "death of Lincoln," I will instantly be able to recite various facts about the death of Abraham Lincoln, such as that it occurred because John Wilkes Booth shot Lincoln through the back of his head at Ford's Theater in April, 1865.  If we believe that a memory is stored in some tiny little spot in the brain, such as storage spot 186,395 out of 250,000, then we have the problem: how was the brain able to instantly find that exact tiny spot where the memory was formed? This difficulty is a "show stopper" for all claims that a memory is stored in one exact spot of a brain, an insuperable difficulty.  We cannot get around such a difficulty by imagining that a brain uses the type of things that a book or a computer use to allow instant retrieval.  Books and computers use information addressing and indexes to allow instant access of a particular data item.  The brain has neither addressing nor indexes.  Unlike houses that have street addresses, neurons don't have neuron numbers or any other addressing system. Storing a memory in a brain would be like throwing a little 3" by 5" card into a giant swimming pool filled to the top with a million little 3" by 5" cards.  Just as it should take you ages to find a specific piece of information stored in such a swimming pool, it would take you ages to find in the brain some particular piece of learned information, if it was stored in one tiny spot, like a book stored in one spot on the shelves of a huge library.  

 

(4) The reading and decoding problem.  If a memory was stored in one particular spot, there would be the problem of how a memory could be read from that exact tiny spot. The brain seems to have nothing like a read mechanism.  Nor is there any known mechanism by which information that had been stored as neural states or synapse states could be translated into a thought that would appear in your mind. 

But there is another form of the idea that brains store memories.  There is the idea that the brain stores a memory throughout the brain, rather than writing the memory only in one little spot.  However, the difficulties in this idea are even worse than in the idea that the brain stores each memory in one specific spot. The idea that a memory is stored throughout the brain has the following difficulties:

(1) A greatly worsened writing and encoding problem. The idea that a memory is stored throughout the brain has the same writing and encoding problems mentioned above, except that now the problem is much worse. This is because now rather than just imagining that a memory is written in one tiny spot by a brain without any known writing mechanism, we must now imagine that such a brain manages to write all over itself each time that a memory is stored.  This would take much more time than writing to a single spot in the brain. Humans routinely show the ability to instantly form new memories,  an ability that neuroscientists cannot credibly explain. You only make that problem worse if you imagine that each time a memory is formed, the brain is writing to many different places rather than one. 

(2) A memory disassembly problem. The idea that a memory is stored throughout the brain creates a gigantic new problem that did not exist if you assume a memory is written to only one tiny spot: a memory disassembly problem. If you imagine that a memory is broken up into tiny pieces and stored throughout the brain,  then you have the problem that such a disassembly process would require additional time, making it all the more impossible to explain the wonder of instant memory formation. Similarly, it only takes a second for me to store a piece of paper by opening a book in my library and sticking the page inside the book; but if I have to cut up the page into twenty pieces and store the pieces in twenty books, that takes much longer. 

(3)  A memory reassembly problem. The idea that a memory is stored throughout the brain creates a gigantic new problem related to memory recall: a problem of reassembling the memory that had been stored in scattered pieces throughout the brain. If we imagine a brain with only one memory, such a thing does not seem so hard (the brain could just read throughout itself looking for memory pieces, and read them all up). But if we imagine many, many thousands of memories that had each been stored by storing pieces of individual memories throughout the brain, then such an assembly seems impossible to occur, no matter how long it would take.  

I can give an analogy. Suppose I am storing 1000 family photos through a scattered storage method. I take each of the thousand photos, cut them up into little pieces, and store each by putting them in different pages in the books that make up my large library. Now, suppose my wife comes and asks, "Please get me a picture of our trip to Los Angeles."  Retrieving that photo would be a nightmare.  I couldn't just get all the photo pieces by shaking each book in my library.  That's because the pieces of each photo would be mixed up with all the pieces of 1000 other photos.  Similarly, we can imagine no way in which a brain that has scattered pieces of each memory throughout itself could ever reassemble such pieces to produce a good recall of a particular memory.  And if it ever could do such a thing, such a recall would take very long lengths of time, and a recollection could never occur instantly.   

(4) A greatly worsened reading and decoding problem. The idea that a memory is stored throughout the brain has the same reading and decoding problems mentioned above, except that now the problem is much worse. This is because now rather than just imagining that a memory is read from one tiny spot by a brain without any known writing mechanism, we must now imagine that such a brain manages to read from all over itself each time that a memory is retrieved.  This would take much more time that reading from a single spot in the brain. Humans routinely show the ability to instantly retrieve new memories,  an ability that neuroscientists cannot credibly explain. You only make that problem worse if you imagine that each time a memory is recalled, the brain is reading from many different places rather than one.  

There was recently in the news an MIT press release story making the utterly unfounded claim that some research had shown that "a single memory is stored across many connected brain regions." What we have is another misleading claim about engrams from MIT, which for many years has been a notorious source of unfounded claims about neural memory storage.  In the 2018 post here I took a long look at how MIT memory researchers had repeatedly made grandiose but unfounded claims about memory research.  I showed that MIT researchers had again and again made grandiose claims based on shoddy poorly-designed rodent studies guilty of using way too small sample sizes.  The results proclaimed by such researchers are mainly false alarms, the type of false alarms that are very easy for a researcher to get when he uses fewer than 20 subjects per study group. 

The latest memory research announcement by MIT discusses research guilty of the same old shoddy research practices that MIT memory researchers have been guilty of for so many years.  Once again, when we read the scientific paper (which can be read here) we find that the researchers used way-too-small study group sizes, such as one group of only 7 mice, another group of another 9 mice, and another group of only 10 mice. If the scientists had acted like good experimental scientists and had done what is called a sample size calculation, they would have found out that such tiny study group sizes are utterly inadequate to produce a reliable result. But they did no such calculation. They confess in their paper, "No statistical methods were used to predetermine sample sizes."

The scientists fear-conditioned mice by electrically shocking them (this typically involves getting mice to learn there is one little area of a cage where the shocking will occur). The scientists then measured something in lots of different regions in the brain of a very small number of mice, and the scientists have somehow got the idea that some regions were involved in memory storage.  To test such suspicions they "optogenetically stimulated" mice to try to artificially create fear in the mice, zapping the little regions they thought were involved in storing a memory.  This "optogenetic stimulation" is a method of using light to zap the brain of a mouse. 

The thinking behind such strange zappings of mouse brains is that by zapping some little part of a mouse's brain, you can get a mouse to remember some fear memory formed when a mouse was zapped by stepping on an electrical plate.  The underlying theoretical assumption was wildly implausible. It was the idea that if a mouse has a particular memory stored in many brain regions, then you can get the mouse to re-experience that memory by stimulating only one of those regions.  Such an idea makes no sense. It's kind of like thinking that I would get Tom Brady to throw a pass by sticking a sewing needle in his arm, stimulating one of the many muscles he uses in throwing a pass.  

Conclusions about whether the fear memory was recalled were based on a poor low-reliability technique that neuroscientists have long used: a judgment about whether so-called "freezing behavior" occurred (such behavior being defined as mere inactivity). The underlying assumption is that mice freeze when afraid, and that you can judge if a mouse is recalling a fear memory by looking for an instant of non-movement in which a mouse may be "freezing in fear." Given the start-stop, helter-skelter way in which mice move, any judgment about whether a mouse froze is going to be a subjective, unreliable judgment. So there is too much of a possibility of observational bias here, one in which an observer subjectively reports the effect he is hoping to find. Similarly, you might subjectively report that your goldfish in a goldfish bowl tends to move towards you when you are looking into the bowl, but that would probably tell us more about your desire to see something than about the goldfish. The idea that mice freeze when terrified isn't even a very sound one.  I have seen  dozens of mice flee when scared by a human, but I never once seen a mouse freeze when suddenly scared by the presence of a human. 

There is a very reliable way to measure fear in mice: you measure the mouse's heart rate, which undergoes a very sharp spike in mice when they are afraid. Our neuoroscientists senselessly continue to use unreliable subjective judgments about "freezing behavior" to try to measure fear in rodents, rather than sensibly using reliable measurements of heart rate spikes in rodents.  Being guilty of this flaw, the new MIT study has provided no reliable evidence about whether or not the mice remembered fear when parts of their brains were zapped. 

Moreover, when "freezing" (simple non-movement) occurred in the mice, the "freezing effect" could have been produced not by a recall of fearful memories, but by the very fact the energy was being transmitted into the brain of the mice. Imagine you are running along, and suddenly a scientist switches on some weird thing that causes some energy to pour into your brain. This all by itself might cause you to stop, even if it didn't cause you to recall some memory that caused you to stop. What could have been going on in the mice was just a kind of pausing effect caused by a novel stimulus rather than a recalled fear effect. A science paper says that it is possible to induce freezing in rodents by stimulating a wide variety of regions. It says, "It is possible to induce freezing by activating a variety of brain areas and projections, including the hippocampus (Liu et al., 2012), lateral, basal and central amygdala (Ciocchi et al., 2010); Johansen et al., 2010;  Gore et al., 2015a), periaqueductal gray (Tovote et al., 2016), motor and primary sensory cortices (Kass et al., 2013), prefrontal projections (Rajasethupathy et al., 2015) and retrosplenial cortex (Cowansage et al., 2014).”

Neither the paper nor its supplementary information contains any  mention of a blinding protocol, other than the bare statement that "all behavior experiments were collected and analyzed blind to experimental group."  Unless a paper has a detailed discussion of  how an effective blinding protocol was implemented (one that really achieves a blinding effect to prevent observer bias), we should assume that no effective blinding protocol was implemented.  For example, if you had one group of 7 mice with optogenetic wires attached to their brains, and another group of control mice with no such wires, anyone would be able to tell which group was the group that was hoped would show more "freezing" behavior (even if those judging how much the mice froze were not specifically told which group was which).  So some method that can technically be referred to as "blind" may not be blind at all because of a lack of an effective protocol. Whenever any paper claims a blinding protocol but fails to specify how an effective protocol was achieved,  we should assume that no effective methods of blinding were used (a severe defect in an experiment). 

Being guilty of quite a few serious methodological flaws (primarily the use of way-too-small study group sizes), the new MIT study has produced no robust evidence that memories are stored in the brains of mice, and no robust evidence that a memory is stored in many different regions of the brains rather than in some particular spot. According to the paper here, "Quality research practice requires both testing statistical significance and reporting effect size." But the new MIT paper reports no effect size. That is what goes on when shoddy experimental research practices have been followed, such as using way-too-small study group sizes. 

In this paper here we have a discussion of the absurd technique most commonly used to measure fear in rodents:

"In mice, freezing is a common and easily measured response used as an index of fear conditioning (Blanchard and Blanchard 1988; Graef 1994). Fanselow (1990) and Paylor et al. (1994) define freezing as the absence of any movement except for respiratory-related movements. Freezing behavior is measured by direct observation, scoring an animal as either freezing or active per interval of time, usually every 5–10 sec (Fanselow 1990; Paylor et al. 1994) or measuring freezing duration with a stopwatch (Phillips and Le Doux 1992)."

The technique discussed above measures only mouse inactivity, which will vary randomly. There is no sound basis for calling such a measurement a measurement of "freezing behavior." If I take 10 snapshots of a mouse per minute, that show the mouse not moving in three of those snapshots, that is no reason for thinking that the mouse was afraid when three of those ten snapshots were taken.  What is occurring these days among cognitive neuroscientists is deceptive labeling of mouse inactivity measurement. Graphs that should be labeled "mouse inactivity (%") are being misleadingly labeled "mouse freezing (%)."  The term "freezing" should never be used unless a sudden stopping of traversal was observed. 

Big Study Finds Brain Gray Matter and Cortical Thickness Peak at Age 6 or Earlier, Contradicting Brain Dogmas

A new study published in Nature (with very many listed authors)  has produced a result very relevant to claims that the human mind is produced by the brain.  Entitled "Brain Charts for the Human Lifespan," the paper says, "We aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age."  MRI scans are a type of scan that allow you to see the physical structure of the brain. 

Human experience is that intelligence roughly peaks around age 20, with no major decline before age 40.  The lack of intellectual decline before age 40 is partially why nations generally elect leaders that are 40 years old or older, and it is partially why major corporations generally have as their Chief Executive Officer someone who is age 40 or older.    The claim has often been made that gray matter in the brain is some type of neural matter particularly associated with intelligence. It has often been claimed that you think with the gray matter of your brain.  Given human intelligence peaking around age 20, and not declining much before age 40, such claims predict that gray matter in the brain should peak at around age 20, without much decline before age 40. 

But this is not at all what the "Brain Charts for the Human Lifespan" study found. It found that gray matter in the brain peaks at around age 6, with about a 12% decline by age 20, and about a 20% decline by age 40.  This is shown in a chart from the paper:

From the "Brain Charts for the Human Lifespan" paper

According to this chart:

• Gray matter volume peaks around age 6;
• gray matter volume declines by about 12% by age 20;
• gray matter volume declines by about 20% by age 40;
• cortical thickness peaks by about age 2 or 3;
• cortical thickness declines by about 10% by age 20;
• cortical thickness declines by about 15% by age 40;
• white matter volume peaks at about age 30.

The chart above is a bit hard to read, but at a web site set up by the paper authors, the gray matter volume trend by age is graphed in the easy-to-read graph below:

 Source: https://brainchart.shinyapps.io/brainchart/

The data presented in the new study simply does not match human experience regarding intelligence and age.  I cannot cite any numbers showing a growth of IQ by age, because IQ tests are designed to test differences in either children of one age or adults.  But the following are simple facts of human experience

• Children age 6 have an intelligence that only seems to be 50% or smaller than the intelligence of adults (despite the gray matter volume peaking around age 6);
• adults of age 40 do not have an intelligence noticeably less than those of adults at age 20, and have an intelligence much greater than those age 6 or younger (despite such 40-year-olds having 20% less gray matter volume than those age 6, about 15% less cortical thickness than those age 2, and roughly 10%  less gray matter volume than those age  20);
• adults of age 20 have an intelligence much higher than children of age 6 (despite such 20-year-olds having about 12% less gray matter volume than those age 6, and about 10% less cortical thickness than those age 2 or 3);
• adults of age 30 do not seem any smarter than adults age 20 (despite such 30-year-olds having white matter volume peaking at their age);
• children with an age of about 2 or 3 have an intelligence that only seems to be a small fraction of the intelligence of adults (despite their cortical thickness peaking around this age). 

Once again, the "brains make minds" dogma gigantically flunks an empirical test. But you won't hear about this failure in the mainstream media, which tends to keep scientists and the public in a "filter bubble" that allows them to keep thinking that their cherished dogmas are holding up well, no matter how miserably such dogmas are failing empirical tests. So, for example, a Nature article on the "Brain Charts for the Human Lifespan" study completely fails to mention how dramatically the study's data conflicts with human experience about how intelligence changes with age. 

"Brains Make Minds" Idea Flunks an Audit of a Large Brain Scan Database

For many years neuroscientists have been claiming important results about brains and minds, after doing brain imaging experiments using small sample sizes.  Typically such claims will be based on way-too-small sample sizes smaller than 15.  A new press release from the University of Minnesota Twin Cities announces results which indicate that such small-sample correlation-seeking imaging experiments are utterly unreliable.  The headline of the press release is "Brain studies show thousands of participants are needed for accurate results."

There is a technique to measure the reliability of brain scans when used to make claims about supposed neural signs of cognitive activity.  The technique involves measuring what is called the test-retest reliability of brain scans.  The technique involves trying to determine to what extent some claimed neural sign of cognitive activity shows up both times when two different brain scans are taken of the same person. 

So, to imagine a hypothetical example, suppose some claim is made that the hippocampus of some subject activated more strongly when the subject recalled something. A check can be made as to whether the same thing was seen when the same subject had his brain scanned a second time, doing the same recall task.  If no such increased activation is seen on the second brain scan, we have a good reason for thinking that the claim about the first scan is unwarranted, and that the first scan has simply given a false alarm, a result of random brain fluctuations.  

Conveniently "covering their tracks," the vast majority of neuroscientists fail to do a retest of subjects when doing brain scanning experiments. However, there are some large databases of brain scans that include scanning retests of many subjects. It is therefore possible to judge how well claimed neural correlations of cognitive activity tend to replicate when a second test is done of the same subject. 

One such brain imaging database is the Adolescent Brain Cognitive Development Database. The database includes scans of thousands of subjects doing particular tasks such as a Monetary Incentive Delay task. a Stop Signal task and an n-back or nBack task (as described here). The database includes brain scans of more than 10,000 adolescents, and for more than 7000 of these adolescents a second set of scans were taken two years later, with the subjects performing the same tasks as in the first scan.  Such a database provides an excellent platform to test whether correlations between brain states and mental activity tend to repeat when the same subjects were scanned two years later.  

Such an examination is reported in the scientific paper entitled "Reliability and stability challenges in ABCD task fMRI data" by James T. Kennedy and others, which you can read here or here.  The study used a measure of retest reliability called the intraclass correlation. An intraclass correlation of less than .4 is generally regarded as "poor." In the wikipedia.org article on the intraclass correlation we read the following:

"Cicchetti (1994) gives the following often quoted guidelines for interpretation for kappa or ICC inter-rater agreement measures:

• Less than 0.40—poor.
• Between 0.40 and 0.59—fair.
• Between 0.60 and 0.74—good.
• Between 0.75 and 1.00—excellent.

A different guideline is given by Koo and Li (2016):

• below 0.50: poor
• between 0.50 and 0.75: moderate
• between 0.75 and 0.90: good
• above 0.90: excellent"

The results reported in the scientific paper entitled "Reliability and stability challenges in ABCD task fMRI data" by James T. Kennedy and others were devastatingly negative.  In the paper's abstract we read this: 

"Reliability and stability [quantified via an intraclass correlation (ICC) that focuses on rank consistency] was poor in virtually all brain regions, with an average ICC of .078 and .054 for short (within-session) and long-term (between-session) ICCs, respectively, in regions of interest (ROIs) historically-recruited by the tasks. ICC values in ROIs did not exceed the ‘poor’ cut-off of .4, and in fact rarely exceeded .2 (only 5.9%).... Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations."

What this means is that there was extremely low level of repetition of effects between one scan on a subject and a later scan on the same subject. As mentioned above, an intraclass correlation of less than .4 or .5 is commonly described as "poor." The very low intraclass correlations reported (only .078 and .054) can be described as extremely poor or appallingly poor.  In the quote below, the authors of the study describe their results as a "particularly disappointing outcome," and wonder what factors contributed to so poor an outcome. We read the following: 

"Our main finding was that within-session reliability and longitudinal stability of individual differences in task-related brain activation was consistently poor for all three ABCD tasks. Data cleaning approaches like outlier removal, movement regression, and rank normalization significantly increased reliability and stability, but by a small, seemingly inconsequential amount (average change of less than .025). While the finding of poor within-session reliability and longitudinal stability in the ABCD task fMRI data did not come as a surprise, given the mounting evidence for generally lackluster reliability of task-fMRI in mostly adult samples (Elliott et al., 2020; Herting et al., 2018; Noble et al, 2021), the present estimates are far below the .397 average reliability of task-fMRI activation estimated in the meta-analysis by Elliott et al. (2020). The question then arises, what factors could contribute to this particularly disappointing outcome? "

These results are what we would expect under the idea that the brain is not the source or cause of human mental activity, and not the storage place of memories.  In such a case we would expect that when scientists claimed some correlation between brain activity and mental activity after brain scanning some subjects, they would almost always be finding mere false alarms that would strongly tend to disappear when a second brain scan was made of the same subjects.