No, Brains Have Nothing Like a Large Language Model (LLM)

Quanta Magazine is a widely-read online magazine with slick graphics. On topics of science the magazine again and again is guilty of the most glaring failures. Quanta Magazine often assigns its online articles about great biology mysteries (involving riddles a thousand miles over the heads of PhDs) to writers who lack even a bachelor's degree in biology, and who may also lack any history of writing very much about biology. Often it will assign such articles to be written by people identified as "writing interns."  The articles at Quanta Magazine often contain misleading prose, groundless boasts or glaring falsehoods. I discuss some examples of such poor journalism in my posts here and here and here and here.

The writers at Quanta Magazine often sound like the most credulous pushovers for scientists making dubious boasts. They often seem like the science journalists depicted below:

A recent  example of a puff-piece article in Quanta Magazine is its fawning article entitled "The Polyglot Neuroscientist Resolving How the Brain Parses Language." The article title is a very misleading one. It is not brains that parse language. It is people who parse language. Parsing language (interpreting what someone meant upon hearing or reading something) is an example of a very high-level mental faculty.  Neuroscientists have no credible tale to tell of how a brain could produce such a faculty. 

The puff piece article is written by someone identified as a "writer and filmmaker," who has written many articles at the Quanta Magazine dealing with topics related to AI and technology, but almost no articles at the site relating to psychology or neuroscience.  What we get is an article devoted to glorifying neuroscientist Ev Fedorenko. You can tell what a "going gaga" scientist glorification affair is occurring by the fact that the article has five huge photos of Fedorenko, each of which fills up my computer monitor. 

We have this extremely misleading statement by the neuroscientist, not matching anything ever found in a brain:

" 'You can think of the language network as a set of pointers,'  Fedorenko said. 'It’s like a map, and it tells you where in the brain you can find different kinds of meaning. It’s basically a glorified parser that helps us put the pieces together — and then all the thinking and interesting stuff happens outside of [its] boundaries.' ”

The brain has no maps, and it has no pointers. I know pointers well, having used them extensively when I was a C programmer early in my programming career. A pointer is a variable that stores the address where some data is stored. Brains have no addresses, and nothing corresponding to a programming variable. So in a brain there can be nothing like a pointer. 

The article has a reference to a scientific paper by Fedorenko, one entitled "The language network as a natural kind within the broader landscape of the human brain." The paper says, "In this Review, we discuss brain areas that are specific to language —what we refer to as the language network — and position them in relation to perception, motor planning and cognition (Fig. 1)." There are no regions in the brain "specific to language" in the sense of being able to parse language. 

The paper announces that it will back up such claims largely by appealing to brain scan studies, saying, "We primarily draw on fMRI data from studies that have relied on the individual-subject functional localization approach14,19 (Box 1), which was essential in clarifying the distinctions discussed.." We then have in Figure 1 an extremely dubious visual showing five brains. We have some "function localization" claims:

• One brain visual has a small fraction of it colored blue, and this is labeled as the "perception" area of the brain. 
• Another brain visual has a very small fraction of it colored red, and we are told that is the "motor planning" area of the brain. 
• Another brain visual has five parts of it colored purple, and we are told that this is the "language" areas of the brain. 
• Another brain visual has quite a few parts of it colored green, and we are told that this is the "knowledge and reasoning" areas of the brain. 
• Another brain visual has quite a few parts of it colored green, and we are told that this is the "intended meaning" areas of the brain. 

People have been making these kind of functional localization claims about the brain for almost 200 years, and almost always the claims have been very dubious. For example, below is an illustration from the beginning of the 1834 book A System of Phrenology by George Combe. Notice how the brain areas have little numbers next to them, with the bottom legend explaining the claims made about localization of function in the brain. 

What basis does Fedorenko have for the brain function area maps in her paper, Figure 2, one rather reminding you of the phrenology image above? She says her basis is brain scan data.  The Supplementary Data part of the paper promises that if you click on a link you will get the data that was used to generate the brain function area maps. Following the links takes you to a little page that is almost worthless for inspecting data, as it involves a .zip file consisting of files in an .nii format that the average person will be unable to load or read. Consequently it is all but impossible for anyone to check the evidence basis for Fedorenko's brain function area maps, which should not be regarded as reliable. Neuroscientists dramatically disagree with each other when they produce such brain function area maps, which typically fail to have a sound evidence basis. 

A figure on page 294 of the paper  gives us the bar chart below: 

The purple bars at left are probably incorrect

The scale on the left shows us that the bars refer to fMRI percent signal changes in different regions of the brain during different activities. You should note well that the largest bars show a variation of only 2%. For almost all of the activities listed, the percent signal change is less than 1%. On average the change in brain signal strength is merely about 1 part in 200. A change that small is no real sign of brains working harder during some cognitive activity. We might expect that mere chance variations would produce differences that small. 

Except for the two purple bars on the left-most edge, the diagram is consistent with what I have often stated on this blog. For example, in my post "Brain Imaging Shows No Appreciable Neural Correlates of Memory Activity," I quoted quite a few studies showing a variation of only  1 part in 200 when people had their brains scanned by fMRI scanners while they were doing various memory-related activities. I noted that so small a change fails to provide any good evidence of brains causing mental activities, as we might expect a 1 in 200 fluctuation in brain activity to occur by chance, even if brains don't make minds. 

But what about the purple bars on the left of the chart? What is the basis for the claim being made in the graph that during sentence comprehension there is up to a 2% change in brain signal strength? We fail to get a justification for the data shown. The references in the paper include some papers referring to sentence comprehension. But none of them seem to back up the claim above. Specifically:

• We have a reference to a paper "Cognitive control and parsing: Reexamining the role of Broca’s area in sentence comprehension." It makes no claim about percent signal changes during sentence comprehension. 
• There's a reference to a paper "Retrieval and Unification of Syntactic Structure in Sentence Comprehension: an fMRI Study Using Word-Category Ambiguity." It's behind a paywall, and its abstract makes no claim about percent signal changes. 
• There's a reference to a paper "The cortical language circuit: from auditory perception to sentence comprehension."  It's behind a paywall, and its abstract makes no claim about percent signal changes. 
• There's a reference to a paper "fMRI reveals language-specific predictive coding during naturalistic sentence comprehension." The paper does not make any claim about a percent signal change during sentence comprehension. 
• There's a reference to the paper "Sentence complexity and input modality effects in sentence comprehension: an fMRI study." It's a study involving only 20 subjects, and does not claim in the main body of its text to have detected any percent signal change of 1% of higher. But there is a footnote in which the authors say that after doing some dubious-sounding fiddling with the data they got some kind of 1% difference of some type. We cannot have much confidence in such claim, as it stated only in a footnote. 
• There's a reference to the paper "Form and Content: Dissociating Syntax and Semantics in Sentence Comprehension." It does not make a claim about a percent signal change. 
• There's a reference to a paper "Location of lesions in stroke patients with deficits in syntactic processing in sentence comprehension." We can ignore it, because the issue is how much normal brain signals change during sentence comprehension.
• There's a reference to a paper "Time course of semantic processes during sentence comprehension: an fMRI study." It does not make a claim about a percent signal change. 

Rather than relying on the references Fedorenko has given, we can follow the alternate approach of doing a Google image search using the phrase " 'percent signal change' + ' sentence comprehension' ." This gives us papers such as these:

• The paper here involving sentence comprehension indicates a percent signal change of less than 1 part in 200 (less than half of a percent). 
• The paper here ("Neural correlates of syntactic movement: converging evidence from two fMRI experiments") reports a relatively large percent signal change of about 1 part in 100 for people listening to sentences. But the study group sizes are so small (involving only 11 subjects for one experiments, and 10 subjects for another experiment) that the paper cannot be counted as good evidence for anything. 
• The very low-quality paper here ("Neural correlate of the construction of sentence meaning") co-authored by Fedorenko must be disregarded because it reported a "percent signal change" based on EEG readings rather than fMRI readings, because it used a sample size of only 6 epilepsy patients with implanted electrodes, and also because of the unreliability of looking for percent signal changes in the brain waves of seizure-prone patients, whose brains produce all kinds of unpredictable EEG spikes. 
• The paper here ("Top-down and bottom-up contributions to understanding sentences describing objects in motion")  used an almost equally poor study group size of 12 subjects, and found a percent signal change of .05 percent, 1 part in 2000. 
• The paper here ("Language processing in the occipital cortex of congenitally blind adults") used one too-small study group size of 9 blind adults and another possibly halfway-adequate study group size of 22 control subjects with normal vision. Its Experiment 1 involving language processing found a percent signal change of less than .1 (less than 1 part in 1000) for the 22 control subjects. Another experiment involve language processing found a percent signal change of less than .5 (less than 1 part in 200) for the 22 control subjects.
• The paper here ("Brain activity associated with selective attention, divided attention and distraction") finds about a 1% percent signal change in the auditory cortex during language processing. But the study group size is an unimpressive 15 subjects. This auditory cortex region of the brain is not part of the "language processing" area claimed by Fedorenko. In her paper she refers to some regions of the brain and says, "These areas are distinct from the language network as well as from general-purpose sensory and motor areas, such as the primary auditory or primary motor cortex," apparently indicating that what she thinks is a "language network" in the brain is something outside of the auditory cortex. 

Typically there is no test/retest reliability when doing these little studies involving only 15 or 20 or 25 subjects. Robust evidence would only come from a much larger study group size. The New Scientist story below tells us that thousands of participants are needed for studies like these:

The purple bars on the graph shown above are very probably incorrect. Scientific papers documenting brain scanning during language comprehension do not show any robust evidence of a 1% or 2% change in brain activity during sentence comprehension. And there is no sound evidential basis for Fedorenko's diagram claiming to show the location of five language areas of the brain. 

As discussed here, a recent study indicated that 40% of MRI signals do not even correspond to brain activity, which further undermines the knowledge boasts that  Fedorenko has made, and provides another reason for disbelieving in the accuracy of her brain activity maps.  We read this: "Researchers at the Technical University of Munich (TUM) and the Friedrich-Alexander-University Erlangen-Nuremberg (FAU) have found that an increased fMRI signal is associated with reduced brain activity in around 40% of cases. At the same time, they observed decreased fMRI signals in regions with elevated activity." This is the kind of finding that should make us disbelieve Fedorenko's fMRI-based claims of a brain "language network." 

Link

The puff piece article in Quanta has a "softball questions only" interview with Fedorenko in which she states this:

"There’s a core set of areas in adult brains that acts as an interconnected system for computing linguistic structure. They store the mappings between words and meanings, and rules for how to put words together. When you learn a language, that’s what you learn: You learn these mappings and the rules. And that allows us to use this 'code' in incredibly flexible ways."

There is no robust scientific basis for these imaginative claims. To the contrary, the most powerful microscopes have examined very much brain tissue from very many still-living people and very many very recently deceased people, and no one has found the slightest trace of any sentence, word or letter stored in a brain. 

Later on Fedorenko states, " Then the language network parses that, finding familiar chunks in the utterance and using them as pointers to stored representations of meaning." Excluding the merely genetic chemical representations in DNA, there is zero evidence for any such "stored representations of meaning" anywhere in the brain.  Later she kind of gives away the speculative nature of her claims, by saying "there may well be cells that respond to particular aspects of language."  Note the use of "there may well be" rather than "there are."   

Fedorenko offers as evidence for this claim the preprint "Modality-Specific and Amodal Language Processing by Single Neurons."  It is a "Jesus in my toast" exercise in noise-mining. Recordings of neuron firings were taken after 1400 invasive microwire electrodes had been implanted in the brains of 21 very sick patients with the worst types of epilepsy. The authors tried to find some neurons that fired more often when certain words were spoken. Because neurons fire between 1 and 200 times per second, any such exercise will always be able to find some neurons that fired more often when certain words were spoken. It is misleading to call such a thing "selectivity" as the paper did. Similarly, if I set up a computer program to try to correlate the passing of cars outside my house and the speaking of words on my TV set, and let the program run long enough, I will probably be able to find that certain words were spoken more commonly on my TV set when a car passed in front of my house.  That would be mere silly noise-mining, and that is all that is going on in this preprint, which fails to provide any decent evidence of any neuron responses to language different from what we would expect from chance variations, giving 1400 microwires implanted in brains. 

Noise-mining studies like this raise grave moral concerns about the reckless research endangerment of very sick epilepsy patients.  The sickest of epilepsy patients often have electrodes implanted for evaluation of where surgery should be done to reduce their symptoms. But such electrodes are usually not the microwire electrodes used for experiments like this. The deep implanting of such microwire electrodes in brains (70 microwires per patient for this study) has serious risks that are not medically justified, such as a risk of brain bleeding.  We should doubt any claim to have got any adequate degree of "informed consent" from the seizure-racked people who have these kind of deep microwire electrode implants. 

A scientific paper tells us, "Sixty-five years after single units were first recorded in the human brain, there remain no established clinical indications for microelectrode recordings in the presurgical evaluation of patients with epilepsy (Cash and Hochberg, 2015)." In other words, there is no medical justification for implanting microelectrodes or microwires in the brains of epilepsy patients. Complications from the insertions of such electrodes may include death, with two deaths reported here. I consider the exploitation and endangerment of sickest epilepsy patients in these type of poorly designed noise-mining experiments to be a serious moral scandal. 

Does it work like this?

For much more on the topic of why most subjects in neuroscience experiments do not understand the risks they are taking, read my post here. Epilepsy patients requiring surgery are often those who cannot read well (because of learning delays caused by their very frequent seizures), and who will not well-understand any jargon-filled form they are asked to sign. Typically neuroscientists doing electrode implant experiments with epilepsy patients fail to provide in their papers the informed consent form used, and also fail to describe whether special measures were taken to make sure those getting the microwire electrode implants really understood the risks of participating in an experiment of this type. 

We should laugh at Fedorenko's answer when she is asked whether there is an LLM (Large-Language Model) inside the brain, and she replies "pretty much." Nothing like that exists in the brain. An LLM or Large Language Model is a gigantic structure of data, computer programming software and data processing software built after some big server farm (consisting of very many individual computers) crunches data gobbled up by crawling the Internet, scooping up the contents of millions of web pages. That does not correspond to anything in a human brain. 

The idea of a language-parsing ability in a brain seems implausible when you consider that human brain anatomy has not changed in more than 20,000 years, but the main human languages (such as English) are less than 3000 years old. 

It is commonly claimed that the left half of the brain is needed for language. But if you read my series of posts labeled "loss of left half of brain," using the link here, you will find many cases that defy such a dogma.  For example, you will read of a case described like this: "He exhibited no lack of intelligence, yet after his death it was discovered that his left brain was practically destroyed and replaced by a watery substance." And you will read of how Beth Usher could still tell all of her "knock-knock" jokes just after the left  half of her brain had been removed to treat intractable seizures. You will read that on page 109 of a paper we read of three other cases of the removal of the left half of the brain, and we read that "speech and verbal comprehension were present immediately after left hemispherectomy in all three cases." The same post describes good verbal comprehension in patient E.C. after the left half of his brain was surgically removed. 

In a scientific paper ("Why Would You Remove Half a Brain? The Outcome of 58 Children After Hemispherectomy −−The Johns Hopkins Experience: 1968 to 1996") we read about how surgeons at Johns Hopkins Medical School performed fifty-eight hemispherectomy operations on children over a thirty-year period. Eleven of these children had the left hemisphere of their brains removed; most of the rest had the right hemisphere of their brains removed.  The paper states this:

"Despite removal of one hemisphere  [i.e. one half of the brain], the intellect of all but one of the children seems either unchanged or improved....Although there have been major concerns about loss of language after left hemispherectomy, all eleven of these children have regained virtually normal language....It is tempting to speculate, that the continuous electrical activity of these severely dysfunctional hemispheres interferes with the function of the other, more normal hemisphere. This might explain why motor function improves after hemispherectomy and why language recovers after removal of the dysfunctional left hemisphere, but does not seem to fully transfer before surgery. Perhaps it also partially explains intellectual improvement in these children after removal of half of the cortex. We are awed by the apparent retention of memory after removal of half of the brain, either half, and by the retention of the child’s personality and sense of humor."

Cognitive Neuroscience Is Floundering, So the Kavli 2024 Neuroscience Prize Went to Low-Quality Research

If you look up the phrase "cognitive neuroscience" you will typically get a description that does not correspond to any scientifically established reality. You will typically get a description saying something like "the branch of science that investigates the neural causes of cognition," a description that presupposes the incorrect claim that cognition has a neural basis. Cognitive neuroscience is getting nowhere, mainly because it is based on the false assumption that the brain is the cause of the mind. Honest and properly designed studies will never produce evidence showing a neural basis for cognition. But there are 1001 ways to do poorly designed Questionable Research Practices studies that may fool someone into thinking that a little progress has been made in showing a neural basis of minds. 

The Kavli Foundation is a foundation founded by millions of dollars in grants from the late Fred Kavli. The foundation issues science prizes and science grants. One of its semi-annual prizes is in neuroscience. The 1 million dollar Kavli Prize in neuroscience was recently announced when a  false claim was made by the Kavli Foundation. The official citation appearing in the award announcement says that the 2024 prize was announced "for the discovery of a highly localized and specialized system for representation of faces in human and non-human primate neocortex." No such thing was ever discovered.  There are no representations of faces anywhere in the brain.  What we have in the 2024 Kavli neuroscience prize announcement is a bogus achievement legend, a claim that something big was done, when no such thing was ever done. 

Claims by neuroscientists that they have found "representations" in the brain (other than genetic representations) are examples of what very abundantly exists in biology: groundless achievement legends. There is no robust evidence for any such representations. 

Excluding the genetic information stored in DNA and its genes, there are simply no physical signs of learned information stored in a brain in any kind of organized format that resembles some kind of system of representation. If learned information were stored in a brain, it would tend to have an easily detected hallmark: the hallmark of token repetition.  There would be some system of tokens, each of which would represent something, perhaps a sound or a color pixel or a letter. There would be very many repetitions of different types of symbolic tokens.   Some examples of tokens are given below. Other examples of tokens include nucleotide base pairs (which in particular combinations of 3 base pairs represent particular amino acids), and also coins and bills (some particular combination of coins and bills can represent some particular amount of wealth). 

Other than the nucleotide base pair triple combinations that represent mere low-level chemical information such as amino acids, something found in neurons and many other types of cells outside of the brain, there is no sign at all of any repetition of symbolic tokens in the brain. Except for genetic information which is merely low-level chemical information, we can find none of the hallmarks of symbolic information (the repetition of symbolic tokens) inside the brain. No one has ever found anything that looks like traces or remnants of learned information by studying brain tissue. If you cut off some piece of brain tissue when someone dies, and place it under the most powerful electron microscope, you will never find any evidence that such tissue stored information learned during a lifetime, and you will never be able to figure out what a person learned from studying such tissue.  This is one reason why scientists and law enforcement officials never bother to preserve the brains of dead people in hopes of learning something about what such people experienced during their lives, or what they thought or believed, or what deeds they committed.    

But despite their complete failure to find any robust evidence of non-genetic representations in the brain, neuroscientists often make groundless boasts of having discovered representations. What is going on is pareidolia, people reporting seeing something that is not there, after wishfully analyzing large amounts of ambiguous and hazy data. It's like someone eagerly analyzing his toast every day for years, looking for something that looks like the face of Jesus, and eventually reporting he saw something that looked to him like the face of Jesus.  It's also like someone walking in many different forests, eagerly looking for faces on trees, and occasionally reporting a success. Read here for why claims of "place cells" in the hippocampus are unfounded claims based on low-quality research guilty of pareidolia. 

 The official citation page makes these claims:

"The present laureates used functional magnetic resonance imaging (fMRI) to localize different areas in neocortex specialized for face processing. Nancy Kanwisher pioneered the establishment of the functional region of interest (fROI) approach to localize the fusiform face area (FFA) in humans using fMRI. Kanwisher was the first to develop and employ a paradigm to identify a region sensitive to faces in each person. This finding strongly supported the idea of modular localization of cognitive function in the neocortex." 

The claims above are not accurate. No robust evidence has ever been provided that there are "different areas in neocortex specialized for face processing." One paper gets a result of only about 1% percent signal change when testing face recognition in different brain areas, getting only about a 1% signal difference for this FFA region.  Two other papers (this paper  and this paper) also find less than a 1% signal difference in this FFA region when testing facial recognition.  Another paper finds only a half of 1% signal change in the FFA during face recognition. Another paper using a larger sample size of 26 people reports a signal change of much less than 1% (only a small fraction of one percent) when testing this FFA region with face recognition. 

Such tiny percent signal changes do nothing to establish any reading of information from brains when visual recognition occurs. For one thing, since the sample sizes are mostly small (around 15 people per study), you could easily get a 1% or 2% signal variation by chance (just as you can easily get 55% of your coin flips being "heads" if you only flip 20 or 40 times).  If there is some tiny little signal change in one region of the brain when a face is recognized, that might be something that has nothing to do with reading memory information from brains. For example, it might be a little of an alert effect or an "aha" emotional boost effect caused by the mere fact of a successful recognition. 

The citation page of the 2024 Kavli neuroscience prize has a link to two papers by Nancy Kanwisher, neither one of which should create any confidence that she achieved what the citation page claims she did. The first paper is the 1997 paper by Kanwisher and others entitled "The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception." The paper is a bad example of a Questionable Research Practices study. A well-designed experimental neuroscience study will have all study group sizes being at least 15 or 20 subjects. That wasn't done in this study. The scientists started out with 15 subjects, and then started doing experiments on subsets of those subjects, those who performed in a way that the scientists found most encouraging. In the resulting study we have way-too-small study group sizes such as only five subjects per study group.  The study used no control subjects, and also completely failed to use any blinding protocol (a necessity for any study like this to be taken seriously). A sample size calculation would have revealed how inadequate the study group sizes were, but the authors failed to do any such calculation (or at least they do not have claim to have done such a calculation). The study was not a pre-registered study, and we get a strong feeling of the authors making up their methods as they gathered data, in violation of sound scientific procedure. 

It is amazing that a study this poorly designed is now being cited  on a prize page as an example of laudatory scientific work. The study would be more properly mentioned on some kind of "Hall of Lame"  page giving examples of poor-quality neuroscience research. Most ridiculously the citation page of the 2024 Kavli neuroscience prize includes a graph showing a 3% percent signal change in the brain of only a single subject. Citing this as evidence for a general effect in brains is as silly as claiming that male heartbeats increase when a male sees a picture of Taylor Swift, and using as your evidence a graph showing a very slight 3% heart rate increase in a single male. 

The only other paper by Nancy Kanwisher mentioned on the citation page of the 2024 Kavli neuroscience prize is her 2017 paper "The Quest for the FFA and Where It Led." That paper is a strange affair that diverges from the conventions of sound scientific research papers. It's a kind of "my glorious quest" paper that seemed to be telling us the wonderful story of how  Nancy Kanwisher progressed against her critics who complained about how weak her evidence was. The paper fails to provide any convincing evidence for Kanwisher's claim that she found a region of the brain specialized for recognizing faces. 

The paper has as its Figure 1 a groundless-looking "schematic map" depicting supposed brain regions specialized for particular tasks. No source is given for this map, which looks like one of those groundless phrenology maps that long appeared in scientific publications. No justification is given for any of the colors that appear in the map. Who made this map, and what data caused them to color the brain regions in these particular ways? The paper does not tell us. For all we know, it's just Nancy's wild guesses.  This extremely dubious Figure 1 is reproduced on the citation page of the 2024 Kavli neuroscience prize as if it was some kind of scientific data, which it is not. 

The citation page of the 2024 Kavli neuroscience prize discusses two other scientists who shared the prize money.  We read these incorrect triumphal claims on the page:

"Winrich Freiwald and Doris Tsao together used fMRI to localize similar face patches in macaque monkeys. Having localized these, they recorded from single neurons in each patch. They showed that the overwhelming majority of visually responsive neurons in the largest such region were face-selective. They proceeded to outline a system of multiple face patches, detailing their interconnections and functional specialization. Face recognition in the earliest patches was dependent on viewpoint, but later became viewpoint-independent through a series of processing stages. Winrich Freiwald in further work characterized populations of cells selectively responsive to faces familiar to the viewer. Doris Tsao identified different features of the face that make up a code enabling single cells to identify faces."

There are no literally "face-selective" neurons in any brain, and the very concept is obscure and implausible. There is no evidence that brains or any part of brains recognize faces. There is merely evidence that people and monkeys recognize faces. 

Let's look at the papers the  citation page of the 2024 Kavli neuroscience prize cite to try to back up the claims quoted above. The first is a 2008 paper by Winrich Freiwald, Doris Tsao and another researcher, one entitled "Comparing face patch systems in macaques and humans." This is a low-quality paper guilty of the same Questionable Research Practices so predominant in today's neuroscience, such as a total failure to follow any blinding protocol. The paper shows us brain scans of some monkeys, but only a too-small study group size of only 9 monkeys. The percent signal changes are shown in the paper's supplemental information document, and my guess is that they were buried there so that there would be a minimum chance of a reader discovering how unimpressive they were:  changes of only about 1% (Figure S8). Such changes are very unimpressive as evidence of monkey brain regions responding differently when they see faces. Given the too-small study group sizes, we have here no robust evidence to back up the paper's claim that there are "face-selective regions in monkeys." (A similar study also finds brain percent signal changes of only about 1% in the same type of monkeys when exposed to faces, and finds similar responses when the monkeys were exposed to faces and non-face objects.) 

The authors have used the typical misleading practice of showing a brain visual with some region colored in a bright color (such as yellow), to fool us into thinking that there was some big difference in some region, when the difference was only about 1%. Instead of giving us Figure S8 (which would let us know the difference was only about 1%), the   citation page of the 2024 Kavli neuroscience prize has reproduced one of those misleading diagrams. 

The other scientific paper of Winrich Freiwald and Doris Tsao referred to by the  citation page of the 2024 Kavli neuroscience prize is the 2009 paper "A face feature space in the macaque temporal lobe." That's a paper as low-quality as the 2008 paper I refer to above. In the 2009 paper the study group size used is a mere three subjects. Also, the paper failed to use any blinding protocol, something necessary for any paper of this type to be taken seriously.  The method described is a rather ridiculous one, in which the authors select particular cells for study, in an arbitrary manner, without any assurance that these were randomly selected cells, and not cells cherry-picked for some characteristic the authors were hoping to find. Based on arbitrary criteria chosen by the authors, the authors make the claim that an individual cell in the brain can be "face selective." What exactly was the criteria used to judge whether a cell was "face selective"?  The paper gives us no answer other than giving us a link to some external page that gives no answer. 

In another study by Tsao we seem to be given the idea that she regards a "face selective" cell as one that responds more when a monkey is shown a face, as opposed to some image that is not a face. A proper term for that would be "face responsive" not "face selective."  We can explain such a thing without any belief that brain cells are producing recognition. When shown a face (as opposed to a neutral sight such as a geometric shape), a monkey may become slightly more alert or attentive. Some cells may therefore "perk up" a bit. But that isn't evidence that cells in the brain are involved in face recognition. Similarly, your eyebrow may raise slightly when a pretty lady walks by, but that does nothing to show that your eyebrows produce recognition of pretty ladies. And a man's penis may start to slightly enlarge when a buxom lady walks by in a bikini, but the penis can't see anything and isn't recognizing anything.  It's the person who is recognizing something, not some part of his body. 

The citation page of the 2024 Kavli neuroscience prize also refers to a low-quality neuroscience paper by Doris Tsao and Le Chang entitled "The Code for Facial Identity in the Primate Brain." This is an example of what can be called parlor-trick neuroscience. They have  used  brain scan data from only two monkeys, and an arbitrary selection of particular cells from such monkeys. The authors have also used a database of 200 face images and some extremely elaborate statistical analysis and computer programming  they did on such images and their sparse brain data, with it all having a sound of "keep playing with the data until we get something publishable."  They claimed to have predicted something, but it's one of those deals where the predictive model (a product of abundant statistical and programmatic fiddling) is leveraging data outside of the brain scans, data found in the external database. We get no robust evidence of anything important, and the title is a misleading one. No evidence has been produced of any such thing as a code for facial identity in the primate brain.  We have mainly what sounds like scientists fiddling with data in arbitrary ways for a long time until they finally ended up with something that pleased them, after conjuring up a "spaghetti code" analysis pathway. Misleading studies like this are getting more and more common in the world of neuroscience. They involve combining data from external databases with data from brain scans or brain EEG readings, mixed with lots of arbitrary computer programming and extremely complex or convoluted make-things-up-as-you-go-along analytics, in some way that everything is so confusingly  entangled that the authors may get away with grand claims that are not justified. Typically what goes on is that the authors give you an impression "we got this from brain scans" when the reality is really "we got this from a very confusing mashup of brain scans, arbitrary computer programming and statistics fiddling,  and data pulled from outside of brain scans," with the mashup being so complex and entangled that you have no good evidence of something about brains alone. The paper critically depends on much computer programming, but the authors failed to follow good practice by publishing a link allowing anyone to inspect their code, and see whether it followed good practices. 

Because of an abundance of neural noise all over the place in brains (a strong reason for thinking brains cannot explain thought and memory recall that can occur with 100% accuracy), reliability of spike readings is a massive problem in some of the study types mentioned above, there being a huge problem of false positives in which cells are incorrectly identified as responding to some stimulus they did not actually respond to. A paper on the topic of single-unit neuron recording tells us this:

"Next, we examined a recording of 283,469 spikes recorded from mPFC [a brain region] in detail (Fig. 6b). Approximately 95% of the spikes recorded were considered noise as a result of low-amplitude spikes on several channels (Fig. 6b, left, gray points)."

Alas, the 2024 Kavli Neuroscience Prize has been awarded for low-quality research work which has not been well-replicated. The fact that this was done tells us something important about how cognitive neuroscience is getting nowhere these days. Cognitive neuroscience is based on false claims such as the claim that the brain is the source of the mind and the claim that the brain is the storage place of memories. It is no surprise that the scientists trying to back up these claims are floundering, running around in circles and getting nowhere. So when the committee of the Kavli Foundation searched for neuroscientists to be given a million dollar prize, all they could find is some low-quality research that was never well-replicated. 

For a scientific paper giving insight into the type of poor research that won the 2024 Kavli Neuroscience Prize, read the 2022 paper "The Failure of Blobology: fMRI Misinterpretation, Maleficience and Muddle" by brain imaging expert Stephen Jose Hanson.  The paper has this quote about the Fusiform Face Area (FFA) and the research of 2024 Kavli Neuroscience prize winner Nancy Kanwisher (I have underlined and boldfaced a few lines):

"Although the absurdity of the discovery of a 'face area' [an area of the fusiform gyrus that was somehow primarily responsible for face processing—fusiform face area (FFA) (Kanwisher et al., 1997)] wasn't sufficient reason to abandon the blobology program, subsequent years showed how bankrupt this phrenology (modularity) program had become. Because functional localization like the 'face area' has no actual anatomical anchors in the brain, but was based on a procedure, using another set or all the existing face stimuli in the experiment to, unfortunately, create circular kind of evidence for where the face area was to be found in the fusiform gyrus ('localizer logic', see Friston et al., 2010 who illustrates the dangers of localizers).... There is not sufficient space here to cover all the other FFA counter- evidence2. But suffice it to say, based on classifier evidence there is no '... fusiform face area, a blueberry-sized region on the bottom surface of the posterior right hemisphere that responds significantly more strongly when people look at faces than when they look at any other stimulus class.' (Kanwisher, 2006) Mathematically, this simply cannot be concluded from the GLM (unsupervised regression) or related tests."

The "Failure of Blobology" paper is an unflattering portrait of brain imaging neuroscientists gone astray, playing misleading games of "keep torturing the data until it confesses." 

Typical content from a cognitive neuroscientist

What goes on in the awarding of prizes such as the Kavli Neuroscience Prize is that there is a strong "help myself" element, in which judges may be doing themselves favors by awarding an award to a particular type of researcher. Researchers who have done a particular type of dubious research may be more inclined to award a prize to other researchers doing the same type of dubious research, because this makes people more likely to hold in high regard the type of dubious research such judges have done. Check the judge list awarding this year's Kavli Neuroscience Prize, and you will find one or two whose careers have been centered around the same type of dubious "blobology" that was awarded with this year's prize. 

Postscript: The paper "Prevalence of Mixed-methods Sampling Designs in Social Science Research" has a Table 2 giving recommendations for minimum study group sizes for different types of research. The minimum subjects for an experimental study are 21 subjects per study group. Some of the studies mentioned above are experimental studies that used less than this minimum number.  The "case study" type mentioned below is a different type of study in which you merely document one or a few occurrences of some condition or situation, without trying to show a cause.