A Recent Study Suggests Very Many Biologists Are "AI Cheating"

 It is huge mistake to rely on AI tools such as ChatGPT or Gemini when dealing with any controversial topic. Such tools make use of computer systems that have no real understanding of anything. The answers they give are produced through a combination of various complicated methods.  The main way in which such AI tools get their "smarts" is by stealing text written by human authors. 

A corporation creating such a system starts out by creating a very massive "question and answer" database consisting of hundreds of millions or billions of entries. A web crawling and book crawling system could look for text passages in any of these forms:

• A phrase or sentence ending with a question mark, followed by some lines of text. 
• A header beginning with the words "How" or "Why" and followed by some lines of text (for example, a header of  "How the Allies Expelled the Nazis from France" followed by an explanation). 
• A header not beginning with the words "How" or "Why" and not ending with a question mark, but followed by some lines that can be combined with the header to make a question and answer (for example, a header of "The Death of Abraham Lincoln," along with a description, which could be stored as a question "How did Abraham Lincoln die?" and an answer).
• A header written in the form of a request or an imperative, and some lines following such a header (for example a header of "write a program that parses a test line and says 'you mentioned a fruit' whenever the person mentioned  a fruit" would be stored so that the header was converted to a question of "how do you write a program" and the solution stored as the answer. 

Crawling the entire Internet and vast online libraries of books such as www.archive.org and Google Books, the corporation can create a database of hundreds of millions or possibly even billions of questions and answers. In many cases the database would have multiple answers to the same question. But there could be some algorithm that would handle such diversity.  The system might give whichever type of answer was the most popular. Or it might choose one answer at random. Or it might give an answer giving multiple answers, adding text such as "Some people say..." or "It is generally believed" and "Some people say." Included in this question and answer database would be the answer to almost every riddle ever posed. So suppose someone asked the system a tricky riddle such as "which timepiece has the most moving parts?" The system might instantly answer "an hourglass." This would not occur by the system doing anything like thinking. The system would simply be retrieving an answer to that question it had already stored. And when you asked the system to write a program in Python that lists all prime numbers between 20,000 and 30,000, the system might simply find a closest match stored in its vast database of questions and answers, and massage the answer by doing some search and replace. 

With such a system there is a big "plagiarism problem." A large fraction of the answers are plagiarized from materials protected by copyright. The system would presumably "cover its tracks" by refusing to provide the sources of its answers. There could also be various types of merging and search-and-replace that would make it hard to track down cases where the system was using plagiarism.  There are all kinds of programmatic ways that text can be massaged to make it harder to detect that plagiarized text was not an original composition. 

There are very many other methods that such an AI system could use to be able to quickly provide answers.  The systems probably include an army of utility programs that can be utilized to calculate answers to various mathematical questions, programming questions and puzzle questions.  Probably the systems make use of general-knowledge relational databases that have been filled up by servers traversing the billions of web pages and millions of books that are online.  Data stored in a relational database can be queried very conveniently by use of the powerful SQL language. 

What are the disadvantages of using such AI tools? For one thing, they often give answers that are dead wrong, wrong in the worst kind of way.  For example, ask ChatGPT whether DNA stores a specification for building a human body, and you will get the dead-wrong answer that DNA does  store such a thing. No such specification exists in DNA, which does not specify how to build any visible thing.  The only thing that DNA specifies is very low-level chemical information such as how to build microscopic protein molecules. DNA contains no information about visible anatomy. 

How did ChatGPT end up giving us an answer so wrong on this very important topic?  The reason is that its answer was not obtained by any actual reasoning process, but by web-crawling, frequency counting and source-ranking.  Every time its web-crawling came across someone attempting an answer to whether DNA stores a specification for making a body,  that attempted answer was added to the system.  With any arrangement like this, whenever there is a preponderance of false answers online to a particular question, the AI system will end up giving a false answer. So if 90% of the people who address online the question of "are those from Madagascar bad people," then if the AI system is asked "are those from Madagascar bad people" it will answer "Yes," even if there is no good basis for such a claim.  For a discussion of why it is that authorities started repeating false claims about what is in DNA, see my post here.  The same post has a list of about 25 quotes from scientists and doctors stating that DNA is not a specification for making a human body, and is not any such thing as a blueprint, recipe or program for making a human body. 

Because such truthful statements are apparently less common online than untruthful and groundless statements claiming DNA is a specification for making a human body,  ChatGPT gives us the wrong answer on this topic.  We must always remember that ChatGPT and other AI systems are myth amplifiers.  Whenever some erroneous idea is held by a majority of authorities, ChatGPT will tend to repeat such an erroneous idea. Ask such an AI system about the source of the human mind and the nature of human memory, and you will get many a false-as-false-can-be answer.  

A recent science news article at the Phys.Org site is entitled "Massive study detects AI fingerprints in millions of scientific papers." Referring to LLM (Large Language Models) that are the basis of AI tools such as ChatGPT and Gemini, we read this:

"This spike in questionable authorship has raised concerns in the academic community that AI-generated content has been quietly creeping into peer-reviewed publications. To shed light on just how widespread LLM content is in academic writing, a team of U.S. and German researchers analyzed more than 15 million biomedical abstracts on PubMed to determine if LLMs have had a detectable impact on specific word choices in journal articles. Their investigation revealed that since the emergence of LLMs there has been a corresponding increase in the frequency of certain stylist word choices within the academic literature. These data suggest that at least 13.5% of the papers published in 2024 were written with some amount of LLM processing."

Here is a quote from the scientific paper the article is referring to. The LLM acronym refers to Large Language Models that are AI.

"Our analysis of the excess frequency of such LLM-preferred style words suggests that at least 13.5% of 2024 PubMed abstracts were processed with LLMs. With ~1.5 million papers being currently indexed in PubMed per year, this means that LLMs assist in writing at least 200,000 papers per year. This estimate is based on LLM marker words that showed large excess usage in 2024, which strongly suggests that these words are preferred by LLMs like ChatGPT that became popular by that time. This is only a lower bound: Abstracts not using any of the LLM marker words are not contributing to our estimates, so the true fraction of LLM-processed abstracts is likely higher."

What is the problem if those writing biology papers are massively using AI tools such as ChatGPT to help write their papers? There are two main problems.

(1) The false statements in abstracts problem. There is a very massive problem in biology papers these days that paper abstracts are very commonly making claims that are not justified by any research done by the authors of the paper. If a scientist uses some AI system to write a paper's abstract after submitting the main text of the paper to the AI system, this problem will tend to become worse. When I ask Google about the topic of "exaggeration when AI is used to summarize a scientific paper," I get this answer:

"A major concern with using AI to summarize scientific papers is the potential for exaggeration and overgeneralization of findings. 

Specifically:

• AI summaries are more prone to overgeneralization than human summaries: Studies have shown that AI summaries are significantly more likely to overstate the scope of research findings compared to summaries written by the original authors or expert reviewers.
• Newer AI models may be worse: Some studies suggest that newer AI models, such as ChatGPT-4o and DeepSeek, may be even more likely to produce broad generalizations than older ones.
• Ignoring nuances and limitations: AI summaries tend to ignore or downplay uncertainties, limitations, and specific conditions mentioned in the original paper, leading to a potentially misleading presentation of the research. This can have dangerous consequences, especially in fields like medicine, where overgeneralized findings could lead to incorrect medical decisions.
• 'Unwarranted confidence': AI models might prioritize generating fluent and confident-sounding responses, even if the underlying evidence does not fully support the strong claims they make in their summaries.

 (2) The bad citation problem and legend recitation problem.  Scientific papers very frequently reiterate false or groundless claims about previous scientific research. For example, in the world of neuroscience very many thousands of very low-quality papers have been published, describing poorly-designed experiments guilty of multiple examples of Questionable Research Practices such as way-too-small study group sizes.  What happens is that these junk science papers end up getting cited over and over again by other papers.  You might call this "the afterlife of junk science." 

Very often when this happens the authors of the scientific paper will not even have ever read the body of the shoddy scientific paper they are citing. Again and again and again we have papers claiming that some grand result was established by neuroscience researchers. There follows a list citing a set of papers. But a careful examination of the papers cited will show that none of them provided any good evidence for the grand result claimed.  The citation of low-quality research is extremely abundant in neuroscience papers. When the citation of low-quality research becomes common, we have a situation in which the neuroscience literature serves to propel and propagate myths and legends, groundless boasts of achievements. 

But when happens when the authors of scientific papers are using AI systems such as ChatGPT to fill up much of the bodies of their papers, the parts dealing with the research of previous neuroscientists? Then there will be an increased tendency towards the propagation and perpetuation of legendary, groundless claims. Here's a "before" and "after":

Before AI: many neuroscientists would not bother to read the papers they were citing, but merely skimmed the abstracts of such papers. 

After AI: now the same neuroscientists do not even bother to read the abstracts of the papers they are citing, but merely copy and paste some answer they got from some AI system. 

We have in the research described above yet another giant reason why all statements in neuroscience papers should by default be distrusted. We cannot trust neuroscientists to write abstracts and paper titles accurately summarizing what was accomplished by the research described underneath such titles and abstracts. And we cannot trust neuroscientists to accurately describe what was demonstrated by research done by other neuroscientists. 

A Neuroscientist Lacking Any Credible Explanation for Memory Resorts to "Contribution" Hand-Waving

Neuroscientists lack any credible explanation for human memory. Neuroscientists cannot credibly answer any of these questions:

• How is a human able to ever instantly learn all of the many different things that humans can learn?
• How is a human ever able to retain memories for decades, something that should be impossible from units such as synapses, which are built from proteins which have average lifetimes of only a few weeks or less?
• How could a brain ever store a memory when nothing in a brain seems to bear any resemblance to some component capable of writing information?
• How could a brain ever read a memory when nothing in a brain seems to bear any resemblance to some component capable of reading memory information?
• How could there possibly be memories stored in brains, when there has been the most careful microscopic examination of so many thousands of brains of very recently deceased people, and the most careful microscopic examination of so many thousands of chunks of brain tissue from living people, without any trace of learned information ever being discovered from such examination? 
• How could there possibly be memories stored in brains, when no one has ever credibly discussed any encoding system whereby episodic memories or learned knowledge could be converted to neuron states or synapse states? 
• How could a human ever instantly remember lots of relevant facts about a person, place or event as soon as he hears the name of such a person, place or event, something seemingly impossible using a brain lacking any of the things that enable fast recall of stored information (addresses, indexes and sorting)?

When writing papers trying to persuade us that they have some understanding of such matters, neuroscientists engage in various types of bluffs, vacuous hand-waving and misleading tricks. They include these:

• The most common trick is to abundantly cite junk science studies. So a neuroscientist might write some paper filled with mentions of experimental neuroscience papers. If you actually examine these papers, you will find they are almost all very low-quality papers guilty of very bad examples of Questionable Research Practices, such as the use of way-too-small study group sizes. 
• Another trick used by empty-handed neuroscientists discussing memory is the trick I can call  concurrent process description. It works like this: using jargon-laden language, a neuroscientist will describe some type of biochemistry activity or electrical activity going on when a person learns or remember, typically some type of activity that is constantly occurring in the brain. An attempt will be made to insinuate that such activity explains something going on during learning or recall.  So we will read statements such as "While you are learning your school lessons, neurotransmitters are traveling across synapses, and new proteins are being synthesized that may strengthen synapses." The fallacy is that such things are occurring in the brain constantly, regardless of whether you are learning anything or forming any new memory or recalling anything. You do not explain things that occur only at some times by referring to types of events that occur constantly in the brain, even when learning and recall is not occurring. 
• The writer may take a kind of approach we may call the "neural miscellany" approach. The approach consists of trying to mention a large variety of neuroscience terms referring to different types of structures or cells in brains or different anatomical parts in the brain or different chemical processes in brains, and so forth. These mentions never add up to a decent explanation for any part of memory, but by given these jargon-laden mentions, some impression of understanding may be created. 
• Vague claims are made that memories are "processed" or "handled" by some particular part of the brain. Typically some very specific reference will be made to some particular fraction of the brain's anatomy, or some particular structures in the brain. No one should be impressed or persuaded by such claims, which typically are completely vague about what this alleged processing was. An example is this claim in Alberini's paper (discussed below): "These memories are processed by the medial temporal lobe-dependent memory system, which consists of the hippocampal region (CA fields, dentate gyrus, and subicular complex) and the adjacent perirhinal, entorhinal, and parahippocampal cortices." 
• To try to create some greater impression of substance or knowledge, references to anatomy or biochemistry will be mixed up with psychology references to details of the phenomena of memory, such as the difference between explicit memory and implicit memory, the difference between episodic memory and muscle memory, the difference between learning and recall,  and the difference between short-term memory and long-term memory. Such psychology references do not involve any neural explanation of memory, but by using them a writer will help to create more of an impression of knowledge on the topic of memory. 
• Another technique is to engage in what we can call "contribution" hand-waving. The technique involves mentioning various parts of the brain or chemicals in the brain, and vaguely claiming that such things "contribute" to memory. No actual explanation is going on when such hand-waving occurs. Claims about hundreds of possible "contributions" may be made without explaining how something occurs. Contribution is not causation. For example, my eyeglasses contribute to the overall process by which I see things and remember things I have seen. But my eyeglasses do not do anything to explain the mystery of memory creation or learning. 

We see an example of such "contribution" hand-waving in the recent paper "Not just neurons: The diverse cellular landscape of learning and memory" by neuroscientist Christina M. Alberini. Alberini has no credible tale to tell to explain how there could occur any of the phenomena of memory. What she mainly does is to make unwarranted or not-very-relevant claims about this or that thing "contributing" to memory or learning. 

Near the beginning of the paper, we have a concurrent process description by Alberini, who mentions "gene expression" and "chromatin regulation" while discussing learning.  Gene expression and chromatin regulation are constantly occurring events in the brain, and there is no evidence they occur more often or differently when a person learns or recalls. 

Alberini makes the statement below, which makes unwarranted boasts, and finally ends with a confession of ignorance:

" These memories are processed by the medial temporal lobe-dependent memory system, which consists of the hippocampal region (CA fields, dentate gyrus, and subicular complex) and the adjacent perirhinal, entorhinal, and parahippocampal cortices. This system can store memories of single episodic experiences for as long as we live—a process of very long-term storage that still lacks an understanding of its biological underpinnings. The implicit memory system, on the other hand, stores and recalls unconscious and automatic memories. These include habits, skills, priming, and simple forms of memories. One example of an implicit type of memory is procedural memory, which guides the execution of skills and tasks without conscious retrieval. These memories include tying shoes, riding a bike, driving a car, skiing, playing the piano, etc. Procedural memories are stored long term through a learning phase characterized by many repetitions while all the relevant neural systems work together to produce the action automatically. Implicit procedural learning is essential for developing any motor skill, and the brain regions involved in forming and storing these memories include the striatum, basal ganglia, cerebellum, and limbic system.10 Implicit memories, once established as long-term representations, can last a lifetime. As for the explicit types of memory, the biological underpinning of this very long-lasting memory storage is not yet understood." 

We have here some examples of the tricks discussed in my bullet list above. Psychology references having nothing to do with neural explanations for memory are mixed up with some specific references to brain anatomy, without any mention of how such brain anatomy can explain such phenomena. We have claims of brain memory storage, which have no specifics of how such a thing could occur: "Procedural memories are stored long term through a learning phase characterized by many repetitions while all the relevant neural systems work together to produce the action automatically." That is the vaguest hand-waving. In the underlined phrases, we have confessions that neuroscientists do not actually have any understanding of how long-lasting memory storage can occur. It makes no sense for someone to be confidently asserting (as Alberini does in the quote above) that brains store memories, and also to confess as she does that we do not understand how long-term memory storage occurs. If you do not understand how long-term memory storage occurs, you should have no confidence that brains store memories, particularly given that short-term memory storage is also not understood. 

Alberini then makes these faulty claims, which involve falsehood, bluffing and speculation:

"Long-term memories do not form instantly upon learning. They are initially fragile, and, in fact, in their early phase, they can be disrupted relatively easily by biological, pharmacological, or behavioral interference. However, over time, or via repeated learning, they build strength and become resistant to disruption through a process known as memory consolidation—a collection of biological changes taking place in several brain regions of the relevant memory system, which may include experience repetitions or reactivations, and eventually result in long-lasting, stable representations."

The first sentence is untrue. Many long-term memories do form instantly upon learning. If you are told of the death of your child or the death of your parent, you will instantly form a permanent new memory for the rest of your life. You do not need repeated notifications of such a death before the memory becomes permanent. The claim that you do not learn something until repeated exposures to it is untrue. Anyone can learn the plot of a movie by watching it a single time, and he may remember that plot for years, even though he has only seen the movie once. Learning something might require repeated exposures, but it very often does not. Scientists define a long-term memory as anything you remember for days or longer. Even many not-very-interesting things can be added to long-term memory after a single sensory experience. I often remember trivial little things I read about or experienced days, weeks, months or years ago, even if I never thought about such things between the time I read or experienced them and the time I remembered them. So neuroscientists deceive us when they make some generalization that learning requires multiple exposures. 

Why do neuroscientists make obviously false claims like those in the quote above? It is because the reality of instant learning is one that defies all claims that learning occurs through brain mechanisms. The brain has nothing like any component that could account for the instant learning that so commonly occurs in a person's life.  When neuroscientists speculate about how memory formation could occur, they speculate about sluggish, very slow processes such as protein synthesis, which would require many minutes. So for neuroscientists, the reality of instant learning is a scandal they must sweep under the rug, by telling us deceits such as the deceit that learning requires multiple sensory exposures. 

Alberini is bluffing when she makes the vague hand-waving statements below, speaking as if she knows things she does not really know: 

"Memory storage refers to the process of holding the learned information. When needed, memories are recalled or retrieved, and this process can temporarily return the memory to a labile state, during which the memory restabilizes—a process known as memory reconsolidation. Each of these memory processes requires the contributions of multiple brain regions, cell populations, and biological pathways that become activated and functionally engaged following learning and evolve over time." 

These "sound like I understand things" claims are contradicted by her previous confession: "As for the explicit types of memory, the biological underpinning of this very long-lasting memory storage is not yet understood." "Labile" means "easily altered." There is no evidence that recalling or retrieving a memory causes a memory to become "labile." To the contrary, verbally recalling a memory will make it less likely to be forgotten. 

In the quote below, Alberini asks some good questions, which she follows with a false boast that is the opposite of the truth:

"What mechanisms underlie the formation and storage of memory? Are the biological mechanisms recruited to form and store different types of memories similar or different? Where do they occur? How can they explain memory storage that lasts for a lifetime? Are they different at different ages? What mechanisms underlie memory recall? And so on. Over these 40 years, monumental progress has been made."

No, the truth is that no progress has been made in answering these questions. No claims of such progress will hold up to critical scrutiny. Typical claims of progress involve appeals to poorly-designed junk science studies such as rodent studies using way-too-small study group sizes and a bad, unreliable "freezing behavior" method for judging how well a rodent remembered. 

The Figure 1 of Alberini's paper is a laughable visual. She has taken a "double bullet list" approach. We have a picture of a brain, with the word "Memory" superimposed over it. On the left of this picture is a bullet list of neuroscience terms, referring to parts of the brain or chemical processes of the brain. On the right of this picture is another bullet list of neuroscience terms, referring to other parts of the brain or chemical processes of the brain. The figure has arrows pointing from these bullet lists to the picture of the brain.  Bullet lists are not explanations.  None of the items listed in the bullet list do anything substantive to explain how a human could form a memory, preserve a memory or instantly recall a memory. 

Alberini makes the untrue claim here: "Far from being just glue or filler cells, astrocytes have been shown by numerous studies to actively contribute to learning and memory through several mechanisms (Figure 2)." The Figure 2 claims that astrocytes do "computing, encoding and storing information." There is no good evidence that any part of the brain does any such thing as encoding memories or storing learned information. No neuroscientist has any credible tale to tell of how any brain component could do such things, and microscopic examination of brain tissue always fails to provide any evidence that such things occur in the brain, with not a single speck of learned information ever being found through microscopic examination of brain tissue. Alberini makes the untrue claim that "recent findings provided compelling evidence for the direct roles of astrocytes, similar to those played by neurons, in computing, encoding, and storing information." 

Here are some examples of the bad studies she cites to try and back up such boasts:

• "Lactate produced by glycogenolysis in astrocytes regulates memory processing." This is a junk science rodent study using way-too-small study group sizes much smaller than 15, and usually much smaller than 10. 
• "Lactate from astrocytes fuels learning-induced mRNA translation in excitatory and inhibitory neurons." This is another junk science rodent study using way-too-small study group sizes much smaller than 15, and usually much smaller than 10. 
• "Astrocyte-Neuron Lactate Transport Is Required for Long-Term Memory Formation."  This is another junk science rodent study using a way-too-small study group size of only 7 mice. 
• "Astrocytic β2 Adrenergic Receptor Gene Deletion Affects Memory in Aged Mice." This is another junk science rodent study using a way-too-small study group sizes such as only 3, 5 and 6. 

These are the results of my examination of a random selection of four papers that Alberini claims as evidence that astrocytes have something to do with memory. We may guess that the other studies she claims are equally bad examples of low-quality neuroscience. 

As a general rule, we should not trust the generalizations that neuroscientists make about human mental performance, because neuroscientists have a long history of mischaracterizing the mental abilities of humans, by making human minds sound much more weakly-performing than they are, so that neuroscientist explanations sound less far-fetched.  As an antidote to such mischaracterizations, remember the facts of your own mental abilities, and also study the very important topic of human best mental performances. A study of human best mental performances will typically blast into smithereens the type of generalizations that neuroscientists like to make about how human minds perform.  You can find many posts about human best mental performances by reading posts like the ones available at the links below:

• exceptional auditory memory
• exceptional language ability
• exceptional mathematics ability
• exceptional memory
• exceptional multitasking
• exceptional musical memory
• exceptional reading ability
• exceptional recall of facts
• exceptional recall of number sequences
• exceptional recall of text sequences
• exceptional recognition memory
• exceptional speed of memorization
• exceptional speed of thinking
• exceptional time-related ability

Below are some results from the annals of the World Memory Championships that show how falsely neuroscientists speak when they depict memory formation as a slow process or claim human minds require multiple sensory exposures before something is memorized. 

Discipline 5, Speed Numbers: Wei Quinru was able to recall 642 digits memorized in a 5-minute period (Korea Open Memory Championship 2024). Four  people were able to each recall more than 800 digits memorized in a 5-minute period (2021 World Memory Championships).

Discipline 6, Dates and Fictional Events:  Prateek Yadav memorized in 5 minutes dates corresponding to 154 fictional events (2019).  Several other contestants memorized in 5 minutes dates corresponding to 700+ fictional events (2021 World Memory Championships).

Discipline 7, "Hour Cards" Card Memorization:  Kim Su Rim memorized 2530 cards in 60 minutes.  

Discipline 8, Random Words:  Prateek Yadev memorized 335 random words in 15 minutes. Several others in 2021 memorized more than 500 random words in 15 minutes. 

Discipline 9, "Spoken Numbers":  Ryu Song I was able to recall 547 decimal digits that had been read at a rate of one per second (WMSC World Championship 2019).  Tenuun Tamir and several other Mongolian or Chinese contestants were able to recall more than 600 decimal digits that had been read to him at a rate of one per second. 

Discipline 10, "Speed Cards":  Munkhshur NARMANDAK memorized 981 cards in five minutes, and several others memorized more than 600 cards in five minutes. 

Postscript: The brain is an organ of constant molecular turnover, as the proteins that it is built from have average lifetimes no greater than a few weeks. Synapses (claimed to be a storage place of memory) are attached to short-lived dendritic spines known to have average lifetimes much shorter than years (and typically not six months).  So how could a human ever remember anything for decades, if memory storage occurred in the brain? Neuroscientists have no credible answer. When asked about such things they appeal to "consolidation," typically using the kind of vacuous, vague hand-waving we got in the quote above, where a scientist referred to "memory consolidation—a collection of biological changes taking place in several brain regions of the relevant memory system, which may include experience repetitions or reactivations."

But it simply is not true that to remember something for decades, you need  periodic reactivations every few years that recharge or reactivate a memory that won't last years without being strengthened.  You can learn trivial little things and remember them decades later, even with no reactivations of the memory. 

An experience I had recently showed this. Someone in my family recently got a teapot, which caused me to recall the beginning of a children's song I had heard only a few times, in a house I have not lived in for about 25 years. I remembered the first line: "I'm a little teapot, short and stout." I tried to remember the second line, but at first I could not. Later I remembered both of the first two lines of the song I had not heard or sung or remembered in about 25 years:

I'm a little teapot, short and stout

Here is my handle, here is my spout

In 2025 (as I noted at the end of the post here) I had a recollection which proved the ability of the mind to recall very old memories that have not been recalled in 50 years. For some reason I recalled a book I had read about 50 years ago, and never since: the science fiction book "Galaxies Like Grains of Sand" by Brian Aldiss. I remembered some lines from the book. I wrote them down on paper like this:

"The mirror of the past lies shattered. The fragments you hold in your hand."

After I wrote this recollection of something I had not read, thought of or heard quoted in fifty years, I borrowed the book on www.archive.org.  I see that the lines were these (almost exactly as I remembered them)

"The long mirror of the past is shattered...Only a few fragments are left, and these you hold in your hand." 

Below is a quote on the same topic from an earlier post discussing why brains cannot be the storage place of very old memories:

"I know for a fact that memories can persist for 50 years, without rehearsal. Recently I was trying to recall all kinds of details from my childhood, and recalled the names of persons I hadn't thought about for decades, as well as a Christmas incident I hadn't thought of for 50 years (I confirmed my recollection by asking my older brother about it). ...Upon looking through a list of old children shows from the 1960's, I saw the title 'Lippy the Lion and Hardy Har Har,' which ran from 1962 to 1963 (and was not syndicated in repeats, to the best of my knowledge). I then immediately sung part of the melody of the very catchy theme song, which I hadn't heard in 53 years. I then looked up a clip on a youtube.com, and verified that my recall was exactly correct."

Misleading Tricks of Those Claiming to Decode "Inner Speech"

You can tell when a person is engaging in muscle activity by analyzing the squiggly lines of EEG readings obtained when someone puts on his head a device containing electrodes. Muscle movements of every type (including speech) produce deviations or disturbances in the wavy lines produced by EEG devices picking up brain waves. Because different types of visual images may produce different types of muscle movements (as illustrated in the visual below), it may be possible to predict above chance which of three photos a person is shown. Different photos may produce different types of muscle movements and different durations of muscle movements. So a computer program analyzing the squiggly lines of EEG readings may score above chance, by considering blips in EEG readings that may have different characteristics when different types of photos are shown. Such an ability is no evidence that brains produce minds, but merely evidence that different visual stimuli may produce different types of reactive muscle movements. 

There is no brain-related technology that allows any person or computer program to figure out what a person is thinking by looking at MRI scans of a brain or EEG electrode readings of brain waves. But there are various tricks and cheats that can be used by someone trying to persuade you that he has decoded a person's thoughts or "inner speech" by analyzing brain states or brain waves. Below are some of these cheats and tricks. 

Trick #1: The leveraging of failures of follow fast-paced hard-to-follow instructions. I have noticed this sleazy trick in some neuroscience papers. It is the trick of doing an experiment that requires an experimental subject to very rapidly switch between speaking a word and merely thinking of a word. So, for example, there may be a computer program that flashes instructions like this, with the instructions appearing on the screen for the times shown below:

Say "hippopotamus" (3 seconds)

Pause (2 seconds)

Say "asparagus" (3 seconds)

Pause (2 seconds)

Think "perfect"  (2 seconds)

Pause (2 seconds)

Say "principle" (3 seconds)

Pause (2 seconds)

Say "asparagus" (3 seconds)

Pause (2 seconds)

Think "inventiveness"  (3 seconds)

Pause (2 seconds)

When instructions like this appear on a computer screen, with a very fast pace, and rapid switches between the type of instruction, there is a good chance that a subject will sometimes fail to follow the instructions exactly. So during some percentage of the time that the subject was supposed to be only thinking of a word, the subject may be speaking a word, in audible speech or all-but-silent speech or silent speech involving lip movement. This may allow a neuroscientist to brag about "above chance" results during intervals when supposed "inner speech" occurred.  What is going on is that the instructions have been almost designed in a way to produce a fair amount of audible speech or all-but-silent speech or silent speech involving lip movement during intervals when subjects were supposed to be engaging in only mouth-motionless "inner speech."  And if you are using very sick patients with speech difficulties (as the main paper discussed below did), and  using a very fast rapidly-switching pace, then it is all-but-certain that a large fraction of the brain waves recorded during intervals that are supposed to be only "inner speech" will instead be audible speech, near-audible speech or mouthed speech, an effect that basically invalidates any boasts experimenters may make about decoding "inner speech."

Trick #2: Failing to prevent mouth-movement during intervals supposed to be "inner speech."  There is a simple way to prevent or minimize muscle movement from the mouth during testing intervals that are supposed to be thought-only "inner speech."  One way is to have a test subject wear something in his mouth designed to prevent any movement of the lips or tongue, with the subject wearing such a device during any test interval in which he is supposed to be engaging in speechless "inner speech."  Another way is to make use of some specialized motion detector that will sound an alarm whenever the subject moves his lips or tongue. No such devices are used by neuroscientists doing experiments claiming to decode "inner speech." So whenever they claim that something involved only "inner speech" we should distrust such claims, and suspect that there was a lot of actual speech or muscle movement (audible or not) going on during the recorded periods of supposed "inner speech."  

Trick #3: The word length cheat.  I have noticed this sleazy trick in some neuroscience papers. It is the cheat of doing an experiment that attempts to predict which of a small set of words a person is thinking about, while leveraging the fact that some of the words have longer lengths than others. So, for example, in some quick-paced instructions appearing on a screen, a user may be asked to think (without speaking) one of these words: dog, chameleon, apple, hippopotamus, triangle. If the pace is fast enough, with enough tricky switches between "say this" and "think this,"  some little traces of muscle movement may show up in the EEG readings, even during intervals when the subject is only supposed to be speaking; and from the length of such muscle movement it may be rather easy to predict which word the user was asked to think of.

Trick #4: No exact specification of the experimental procedure. This is a very bad defect of most papers claiming to decode inner speech from brain scans or EEG readings. Such papers will typically offer some sketchy outline of the experiment that went on, without specifying the exact procedure. The rule of thumb we should follow is: regard as worthless any paper claiming successful experimental results which fails to specify in sufficient detail the exact experience that subjects underwent, in a way sufficient for someone to attempt a replication of the reported results. 

Trick #5: Cherry-picking best results. Using multiple subjects and many different electrodes reading from different regions of the brain, a researcher can cherry-pick a best result from the many results (a result that might easily be obtainable by pure chance), and then try to give the impression that such a result was a typical result. Something similar would be going on if you had 20 people try to guess 50 five-digit numbers, and then had some visual graph heading bragging about "60% accuracy" with the fine print revealing that this was for guess target number 35 and guesser number 17 (when the target was 44392 and the guesser guessed 44291). 

Trick #6: Leveraging data backdoors in a sneaky way. This trick goes on when some researcher claims that they got an impressive result "from brain scans" or "EEG readings" when brain scans or EEG readings were only part of the inputs used, with the success mainly coming from some data backdoor. An example is when researchers have subjects look at images obtained from the COCO image dataset. That dataset includes text annotations corresponding to each of the images, an example being that a picture of an apple may be labeled as "apple" or "fruit."  So a computer program analyzing EEG readings while test subjects saw particular images can find out words corresponding to the observed image, by using the data backdoor of the text annotation corresponding to each image. With a little obfuscation and "clouding the waters," a success so unimpressive might be passed off as "mind reading" even though what is powering the success is 98% simply looking up the text annotations corresponding to the images, a feat no more impressive than looking up the definition of a word. 

Trick #7: Leveraging sound inputs. Some people with speech problems have the ability to produce sounds when trying to speak, sounds that an average person is unable to understand. This may sound like someone trying to speak with his mouth filled with food. Some scientist may connect such a person to some EEG device, either one that is invasive (involving brain-implanted electrodes) or not invasive. Some computer program may then train on the person's speech while he is reading something or trying to read something. The computer may get a good idea about correlations between sounds that a human listener cannot understand, and words that a person is attempting to speak.  Then the computer program may report success at "decoding" something that may be called "inner speech" or "brain states" or "brain outputs," even though the success is coming mainly from sound inputs rather than brain states. The effort may be wrongly called "brain-to-text" or a "decoding of brain speech" although such terms are inappropriate under such circumstances. 

Trick #8:  Leveraging phoneme or attempted phoneme EEG correlates. I noted before that muscle movements of every type (including speech) produce deviations or disturbances in the wavy lines produced by EEG devices picking up brain waves. There may be particular EEG correlates for particular phonemes or attempted phenomes that a person may make. So when someone makes the sound at the beginning of "achoo" and "apple," that may tend to produce a particular type of EEG blip; and when someone makes the sound in the middle of the words "cheese" and "sneeze," that may tend to produce some other type of EEG blip. So if you have a computer program that is trained to recognize such characteristic EEG blips, by training after someone connected to an EEG device tries to read some long body of text, that program may gain some ability to pick up lots of what a person is saying from his EEG brain wave readings. This may be described as "brain reading" although it is more accurately described as muscle movement EEG correlation reading. A program trained to recognize particular type of EEG correlates of phoneme pronunciation or attempted phoneme pronunciation may use some fancy AI "fill in the blanks" algorithm (possibly involving frequentist word-guessing or syllable guessing or phoneme guessing) to enhance some limited success it has at picking up EEG correlates of attempted syllable pronunciations. None of what I describe in this paragraph is correctly described as "decoding inner speech," although it may be described as that, particularly under some fast-pace hurry-up methodology in which a good deal of actual speech or attempted speech is occurring during two-second intervals in which someone is supposed to be only thinking of a word, because of a study design that almost guarantees there will be a large amount of this spillover "talking or trying to talk when you were supposed to only think."

Trick #9: The "as high as X percent accurate" trick. This trick is as old as the hills. You slice and dice the prediction results into something like 100 different portions, and pick the portion with the highest predictive accuracy. You then say something like "my method is up to 75% accurate," mentioning the accuracy of the most successful little portion, rather than the overall results. 

Trick #10: Leveraging AI and large language models. An AI system that has trained on very many web pages and online books may be able to fill in lots of blanks in sentences, using guesswork based on word frequencies and the frequency of words used in a particular type of sentence or sentence fragment. So for example, if you have a fragment of a sentence such as "I'm hungry so __ ____ __ ____ ______," the AI system might be able to predict "I'm going to make some food" or some similar phrase as the missing part. Leveraging such AI systems, an experiment might produce some success level at "decoding inner speech" much higher than it would get without using such an AI system, particularly if some experiment uses carefully chosen test sentences of a type that allow an AI system to predict the full sentence from only half of the sentence.  

The latest example of a misspeaking neuroscience paper boasting about decoding inner speech is the paper "Inner speech in motor cortex and implications for speech neuroprostheses" which you can read here.  We get in the paper various boast soundbites that are not backed up by anything reported in the paper. The paper starts out by making the false claim that "Attempted, inner, and perceived speech have a shared representation in motor cortex."  Speech is not represented in the cortex or any other part of the brain. The beginning of the paper contains quite a few untrue statements about the previous results of researchers, statements that are untrue because of various defects in the results published by such researchers. 

Many of today's neuroscientists misspeak like crazy when they use the words "represent," "representations," "decode" and "decoding."  Misstatements by neuroscientists using these words are extremely abundant. As a general rule you should never trust a neuroscientist using the words "represent," "representations," "decode" and "decoding." When it comes to "representations" neuroscientists are often guilty of very bad pareidolia and noise-mining, which involves a kind of seeing things that are not really there. Nowadays it easy for a scientist to kind of see things that are not there, by using "keep torturing the data until it confesses" tactics that often involve shady manipulations of data by dubious custom-written computer programs. We also should have a default distrust over any neuroscientist statement made by a neuroscientist about a decoding percentage accuracy. Such statements are typically extremely dubious, involving very dubious or easy-to-discredit calculation methods, or claims in which no calculation method is ever adequately specified. Often in a paper some impressive "decoding accuracy" figure is stated, but never justified. 

Our first reason for distrusting the "Inner speech in motor cortex and implications for speech neuroprostheses" paper comes when we read that it involved only four subjects. As a general rule, correlation-seeking neuroscience experiments have no value unless they use a study group size of at least 15 or 20 subjects; and usually the required study group size is much larger. 

Another strong reason for distrusting the "Inner speech in motor cortex and implications for speech neuroprostheses" paper comes when we consider the endangerment-of-the-sickest procedure that its researchers engaged in.  The study involves invasively inserting microelectrodes into the brains of four very sick patients.  This was not done for any medical benefit for these patients.  The very sick patients had diseases such as the muscle-wasting disease ALS, sometimes called Lou Gehrig's disease. The insertion of microelectrodes into brains involves very serious medical hazards, and when used on very sick patients it may worsen their difficulties. In this case the very sick patients were used as "experimental guinea pigs," without any medical benefits coming to them from the medical risks they were enduring. 

Whenever such shady business is going on, we should all-the-more tend to distrust any statements made by the people engaging in the shady business. We should nowhere be giving "the benefit of the doubt" when such researchers make grand boasts, but demand the clearest evidence that such boasts are justified. 

In the case of the paper "Inner speech in motor cortex and implications for speech neuroprostheses" no such clear evidence is given. The paper fails to give any very exact specification of the experimental procedures it followed. But from its Supplemental Information document we should have the strongest suspicion that some of the tricks listed above were used.  

When asked to produce "inner speech," instructions were given that seem designed to produce muscle movement rather than pure thought. According to Table S1, the instructions were these:""

• "Imagine mouthing the word. Focus on what your mouth, tongue, lips, jaw and throat would be doing and how they would feel."
• "Imagine uttering the word aloud. Focus on the sound you would be producing."
•  "Imagine hearing me (or someone’s voice you know well) say the word, focus on the sound of my (their) voice."

The same table tells us that instructions such as these were alternated with instructions like these:

• "Say the word aloud (to the best of your ability.'
• "Mouth the word as if you were mouthing to someone across a room, without sound."

How fast were these instructions switched? We cannot tell exactly, because the paper authors have failed to describe their exact test procedure in a way that would allow anyone to reproduce it exactly.  But from Table S4 in the Supplemental Information, we have every reason to suspect that the authors were guilty of Trick #1 described above. We have some table suggesting that very fast, rapidly switching time intervals were used. The table makes it sound as if the subjects were required to do some super-hurried affair in which they had to very rapidly switch between "speak the word" instructions and "think the word" instructions. 

Now let us look at some of the unwarranted and dubious statements made in the paper:

(1) The caption of Figure 1F refers to a "T16-i6v Decoding Accuracy of 92.1%."  This gives an impression of high accuracy, until you figure out that this referring to only a single subject (subject T16) and a single electrode location (corresponding to the name i6V). The figure seems to have been cherry-picked from Figure 1E, which shows a grid of 63 percentages ranging from 11 to 97.9. We may note how misleading this is. A casual viewer of the paper, looking at the figures, may get the idea that some high decoding accuracy was achieved, when no such thing occurred.  Something shady as this should deepen our distrust of this paper. We have no decent explanation of how these numbers in Figure 1E were obtained, and the whole grid should be regarded with suspicion. What little explanation is given (some mention of a "Gaussian naive Bayes" with a 500 millisecond window)  is something that does not inspire confidence. Figure 1D graphs a suspiciously hurried-up affair that seems to involve a trick like described in Trick #1 above. 

(2) The careful critical reader of the paper will tend to suspect that what is going on is noise-mining and cherry-picking from electrode data corresponding to many different reading locations in the brain. Each of the four patients had multiple electrodes inserted into their brains. So when Figure 1F refers to a "T16-i6v Decoding Accuracy of 92.1%,"  this is referring to only a single subject (subject T16) and a single electrode location (corresponding to the name i6V). It is not at all the average accuracy of decoding attempts using this subject. Do we have here any reason for thinking that the results are better than chance, when you consider the results from each patient's electrodes?  There seems to be no such reason. 

Each of the four subjects had about 6 electrode arrays in their brains. So with 24 or more possible areas to check, it is hardly surprising that some researcher might be able to report a relatively high "decoding accuracy" involving one of those areas and one of these subjects. Similarly, if I ask 24 people to pick the score and teams of the next Super Bowl, I will probably have one that I can claim as having a high predictive accuracy, even if mere chance is involved. 

We also have some insinuations in the paper ""Inner speech in motor cortex and implications for speech neuroprostheses" that some  relatively high accuracy was achieved in experiments involving a 125,000 word vocabulary. None of the claims should be trusted, because the procedure involved is not described in adequate detail.  We have a link to a video showing a woman (subject T16)  seeing a computer screen that displays some text. The video says, "In this task the target sentence appears at the top of the screen, and the inner speech BCI [brain computer interface] is shown below, generated in real time."  First, the computer displays the sentence "That's probably a good idea." Then we see below that a line slowly appearing; "That's probably a good idea."

We should treat with the greatest skepticism any claim that this is a "decoding" of what the very sick subject was thinking. Some computer program already knew the target sentence. We don't know what tricks are going on for the computer program to go from this known target to a supposed "decoding" matching the target, because the testing procedure and programming is nowhere decently described in the paper or its Supplemental Information. Were the sentences randomly selected from some very large set such as a group of 100,000 sentences? Or were the sentences only a very limited number of sentences that some AI program had trained on, which would tend to create a vastly higher chance of success? We don't know, because the authors haven't explained their method decently. We have no idea of what kind of tricks and cheats may have helped produced this impressive-looking result.  Part of what is going on seems to be AI prediction based on phrase frequencies in sentences starting a particular way. An AI system can predict "a good idea" as one of the most likely endings of a sentence beginning "That's probably..."

Seeing the video you might assume that there was some "Chinese wall" affair in which one part of the software knew that the target sentence was "That's probably a good idea," and some other part of the software (a decoding part) did not know that this sentence was the target, and figured out the target from brain waves. But you should not make any such assumption, because it is never made explicitly in the paper; and what was going on when you see that video clip is never adequately explained. The paper authors have given us reasons for distrusting their work, and our default attitude should be distrust, rather than making generous assumptions the authors are trying to suggest. 

The video is attempting to give us the impression that some randomly generated sentence (created from a vocabulary list of 125,000 words) is being decoded by brain signal analysis. But nowhere in the text do we actually have a claim that any of the sentences were randomly generated from such a vocabulary list; and nowhere in the text do we have an assertion that the sentence was randomly chosen from a very large set of sentences such as a set of 100,000 sentences.  For all we know there may be only a very small number of sentences, each of which was previously given to the subjects. So the impressive-looking "decoding" might actually be something a thousand times less impressive, something easily obtainable by a few statistical or programing tricks, even if it is utterly impossible to decode what word someone is thinking of by gathering EEG signals from someone whose mouth is immobile.  

Referring to the subject T16 shown in their little video clip, the paper says, "T16 had online retraining only for the 125,000-word vocabulary evaluation blocks, in which the cued sentences were used as ground truth to retrain the model, but only after those sentences had been decoded online." Although obscure, that sentence should be enough to make us suspect that the video involving subject T16 is just some smoke-and-mirrors affair, not any real decoding of what someone was thinking from the person's brain states or brain waves. 

As the paper lacks adequate documentation on what was going on, we should have no confidence in the results.  The authors of the paper help create a fog of mystery about what they did by having the paper document about five different experiments, none of which is clearly and consistently named, and none of which is very well documented in regard to the exact procedure followed. This is not how to do a persuasive experiment showing an ability to decode "inner speech" from brain waves. Instead, do a single experiment in which everything that went on is so well-documented that someone else might be able to reproduce the result.  

Whenever a completely silent person's lips and tongue are motionless, and he is not moving any of his muscles,  it is impossible to decode what a person is thinking or imagining (or a sentence he is trying to speak) using only brain scans produced by MRI machines or the brain waves picked up by EEG readings. But using a variety of misleading tricks such as the ones listed above and many other possible misleading tricks, researchers can create misleading impressions that they are making progress at a task that is impossible. 

He Had Almost No Brain, But Was Bilingual With Near-Normal Verbal Skills

Neuroscientists are members of a belief community dedicated to preserving the dogma that the brain is the source of the human mind. So when neuroscientists discover case histories that seem to defy such a dogma, neuroscientists tend to write up the results with papers having a title not likely to be noticed. An example of this was when neuroscientists discovered that a French civil servant had almost no brain. The case was written up in the British medical journal The Lancet with a paper having the title "Brain of a White Collar Worker," as if the authors were trying to get as few readers as possible by creating the dullest-sounding title they could create.  The paper had the visual below, in which missing parts of the brain are shown in black:

The 2007 paper told us that the subject had an overall IQ of 75 and a verbal IQ of 84, and that he was a father employed as a civil servant (a government worker). Such occupational success with so little brain defies claims that the brain is the source of the human mind. The Reuters story here discusses the case. 

An equally dramatic case of high mental function and very little brain was discussed in a 2018 paper with a title that also seemed chosen to attract as little attention as possible. The title of the paper (which can be read here) was "Volumetric MRI Analysis of a Case of Severe Ventriculomegaly," a title that sounds as dull as dishwater. But the case is a fascinating one. We read of a bilingual 60-year-old man with near-normal verbal skills but very little brain. 

We are given the visual below, which shows on the left the brain of the 60-year-old man, and on the right what a normal brain looks like. The black areas are areas in which normal brain tissue is gone, having been replaced by fluid. 

We read this about tests performed on the person whose brain is 

"The Wechsler Adult Intelligence Scale (WAIS-III; Wechsler, 1997) revealed a borderline IQ of 79, with a verbal IQ of 88, non-verbal performance IQ of 74, poor working memory IQ of 71, verbal comprehension IQ of 93, and visual-spatial IQ of 80. The patient had difficulty completing tasks requiring working memory, which was in the 3rd percentile, and processing speed was extremely slow (in the 1st percentile)."

A verbal IQ of 88 is near-normal, and a normal verbal IQ is about 100. 

We read that this person with little brain tissue was bilingual (in other words, someone who could speak two languages). We read that he "plays guitar well." We hear some vague, vacuous speculation trying (in the thinnest way) to offer a bit of explanation of how someone with so little brain could have "preserved function, including being fluent in two languages and mastering playing a musical instrument."

I have another example of a neuroscientist paper with a dull-as-dishwater title but a sensational case of high mental function and little brain tissue. It is the paper "Colpocephaly in adults" which you can read here. We read of a 60-year-old woman who had the great majority of her brain destroyed by a congenital disease, apparently one present from birth or from early childhood. We read this:

"Growing up, she had a reading learning disability; however, she graduated from high school with average grades, married in her 20s and had one child. She worked in a factory and most recently as a home health aide. At the time of presentation...She was alert, appropriately oriented and had normal language function."  

Below is what the woman's brain looked like. The black areas are areas hollowed out by the congenital disease. 

The cases discussed here are only a fraction of the cases of high mental function despite extremely severe brain loss. You can read of many more such cases in my post here. Collectively the cases provide one of the strongest reasons for thinking that the brain is not the source of the human mind.