Neuroscientists Senselessly Think They Can Perform Innumerable Contortions of Brain Data, and Then Claim a Discovery

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 face shapes on trees, and occasionally reporting a success, or like someone scanning the sky, looking for clouds that look like animal shapes.

The latest example of nonsensical neuroscientist pareidolia is to be found in a press release from Columbia University, and the paper that press release describes in very misleading terms.  The press release has the phony headline "Scientists Capture Clearest Glimpse of How Brain Cells Embody Thought." When you read a headline like that, you should remember a sad truth that has been glaringly obvious for many years now: the fact that university press releases on topics of science are no more trustworthy than corporate PR press releases.  There is the most gigantic amount of lying, hype and misrepresentation in university press releases these days, and such baloney occurs in equal amounts in the press releases of every major university. So don't think for a moment than you can  trust a press release because it came from Harvard or Columbia or Yale or Oxford University. I wish I had a dollar for every bogus press release that has been issued by such institutions. The subtitle of the press release is the 100% untrue claim "Recordings from thousands of neurons reveal how a person’s brain abstractly represents acts of reasoning."

We have an utterly groundless claim by a neuroscientist that he and his colleagues found a “uniquely revealing dataset that is letting us for the first time monitor how the brain’s cells represent a learning process critical for inferential reasoning." We then have an equally groundless claim by another neuroscientist that "this work elucidates a neural basis for conceptual knowledge, which is essential for reasoning, making inferences, planning and even regulating emotions.”

Do the authors claim to have seen some structure in the brain corresponding to these claims? Certainly not. They did not do any brain imaging such as using MRI scans. All they had to work with is EEG readings, readings of brain waves. Would anyone have seen any sign of such claimed representations by visually examining the wavy lines of these EEG readings? Certainly not. 

The press release reveals that what is going on is an affair that can be described as "keep torturing the data until it confesses in the weakest voice." We read this:

"The researchers recast the volunteers’ brain activity into geometric representations – into shapes, that is – albeit ones occupying thousands of dimensions instead of the familiar three dimensions that we routinely visualize. 'These are high-dimensional geometrical shapes that we cannot imagine or visualize on a computer monitor,' said Dr. Fusi. 'But we can use mathematical techniques to visualize much simplified renditions of them in 3D.' ” 

What a joke. They didn't see any such representations of knowledge or thought by a simple examination of the brain wave data they acquired. So they kept fiddling with their data and manipulating the data and contorting the data with some kind of absurdly convoluted and byzantine analysis pathway, and then claimed to see representations or shapes in such super-manipulated data. It's like someone taking 1000 pictures of the clouds in the sky, and then playing around all day with image manipulation filters until he got something that looked like an animal shape in one of the clouds. 

The paper being described (which you can read here) describes the huge chain of arbitrary manipulations and arbitrary naming that went on. It's a laughably arbitrary and byzantine analysis pathway in which many dozens of arbitrary analysis decisions are being made. 

Here from the paper is a description of just a small fraction of the "keep torturing the data until it confessed" spaghetti code craziness that went on:

" A cross-session-group PS was then computed by applying the same alignment to a pair of held-out conditions, one on either side of the current dichotomy boundary. Alignment and cross-group comparisons were performed in a space derived using dimensionality reduction (six dimensions). For a given dichotomy, two groups of sessions with N and M neurons were aligned by applying singular value decomposition to the firing-rate normalized condition averages of all but two of the eight task conditions, one on either side of the dichotomy boundary. The top six singular vectors corresponding to the non-zero singular values from each session group were then used as projection matrices to embed the condition averages from each session group in a six-dimensional space. Alignment between the two groups of sessions, in the six-dimensional space, was then performed by computing the average coding vector crossing the dichotomy boundary for each session group, with the vector difference between these two coding vectors defining the ‘transformation’ between the two embedding spaces. To compare whether coding directions generalize between the two groups of sessions, we then used the data from the two remaining held-out conditions (in both session groups). We first projected these data points into the same six-dimensional embedding spaces and computed the coding vectors between the two in each embedding space. We then applied the transformation vector to the coding vector in the first embedding space, thereby transforming it into the coordinate system of the second session groups. Within the second session group embedding space, we then computed the cosine similarity between the transformed coding vector from the first session group and the coding vector from the second session group to examine whether the two were parallel (if so, the coding vectors generalize). We repeated this procedure for each of the other three pairs of conditions being the held-out pair, thereby estimating the vector transformation of each pair of conditions independently. The average cosine similarity was then computed over the held-out pairs. All possible configurations of conditions aligned on either side of the dichotomy boundary are considered (24 in this case), and the maximum cosine similarity over configurations is returned as the PS for that dichotomy (plotted as ‘cross-half’ in Extended Data Fig. 3z)."

This is only a very small fraction of the "manipulate the data like crazy" nonsense that was going on. The paper lists a total amount of statistical rigmarole that seems ten times more complicated than what is described in the quote above. Never be impressed when you read about such operations. The more complicated such paragraphs are, the more it shows that the original data did not have the final result claimed, and that the authors had to play "keep torturing the data until it confesses" games, using a long series of arbitrary data manipulations  and data contortions to try to "gin up some success."  Strangely, we read almost nothing in the way of justifying these bizarre data manipulations and data contortions. It is as if the authors thought they had the right to dream up the most enormously  convoluted data manipulation scheme, without justifying the bizarre data distortions they were doing. 

A look at some of the programming code used shows that all the data was being passed through doubly-nested loops that were doing God-only-knows-what:

%for every area in the dataset
for i = 1:length(cell_groups)
    
    %run analysis for the inference absent sessions
    idx_current = intersect(cell_groups{i},find(ismember(sessions,inference_absent)));

    for i_rs = 1:n_resample
        [avg_array,~] = construct_regressors(neu,n_samples(i),idx_current);
    
        [t_1,t_2]        = sd(avg_array,n_perm_inner,n_samples(i));
        sd_{i,1}         = cat(2,sd_{i,1},t_1);
        sd_boot{i,1}     = cat(2,sd_boot{i,1},t_2);
        [ccgp_{i,1}]     = cat(2,ccgp_{i,1},ccgp(avg_array,...
                           n_perm_inner,false,n_samples(i)));
        [ccgp_boot{i,1}] = cat(2,ccgp_boot{i,1},ccgp(avg_array,...
                           n_perm_inner,true,n_samples(i)));
        ps_{i,1}         = cat(2,ps_{i,1},ps(avg_array,...
                           n_perm_inner,false));
        ps_boot{i,1}     = cat(2,ps_boot{i,1},ps(avg_array,...
                           n_perm_inner,true));
    end
    
    %run again for the inference present sessions
    idx_current = intersect(cell_groups{i},find(ismember(sessions,inference_present)));

    for i_rs = 1:n_resample
        [avg_array,~] = construct_regressors(neu,n_samples(i),idx_current);
    
        [t_1,t_2]        = sd(avg_array,n_perm_inner,n_samples(i));
        sd_{i,2}         = cat(2,sd_{i,2},t_1);
        sd_boot{i,2}     = cat(2,sd_boot{i,2},t_2);
        [ccgp_{i,2}]     = cat(2,ccgp_{i,2},ccgp(avg_array,...
                           n_perm_inner,false,n_samples(i)));
        [ccgp_boot{i,2}] = cat(2,ccgp_boot{i,2},ccgp(avg_array,...
                           n_perm_inner,true,n_samples(i)));
        ps_{i,2}         = cat(2,ps_{i,2},ps(avg_array,...
                           n_perm_inner,false));
        ps_boot{i,2}     = cat(2,ps_boot{i,2},ps(avg_array,...
                           n_perm_inner,true));
    end

end

Every single piece of data is being passed into a function called construct_regressors(), but what is that function doing? We cannot tell, because the code for that function has not been supplied.  We should be suspicious that this construct_regressors() function was doing something so arbitrary and convoluted that the authors were embarrassed to publish the code for that function. 

What we have here is something like the situation described in the visual below:

What we have here is something like the situation described in the visual below:

To pass off the results of so vast an amount of data monkeying as a discovery is a case of BS and baloney. No representations in the brain or brain waves have been discovered here. All we have is scientists manipulating and contorting data like crazy, and then displaying some pareidolia by passing off their super-manipulated data as an example of "representations."  

Can you imagine what a scandal would arise if climate scientists tried to get away with even one tenth of this amount of manipulating and contorting and distorting their data? Skeptics of their work would start "screaming bloody murder," and howl about how scientists were failing to use their original data, and using instead manipulated, contorted, twisted, distorted data.  But it seems that neuroscientists senselessly think that it is okay for them to play around endlessly with data from brains,  and that they have the right to contort and twist and distort such data in dozens of different ways, and then pass off the result (an utterly artificial construction) as something they can then call "what the brain does."  

What can you call data like this which has undergone so many contortions and manipulations and distortions and transfigurations that it is something almost totally different from the raw data originally gathered? You might be tempted to call it "fake data," but that isn't quite right, because the authors have described all the transformations they did of the data. So rather than calling it "fake data," a description that is not quite right, we can merely say that it is data that has been so enormously contorted and manipulated and transfigured that it is data that cannot be claimed as evidence telling us about brain states or brain activity. 

Experimental neuroscience is in a state of great sickness and dysfunction. The use of Questionable Research Practices seems like more the rule in experimental neuroscience than the exception. When scientists think that it is okay for them to perform endless manipulations and contortions and distortions and transfigurations of their data, and to then pass off the resulting artificial mess as "what we got from the brain," then it is a sign that neuroscience has sunk to an extremely low nadir of dysfunction. 

Brain waves don't represent anything that someone has learned. Brain waves are no more representations of something learned than clouds are representations of something learned. Brain waves are streams of random data, as random as the stream of clouds passing above a house or  a city.  Not one iota of evidence of brain representations has been presented in this paper. The paper is entitled "Abstract representations emerge in human hippocampal neurons during inference." An honest title for the paper would have been "We got something we called 'abstract representations'  after we manipulated and contorted brain wave readings in dozens of weird ways."

Contortion craziness