Category Archives: scientific discussion

I was wrong…

It has been almost a year since I last posted on this blog. I apologise for this hiatus. I’m afraid it’ll continue as it will probably be even longer before my next post. I simply don’t have the time for the blog these days. But in a brief lull in activities I decided to write this well-overdue post. No, this is not yet another neuroscientist wheeling out his Dunning-Krugerism to make a simplistic and probably dead-wrong (no pun intended) model of the CoViD-19 pandemic, and I certainly won’t be talking about what the governments are doing right or wrong in handling this dreadful situation. But the post is at least moderately related to the pandemic and to this very issue of expertise, and more broadly to current world events.

Years ago, I was locked in an extended debate with parapsychology researchers about the evidence for so-called “psi” effects (precognition, telepathy, and the like). What made matters worse, I made the crucial mistake of also engaging in discussion with some of the social media followers of these researchers. I have since gotten a little wiser and learned about the futility and sanity-destroying nature of social media (but not before going through the pain of experiencing the horrors of social media in other contexts, not least of all Brexitrump). I now try my best (but sometimes still fail) to stay away from this shit and all the outrage junkies and drama royalty. Perhaps I just got tired…

Anyway, in the course of this discussion about “psi” research, I uttered following phrase (or at least this is it paraphrased – I’m too lazy to look it up):

To be a scientist, is to be a skeptic.

This statement was based on the notions of scientific scrutiny, objectively weighing evidence for or against a proposition, giving the null hypothesis a chance, and never to take anybody’s word for granted. It was driven by an idealistic and quite possibly naive belief in the scientific method and the excitement about scientific thinking in some popular circles. But I was wrong.

Taken on their own, none of these things are wrong of course. It is true that scientists should challenge dogma and widely-held assumptions. We should be skeptical of scientific claims and the same level of scrutiny should be applied to evidence confirming our predictions as to those that seem to refute them. Arguments from authority are logically fallacious and we shouldn’t just take somebody at their word simply because of their expertise. As fallible human beings we scientists can fool ourselves into believing something that actually isn’t true, regardless of expertise, and perhaps at times expertise can even result in deeply entrenched viewpoints, so it pays to keep an open mind.

But there’s too much of a good thing. Too much skepticism will lead you astray. There is a saying, that has been (mis-)attributed to various people in various forms. I don’t know who first said it and I don’t much care either:

It pays to keep an open mind, but not so open that your brains fall out.

Taken at face value, this may seem out-of-place. Isn’t an open mind the exact opposite of being skeptical? Isn’t the purpose of this quote precisely to tell people not to believe just about any nonsense? Yes and no. If you spend any time reading and listening to conspiracy theories – and I strongly advise you not to – then you’ll find that the admonition to keep an open mind is actually a major hallmark of this misguided and dangerous ideology. I’ve seen memes making the rounds that most people are “sheeple” and only those who have awoken to the truth see the world as it really is, and lots of other such crap. Conspiracy theorists do really keep a very open mind indeed.

A belief in wild-eyed conspiracies goes hand-in-hand with the utmost skepticism of anything that smells even remotely like the status quo or our current knowledge. It involves being open to every explanation out there – except to the one thing that is most likely true. It is the Trust No One philosophy. When I was a teenager, I enjoyed the X-Files. One of the my favourite video games, Deus Ex, was strongly inspired by a whole range of conspiracy theories. It is great entertainment but some people seem to take this message a little too much to heart. If you look into the plot of Deus Ex, you’ll find some haunting parallels to actual world events, from terrorist attacks on New York City to the pandemic we are experiencing now. Ironically, one could even spin conspiracies about the game itself for that reason.

dxcover

Conspiracy theories are very much in fashion right now, probably helped by the fact that there is currently a lunatic in the White House who is actively promoting them. It would be all fun and games, if it were only about UFOs, Ancient Aliens, Flat Earth, or the yeti. Or even about the idea that us dogmatic scientists want to suppress the “truth” that precognition is a thing*. But it isn’t just that.

From the origins of the novel coronavirus disease over vaccinations to climate change, we are constantly bombarded by conspiratorial thinking and its consequences. People apparently set fire to 5G radio masts because of this. Trust in authorities and experts has been eroded all over the globe. The internet seems to facilitate the spread of these ideas so they become far more influential than they would have been in past decades –¬† sometimes to very damaging effects.

Can we even blame people? It does become increasingly harder to trust anything or anybody. I have seen first-hand how many news media are more interested in publishing articles to make a political point than in providing factual accuracy. This may not even be deliberate; journalists work to tight deadlines and they are a struggling industry trying to keep financially afloat. Revelations about the origins of the Iraq War and scandals of collusion and election meddling, some of which may well be true conspiracies while others may be liberal pipe dreams (and many may fall into a grey area in between), don’t help to restore public trust. And of course public trust in science isn’t helped by the Replication Crisis**.

Science isn’t just about being skeptical

Sure, science is about challenging assumptions but it is also about weighing all available evidence. The challenging of assumptions we see in conspiracies is all too often cherry-picking. Science is also about the principle of parsimony and it requires us to determine the plausibility of claims. Crucially, it is also about acknowledging all the things we don’t know. That last point includes recognising that, you know, perhaps an expert in an area actually does occasionally know more about it than you.

No, you shouldn’t just believe anything someone says merely because they have PhD in the topic. And I honestly don’t know if expertise is really all that crucial in replicating social priming effects – this is for me where the issues with plausibility kick in. But knowing something about a topic gives experts insights that will elude an outsider and it would serve us well to listen to them. They should certainly have to justify and validate their claims – you shouldn’t just take their word as gospel. But don’t delude yourself into thinking you’ve uncovered “the Truth” by disbelieving everybody else. If I’ve learned anything from doing research, it is that the greatest delusion is when you think you’ve actually understood anything.

I have observed a worrying trend among some otherwise rather sensible people to brush aside criticism of conspiracy theories as smugness or over-confidence. This manifests in insinuations like these:

  • Of course, vaccines don’t cause autism, but perhaps this just distracts from the fact that they could be dangerous after all?
  • Of course, 5G doesn’t give people coronavirus but have governments used this pandemic as an opportunity to roll out 5G tech?
  • Of course, the CoViD-19 wasn’t manufactured in a Chinese lab, but researchers from the Wuhan Institute of Virology published studies on such coronaviruses and isn’t it possible that they already had the virus and it escaped the lab due to negligence or was even set loose on purpose?

Conspiracy theories are always dealing in possibilities. Of course, they require ardent believers to promote their tinfoil hat ideas. But they also feed on people like us, people with a somewhat skeptical and inquisitive mind who every so often fall prey to their own cognitive biases. Of course, all of these statements are possible – but that’s not the point. Science is not about what is possible but what is probable. Probabilities change as the evidence accumulates.

How plausible is the claim and even if it is plausible, is it more probable than other explanations or scenarios? Even if there were evidence that companies took advantage of the pandemic to roll out 5G (you know, this thing that has been debated for years and which had been planned ages before anyone even knew what a coronavirus is), wouldn’t it make sense to do this at a time when there is an unprecedented need of a world population in lockdown to have reliable and sophisticated mobile internet? Also, so fucking what? What concrete reason is it why you think 5G is a problem? Or are you just talking about the same itchy feeling people in past ages had about the internet, television, radio, and doubtless at some point also about books?

Let us for a moment ignore the blatant racism and various other factors that make this idea actually quite unlikely and accept the possibility that the coronavirus escaped from a lab in Wuhan. Why shouldn’t there be a lab studying animal-to-human transmission of viruses that have the potential for causing pandemics, especially since we already know this happened with numerous illnesses before and researchers have already warned years ago that such a coronavirus pandemic was coming? Doesn’t it make sense to study this at a place where this is likely to occur? What is more likely, that the thing that we know happens happened or that someone left a jar open by accident and let the virus escape the lab? How do you think the virus got in the lab in the first place? What makes it more likely that it escaped a lab than that it originated on a market where wild exotic animals are being consumed?

There is also an odd irony about some of these ideas. Anti-vaxxers seem somewhat quiet these days now that everybody is clamouring for a vaccine for CoViD-19. Perhaps that’s to be expected. But while there is literally no evidence that widely used vaccines are making you sick (at least beyond that weakened form of creating an immune response that makes you unsusceptible to the actually disease anyway) there are very good reasons to ask whether a new drug or treatment is safe. This is why researchers keep reminding us that a vaccine is still at least a year away and why I find recent suggestions one could become available even this September somewhat concerning. It is certainly great that so much work is put into fighting this pandemic and if human usage can begin soon that is obviously good news – but before we have wide global use perhaps we should ensure that this vaccine is actually safe. The plus side is, in contrast to anti-vaxxers, vaccine scientists are actually concerned about people’s health and well-being.

The real conspiracy

Ask yourself who stands to gain if you believe a claim, whether it is a scientific finding, an official government statement, or a conspiracy. Most conspiracy theories further somebody’s agenda. It could help somebody’s reelection or bring them political influence to erode trust in certain organisations or professions, but it could also be much simpler than that: clickbait makes serious money, and some people actually sow disinformation simply for the fun of it. We can be sure of one real conspiracy: the industry behind conspiracy theories.

 

* Still waiting for my paycheck for being in the pocket of Big Second-Law-of-Thermodynamics…

** This is no reason not to improve the replicability and transparency of scientific research – quite the opposite!

Imaging

Inspired by my Twitter feed for the past few weeks, and in particular this tweet in which I suggested science should be about the science not about the scientist, the revelation that there is such a thing as “negotiated submissions“, the debate on whether or not people should sign their peer reviews, and last but not least my inability to spell simple words, I give you my most embarrassing blog post yet. To the tune of a famous John Lennon song:

Imaging there’s no tenure
It’s easy if you try
No self-promotion needed
No reviewer makes you cry

Imaging all researchers
Searching for the truth

Imaging there’s no Twitter
It isn’t hard to do
Nothing to kill or die for
And no arguments, too

Imaging all researchers
Living life in peace

You may say I’m a procrastinator
That I should apply for grants
I should be preparing lectures
And write no stupid rants

Imaging no more authors
Results published as they came
No need for impact factors
Nobody cares about fame

Imaging all researchers
Sharing all data

You may say that I’m a dreamer
But I’m not the only one
I hope someday you’ll join us
And science will again be fun

What is a “publication”?

I was originally thinking of writing a long blog post discussing this but it is hard to type verbose treatises like that from inside my gently swaying hammock. So y’all will be much relieved to hear that I spare you that post. Instead I’ll just post the results of the recent Twitter poll I ran, which is obviously enormously representative of the 336 people who voted. Whatever we’re going to make of this, I think it is obvious that there remains great scepticism about treating preprints the same as publications. I am also puzzled by what the hell is wrong with the 8% who voted for the third option*. Do these people not put In press articles on their CVs?

screenshot 2019-01-19 at 16.53.25

*) Weirdly, I could have sworn that when the poll originally closed this percentage was 9%. Somehow it was corrected downwards afterward. Should this be possible?

On optimal measures of neural similarity

Disclaimer: This is a follow-up to my previous post about the discussion between Niko Kriegeskorte and Brad Love. Here are my scientific views on the preprint by Bobadilla-Suarez, Ahlheim, Mehrotra, Panos, & Love and some of the issues raised by Kriegeskorte in his review/blog post. This is not a review and therefore not as complete as a review would be, and it contains some additional explanations and non-scientific points. Given my affiliation with Bobadilla-Suarez’s department, a formal review for a journal would constitute a conflict of interest anyway.

What’s the point of all this?

I was first attracted to Niko’s post because just the other day my PhD student and I discussed the possibility of running a new study using Representational Similarity Analysis (RSA). Given the title of his post, I jokingly asked him what was the TL;DR answer to the question “What’s the best measure of representational dissimilarity?”. At the time, I had no idea that this big controversy was brewing… I have used multivoxel pattern analyses in the past and am reasonably familiar with RSA but I have never used it in published work (although I am currently preparing a manuscript that contains one such analysis). The answer to this question is therefore pretty relevant to me.

RSA is a way to quantify the similarity of patterns of brain responses (usually measured as voxel response patterns with fMRI or the firing rates of a set of neurons etc) to a range of different stimuli. This produces a (dis)similarity matrix where each pairwise comparison is a cell that denotes how similar/confusable the response patterns to those stimuli are. In turn, the pattern of these similarities (the “representational similarity”) then allows researchers to draw inferences about how particular stimuli (or stimulus dimensions) are encoded in the brain. Here is an illustration:

rsa

The person called Warshort believes journal reviews, preprint comments, and blog posts to be more or less the same thing, public commentaries on published research. The logic of RSA is that somewhere in their brain the pattern of neural activity evoked by these three concepts is similar. Contrast this to person Liebe who regards reviews and preprint comments to be similar (but not as similar as Warshort would) but who considers personal blog posts to be diametrically opposed to reviews.

What is the research question?

According to their introduction, Bobadilla-Suarez et al. set themselves the following goals:

“The first goal was to ascertain whether the similarity measures used by the brain differ across regions. The second goal was to investigate whether the preferred measures differ across tasks and stimulus conditions. Our broader aim was to elucidate the nature of neural similarity.”

In some sense, it is one of the overarching goals of cognitive neuroscience to answer that final question, so they certainly have their work cut out for them. But looking at this more specifically, the question of the best measure of comparing brain states across conditions and how this depends on where and what is being compared is an important one for the field.

Unfortunately, to me this question seems ill-posed in the context of this study. If the goal is to understand what similarity measures are “used by the brain” we immediately need to ask ourselves¬† whether the techniques used to address this question are appropriate to answer it. This is largely a conceptual point, and the study’s first caveat for me. We could instead reinterpret this into a technical comparison of different methods, but therein lies another caveat and this seems to be the main concern Kriegeskorte raised in his review. I’ll elaborate on both these points in turn:

The conceptual issue

I am sure the authors are fully aware of the limitations of making inferences about neural representations from brain imaging data. Any such inferences can only be as good as the method for measuring brain responses. Most studies using RSA are based on fMRI data which measures a metabolic proxy of neuronal activity. While fMRI experiments have doubtless made important discoveries about how the brain is organised and functions, this is a caveat we need to take seriously: there may very well be information in brain activity that is not directly reflected in fMRI measures. It is almost certainly not the case that brain regions communicate with one another directly via reading out their respective metabolic activity patterns.

This issue is further complicated by the fact that RSA studies using fMRI are based on voxel activity patterns. Voxels are individual elements in a brain image, the equivalent to pixels in a digital image. How a brain scan is subdivided into voxels is completely arbitrary and depends on a lot of methodological choices and parameters. The logic of using voxel patterns for RSA is that individual voxels will usually exhibit biased responses depending on the stimulus – however, the nature of these response biases remains highly controversial and also quite likely depends on what brain states (visual stimuli, complex tasks, memories, etc) are being compared. Critically, voxel patterns cannot possibly be directly relevant to neural encoding. At best, they are indirectly correlated with the underlying patterns, and naturally, the voxel resolution may also matter. In theory, two stimuli could be encoded by completely non-overlapping and unconnected neuronal populations which are nevertheless mixed into the same voxels. Even if voxel responses were a direct measure of neuronal activity, they might not show any biased responses at all, and the voxel response pattern would therefore carry no information about the stimuli whatsoever.

But there is an even more fundamental issue here. This is also unaffected by what actual brain measure is used, be it voxel patterns or the firing rates of actual neurons. The authors’ stated goal is to reveal what measure the brain itself uses to establish the similarity of brain states. The measures they compare are statistical methods, e.g. the Pearson correlation coefficient or the Mahalanobis distance between two response patterns. But the brain is no statistician. At most, a statistical quantity like a Pearson’s r might be a good description for what some read-out neurons somewhere in the processing hierarchy do to categorise the response patterns in up-stream regions. This may sound like an unnecessarily pedantic semantic distinction, but I’d disagree: by only testing predefined statistical models of how pattern similarity could be quantified, we may impose an artificially biased set of models. The actual way this is implemented in neuronal circuits may very well be a hybrid or a completely different process altogether. Neural similarity might linearly correlate with Pearson’s r over some range, say between r=0.5-1, but then be more consistent with a magnitude code at the lower end of similarities. It might also come with built in thresholding or rectifying mechanisms in which patterns below a certain criterion are automatically encoded as dissimilar. Of course, you have to start somewhere and the models the authors used are reasonable choices. However, this description should be more circumspect in my view because in the best case we could really say that the results suggest a mechanism that is well described by a given statistical model.

Finally, the authors seem to make an implicit assumption that does not necessarily hold: there is actually no reason to accept up-front that the brain quantifies pattern similarity at all. I assume that it does, and it is certainly an important assumption to be tested empirically. But in theory it seems entirely possible that spatial patterns of neural activity in a particular brain region are an epi-phenomenon of how neurons in that region are organised. This does however not mean that downstream neurons necessarily use this pattern information. I’d wager this almost certainly also depends on the stimulus/task. For instance, a higher-level neuron whose job it is to determine whether a stimulus appeared on the left or the right presumably uses the spatial pattern of retinotopically-organised responses in the earlier visual regions. For other, more complex stimulus dimensions, this may not be the case.

The technical issue

This brings me to the other caveat I see with Bobadilla-Suarez et al.’s approach here. As I said, this is largely the same point made by Kriegeskorte in his review and since this takes up most of his post I’ll keep it brief. If we brush aside the conceptual points I made above and instead assume that the brain indeed determines the similarity of response patterns in up-stream areas, what is the best way to test how it does this? The authors used a machine learning classifier to use pair-wise decoding of different stimuli and construct a confusability matrix. Conceptually, this is pretty much the same as the similarity matrix derived from the other measures they are testing (e.g. Pearson’s r) but it instead uses a classifier algorithm the determine the discriminability of the response patterns. The authors then compare these decoding matrices with those based on the similarity measures they tested.

As Kriegeskorte suggests, these decoding methods are just another method of determining neural similarity. Different kinds of decoders are also closely related to the various methods Bobadilla-Suarez et al. compared: the Mahalanobis distance isn’t conceptually very far from a linear discriminant decoder, and you can actually build a classifier using Pearson’s r (in fact, this is the classifier I mostly used in my own studies).

The premise of Bobadilla-Suarez et al.’s study therefore seems circular. They treat decodability of neural activity patterns as the ground truth of neural similarity, and that assumption seems untenable to me. They discuss the confound that the choice of decoding algorithm would affect the results and therefore advocate using the best available algorithm, yet this doesn’t really address the underlying issue. The decoder establishes the statistical similarity between neural response patterns. It does not quantify the actual neural similarity code – as a matter of fact, it cannot possibly do so.

It is therefore also unsurprising if the similarity measure that best matches classifier performance is the method that is closest to what the given classifier algorithm is based on. I may have missed this, but I cannot discern from the manuscript which classifier was actually used for the final analyses, only that the best of three was chosen. The best classifier was determined separately for the two datasets the authors used, which could be one explanation for why their outcome results differ between them.

Summary

Bobadilla-Suarez et al. ask an interesting and important question but I don’t think the study as it is can actually address it. There is a conceptual issue in that the brain may not necessarily use any of the available statistical models to quantify neural similarity, and in fact it may not do so at all. Of course, it is perfectly valid to compare different models of how it achieves this feat and any answer to this question need not be final. It does however seem to me that this is more of a methodological comparison rather than an attempt to establish what the brain is actually doing.

To my understanding, the approach the authors used to establish which similarity measure is best cannot answer this question. In this I appear to concur with Kriegeskorte’s review. Perhaps I am wrong of course, as the authors have previously suggested that Kriegeskorte “missed the point”, in which case I would welcome further explanation of the authors’ rationale here. However, from where I’m currently standing, I would recommend that the authors revise their manuscript as a methodological comparison and to be more circumspect with regard to claims about neural representations.

The results shown here are certainly not without merit. By comparing commonly used similarity measures to the best available decoding algorithm they may not establish which measure is closest to what the brain is doing, but they certainly do show how these measures compare to complex classification algorithms. This in itself is informative for practical reasons because decoding is computationally expensive. Any squabbling aside, the authors show that the most commonly used measure, Pearson’s correlation, clearly does not perform in the same way as a lot of other possible techniques. This finding should also be of interest to anyone conducting an RSA experiment.

Some final words

I hope the authors find this comment useful. Just because I agree with Kriegeskorte’s main point, I hope that doesn’t make me his “acolyte” (I have neither been trained by him nor would I say that we stem from the same theoretical camp). I may have “missed the point” too, in which case I would appreciate further insight.

I find it very unfortunate that instead of a decent discussion on science, this debate descended into something not far above a poo-slinging contest. I have deliberately avoided taking sides in that argument because of my relationship to either side. While I vehemently object to the manner with which Brad responded to Niko’s post, I think it should be obvious that not everybody is on the same wavelength when it comes to open reviewing. It is depressing and deeply unsettling how many people on either side of this divide appear to be unwilling to even try to understand the other point of view.