Comments rejected by the new, improved PubPeer moderators

Ongoing list…

 1. Van Zyl (2018) New Ideas in Psychology 

It is my understanding from reading various texts that the chief distinction made by “Bayesians” between their practices and those of “frequentists” is that frequentists are referring to the actual probabilities or relative probabilities of events resulting from a test of a predictive hypothesis, while “Bayesians” are referring to “subjective” probabilities encompassing a much broader framework (extending beyond the experimental test).

While there have been attempts to carve out a distinction between “objective” and “subjective” “Bayesian analysis,” these efforts seem, so far, unconvincing. I quote below from the Wikipedia entry on “Bayesian probability”:

“Broadly speaking, there are two interpretations on Bayesian probability. For objectivists, interpreting probability as extension of logic, probability quantifies the reasonable expectation everyone (even a “robot”) sharing the same knowledge should share in accordance with the rules of Bayesian statistics, which can be justified by Cox’s theorem.[2][8] For subjectivists, probability corresponds to a personal belief.[3] Rationality and coherence allow for substantial variation within the constraints they pose; the constraints are justified by the Dutch book argument or by the decision theory and de Finetti’s theorem.[3] The objective and subjective variants of Bayesian probability differ mainly in their interpretation and construction of the prior probability.”

Thus, we see that even the “objective” version of “Bayesianism” entails subjective cognitive evaluations – “reasonable expectations,” “rationality constraints,” “interpretation of the prior probability.”

Continuing in the Wikipedia entry, we see that, indeed, the “objective” category is the subject of much (subjective) disagreement:

“[Many theorists] have suggested several methods for constructing “objective” priors. (Unfortunately, it is not clear how to assess the relative “objectivity” of the priors proposed under these methods)…The quest for “the universal method for constructing priors” continues to attract statistical theorists.”

Given all the above, given the absence of a “universal method for constructing [objective] priors” I would say that a strong case for “Bayesian probability,” as regards its use in objective science, is still incomplete.

 

2. Wei Wei (2018) Annual Review of Vision Science

I am puzzled by the author’s unqualified use of the term “receptive field,” a term whose meaning appears to be in flux. The simple notion that there is a circumscribed part of the retina, and a corresponding circumscribed part of perceived space, events in which affect particular neurons, has long been debunked and labelled “classical receptive field.” The understanding that the early concept was problematic came almost from the start, as indicated by the excerpt from Spillma’s (2015/JOV) article, “Beyond the classical receptive field (the two paragraphs are consecutive in the original text):

“Our perception relies on the interaction between proximal and distant points in visual space, requiring short- and long-range neural connections among neurons responding to different regions within the retinotopic map. Evidently, the classical center-surround RF can only accommodate short-range interactions; for long-range interactions, more powerful mechanisms are needed. Accordingly, the hitherto established local RF properties had to be extended to take distant global inputs into account.”

“The idea of an extended (called nonclassical or extraclassical today) RF was not new. Kuffler (1953, p. 45) already wrote, “… not only the areas from which responses can actually be set up by retinal illumination may be included in a definition of the receptive field but also all areas which show a functional connection, by an inhibitory or excitatory effect on a ganglion cell. This may well involve areas which are somewhat remote from a ganglion cell and by themselves do not set up discharges.””

The language of the above text is a bit misleading, implying as it does that the “hitherto established” local RF remained in place and merely needed elaboration. It should be clear that if the firing rate of a neuron “x” can be altered by the conditions of stimulation applying to the whole retina, then it is not possible to experimentally define a local area as in any way special based on the local conditions of stimulation. Or rather, it is artificial, privileging one set of global conditions other an infinite number of alternatives in producing a definition (which even then has not been proven to replicate). Even the verbal expansion of the term to include “non-classical receptive fields” does not rescue the concept from this problem.

The extreme confusion that the concept has produced as researchers have attempted to specify its elusive properties may be appreciated in reading Carandini et al’s (2005) “Do we know what the early visual system does?” The discussion includes reference to a black box “saving device.”

The concept of “direction-selectivity” is closely tied to the receptive field concept. It is difficult for me to understand how Wei can avoid addressing these theoretical problems.

Unqualified use of the term “receptive field” and associated concept is quite common; I’ve highlighted it in several PubPeer comments, including on El Boustani et al (2018)/Science; onBeltramo & Scanziani (2019)/Science; on Olshausen & Field (1996)/Nature

A second stab at Wei Wei also failed:

As noted in the PubPeer comment on Olshausen & Field (1996) as well as other PubPeer comments linked therein, the concept of “receptive field” is currently missing a theoretical definition. Various researchers employ different de facto definitions of the term, strictly tied to the procedures they happen to use. The use of the term by Wei in this review, without qualification or clarification, renders the discussion incomplete.

 

3. Bakkour, Palombo, Zylberberg, Kang, Reid, Verfaellie , Shadlen , Shohamy (2019) eLife “The hippocampus supports deliberation during value-based decisions.” 

(In addition to the comment submitted to PubPeer, I note here that the authors’ use of the term “supports” is an example of Neuroscience Newspeak.)

“Bakkour et al state:

We fit a one-dimensional drift diffusion model to the choice and RT on each decision. The model assumes that choice and RT are linked by a common mechanism of evidence accumulation, which stops when a decision variable reaches one of two bounds.

I’m confused about what the authors are claiming. Experiments are based on two-alternative forced choices and structured so that the data produced may be “modelled” based on the “drift diffusion model.” The fitting procedures allow modellers quite a bit of leeway in adjusting free parameters, and many quantitative choices are unconstrained by theory. The above-stated assumptions of the “drift diffusion model”, i.e. that “choice and RT are linked by a common mechanism of evidence accumulation” are vague; no concrete description (even a vague one) in terms of neural function has ever been proposed. The drift diffusion model is an extension of “signal detection theory;” and the assumptions of this “theory” seem to lack face validity. SDT curves tend to be specific to particular experiments and not to generalize.

In short, under the circumstances I’m not sure that fitting the data acquired to the model under consideration is enough to license inferences about brain function.”

4. Mueller & Weidemann (2008) Psychonomic Bulletin and Review

“SDT assumes that percepts are noisy.”

The term “percept” refers to what is consciously experienced, and generally to what is experienced visually. What we experience visually is not noisy, and does not necessitate any conscious decision-making on the viewer’s part. Conscious decision-making is, both implicitly and explicitly, what we are talking about here. Implicitly, because if the conscious perceptual experience (the percept), is noisy, then the viewer must be called on to make a conscious decision as to how to interpret it. Explicitly, because the associated experiments refer to participants’ decisions, usually binary forced-choice decisions often requiring guesses.

Given all of this, the statement that “SDT assumes percepts are noisy” is hard to interpret. The assumption seems to lack face validity, and no explanation or references, or proposals of how to test it, are offered. On what basis is the assumption considered valid?

5. Oberauer & Lewandowsky (2019) Psychonomic Bulletin and Review.

[It seems particularly unkind of PubPeer moderators to censor my reply to another commenter’s reply to my initial comment, which did post.]

The text you cite seems very confused and waffling to me. Which of Mayo’s arguments have you found compelling with respect to making the fruits of post hoc correlation-fishing reliable – something that, as mentioned, is virtually never the case? Has she proposed and tested a method of post hoc statistical inference that produces replicable outcomes?

 

6. Chen, Yeh & Tyler (2019) Journal of Vision

I’d like to discuss the authors’ dichotomization of the images used in their procedures into “target” and “noise.” It seems to me that this dichotomy is not a valid one, for fairly obvious reasons.

In this comment, I’ll be using the term “image” to refer to a surface reflecting light into the eyes of a seeing human.

By target, the authors are referring to certain more-or-less smoothly-changing bands of light and dark which they call “Gabors.” These patterns are typically perceived either as alternating bars or, if the transitions are fairly gradual, as partly-shaded cylinders. By “noise,” they mean a different type of pattern, consisting of variously-arranged dots.

Collections of dots always tend to be grouped spontaneously by both human and species of non-human viewers to produce various perceived shapes or patterns, among the simplest examples being the simple “rows” or “columns” of dots typically used to demonstrate principles of organization. We may see the same tendency in the grouping of stars into constellations, and the perception of objects in clouds.

Here, we have two types of patterns; one rather orderly both physically and perceptually, the other less so – but both requiring and eliciting perceptual/neural organizing processes in order for perceived structures, stable or unstable, to arise in consciousness.

When two patterns are super-imposed in an image, the structures that will emerge in consciousness are not necessarily the sum of these two. They may be – it depends on the combined pattern, and how it is interpreted by the perceptual organizing processes. The combination of the two may destroy the structural coherence of one or the other or both; and new structures may be perceived. A classic series of experiments by Gottschaldt demonstrated, many decades ago, that “targets” may not be perceived in certain contexts, even when observers expect and are actively watching for them.

It seems to me the above facts are relevant and should be addressed in the authors’ theoretical discussion.

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This comment was just rejected by PubPeer. Why?

 

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Is this April 2019 Science article an example of fake visual neuroscience?

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In the past two posts I’ve tried to explain why prevailing methods in visual neuroscience amount to a fake science even less demanding than astrology. A recent Science article by Stringer, Pachitariu, Steinmetz, Reddy, Carandini & Harris (2019) seems a perfect example of such methods. The article is titled “Spontaneous behaviors drive multidimensional, brainwide activity.”

Stringer et al (2019) wave some objects around in the lab while recording from a few thousand neurons, then mine their data for coincidences between (their partial descriptions of) those external events and the electrical activity they have recorded from 10,000 neurons.

As is well-known, even if those external events had been random numbers from a random number generator, correlations would be found.

In other words, there’s no necessary, rational link between Stringer et al’s experimental conditions and the “data” they collect. The same methodological principle could be used to support any thesis whatsoever, e.g. to identify supposed psychics in our midst.

Big data doesn’t help, either; it just makes things worse, as Calude and Longo (2016) recently showed in a paper titled The Deluge of Spurious Correlations in Big Data.

The hallmark of scientific practice is, of course, an investigator’s ability to show a tight, necessary link between theory and experimental conditions, and experimental conditions and results. Again, that crucial connection here is completely lacking. Their method, in other words, does not allow the authors to distinguish between chance and necessity.

Relatedly: As Gary Smith explains in The AI Delusion, the principle component analysis (PCA) technique used by Stringer et al is a tool for data reduction whose output – the “components” – need have no predictive value:

“A goal of summarizing data is very different from a goal of using the data to make predictions….the principle components are chosen based on the statistical relationships [in the sample] among the explanatory variables, with no consideration whatsoever of what these components will be used to predict. For example, a person’s birth month or favorite candy might end up being included among the principal components used to predict whether someone will be involved in a car accident. Moreover, if the principal components are based on a data-mining exploration of hundreds or thousands of variables, it is virtually certain that the components will include nonsense. Once again, Big Data is the problem, and principal components is not the solution.”

To avoid confusion, it should be noted that Smith is using the word “predict” in the normal, forward-looking sense, not in the neuroscience newspeak, post hoc manner of Stringer et al. (2019) (see below).

The “thousands of variables” here correspond to the 10,000-plus putative neurons being recorded from. They constitute only a small subset of a highly integrated system involving billions or trillions of synapses. The idea that meaningful inferences about how a such a complex system, whose basic functional principles are as yet unknown, may be drawn via random correlation-fishing beggars belief.

Correlation-fishing is also, naturally, the basis of the literature Stringer et al inappropriately cite.

They state, for example, that “The firing of sensory cortical [] neurons correlates with behavioral variables such as locomotion…,” citing DiPoppa et al (2018). But the claims of DiPoppa et al were arrived at via straightforward p-hacking.

The discovery of such correlations in a sample of data is, again, no basis for making causal neuroscientific claims (as pointed out recently by Mehler and Kording (2018)), due to the obvious problem of massive confounding. (One of Mehler and Kording’s main points was the impropriety of employing causal language – like the term “drive” used in the title of the present paper – to describe correlation-fished neuron-stimulus associations as though they implied a causal relationship). And such associations are known not to replicate.

Stringer et al also tell us that:

“[N]eurons’ responses to classical grating stimuli revealed robust orientation tuning as expected in visual cortex (fig. S1).”

As someone who has studied this literature closely, this statement reads to me like a lie. Claims of orientation-tuning have always been correlation-fished, exactly in the way we could identify psychics based on a series lucky guesses of the results of dice rolls. If we go to Stringer et al’s figure S1, the situation becomes quite clear:

“Orientation tuning curves of the 400 most tuned neurons in each experiment (as assessed by orientation selectivity index)…” As in DiPoppa et al, neurons that happen to be firing at highish rates (according to some arbitrary criterion) coincidentally with the presentation of the “stimulus” are defined as tuned, and their firing is causally attributed to the “stimulus.” Practitioners of such methods seem to be totally unaware of the massive confounds involved.

Finally, I have to note the reference to “classical grating stimuli.” The only meaning of “classical” here is to indicate stimuli that have been used continuously for at least fifty years, so that the correlation-fishing nature of the neuron-stimulus correlations will not be obvious. The method could just as (in)validly be used to identify “kitty-tuned” neurons. Even more plausibly, perhaps, given the utter absurdity of the rationale underlying the use of gratings.

Why It’s Easier To Be A Neuroscientist Today Than An Astrologer

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Believe it or not, by today’s standards, the demands made on astrologers are more stringent than those made on “neuroscientists.”

Sure, neuroscientists today use lots of high-tech equipment and fussy, complicated techniques; but they’ve arranged things so that they’ll will always (seem to) turn out the way they want; so that their experiments can never prove their most basic assumptions wrong, even if they are.

If you’re an astrologer, you believe that people’s personality traits are determined by the stars. If you’re a Gemini, you’re a certain way, a Taurus, another, and so on. Lots of times, these “predictions” come true – no wonder Joe has a hot temper, he’s an Aries! But they can also be challenged; Shannon is hot-tempered, too, which is strange, because she was born under a milder star. Of course, astrologers can always resort to more detailed astrological analyses to rationalize apparent discrepancies; but at each step, their “predictions” may be falsified or challenged. The lack of reliability of astrologers’ predictions are one reason we don’t let them publish in scholarly journals.

Now, imagine this:

All records of birth dates disappear, as well as memories. The astrologers step in; they can fix it! They ask everyone for a detailed bio, and perform analyses based on their astrological assumptions about the connection between the stars and personality. Approximate dates of birth are then assigned based on the results of the test; if the test says you’re a Pisces, then we’ll presume you were born in February or March, and so on. Note the difference between this scenario and the previous one. In the previous case, astrologers assumptions could be shown to be wrong, based on failure to make accurate predictions. In this one, the assumptions are taken as true a priori, and analyses simply lie on top. The assumption, in other words, that having a particular personality/behavior is caused by particular alignment of the stars at a certain moment (of your birth) is used to label/define you as an Aquarian, etc. What if your future behavior doesn’t align with the label assigned? Well, in that case the astrologers, as mentioned above, are allowed to keep the label, but argue that the discrepancy is due to other, complicating factors.

As I discussed in the previous post, neuroscientists do pretty much the same thing: They assume that a particular neuron’s (part of a network of billions or trillions of connections) “personality” falls into a small number of simple categories of “preference,” and that an instance of a coincidence in time between a neuron’s high activity and the presence of an exemplar of that “preferred” category licenses them to label the neuron (post hoc) as, e.g. an “orientation detector” with a particular “tuning curve.” The fact that such findings do not replicate (the coincidences don’t repeat) is treated as “variability” in neural activity due to complicating factors. The “orientation preference” assumption, in other words, is carved in stone, and violations are explained away.

That this technique may be used to support any assumptions, even the most untenable, is evidenced dramatically by the continued claims of the existence of “spatial filters.” In general, the small set of claimed “preferences” of “visual” neurons are historical artifacts dating back many decades. They survive because never challenged.

Going back to our astrologers: Let’s imagine that, having been given license to treat their assumptions about birth and stars as true, astrologers then decided to expand their research program. They could, for example, ask question about the role of star sign in determining success in various professions. They could collect data on professional success, employing various parameters, and perform linear regressions to find whether it’s better to be a Virgo or a Libra if you want to be a neuroscientist. Or they could dig deeper to see whether there is an interaction between the sign of a student and their PI. Naturally, they would couch their results in probability terms, employing Bayesian “default priors” to fit in with the current zeitgeist.

Note, again, the astrologers would be taking no risks here; again, their underlying assumptions are not on the line. They aren’t required to test them by making any predictions about the results of their investigation; they simply describe certain arbitrary parameters of their sample, with whatever mathematical techniques and assumptions they choose to assess them. To data-mine/p-hack their sample, in other words. This is what neuroscientists are doing when they collect “data” and then mine it for correlations with “behaviors” on certain “tasks,” etc.

Will such correlations found post hoc apply in general? As a rule, they don’t.

This is the case with the analogous practices in neuroscience, as many have acknowledged. They include Konrad Kording of UPenn, speaking on Waterloo Brain Day 2017. Addressing the issue of why generalization studies of correlation-fished results are “never, or almost never” performed, he replies:

I’ll tell you why…All my generalization studies fail, almost all of them, both in psychophysics and in data analysis.”

If our astrologers were, as a result of their inability to achieve reliable results via correlation-fishing built on arbitrary assumptions, to engage in years of earnest discussions about their field experiencing “replication” or “reproducibility” crises, and found (and fund) a “Center for Reproducible Astrology,” and still continue on with business as usual…they would be acting like neuroscientists in good standing.

FURTHER READING FROM THE BLOG

Contemporary Neuroscience Depends on Outright p-hacking

Bondy, Haefner & Cumming Base Their Post Hoc Correlational Study on Correlations They Say (Correctly) Don’t Exist

It Is Bullshit: None of it Replicates

Neuroscience Newspeak, Or How to Publish Meaningless Facts

The Miracle of Spatial Filters

Why Correlational Studies Are Fake Science

Nature Neuroscience Starts Year Strong With Correlation-Fishing from Yale, Mount Sinai

 

Why “Correlational Studies” Are Fake Science

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The brain as “neuroscientists” crystal ball; they see what they want to see.

It seems that the dominant practice in “visual neuroscience” today is to take some “stimulus,” wave it in front of a human or animal subject, and record brain activity.  Correlations in time between this activity (as defined by some arbitrary metric, e.g. averaging over arbitrary time intervals), and exposure to the “stimulus” are then described as “responses” to the “stimulus.”

The metrics are ad hoc and flexible. In Lau et al (2019), for example, we have this:

Responses that fell above the top 2.5%, or below the bottom 2.5%, of this distribution were considered significantly excitatory or inhibitory respectively.

Even the neurons to which the “data” are supposed to correspond are “putative:”

…neurons with waveforms that had an interval of 0.5 ms or less and a trough/peak amplitude ratio of >0.5 were designated as putative PV neurons.

Do you see what’s going on here?  The expectation that there exist certain neurons that “respond” to whatever investigators imagine their “preference” to be is, in the circumstances, a sure-fire prediction. There will always be more or less electrical activity at any given brain location at any given moment. We could “link” highish or lowish points of activity with any external event we like –  it’s a low-to-no-risk operation. The method doesn’t punish you for being wrong, for not understanding anything about your system.. If you want to rack up a relatively higher number of coincidental correlations, you simply use a low p-value, such as the p < .05 criterion (understandably) still very popular in the “neuroscience” literature.

And voila – Nature paper, Science paper, Neuron paper, Current Biology paper, etc.

The procedure is exactly like trying to discover psychics among a group of people. First, you assume that some people are psychic. Then, you choose a decision criterion – on what basis will you classify certain people as psychic? You could, for example, ask them to guess at the the number on a playing card without looking, and classify the ones that got a certain proportion correct as psychic. The idea that some people are psychic wouldn’t need to be true for us to be able to classify some of our subjects as such. It wouldn’t matter that the idea violates the known laws of physics.

Similarly, it doesn’t matter that the idea of neurons as “detectors” “signalling” things (the notion is implicitly homuncular) via highish firing rates violates basic logic and known facts; post hoc correlation-fishing doesn’t care about fact, doesn’t care about logic, doesn’t care about truth. It’s a racket.

 

Image credit: Screenshot taken from video by shaihulud. 

 

 

 

 

 

Signal detection theory’s “Ideal observer:” A device for obscuring empirical failure.

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Mr. Perfect: The Ideal Observer

The “signal detection theory” approach, despite the self-evident absurdity of its assumptions, forms part of the bone structure of contemporary neuroscience. The most recent example I’ve examined at is a biorxiv preprint from the Churchland lab at Cold Spring Harbor, and it got me thinking again about how the whole scheme works.

The title of the paper is “Lapses in perceptual judgments reflect exploration.” The term “lapses” (like the term “perceptual judgments) is part of SDT terminology. Unpacking it helps reveal the way this cargo cult (and I’m not being dramatic) functions.

SDT’s concept of “lapse rate” is intrinsically tied to SDT’s concept of the “ideal observer.”  It took me a while to appreciate the role of this device; thinking about the Churchland paper helped bring it home.

They key to a successful pseudoscience – and SDT, with its sixty-year run so far, is certainly successful – is to immunize its assumptions from the challenges and insults of reality. This involves, first, a willful blindness to logical contradictions and contradiction with fact – but that is not the topic of this post – and, second, an arrangement of experimental practices and analytical techniques that eternally ensure a superficial consistency with “prediction.”

One of the safest ways, of course, to ensure that your predictions are consistent with the results of experiment are to make predictable predictions.

The basic “prediction” of SDT is that data will have a sigmoid shape. Obviously, data having such a form can mean a million different things – it depends on the conditions, the manipulations, etc. For SDT’ers studying the brain, it reflects the effects of very specific assumptions about brain function (which I’ve described in the link above).

The sigmoid shape of the data is easily achieved by setting up a situation where subjects are required to make a binary choice, and in which giving the “correct” answer becomes more and more difficult. If you set up such a situation, and then refer to the stimuli that produce correct responses as the “preferred (by neurons) stimuli” and the more ambiguous stimuli as “noisy” and the stimulus set as a whole as “sensory evidence” consisting of different “stimulus levels” then congratulations, you can be an SDT’er in good standing! Your results may now be interpreted via the assumptions of SDT.

Wait, there’s another step. Because subjects are being forced to make a choice, -regardless of whether that choice reflects any relevant experience (due to stimulus ambiguity) – SDT practitioners realized that subjects might be guessing! If subjects are guessing, SDT’ers reason, then the proportion of “correct” answers will be 50%. So, prior to their experiments, SDT’ers adjust their basic stimuli and conditions to produce some particular level of correct choices, somewhere between 65% and 80%. Raposo et al (2012), for example, (a paper to which the “Lapse rate” paper refers us for detailed methods), states that:

“The amplitude of the events was adjusted for each subject so that on the single-sensory trials performance was70–80% correct and matched for audition and vision.”

For some reason, such figures are treated by SDT’ers as not subject to statistical uncertainty – that is, they never seem to report having conducted a test to show that their 75% etc, figure is statistically different from chance.

Now we’re all prepped to ensure we’ll get sigmoidish curves going forward to our “experiments,” and will be free to apply the neurons-as-noisy-detectors-binary-probability-distribution-comparison mechanism explanation to our data.

BUT. Even with such conscientious preparation, data don’t tend to come out quite in line with SDT prediction. Something about the tails…Even turning a blind eye to the basic absurdity of its assumptions, empirical failures might make an SDT’er stop and think. And indeed, they did…and concluded that the theory was right and the data were wrong. Fortunately, math comes to the rescue to correct the curves, in the form Mr. Perfect, the “ideal observer.”

The ideal observer is an imaginary subject who would produce the results SDT “predicts,” and thus (superficially) license the claim that subjects’ choices are based on comparing firing rates of individual neurons with distributions of potential firing rates of these neurons. (The fact that this scenario doesn’t correspond to any aspect of subjects’ experience doesn’t seem to matter).

Unlike the ideal observer, real subjects are sloppy, biased, whatever, and tend to “lapse,” producing undesirable curves. As the Churchland team writes:

In practice, the shapes of empirically obtained psychometric curves do not perfectly match the ideal observer [i.e. don’t match prediction] since they asymptote at values that are less than 1 or greater than This is a well known phenomenon in psychophysics (Wichmann and Hill, 2001), requiring two additional lapse parameters to precisely capture the asymptotes.”

A few free parameters later, the assumptions of SDT with respect to the basic mechanism underlying the “data” remain intact. Now, proposals for additional mechanisms may be layered on top to “explain” deviations in the “data.”

Notice that it doesn’t matter whether or not the assumptions underlying the “ideal observer” concept are true or false. They’re never tested, just taken for granted, with mathematical fixes taking care of prediction failures, which occur despite strenuous preliminary stage-managing.

SDT’ers are apparently not confident enough about the lapse rate concept for it to rate a mention on Wikipedia, and discussion is very patchy. The Churchland team runs with it, though, and proposes a mechanism for the deviations of their “data” from prediction. The proposal is, of course, silly, but that’s not the subject of this post.

 

 

 

Adelson & Movshon’s (1982) “Phenomenal coherence of moving visual patterns,” a classic in an ongoing pseudoscientific tradition in motion perception

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Adelson & Movshon’s 1982 Nature paper, “Phenomenal coherence of moving visual patterns,”  cited to this day, is a good illustration of the difference between science and pseudoscience.

The brand of pseudoscience I will be discussing here has several basic features.

First, it draws general inferences from post hoc observations of special cases while studiously ignoring cases that contradict these inferences, as well as logical inconsistencies within among their theoretical assumptions. The chief characteristic of real science – the ability to make forward-looking predictions that put its assumptions to the test – is lacking. Pseudoscience is a riskless game. When stories built around a set of special cases are perpetuated and elaborated over generations, then we are dealing with a pseudoscientific tradition.

Before going on to show that Adelson & Movshon and their successors are part of such a pseudoscientific tradition, I want to offer an imaginary analogue of how they operate.

Imagine that Martians come to Earth and discover a cylindrical object floating in a pond filled with fish. It happens to be what we call a cork. They then proceed to construct an account of why this object floats. Their explanation hinges on certain features of the particular object – e.g. that it is cylindrical, that it has a little nick on one side, that it is tan-colored, that it has a particular texture, that it’s contains a molecule with a particular structure; and  also on certain features of the pond, e.g. that it contains fish. They write up their report, successfully submit it to Martian Nature and advance in their careers.

Naturally, an investigation of a wider set of objects and conditions would expose the Martians’ mistake in treating incidental features of the cork and pond as causal factors underlying the phenomenon of interest. To protect their pet “theory,” the Martians make sure that future studies of flotation always revolve around corks and ponds, on which they  make more and more post hoc observations referencing more and more incidental features and around which they construct ever more elaborate ad hoc stories.When a colleague asks, “What about that floating light bulb over there?” they pretend not to hear, or smugly describe their ad hoc theories as “partial explanations.”

It should be obvious that constructing robust, general explanations of why objects float would require much more time, effort, ingenuity and a wider field of view than concocting casual explanations based on easily observed or incidental features of particular cases while failing to examine this explanation critically or acknowledge contradictory evidence. It should also be evident that the stories constructed by these Martians will not figure additively in the construction of the true explanation.

Why the obsession with Gabor patches?

If you’ve ever wondered why vision science for decades seems almost exclusively interested in stimuli consisting of circular areas of light and dark stripes – the famous “Gabor patches” – it is for this reason; to provide perpetual, if thinly cosmetic, cover for the simplistic, fragile, irrational ad hoc stories built around these forms.

The story Adelson & Movshon are offering about motion perception in this paper is such a story.

They begin their abstract by stating that:

When a moving grating is viewed through an aperture, only motion orthogonal to its bars is visible…

The statement is false; it only applies to a narrow set of conditions. A more complete picture is described in a 1935 text cited by Adelson & Movshon:

There exists a tendency for line motion to be perpendicular to line orientation, and also a tendency to perceive motion along one of the cardinal directions of space. Above all, however, the perceived direction of motion runs as parallel as possible to the direction of the edge of the aperture that the line happens to intersect. If the two aperture edges that are intersected simultaneously have different orientations, then the line pursues an intermediate direction; being a subject of psychophysical `self-organization’…” (Wallach, 1935).

Wallach’s is a much more subtle and complex description with profound and difficult theoretical implications.

 Adelson and Movshon prefer the more easily digestible, ad hoc version.

Their claim is descriptively correct when the “aperture” is circular. If we were to change the shape of the aperture – to make it rectangular, for example – then Wallach’s general statement would remain true, but A & M’s would fail. To put it another way: Wallach’s claim applies as a general principle over all known cases; Adelson & Movshon’s claim is a description of a special case. General hypotheses founded on it cannot be seriously entertained as explanatory. They are not robust. Scientists respect the phenomena, take on challenges, and actively look for weaknesses in their accounts; pseudoscientists avoid challenges and turn a blind eye to contradictions.

The use of circles and the false statements about the nature of aperture motion continues. We may find the same unqualified claim about orthogonality in a review on motion perception and the brain by Born & Bradley (2005), who state simply: 

A moving edge seen through an aperture appears to move perpendicularly to itself…

A very recent example of a study treating employing circular apertures and treating orthogonal motion as the general case are Junxiang et al (2019)/Journal of Neuroscience.

So, a false, simplistic assumption about motion perception has been imported into the neuroscience age and serves as the basis for explanations about brain function; and since neuroscience as a whole has adopted the post hoc/ad hoc approach to theorizing – as a result suffering from a replication crisis that shows no signs of abating, – and has adopted Orwellian language to hide its barrenness, this false assumption remains safe in its pseudoscientific cocoon, and the pseudoscientific tradition it underpins remains strong and has even colonized new lands.

More on Gabors

The stimuli Adelson and Movshon employ (and which are employed generally under the label “Gabor patches”) are not “gratings” consisting of simple lines or bars, with solid black areas alternating with solid white areas. Their colors gradually change from light to dark.

The use of these patterns is linked to another pseudoscientific notion, this one quite bizarre, irrational and,  of course, unsupported. It is the idea, to quote Adelson and Movshon, that, “visual analyzing mechanisms selective for…spatial frequency exist at a relatively early stage in the visual pathway.”

The idea that mechanisms analyzing “spatial frequency” of patterns exist at any stage of the visual process is patently absurd. I’ve addressed the reasons why in a separate blog post; among them is the fact that there is no possible utility to such a function, nor is there any conceivable mechanism by which it could be achieved, given that this “analysis” first requires synthesis of the point stimulation on the retina.

The assumption was protected by, first and foremost, being deaf to reason and restricting experimental activity to manipulations of the features of this narrow category of patterns, describing effects post hoc, and and drawing ad hoc inferences presented as general principles.

This is exactly what Adelson and Movshon do here. They are drawing various technical-sounding inferences by acting like the Martians messing around with corks and ponds and pretending to make general discoveries about the nature of floating objects. If you pointed to a floating feather, or a floating  light bulb, or a floating corpse, they would be at an utter loss for words. Similarly, if you pointed Adelson and Movshon, even today, to known facts of motion perception – you can read about them in an online translation of a chapter in an 82-year-old text (Metzger, 1937, translation made available by Brandeis University) – these practitioners of pseudoscience would be at a total loss. They would do what they’ve always done, simply look away. It’s worked well so far.

Update #1: Pseudoscientists never lose

It occurred to me after finishing this post that there is a very well-known effect that contradicts the “orthogonal motion” claim, and this is the barberpole illusion. Again, it’s the nature of pseudoscience that gross contradictions go unnoticed or politely ignored. 

Update #2: Pseudoscientists never lose redux

Two recent articles illustrate the way that the dogma of “spatial frequency tuning” is protected by its followers. In “Mechanisms of Spatialtemporal Selectivity in Cortical Area MT” (2019/Neuron), Pawar et al describe how this selectivity is contingent on a variety of stimulus “features:” 

“…even interactions among basic stimulus dimensions of luminance contrast, spatial frequency, and temporal frequency strongly influence neuronal selectivity. This dynamic nature of neuronal selectivity is inconsistent with the notion of stimulus preference as a stable characteristic of cortical neurons.”

Even when results are all over the place, the spatial tuning concept remains in place – it is now merely described as “unstable.” The idea that neurons are “signalling” spatial frequency via their firing rate but that that firing rate is contingent on a bunch of other factors is even more senseless than the simpler notion.

In “Single-Neuron Perturbations Reveal Feature-Specific Competition in V1,” (2019/Nature), Chettih & Harvey also find instability in their desired correlations: 

“Precise levels of decoding accuracy were variable from experiment to experiment, depending on the number and tuning of imaged cells as well as overall signal quality. …This is of note because the tuning bias also causes different grating orientations to be more or less likely to be matched to the tuning preferences of photostimulated neurons.”

Again, the fundamental tenet that neurons are tuned to “spatial frequency,” as irrational as it is, is never questioned, despite needing to be qualified beyond recognition.

Naturally, the data in both papers is correlation-fished and fitted via assumptions – such as linear models, Gaussian “priors” – chosen because they make the math easy, not because of any rationale. None of the authors seems to have considered how their mathematical acrobatics and probability functions can illuminate how we see that elephant over there.