An “interview” with Laboratory Life

Note: Reading Latour’s “Laboratory Life”, I found that there were too many great quotes to summarize. Having once been chastened by a High School teacher that “when there is a great writer, you should let them have their voice”, I thought that a Q&A with the book would be a suitable way to get the ideas across. All answers are quotes from the book in one way or another. This is not meant to be an introduction to Laboratory Life but rather a series of quotes that I found particularly interesting.

Q: You are a book about how facts – or should I say, “facts” – are constructed in science. In order to understand how that happens, you spent some time in Roger Guillemain’s neuroendocrinology laboratory at the Salk Institute. How would you describe – anthropologically – what you saw there?

Firstly, at the end of each day, technicians bring piles of documents from the bench space through to the office space. In a factory we might expect these to be reports of what has been processed and manufactured. For members of this laboratory, however, these documents constitute what is yet to be processed and manufactured. Secondly, secretaries post off papers from the laboratory at an average rate of one every ten days. However, far from being reports of what has been produced in the factory, members take these papers to be the product of their unusual factory.

By dividing the annual budget of the laboratory by the number of articles published (and at the same time discounting those articles in the laymen’s genre), our observer calculated that the cost of producing a paper was $60,000 in 1975 and $30,000 in 1976 (ed: approximately $260,000 and $123,000, respectively, in 2013 terms). Clearly, papers were an expensive commodity!

Moreover, nearly all the peptides (90 percent) are manufactured for internal consumption and are not available as output. The actual output (for example, 3.2 grams in 1976) is potentially worth $130,000 at market value, and although it cost only $30,000 to produce, samples are sent free of charge to outside researchers who have been able to convince one of the members of the laboratory that his or her research is of interest.

Q: Then it seems like if one were an outsider observing a laboratory, it was the papers that were important, not the experiments. How did the scientists react when they heard this?

Indeed, our observer incurred the considerable anger of members of the laboratory, who resented their representation as participants in some literary activity. In the first place, this failed to distinguish them from any other writers. Secondly, they felt that the important point was that they were writing about something, and that this something was “neuroendocrinology.” They claimed merely to be scientists discovering facts; [I] doggedly argued that they were writers and readers in the business of being convinced and convincing others.

Q: If the work of science is fundamentally literary, it must have a set of precursors – myths, legends, etc – that it draws on. Could you illustrate that somehow?

Neuroendocrinology seemed to have all the attributes of a mythology: it had had its precursors, its mythical founders, and its revolutions. In its simplest version, the mythology goes as follows: After World War II it was realised that nerve cells could also secrete hormones and that there is no nerve connection between brain and pituitary to bridge the gap between the central nervous system and the hormonal system. A competing perspective, designated the “hormonal feedback model” was roundly defeated after a long struggle by participants who are now regarded as veterans. As in many mythological versions of the scientific past, the struggle is now formulated in terms of a fight between abstract entities such as models and ideas. Consequently, present research appears based on one particular conceptual event, the explanation of which only merits scant elaboration by scientists. The following is a typical account: “In the 1950s there was a sudden crystallization of ideas, whereby a number of scattered and apparently unconnected results suddenly made sense and were intensely gathered and reviewed.”

However, the mythology of its development is very rarely mentioned in the course of the day-to-day activities of members of the laboratory. The beliefs that are central to the mythology are noncontroversial and taken for granted, and only enjoy discussion during the brief guided tours of the laboratory provided for visiting laymen. In the setting, it is difficult to determine whether the mythology is never alluded to simply because it is a remote and unimportant remnant of the past or because it is now a well-known and generally accepted item of folklore.

Q: Okay, but most scientists would say that they spend their time performing experiments in order to establish facts. 

Let us start with the concept of noise. Information is a relation of probability; the more a statement differs from what is expected, the more information it contains. It follows that a central question for any participant advocating a statement in the field is how many alternative statements are equally probable. If a large number can easily be thought of, the original statement will be taken as meaningless and hardly distinguishable from others. If the others seem much less likely than the original statement, the latter will stand out and be taken as a meaningful contribution.

The whole series of transformations, between the rats from which samples are initially extracted and the curve which finally apears in publication, involves an enormous quantity of sophisticated apparatus. By contrast with the expense and bulk of this apparatus, the end product is no more than a curve, a diagram, or a table of figures written on a frail sheet of paper. It is this document, however, which is scrutinised by participants for its “significance” and which is used as “evidence” in part of an argument or in an article. Thus, the main upshot of the prolonged series of transformations is a document which, as will become clear, is a crucial resource.

Instead of a “nice curve,” it is all too easy to obtain a chaotic scattering of random points of curves which cannot be replicated. Every curve is surrounded by a flow of disorder, and is only saved from dissolution because everything is written or routinised in such a way that a point cannot as well be in any place of the log paper. The investigator placed a premium on those effects which were recordable; the data were cleaned up so as to produce peaks which were clearly discernible from the background; and, finally, the resulting figures were used as sources of persuasion in an argument.

It was obvious to our observer, however, that everything taken as self-evident in the laboratory was likely to have been the subject of some dispute in earlier papers.

Q: Could you describe the types of facts?

Statements corresponding to a taken-for- granted fact were denoted type 5 statements. Precisely because they were taken for granted, our observer found that such statements rarely featured in discussions between laboratory members. The greater the ignorance of a newcomer, the deeper the informant was required to delve into layers of implicit knowledge, and the farther into the past. Beyond a certain point, persistent questioning by the newcomer about “things that everybody knew” was regarded as socially inept.

More commonly, type 4 statements formed part of the accepted knowledge disseminated through teaching texts. It is, by contrast with type 5 statements, made explicit. This type of statement is often taken as the prototype of scientific assertion.

Many type 3 statements were found in review discussions and are of the form, “A has a certain relationship with B.” By deleting modalities from type 3 statements it is possible to obtain type 4 statements. For instance, “Oxytocin is generally assumed to be produced by the neurosecretory cells of the paraventricular nuclei.”

Type 2 statements could be identified as containing modalities which draw attention to the generality of available evidence (or the lack of it). For example: “There is a large body of evidence to support the concept of a control of the pituitary by the brain.”

Type 1 statements comprise conjectures or speculations (about a relationship) which appear most commonly at the end of papers.

It would follow that changes in statement type would correspond to changes in fact-like status. For example, the deletion of modalities in a type 3 statement would leave a type 4 statement, whose facticity would be correspondingly enhanced.

Q: Okay, let’s take this metaphor seriously. If the purpose of a laboratory is to construct papers for the purpose of persuasion – why does a scientist do this?

It is true that a good deal of laboratory conversations included mention of the term credit. The observers’ notebooks reveal the almost daily reference to the distribution of credit. It was a commodity which could be exchanged. The beginning of a scientist’s career entails a series of decisions by which individuals gradually accumulate a stock of credentials. These credentials correspond to the evaluation by others of possible future investments in that scientist. The investments have an enormous payoff both because of a concentration of credit in the institute and because of a high demand for credible information in the field. In terms of his pursuit of reward, his career makes little sense; as an investor of credibility it has been very successful.

For example, a successful investment might mean that people phone him, his abstracts are accepted, others show interest in his work, he is believed more easily and listened to with greater attention, he is offered better positions, his assays work well, data flow more reliably and form a more credible picture. The objective of market activity is to extend and speed up the credibility cycle as a whole. Those unfamiliar with daily scientific activity will find this portrayal of scientific activity strange unless they realise that only rarely is information itself “bought.” Rather, the object of “purchase” is the scientist’s ability to produce some sort of information in the future. The relationship between scientists is more like that between small corporations than that between a grocer and his customer.

Another key feature of the hierarchy is the extent to which people are regarded as replaceable. When, for example, a participant talks about leaving the group, he often expresses concern about the fate of the antiseras, fractions, and samples for which he has been responsible. It is these, together with the papers he has produced, that represent the riches needed by a participant to enable him to settle elsewhere and write further papers. Since the value of information is thought to depend on its originality, the higher a participant in the hierarchy the less replaceable he is thought to be.

Q: Credit is important because it means that the science is deemed more credible. Talk about how this affects the perception of the science.

For instance, the standing of one scientist might be such that when he defines a problem as important, no one feels able to object that it is a trivial question; consequently, the field may be moulded around this important question, and funds will be readily forthcoming. One review specified fourteen criteria which had to be met before the existence of a new releasing factor could be accepted. These criteria were so stringent that only a few signals could be distinguished from the background noise. This, in turn, meant that most previous literature had to be dismissed. By increasing the material and intellectual requirements, the number of competitors was reduced. 

Whether or not the number and quality of inscriptions constituted a proof depended on negotiations between members. Let’s say that Wilson wants to know the basis for the claim that the peptides have no activity when injected intravenously, so that they can counter any possible objections to their argument. At first sight, a Popperian might be delighted by Flower’s response. It is clear, however, that the question does not simply hinge on the presence or absence of evidence. Rather Flower’s comment shows that it depends on what they choose to accept as negative evidence. For him, the issue is a practical question. This example demonstrates that the logic of deduction cannot be isolated from its sociological grounds.

Similarly, a colleague’s claim was dismissed by showing an almost perfect fit between CRF, an important and long sought-after releasing factor, and a piece of haemoglobin, a relatively trivial protein. The dismissal effect is heightened by the creation of a link between his recent claim and the well-known blunder which the same colleague had committed a few years earlier

They appear to have developed considerable skills in setting up devices which can pin down elusive figures, traces, or inscriptions in their craftwork, and in the art of persuasion. The latter skill enables them to convince others that what they do is important, that what they say is true, and that their proposals are worth funding. They are so skillful, indeed, that they manage to convince others not that they are being convinced but that they are simply following a consistent line of interpretation of available evidence.

Q: If you could summarize everything, how would you do it?

Our argument has one central feature: the set of statements considered too costly to modify constitute what is referred to as reality.

The result of the construction of a fact is that it appears unconstructed by anyone; the result of rhetorical persuasion in the agnostic field is that participants are convinced that they have not been convinced; the result of materialisation is that people can swear that material considerations are only minor components of the “thought process”; the result of the investments of credibility, is that participants can claim that economics and beliefs are in no way related to the solidity of science; as to the circumstances, they simply vanish from accounts, being better left to political analysis than to an appreciation of the hard and solid world of facts!

By being sufficiently convincing, people will stop raising objections altogether, and the statement will move toward a fact-like status. Instead of being a figment of one’s imagination (subjective), it will become a “real objective thing,” the existence of which is beyond doubt.

Neil deGrasse Tyson hates on philosophy, and that’s a shame

A common sentiment among scientists is that they find nothing useful in philosophy. Neil deGrasse Tyson is one of those:

It seems like my friend Neil deGrasse Tyson [1] has done it again: he has dismissed philosophy as a useless enterprise, and actually advised bright students to stay away from it. It is not the first time Neil has done this sort of thing…

Neil’s comeback was: “That can really mess you up.” The host then added: “I always felt like maybe there was a little too much question asking in philosophy [of science]?” And here is the rest of the pertinent dialogue:

dGT: I agree.

interviewer: At a certain point it’s just futile.

dGT: Yeah, yeah, exactly, exactly. My concern here is that the philosophers believe they are actually asking deep questions about nature. And to the scientist it’s, what are you doing? Why are you concerning yourself with the meaning of meaning?

Tyson has a bit of a point: the reason that science was initially called Natural Philosophy was because that was where it grew out of. Before we had the intellectual tools (ie empricisim) to perform what we consider “science”, humanity used a lot of logical reasoning to learn about the world. Because it was the best we had! And sure, there are many places that we no longer use philosophy to learn about the world because we’ve got a lot of data. But there’s a lot of other things where we don’t have enough data! And we have to use our reasoning to figure things out. That’s called philosophy.

I was a philosophy major as an undergrad and though much of what I did was not ‘useful’ per se, it was pivotal in teaching me how to think. Personally, I would encourage any scientist to read more philosophy to improve the clarity of their thinking.

Nietzsche on science

While searching for the appropriate epigraph for my thesis – y’know, important things – I found a lot of great Nietzsche quotes that vaguely relate to science:

Being deep and appearing deep.–Whoever knows he is deep, strives for clarity; whoever would like to appear deep to the crowd, strives for obscurity. For the crowd considers anything deep if only it cannot see to the bottom: the crowd is so timid and afraid of going into the water.

Profundity of thought belongs to youth, clarity of thought to old age.

There are no facts, only interpretations.

There cannot be a God because if there were one, I could not believe that I was not He.

It is my ambition to say in ten sentences what others say in a whole book.

The man of knowledge must be able not only to love his enemies but also to hate his friends.

Cause and effect: such a duality probably never exists; in truth we are confronted by a continuum out of which we isolate a couple of pieces, just as we perceive motion only as isolated points and then infer it without ever actually seeing it. The suddenness with which many effects stand out misleads us; actually, it is sudden only for us. In this moment of suddenness there are an infinite number of processes which elude us. An intellect that could see cause and effect as a continuum and a flux and not, as we do, in terms of an arbitrary division and dismemberment, would repudiate the concept of cause and effect and deny all conditionality.

Convictions are more dangerous enemies of truth than lies.

What are man’s truths ultimately? Merely his irrefutable errors.

What then is truth? A mobile army of metaphors, metonyms, and anthropomorphisms — in short, a sum of human relations, which have been enhanced, transposed, and embellished poetically and rhetorically, and which after long use seem firm, canonical, and obligatory to a people: truths are illusions about which one has forgotten that is what they are; metaphors which are worn out and without sensuous power; coins which have lost their pictures and now matter only as metal, no longer as coins.
We still do not know where the urge for truth comes from; for as yet we have heard only of the obligation imposed by society that it should exist: to be truthful means using the customary metaphors – in moral terms, the obligation to lie according to fixed convention, to lie herd-like in a style obligatory for all…

Nietzsche loved to pile endless epigrams in his book; he was essentially the greatest Twitter philosopher of all time. Not only was he fairly straightforward in how he presented his ideas, but he was a great stylist. Read, say, Twilight of the Idols and then Dostoevsky’s Notes from Underground and tell me they aren’t both products of similar minds.

How should you judge a theoretical model?

When faced with a model of the world (in physics, neuroscience, economics, ecology), how should you judge that theory? Cyrus Samii suggests 5 ways. Here is number 2:

2. If any result can be engineered then results themselves have no special ontological status.

This is another way of asking whether a model has empirical content, which we typically take as falsifiability. Yet Karl Popper suggested:

The empirical content of a statement increases with its degree of falsifiability: the more a statement forbids, the more it says about the world of experience.

And he suggested “two criteria determine the empirical content of a theory are their level of universality (Allgemeinheit) and their degree of precision (Bestimmtheit).”

I also really like the question at the start of number 4:

How complicated can the problems be that we allow our agents to solve in a model? Is a dynamic program ever admissible as a reasonable assumption on the objective function of an agent?

Charles Krebs (or Judy Myers) says:

Recommendation – no paper on models should be published or talked about unless it makes specific, testable predictions of how the model can be tested.

I actually disagree with this rather strenuously. There are several reasons to make models, only one of which is to make predictions. Another is to confirm hypotheses.

Let’s say that you think that honeybees are dying because of the excessive use of mint toothpaste and you collect data to prove it. The problem is that data is simply a collection of facts (or “facts”) with no organizing structure. A model can give those facts that structure: you put what you know together with some of the data, and see if what you know is sufficient to replicate the observations of the world. Of course, you have to interpret these types of models carefully; they are not predictive models in the sense that they tell you anything about the world. Rather, they tell you about whether you have a consistent and complete story. But it’s still just a story.