The Matter With Things. Chapter 11. Science’s Claims on Truth

Another interesting and enjoyable chapter in this very long book, which I am not even halfway through, despite having bought it very shortly after publication in November 2021. At this rate it will be 2024 before I finish it!

As mentioned before, the book is in three parts. The first part is made up of nine chapters and a coda, in which McGilchrist writes about The Hemispheres and the Means to Truth. I have yet to finish reading the last two chapters of Part 1, but for notes on Chapters 2 to 7, see https://jennymackness.wordpress.com/the-matter-with-things-2/

In Part 2 McGilchrist writes about The Hemispheres and the Paths to Truth. In the first chapter, Chapter 10, he explores the question What is Truth? . Chapter 11, Science’s Claims on Truth is the second chapter. So far, I am enjoying Part 2 more that Part 1.

The main thrust of this chapter is that in the West, recent history has seen a move away from religion to science, and more particularly, to scientism (the belief that science will one day answer all our questions), in our search for truth. But, says McGilchrist, there are intrinsic limits to science, which tends to make exaggerated claims, use models that distort, and succumb to institutional pressures. Science has come to be thought of as the only path to truth and a discussion of its limits is often not welcome. Whilst McGilchrist is emphatic about the value of science, his concern is that it can’t answer the big questions; it can’t, for example, tell us what it means to be in love. Science finds it hard to deal with all that is experiential, but most of what we value in life is experiential, not observable, or measurable. The bigger the human meaning, the less science can offer. What we are asking science to do is to give us information/data but can that be converted into an understanding and what part does science play in the achievement of an understanding? Science can answer questions where explicit, mechanistic explanations are required, but not where understanding is required.

Explanation, metaphor, and models

Science cannot escape using models and metaphors because they are the basis of all understanding, so science depends on metaphors derived from concrete experience. All understanding depends on metaphor. Science uses models. Models are simply extended metaphors. The choice of the model is critical because:

‘We never just see something without seeing it as a something. We may think that our theories are shaped by observations, but it is as true that our observations are shaped by theories. This means that we can be blind to some very obvious things in our immediate environment. We don’t look where we don’t expect to see, so that our expectations come to govern what we can see.’ p.410

In the past the dominant model was a tree, a river, a family – something in the natural world. These days the dominant model is the machine (as favoured by the left hemisphere). The machine model is science’s defining paradigm, but it is a form of metaphor, and not all metaphors are good metaphors. All models are only a partial fit. A model determines not only what we do see but also what we don’t see, and we affect the model. No one model will ever be the perfect fit. We need to try and test different models, even though we may ultimately need to jettison them. Ideas of 100% truth cannot be sustained. In science certain things will be neglected. We may think that only things that are quantifiable are real (a left hemisphere perspective), but we have to rethink objectivity.

Objectivity

We cannot do without objectivity, but it is easily misinterpreted. To quote McGilchrist:

‘Science provides us with that objective knowledge by taking ‘us’ out of the picture, so removing subjective distortion from its objective presentation of how, in itself, the world actually is.’ p.413

We have already seen, however, that this aspiration to take ‘us’ out of the picture is compromised by the fact that science can’t get going without metaphor and metaphor is something from which ‘we’ cannot possibly be divorced.’ p.413

‘Objectivity is always someone’s position, situated somewhere and making some assumptions.’ p.414

Objectivity should be able to inhabit a lot of different perspectives – we ought to try to see from different perspectives.

Between us and the world there is always the barrier of our brains, and since we have two hemispheres in our brains each with their own view of the world, there are at least two views that science must take into account.

All methods rely on our judgements and values, even though these can’t be measured. Science frequently passes over what can’t be measured. It can’t cope with things that are imprecise or can’t be generalised. When considering objectivity, we need a more nuanced interpretation which recognises that existing answers are inadequate and provisional; there are always alternative answers. There are no whole truths, only half-truths, and context is of critical importance. Science tends to take things out of context. In trying to make science robust, we veer unstably between black and white positions, but we shouldn’t make statements that are too great or absolute. Instead of trying to make science robust, we should make it anti-fragile.

Hidden Assumptions

There are many assumptions in science. Science assumes that everything is understandable in physical terms, but science’s explanations both reveal and conceal. Sometimes assumptions are justified, but we must acknowledge them. Science can do very, very much, but not everything. As mentioned above, it cannot answer the very big questions, about values, meaning and purpose in life. Science is far from having all the answers – it is alive, provisional, and uncertain.

On p.420 McGilchrist quotes Max Planck as saying – ‘we have no right to assume that any physical laws exist, or if they have existed up to now, that they will continue to exist in a similar manner in the future.’

The scientific method

The scientific method is a myth. A belief in the notion of scientific objectivity, has led to a loss of imagination in science, but science requires imagination to come up with fruitful hypotheses. Chance and serendipity, intuition and inspiration play important roles in science.

Great discoveries are often made through images and metaphors rather than through chains of logic. Big insights are not made by following a logical linear sequence of steps, but by things like pattern recognition. Results can come in a flash of intuition and often precede arguments. Good hypotheses always ‘go beyond’ the immediate facts.

‘….. this does not discredit science in any way: it shows, instead, what an exciting and humbling business science is. We collaborate with nature, and with fortune, pay attention and learn from her. We neither withdraw the human element, as the myth of the scientific method implies, nor force nature to our preconceived ends.’ p.425

‘…. Just because what we rightly take to be scientific truths are not ‘objective’ in the sense that nothing human, contingent, and fallible enters into them, this does not mean they have no legitimate claim to be called true. … truth is never objective ….. All knowledge whatsoever is contextual and contingent. p. 429

Scientists must have faith, and science must be aware of its own limits.

For a discussion about this chapter between Iain McGilchrist and Alex Gomez-Marin, see:

References

McGilchrist, I. (2021). The Matter With Things. Our Brains, Our Delusions, and the Unmaking of the World. Perspectiva Press.

The Value and Limits of Science

A bit of background

On the recent Field and Field four-day course (June 8th – 11th 2019), Iain McGilchrist discussed key ideas from his book The Master and His Emissary. The Divided Brain and the Making of the Western World, talking for an hour on each. For the most part these talks were familiar as I have attended this course before.

  • Introduction to the Hemispheres
  • Brain Disorders of the Hemispheres
  • What is Language For?
  • Are we Becoming Machines?
  • What Does it Mean to Think?
  • The Power of No

I have blogged about these topics after attending previous courses.  See my page on The Divided Brain, on this blog.

But Iain is now writing a new book which will have the title (proposed, but not yet confirmed) – “The Matter With Things”. It was good to get this update, as on the last course I attended we were told that the title of the book would be There are no Things. I think Iain feels that his philosophical position is clearer with the newer title. This new book will argue against reductionism and materialism and for betweenness.

In the second part of this new book, which Iain is still working on, he will discuss what he told us are the four main paths to knowledge: science, reason, intuition and imagination. He stressed that we need all four, but that intuition and imagination have been downgraded in favour of science and reason, a result of left hemisphere dominance. So we were very fortunate to hear five one hour talks about these most recent ideas.

  • The Value and Limits of Science
  • The Value and Limits of Reason
  • The Values and Limits of Intuition
  • The Value and Limits of Imagination
  • Everything Flows

The value and limits of science  (These are the notes from Iain’s talk. Any errors are mine and I do not at all mind being corrected in the comments).

Collingwood wrote: Science and metaphysics are inextricably united, and stand or fall together.

And Heidegger wrote: Science does not think, science does not venture in the realm of philosophy. It is a realm, however, on which without her knowing it, she is dependent. (translated from the original by Iain McGilchrist)

(I cannot find these quotes online to verify them, and I learned on this course that my note-taking has slowed down, so I am not absolutely sure of their accuracy, but, as written, they provide the gist of Iain’s argument. For more on this, see the Update – 17-06-19 – at the end of this post.)

The word science simply means knowledge. We need science, but we rely too much on the left hemisphere. Public science is different to what good science is telling us.

The two hemispheres find two different worlds. Objectivity is not about what is out there. There isn’t a thing out there that we can know. Things only come into being through interaction with our consciousness. The more you dig into a tiny hole, the less you can see the whole. So the question is: What constitutes evidence in life? The ‘howness’ of the ‘what‘ matters a lot. Objectivity is a ‘howness’ – a disposition towards the world. You try to be just and truthful, to bring an understanding. This reminded me of the work of Gayle Letherby et al. on Objectivity and Subjectivity in Social Research .

There are no things that are not unique. How does science cope with this? In science when we say we understand something, we are comparing it to something else. Everything is built on analogy.

Science is not chaste (pure and virtuous). It starts from certain axioms/assumptions, e.g. the world is fully comprehensible physically. This is an unlikely but reasonable assumption. But why do we want to understand the physical?  Iain thinks this is related to ‘the matter with things’, the title of his new book, so I expect we will learn more about this when the new book is published (hopefully by the end of 2020).

Science is reluctant to accept anything that can’t be measured. It is based on a false dichotomy between facts and value. There is always a value involved in seeking any kind of truth. We try to rise to meet this through objectivity. Many things in science can’t be separated from value, but there is value involved in appreciating what is a fact.

Problems with science

There are 3 problems with science

  • Intrinsic problems built on assumptions
  • Problems of the model of the machine
  • Institutional problems – the way science promulgates what it is doing

Intrinsic problems built on assumptions

There is no one truth, only more or less truth, but we must be loyal and faithful to truth. (See Where Can we go for Truth? for more of Iain’s thoughts on truth). So how do we decide which questions are worth asking?

Values, judgement and insights are very important in science. Great scientists allow ideas to incubate for a long time. Science eliminates the idea of purpose. This is a tenet of science; there is no purpose to science. Science cannot address things like love or an understanding of God. We can see these in operation, but they cannot be explained by science. But science is teleological – things happen for a reason, although the value of reason itself can’t be reasoned.

An example of a problem built on assumptions is DNA. DNA is not a building block; there is just not enough information in DNA. DNA is a resource from which the cell can draw. It is not a script. Only 2% of it expresses anything. Quarter of a million new neurones a minute are developed in the brain. We cannot get this from a linear script. The genome is not the answer.

Problems of the model of the machine

We are not machines. A machine can be switched off, but life is constant and cannot be switched off. A machine operates close to equilibrium; you have to put energy in to make it change. Life is the exact opposite. It is always changing, but how does it remain stable enough to keep going better? Through homeostasis. Human beings and living things change. Natural selection is the thing that stops change, it doesn’t cause change.

Organisms are not on/off. They involve inconceivably complex reactions to maintain stability between motion and stasis. They are non-linear, action is not one-way as in machines. The parts of organisms themselves are changing. This doesn’t happen in machines. The genome restructures itself all the time. DNA is not the robot master. The same genes can give rise to different effects, e.g. Pax6 gives rise to different eyes in the fly, the frog and humans. Some animals can regenerate parts of their body. If you cut off the head of a nematode worm, it will grow a new head with the same memories. Living organisms are not machines. The instructions for life are within the organism.

See also a previous post – The Human Versus the Machine 

Institutional problems

Science is carried out by normal people with egos etc. Fashions of thinking dominate. Science depends on results, safety, conformity, narrowness. There are many dogmas that can’t be broken.

Scientists are expected to publish or perish. This is destructive to morale. Scientists are rated on the number of papers they can churn out, but they need fallow periods, and they can get caught up in administration, particularly if they get promoted.

Lots of science papers need to be retracted, because they have been made up. And Ceci and Peters’ research raised doubts about the reliability of the peer- review process.

Scientists are also subject to predatory journals to the extent that Jeremy Beale published a list of journals which researchers should avoid.

Truth matters, but these problems with science show that finding out what is true is more difficult. We need more replication work. The amount of replication work is very low.

Why is truth important? We are here to engage with the world. If it is pointless why go with truth?

Update (17-06-19) re the Heidegger quotes (with thanks to Iain McGilchrist for this information)

The first part, » Die Wissenschaft denkt nicht «, is originally from page 4 of Heidegger’s Was heißt denken?, the version of his lectures given in Freiburg in 1951-2 published by Max Niemeyer Verlag, Tübingen (1954), and later translated into English by FD Wick & JG Gray as What is Called Thinking?(Harper & Row, 1968).  Heidegger then repeated it in a conversation with his pupil the German philosopher Richard Wisser on the 17th September 1969, in which he follows it by another phrase in explanation, thus: » Und dieser Satz: die Wissenschaft denkt nicht, der viel Aufsehen erregte, als ich ihn in einer Freiburger Vorlesung aussprach, bedeutet: Die Wissenschaft bewegt sich nicht in der Dimension der Philosophie. Sie ist aber, ohne daß sie es weiß, auf diese Dimension angewiesen «. In H Heidegger (ed), Martin Heidegger: Gesamtausgabe, Part One, Veröffentlichte Schriften 1910-1976, vol 16, Reden und Andere Zeugnisse eines Lebensweges, Vittorio Klostermann, Frankfurt am Main, 2000, 702-710 (705).

#SOCRMx Week 7: Objectivity and Subjectivity in Social Research

The past two weeks in the Introduction to Social Research Methods MOOC (SOCRMx) have covered ‘How to Lie with Statistics’ (Week 6 Quantitative data analysis) and ‘Interpretation and Trustworthiness in Qualitative Data Analysis’ (Week 7).

Lots of resources have been provided for the Week 6 topic on quantitative data analysis. I have saved them for future reference, should I ever go down that track. It was interesting to see that more course participants got involved in discussion in the Week 6 forums. I’m not sure if that’s because a positivist approach comes more easily to those new to research, or whether it was because once again the resources were extremely good and the tutor for this week – Rory Ewins – actually engaged in the forums and provided feedback!

The resources for Week 7 on qualitative data analysis are very familiar given that all my research has taken this approach, but once more the resources provided on what we mean by trustworthiness and how to code qualitative data are helpful and thought provoking. Again I have saved these resources for future reference when I can explore them with more time. I think both these topics require and deserve more time than one week to get to grips with.

The topic of qualitative analysis reminded me of an excellent video that was shown in the first week of the course under the title of theoretical considerations in research design. This is a SAGE research methods video and therefore not open access, but if anyone from SAGE visits this post, I would urge them to make this video publicly available. I think both novice and experienced researchers would find it hugely helpful.

The video shows John Scott (Professor of Sociology, Plymouth University), Malcolm  Williams (Professor in the School of Social Sciences, Cardiff University) and Gayle Letherby (Professor of Sociology , Plymouth University) discussing objectivity and subjectivity in social research, having published a book about this in 2014:

Scott, J, Williams, M & Letherby, G. (2014). Objectivity and subjectivity in social research, SAGE Publications Ltd., London.

These are the notes I made from watching the video.

John Scott starts by drawing on the work of Kant and Mannheim to discuss how subjectivity, objectivity, relativity and truth have multi-faceted meanings. We see the world differently according to our social location (our perspective) and we construct knowledge relative to that location. But if this is so, how, as researchers, can we present a truthful representation of the world. One way we can do this (which Iain McGilchrist has also discussed in his work on the Divided Brain) is to try and synthesise different standpoints (alternative perspectives) to achieve a better model for understanding the world.

The three authors each approach research from different perspectives. Malcolm Williams adopts a socially situated objectivity approach, Gayle Letherby a theorised subjectivity approach and John Scott, is somewhere in the middle of these two perspectives, and the point was made that it’s not necessarily a question of either/or, as shown by mixed methods approaches.

Malcolm Williams sees objectivity as a value which is socially constructed. The pursuit of truth is a key research value and he argues that a necessary condition of objectivity is to begin with subjectivity.

Gayle Letherby recognises the personhood of research and the complex relationship between researcher and respondent. From this perspective research is subjective, power laden, emotional and embodied. A theorised subjectivity approach is concerned with how identity plays out in the research process. Gayle Letherby tells us that interrogation of the self in research, with reference to the ‘other’, gets us closer to a position that we might call objective and that autobiography is always relevant. We need to interrogate our biases, but we should avoid demonization of subjectivity. Subjectivity is not a redefinition of objectivity, but starts from a different place. Objectivity and subjectivity are interrelated.

Whether we take a subjective or objective approach, social science research does have validity and needs to be defended. This does not mean that one account is as good as any other. Researchers must be responsible and explain how we got what we got and how what we did affects what we got. Research always involves a view from somewhere and we need to write about our subjective positions. Where did the research question come from? Why was a particular approach adopted? Can we justify why our findings are important? What is our ethical position? Have we acknowledged that ideas about knowledge can’t be separated from ideas about power? (Foucault’s work is relevant here) The social and political role of research means that there is no escape from issues of power.

Scott, Williams and Letherby conclude that the validity of knowledge depends on having:

  • Open societies (Karl Popper)
  • An ideal speech situation (Habermas)
  • Free discussion (Mannheim)
  • Leaving power outside the door as much as possible.