Wednesday, 21 September 2022

About Learning and de-growth

Seymour Papert argued that we do not have a word for the art of learning in the same way that we have words for the art of teaching (pedagogy, didactics) (see his "A word for learning": http://ccl.northwestern.edu/constructionism/2012LS452/assignments/2/wordforlearning.9-24.pdf). Papert then suggests the word "mathetics", drawing attention to the fact that "mathematics" appropriated the word for learning to refer to its specialised practices, when the word "Mathematikos" simply meant "disposed to learn". There may be deeper things to explore in this etymological relationship. 

We tend to think of learning as a kind of growth. As we learn, we know "more stuff", we gain "more knowledge", and we might even imagine that we get bigger heads! Babies start small and get bigger (up to a point), and as they get bigger they learn. Learning produces material artefacts which certainly do increase in size - before the internet, knowing more stuff meant more books, and (perhaps) a bigger library (to display as our zoom background!). The bigger the library the cleverer the people.

I was listening to Neil Selwyn talking about "de-growth" as a possible response to climate change and thinking about how education might support this (here: https://media.ed.ac.uk/playlist/dedicated/79280571/1_6u9a41zh/1_l7anxlgx). Crudely, we imagine that our ecological crisis is caused because things have grown too big, and that to address it, we need to "degrow". But what do we mean by "big" or even "growth"? My favourite source for thinking about this is Illich's "Tools for Conviviality". He talks about the outsized growth of technology and institutions beginning as beneficent, and becoming malevolent. The causes for the transition from beneficence to malevolence are mysterious - they may lie in our physiology and evolutionary biology (that's another post). But the actual manifestation of pathology is not size - it is a reduction in variety. Illich's clearest example is 100 shovels and 100 people digging a hole, which is eventually replaced by one person and a JCB. Which has the greater variety? The loss of variety as the technology becomes more powerful results in an increase in the creation of scarcity - and the "regimes of scarcity" are the ultimate propellent for positive feedback loops and accelerating crisis. 

The ecological crisis is a crisis resulting from the loss of variety caused by modern living, and within modern living, we must include education. No human institution excels in the art of producing scarcity more than education. The rocket fuel for the rest of the ecological crisis lies at the classroom door. But we can't seem to help ourselves. We see education as the solution to our troubles, not the cause! Education will teach us to "de-grow"... quick! roll-up for "degrowth 101"! Why do we do this? It is because we mistake education for learning. 

We tend not to see learning but instead see "education" in the same way that we tend not to see health but instead see "health systems". "Education" (and "health systems") get bigger and more powerful - rather like the library which forms part of educational institutions. As they get bigger and more powerful they lose variety (look at the NHS today). But "learning" (and "health") do not grow or get bigger. Both of these terms refer to processes which relate an organism (a person, a community, an institution) with its environment. These terms relate to the capacity of any organism to maintain their viability within their environment - indeed "health" and "learning" are deeply connected concepts. Learning is not about growth, but about homeostasis. 

Having said this, it's obvious that as we get older, we learn more stuff, we can do more things, we talk to more people, and so on. But we are really in a continual process of communion with a changing environment. Babies may seem to learn to scream to get attention, but their physiological context is changing alongside an epigenetic environment within which what it is to remain viable is a continual moving target. The education system appears to be a way of forcing certain kinds of environmental change, and as a result insisting on certain physiological responses (which appear to reproduce regimes of scarcity, and social inequality). Indeed, what we call "growth" is an outward manifestation of an unfolding of physiological potential in a changing environment. If growth was as fundamental as the "de-growth" people say, why does anything stop growing?

So if learning is not about growth, but about the viability of an organism in an environment, how can we visualise it differently? One way is to think about it mathematically - and so to draw back to the origin of the word for mathematics, mathematikos and "mathetic". If learning is a process of variety management, and a developing environment has differing levels of variety (and indeed, increasing entropy), then learning is really a process of finding a kind of resonance with that environment. These orders of variety and variety management might be rather like orders of prime numbers, or different levels of scale in a fractal, or different orders of infinity. Mathematically, we might be able to see learning in geometrical forms produced through cymatic patterns:

or knot topologies, 

Or Fourier analysis, or even in Stafford Beer's syntegrity Icosahedron (see Beer's book "Beyond Dispute" https://edisciplinas.usp.br/pluginfile.php/3355083/mod_resource/content/1/Stafford%20Beer_Beyond%20Dispute.pdf):

These forms are expressions of relations, not quantifications of size. If we see size (and growth) as the problem we don't only miss the point, but we feed the pathology. 

We urgently need a more scientific approach to learning. We are going to need our technologies to achieve this. This is not ed-tech, but technology that is necessary to help us understand the nature of relationship. I fear that for those consumed with ed-tech, blaming it for the demise of "education", a different kind of approach to technology and a more scientific approach to learning is not a thinkable thought. 

I feel the need to make this thinkable is now very important. 




Monday, 19 September 2022

Rethinking Education and a "Trope Recognition Machine"

I went to a conference at the weekend on "Rethinking Education". As is often the case with these things, there were some good people there and some good intentions. But I came away rather depressed. It's often said that there is nothing new in education, and events like this prove it. What it amounted to was a series of tropes uttered by various people, some of whom were aware that they were tropes, and others who genuinely thought they were saying something new. Meanwhile the system trundles on doing its thing - and while everyone there might admit that the thing it does is not very good, there is a surprising lack on clarity on what the system actually does. 

When we ask people to rethink something, it is often framed as an invitation to think about the future - to say, "let's bracket-out the system we have, and conceive of the system we want". But this is naive because the system we want is always framed by the system we are in, and it is always difficult to see the frame we are in, and what it does to our thinking. Frame-blindness has specific effects - one of which is the tropes.

At one point I was getting a bit frustrated by the degree of repetition in the tropes that a wicked thought occurred to me: what if we had a trope recognition machine? What if there was some device that could process all the utterances and classify them according to their trope identity. And of course, current machine learning is very good at this kind of job. But if you had a trope recognition machine, what use might it be? 

If we look at "rethinking education" as a problem situation - not the problem of "rethinking education" but the problem of talking about "rethinking education" - this problem is one of time-consuming redundancy of utterances. Basically many people say the same thing, and feel the need to say the same thing. Indeed, I suspect meetings like this owe their appeal to the opportunity they present to people to say what's in their heads in the confidence that what they say will "resonate" with what else is said. In other words, the redundancy is there in the desire to attend and speak in the first place. Perhaps we need to think about this - about the dynamic of redundancy in communication. 

One of the most interesting things about redundancy is how attractive it seems to be - it is after all about pattern, and patterns are what we look for when we try to make sense of something. So if we want to make sense of education, we need to go somewhere where we can fit into a pattern - a conference. But this is curious because the motivation of most people at conferences is to "get noticed" - to have their version of a trope which is distinct that everyone looks to them as some kind of originator of something which has been said before (actually the whole academic discourse is like this, but let's not go there!). So how does that work? How does the desire for collective sense-making through pattern and egomania fit together?

I've been reading Elias Canetti's "Crowds and Power" and I think there is something in there about this tension between the search for redundancy and pattern, and the expression of the ego. Canetti sees the individual as someone who wants to preserve the boundary of their self. They don't even want to touched by someone else most of the time. And yet, they also want to belong to the crowd. Although Canetti was opposed to Freudian psychodynamics, clearly his analysis of the crowd is treading similar territory to psychodynamics: the crowd is the Freudian super-ego. 

The search for redundancy in going to conferences and saying similar things to everyone else is crowd-like behaviour. It seems to be driven by egos who want to get noticed - to preserve and reinforce their boundary of the self. 

I think the best way to think about this is to see both the ego and the superego as essentially dealing with contingency. They have to find a way to maintain a balance between their internal contingency and the external contingency. That means that it is necessary to understand and control the external contingency. Creating redundancy through utterances is a way of establishing some degree of control over external contingency: it is a way of establishing a "niche" in which to survive (my favourite example of an organism using redundancy to create a niche is a spider spinning a web). 

What is discovered about the external contingency has an effect on internal contingency. The ego is troubled by the subconscious, which contains the vestiges of experience and desire from infancy - and the legacy of education. The ego is satisfied with the niche it creates in talking about education and feels more secure. (What appears as egomania may simply be a need to establish some kind of inside/outside balance). But as a result, conferences like this actually satisfy the psychodynamic needs of individuals struggling in a terrible system for a short time. They are essentially palliative. 

Understanding these dynamics at conferences may be a first step to remedying the problems in the education system itself. A trope-recognition machine could pinpoint the different positions and contingencies which are expressed in a group: it could highlight areas of deep contention and uncertainty and thus focus discussion on those issues, codifying the underlying patterns that everyone is searching for in a way which could save a lot of time and frustration. That might result in some better decision-making perhaps.

Monday, 5 September 2022

Learning and the Redshift of Biology

When we think we observe learning happening in others, we think of a change in an individual. We see each individual as on some kind of trajectory along a path which we have determined through making prior observations throughout history. This is encoded in our formal processes of education. In each individual we observe, there is a wide range of variation in this trajectory. Some lead to "success", some lead to "failure". One of the tragedies of education is that there is never enough time, nor the energy, to look at an individual's learning really closely. Perhaps parents (sometimes) and psychotherapists get a bit closer. Scientists observe ecosystems, stars, and cells with far more intellectual curiosity and desire for precision.

Learning never happens in "others". It happens in relationships - and those relationships inevitably include anyone who wants to "observe". If we imagine that in any relational situation, there are "engrams" - structures in consciousness - and "exterograms" - externally observable phenomena, the only observable aspect of relationship are the exterograms of communication - we can at least write spoken words down, record actions, assess, etc. In education research, this is basically what is done, and these "exterograms" of the learning process are subjected to various kinds of analysis which produce conclusions like "phonics helps children to read", or "people have different (codifiable) learning styles", and other such stuff. These are really political statements about which there is endless debate. Good for the champions of phonics and learning styles - after all, "there's only one thing worse than being talked about..."

The phrases "exterograms" and "engrams" were suggested by Rom Harré as a way of introducing the problems addressed by his "positioning theory". Harré said something very sensible about learning: "you know when learning has happened because the positioning changes". This seems true - the transition from apprentice to master is precisely a change in positioning between master and apprentice. This suggests to me that rather than look at the trajectory of an individual, we should look at the trajectory of positions. 

How could education become focused on the trajectory of positions, rather than the trajectory of individuals? Perhaps a good place to start in thinking about this is how an "individual" focus of education is different from a position-focused education. The former is built around the material consumption and production of students - textbooks, lectures, essays, exams, etc. The latter is built around the energy of communication between people. Is the shift one from a focus on matter to one on energy? 

In an energy oriented focus, there are no "exterograms" really. They are mere manifestations of energy in the learner (or lack of it!). In a dialogical relation, these exterograms have an effect on others in causing the production of other communications. But in the process, there is a  physiological background which is where the thinking and adaptation takes place. These physiological processes obey rules about which we have little understanding - but where an increasing amount of biological evidence is suggesting we might have new and highly productive scientific paths to tread. 

We are all made from the same stuff, and all our stuff - our cells - comes from a point source - a unicell. Through evolutionary history, cells diversified and acquired (through endogenisation of aspects of their historical environment) various features (mitochondria) which we find in us and everywhere else in nature. There is a surprising lack of variety of cell types in the human body - only about 200. Our learning processes are processes of cellular communication - not just within us, but between us. Those processes of communication reference the origins of cells - shared cellular history is a deep coordination mechanism which underpins what we might call "instinct". Instincts arise from cellular relations, just as learning arises from human relations. The same processes are in operation at different orders of scale. 

Looking at learning as the trajectory of "positions" in relations is like looking through the James Webb telescope for the beginning of the universe. Learning shows us the "redshift of biology". The cholesterol from which we are made had its origins at the origin of the universe (see David Deamer's book "First Life"). And there are powerful clues for this at a more mundane level. Simply counting the variety of possible relationship trajectories (just as counting the behaviour of individual learners) will reveal statistical structures which form normal distributions. Regression structures reveal differentiated groups - the learners who like maths, and those who like music - these too will be normally distributed. But simply to refocus on more fundamental things - love or hate - will also produce the same structures. It is fractal. 

To see education in terms of positions raises an important question about how to make education better. Do we want more kids to pass more exams? Or do we want better positions/relations between people? I don't think the answer to that question is too hard, although some might say that education is about "knowing stuff". So what is "knowing stuff"? We can see that too either as a material process - knowing stuff is about material consumption and production. Or we can see "knowing stuff" as being about energy - the capacity to engage with and position well a large set of relations in harmony with (and not against) our biological origins. I think this is the same as being "in dialogue" with one another.


Wednesday, 31 August 2022

Tacit Revolution

As technology advances in society, there is no escaping the increasing specialisation of knowledge. This may seem like a challenge to those like me who believe that the way forwards is greater interdisciplinarity, or a metacurriculum (as I wrote here: https://link.springer.com/article/10.1007/s42438-022-00324-1). While greater specialisation indicates increasing fracturing of the curriculum and the growth and support of niche areas, more fundamentally it represents the organisational challenge to find the best way to support specialised skills from the generalised organisational frameworks of curriculum, assessment, certification, etc. At the root of this challenge is the fact that generalised organisational frameworks - from KPIs to curriculum - all depend on the codification of knowledge. Meanwhile, hyperspecialisation is largely dependent on tacit knowledge which is shared among small groups of professionals in innovative niche industries, startups, and departments of corporations. 

Since Michael Polanyi's seminal work on tacit knowledge in the 1950s in Manchester University, the educational challenge of transmitting the uncodifiable have been grappled with in industry. The Nonaka-Takeuchi model of knowledge dynamics within organisations has been highly influential in understanding the ways that professional knowledge develops in workplace settings (see https://en.wikipedia.org/wiki/SECI_model_of_knowledge_dimensions). It is in these kinds of dynamics that we are seeing technology drive (and perhaps even accelerate) processes of tacit knowing and externalisation. It's now not only highly technical people whose tacit knowledge must be communicated to senior management, but the technics of machine learning (for example) which are so different from the technics of database design or full-stack implementation, or component manufacture, where each of these technical aspects are interdependent. The communication between technical groups is critical - industries survive or fail on the quality of their internal communications. 

What is critical to making the communications work is dialogue. This may be partly why so many successful technical industries adopt flat management structures and dynamic and adaptive ways of configuring their organisation. That is driven by the dynamics of dialogue itself - the need for technical conversations to flow and develop. Compare this to the rigid hierarchical structures of every educational institution, burdened with its bureaucratic codified curricula. It is completely different. One wonders how it will survive the changes in the world. 

Over the last year and a half, I have been involved in a research project in the University Copenhagen on the digitalisation of education. This project was set up because Universities at least recognise the problem - they are getting left behind in a world which is changing too fast. But like all institutions, Copenhagen believed that the way to address this was to tweak its structures and what it "delivered" - basically to change the curriculum. This was not going to work, and our project has shown it. But this is not because of a failure to get the "right" tweaks to the curriculum. It is because technical knowledge is largely tacit and uncodifiable, and the organisational structures of education cannot deal with tacit knowledge. Indeed, with the bureaucratisation of education, it is even less able to deal with tacit knowledge than it was in Polanyi's time. 

I am now working for the Occupational Health department in the University of Manchester. This is a domain for professionals in health and industry to identify and analyse the relationship between environmental circumstances (work places, etc) and personal and public health. There is so much knowledge in professionals working in this area which is the product of years of experience in the field. Much of it is uncodifiable. 

Uncodifiable does not mean untransmissible. Uncodifiable knowledge can be taught dialogically, and more importantly, technology can greatly assist in producing new kinds of dynamic dialogical situations where this transmission can take place. I am currently looking at ways of doing this in occupational health, but as I do so, I am thinking about what it means for technical education, hyperspecialisation and tacit learning. 

This is almost certainly going to be the trajectory of educational technology. It doesn't look like it at the moment because "edtech" sees itself (tacitly!) as "educational management tech" - we don't really have "technology for learning" as such. It's managers who write the cheques for edtech. But that will change. We're going to need "technology for learning".

There's then another challenge for institutions. Because when we do have "technology for learning", the dialogical situations of tacit learning will not need to be bound by classroom, curriculum, assessment, etc. They can be situated in the world alongside the real activities that people engage in. My own experience of co-establishing a medical startup around an AI solution to diabetic retinopathy diagnosis is indicating this, and there are many other similar startups. Mine started with an educational desire to teach people how to diagnose. It ended with a new product that embraced the educational aspect but did something powerful in the actual domain of work too. 

This is where things are going. I'm not sure this "tacit revolution" is going to be quiet though...

Monday, 29 August 2022

Anticipation and Learning as Information

This is a follow-on blog to yesterday's on "Visualising Learning Statistically". The most powerful thing in any scientific inquiry is to have two ways of saying similar things. Yesterday, I suggested a way of thinking about learning as emerging distinction-making in terms of relations between normal distributions (in the manner that psychophysicists like Thurstone thought). Among the powerful features of seeing things this way is the fact that the statistics strongly suggests (indeed, insists) that there must be a common origin to the psychological phenomena which produce normal distributions. 

Another way of thinking about this is to consider what happens when any phenomenon is presented to consciousness and a distinction is made. A distinction might be called a "category" - something like "chair", "table", "book", "dog". In psychological experiments, what is measured is not the perception of difference per se, but rather the articulation of difference. In psychophysics for example, this is the expression of judgements of degrees of similarity to normality. That entails not only the perception of something in the environment, but selecting a word for it. To utter a word in response to a stimulus is a communicative act. 

No word can be uttered without having some idea of the effect of that utterance. We do not make words up for things we don't know. Rather like contemporary machine learning, we fit the word which we know as the most likely utterance which we believe will be understood by others. Unlike machine learning, we might not be sure, so utter the word as a question to see the response, but fundamental we are making a prediction. Paraphrasing George Kelly, who, alongside sociologists like Parsons and later, Luhmann, to make a distinction is to anticipate something in the communication system in which we operate. So we should ask, how might this anticipation work?

To anticipate anything is to recognise a pattern which relates some expected experience to a previous experience. As a pattern, there must be something about a phenomenon which is more general than the specifics of any particular instance. The fact that an anticipation is about something present in relation to something past means that there must be a dimension of time. The time dimension works both in the ongoing unfolding of a present experience, and "backwards" in the sense of reflexivity which relates what is present to what is past. This process of identifying the commonality between what is past and what is present is a selection mechanism for the utterance of whatever one thinks is the category that relates to what is currently seen. To create a selection mechanism for an utterance must entail 1. the selection of an appropriate model of past experience which relates to present experience from a set of possible models; 2. the management of a set of possible models; 3. the ongoing generation of models from present experience. 

Yesterday I said that the psychodynamics of distinction-making mean that the ability to refine distinctions is related to the ability to relax distinctions in a different domain - so Freudian "oceanic" experiences are important as an anchor for new distinction-making. That's the kind of statement which might irritate some, but I don't see it as saying anything more than the need for sleep and dreams in order to do work. It is the push and pull of the imagination - much like music, as I wrote here: https://onlinelibrary.wiley.com/doi/full/10.1002/sres.2738

Because making a distinction relies on a selection mechanism which in turn relies on a pattern, we can see a further argument for why the selection mechanism is dynamic between ongoing refinement and "oceanic nothingness". Patterns are segmented typically through repetition. Repetition itself, from an information theoretical perspective, is "redundancy" - it has an entropy of zero. Thus we can say that the segmentation of pattern is achieved through passages of high entropy followed by low or zero entropy. This helps to explain why repetition (as redundancy) is so important for memory - the essential feature of an effective selection mechanism for identifying a category is the ability to segment patterns of experience from the past to relate it to the future. 

This also reinforces the point that there must be a common biological origin which is responsible for steering this process. Patterns established in communication rely on cellular communication throughout the brain and other organs in the body. Within cells there are also patterns which reference evolutionary processes which themselves are demarcated by nothing. Statistically this can be observed as a normal distribution, but it can be also modelled as a process of evolutionary construction of patterns which act as selection mechanisms for communication. At a cellular level, these points of "nothingness" are homeostatic points of equilibrium between a cell and its environment.

The role of the environment in learning and evolutionary development is critical. The construction of anticipatory systems is a kind of evolutionary dance of endogenising the environment, where specific stages of development are segmented in ways where one stage can be related to other stages. It is this evolutionary dance which is the reason why there is always a distribution of traits and abilities which then give rise to measurable statistical phenomena. 


Sunday, 28 August 2022

Visualising Learning Statistically

To talk of learning as a process which we can observe is very difficult. When we teach teachers, we teach "theories" of learning which are just-so stories with little hard evidence to back them up barring a few (now famous) psychological experiments. The resort to teaching theory is partly because this is so hard that we would struggle to decide what we should talk about if we didn't just talk about theory. The irony is that talking about theory can be very boring, encouraging professors who didn't think of any theory themselves to talk endlessly about what's written in textbooks - not exactly an example of good teaching! Ultimately we end up with what is easiest to deliver, rather than what needs to be talked about. 

I think the birth of cybernetics in the 1940s was the best chance we had of remedying this situation, but for various reasons, a lot of this transdisciplinary insight was lost in the 1950s and 60s, as other disciplines (notably psychology) appropriated bits of it but lost sight of its key insights. Now, the growth of machine learning is providing a new impetus to revisit cybernetic thinking, with people like James Bridle leading the way in a revised presentation of these ideas (see his "Ways of Being"). One of the most impressive things about Bridle's book is the fact that he reconnects cybernetics to biology and consciousness. That connection was at the heart of the original thinking in the discipline. The biology/consciousness thing is really important - but isn't it just another just-so story? If we don't have any way of measuring anything, then I'm afraid it is. 

Here perhaps we need to look a bit deeper at the whole issue of "measurement" as it is practiced in the social sciences. Another historical development from the 1950s was the increasing dominance of statistical techniques in disciplines like economics. Tony Lawson argues that this was directly connected to the McCarthy period, where anything statistical was "trusted" as scientific and anything "critical" was communist! - as Lawson points out in his "Economics and Reality", the greatest economists of the 20th century (including Hayek and Keynes) were highly skeptical of the use of mathematics in economics. 

Statistical techniques are regularly used in academic papers in education to defend some independent variable's impact on learning. These are usually the result of academic training in statistics for researchers - not the result of a critical and scientific inquiry into the the applicability of techniques of probability to education. But there are fundamental questions to ask about statistical procedures. These include:

  • Why do natural phenomena reveal normal (Gaussian) distributions in the first place? 
  • What is an independent variable, and why should an independent variable (if such a thing exists) produce a new normal distribution?
  • All statistics is about counting - but what is counted in something like learning, and how are the distinctions made between different elements that are counted? 
  • What happens to the uncertainty about distinction-making in what is counted (Keynes made this point in his "Treatise on Probability" with regard to his discussion about Hume's distinguishing between eggs)
  • Where is the observer in the counting process? Are they an independent variable?
  • It is well-recognised that "exogenous variables" are highly significant causal factors - particularly in economics (which is often why economic predictions are wrong). Yet normal distributions arise even when exogenous variables are bracketed-out. Why?
  • While one big problem with statistical techniques is the fact that averages are not specifics, averages nevertheless can sometimes prove useful in making effective interventions. Why? 
  • Why does statistical regression (sometimes) work? (particularly as we see in machine learning)
  • Is a confidence interval uncertainty?
These are the kind of "stupid questions" which never get asked in education research, or anywhere else outside philosophy for that matter. I want here to think about the first one because I think it underpins all the others. 

Normal distributions (calculated using mathematical equations developed by de Moivre, Euler and Gauss in the 18/19th centuries) require a statistical mean and standard deviation to produce a model of likelihood of a set of results.  Behind the reliability of these assumptions is the fact that there is - among the phenomena which are measured - some common point of origin from which the variety of possible results can be obtained. Thus the top of a bell curve indicates the result which is maximally probable having passed through all the possible variations that stem from a common point of origin. 

Mathematically, we can produce a normal distribution from techniques arising from Central Limit Theorem (CLT), where a normal distribution will arise from the sums of normalised random data (see https://en.wikipedia.org/wiki/Central_limit_theorem) . According to Ramsey (https://en.wikipedia.org/wiki/Ramsey%27s_theorem) and others, true randomness is impossible. So the normal distribution is really a reflection of deeper order arising from a single point of origin. What is this point of origin? What does a normal distribution in educational research really point to?

It must lie in biology, and (importantly) the fact that biology itself must have a common point of origin. Because we tend to think of education as a cultural phenomenon, not a natural one, this point is missed. But we are all made of the same physiological stuff. And the components of our physiology have a shared evolutionary history, and it is highly likely that this shared evolutionary history has a point source. So looking at your educational bell curve is really looking at the "red-shift" of biological origins. This is an important reason why it "works".

However, this doesn't explain learning itself - it just helps to explain the diversity of features (behaviour) in a population which can be observed statistically. Much more interesting, however, is to look at how the process of making distinctions arises given that normal distributions are everywhere.

This is why psychophysics is so interesting. The psychophysicists were interested in the distributed differences that different stimuli make on a population. Some differences make big differences in perception: for example, hot and cold. Other differences are harder to distinguish - for example, the difference between Titian and Tintoretto. These differences can also be represented statistically. For example, the orange curve below might be "hot", and the blue curve might be "cold". There is little uncertainty between these distinctions, and within any population, there is no question that what is hot is identified as hot (with a little variation of degree).



But here (below), there is much more uncertainty in distinction making. 

It is this kind of uncertainty in making distinctions between things which characterises learning processes at their outset. Whether it is being able to distinguish the pronunciation of words in a foreign language, or being able to manipulate a new piece of software, among the various categories of distinctions to be made, there is a huge overlap which leaves learners initially confused. 

As the learning process continues, this distinction-making becomes more defined:
So given phenomenon x, the likelihood of correct categorisation of that phenomena is improved. 

But it is important to remember what these graphs are really telling us - that the Gaussian distribution implies a common point of origin. The second graph is the result of a conditioning process upon natural origins - rather like a cultivated garden. But perhaps more importantly, this is dynamic, where the point of origin is ever-present, and exerts an influence on distinction-making. This may be why, despite increases in the ability to make distinctions in one domain, there is a biological requirement to relax distinction making in other domains, and these domains may be related.

"Oceanic" experiences - those that Freud associated with the "primary process" of the subconscious remain an important part of the overall dynamic of distinction-making. This looks something like this:

We make the mistake of seeing learning in terms of moving towards graphs 1 and 3, without seeing the dynamic pulse which relates graphs 1 and 3 to graphs 2 and 4. But this process is critical - without the oceanic connection to distinctionlessness, the coordination mechanism (i.e. reference to origins) which facilitates higher-order distinctions (graph 3) cannot coordinate itself and is more likely to collapse in a kind of schizophrenia (this is what Freud talked about in terms of the superego taking over and the psychodynamics breaking down). 

Looking at learning like this does two things. It invites us to think about our methods of scientific measurement differently - particularly statistics - as a means of looking at life processes as processes which refer to a common origin. Secondly, it gives us a compass for assessing the interventions we make. Our current lack of a compass in education and society is quite obvious. 







Saturday, 30 July 2022

Scientific Economy and Artistic Technique

I wrote (10 years ago!) about my struggle to compose: https://dailyimprovisation.blogspot.com/2012/04/music-meaning-and-compositional-process.html). What's changed in me since?

Now I would say the key word is at point 5:  "If I have enough energy, I will battle on to try and get something down in these gaps, although at some point I get tired and give up" It's that reference to "energy" which has changed for me. What is that? This has become of great interest to me (see this more recent post: http://dailyimprovisation.blogspot.com/2021/09/energy-collages-in-vladivostok.html). How do artists maintain the energy to continue?

This has always been a mystery to me - but it is the essence of what compositional technique is meant to do. Most composers work from a germinal idea which generates possibilities. These germinal ideas are highly economical and concentrated forms of energy. Most commonly, in universities and music colleges, students are told that such germinal ideas relate to patterns of pitches. I have to say I always struggled to relate to this. Pitches seem so abstract as entities - they are just frequencies after all. Music isn't made of pitches, it is made of feelings and energy, and the connection between the abstract patterns of pitches and the feelings always seemed too remote for me. 

In science, a germinal idea which is generative of possibilities is called a theory. Theories are used as a guide in the manipulation of nature and the prediction of events. In an important way, this is also a process of concentrating energy flows. Scientific curiosity depends on energy, and theories concentrate and focus the labour of scientific inquiry to produce new knowledge. There is almost certainly a physiological driver for theory production in science.  

Many successful modern composers do not have the hang-ups I experience about the abstractions of notes. Rather like mathematicians, they seem to delight in the manipulation of abstractions as a source of their composing work. What I believe they possess when they do this is a highly compressed representation of the energy of the work which is comparable to the scientist's theory. They use the abstractions to unfold the energy over the long period of time that it takes to get the actual notes down on paper. That is how they are able to get the whole thing done.

Having said this, there is a problem in becoming too fascinated by the mathematical manipulation of pattern to produce sound. It is like a scientist becoming too fascinated by their concepts. While such procedures can unfold music of energy and beauty, sometimes (perhaps quite often) it sounds abstract and remote. Techniques like serialism were developed to free the conscious mind of cliché so as to facilitate the authentic connection between the subconscious creative mind and its conscious expression. It was intended as an "unlocking" procedure. But an obsession with mathematical procedure and pattern carries its own clichés. Brilliance in science also effects a kind of psychodynamic unlocking. 

Over the last couple of years, I've become interested not in notes but in physiology. When I improvise I find that the concentrated and economical forms of energy are not in any pattern of notes, but in the pattern of my fingers. So often my improvisation exploits the economy of my usage of my hands. I've recently started to notate this physiological concentration. The advantage this has is that unlike abstract patterns of notes, the concentrated pattern of physiology does contain feeling and energy. While I can notate the physiology, I can also feel it physically, and in feeling it physically, the energy of its unfolding (so I can get notes down over time) can also be controlled. 

I wonder whether the way artists manage the energy of creation is a determiner of artistic style. Every period in history brings environmental stresses which impinge on the ability to manage the flow of energy in artistic expression. Ours is a time of "entropy pumps" - we live in an age of constant distraction. That may mean that our management of creative energy may have to be situated more closely to our bodies. Overly cerebral approaches may lead to a disconnect between what is said and what needs to be said (although maybe I'm being too cerebral!). It may be ok for writing blogs and academic papers - but really, that is a waste of time and energy. 

The ability to concentrate energy in a germinal form - which may be common to both art and science - is really the ability to facilitate the steering of the creative (or empirical) process. Something that facilitates steering in systems terms is a trim-tab. The creative process - and certainly the process of improvising - is rather like a bird in flight. The very best scientific work also has this quality of free thought. Technique is not there to direct the course of creativity. It is there to loosen the constraints which would otherwise prevent the freedom of movement of creative processes in turbulent times.