Art & Sciences

Two cultures of inquiry – and one shared practice of knowledge-making

Slide 1

Art & Sciences
Two cultures of inquiry – and one shared practice of knowledge-making

Welcome.

Today I would like to speak about a divide that is deeply embedded in our culture: the divide between art and science.

We tend to think of these two fields as opposites.

Art is often associated with intuition, expression, subjectivity, emotion and imagination.
Science is associated with reason, objectivity, method, measurement and proof.

This opposition is familiar. It is also useful in some institutional ways. Art and science do indeed have different histories, different forms of validation, different publics, different languages and different methods.

But when we look more closely at how artists and scientists actually work, the opposition begins to fall apart.

Artists do not simply express themselves. They observe, test, compare, revise and discard.
Scientists do not simply follow rules. They imagine, model, interpret, improvise and make judgements.

So the question today is not: Are art and science the same?

They are not.

The better question is: What kinds of movements of knowledge do they share?

This lecture proposes that art and science are two different cultures of inquiry, but that both participate in a shared practice of knowledge-making.

***

Slide 2 — Guiding thesis

Not equivalence, but a kinship of movements of knowledge

The guiding thesis is this:

Art and science are not equivalent. They do not produce the same kind of knowledge, and they do not validate knowledge in the same way.

Science asks for evidence, method, reproducibility, explanatory power and conceptual clarity.
Art asks what becomes visible, what becomes experienceable, what shifts in perception, what form an experience may take.

But beneath these differences, both fields share a set of fundamental cognitive operations.

They question.
They see.
They imagine.
They test.
They sustain openness.

These are not decorative skills around the edges of knowledge. They are central movements through which knowledge comes into being.

So I will not argue that art should become science, or that science should become art.

I will argue that both fields can help us understand how human beings enter into relation with the unknown.

***

 Slide 3 — The false opposition

The familiar divide obscures actual practice

Let us begin with the familiar opposition.

On one side: art.
Intuitive. Expressive. Subjective.

On the other side: science.
Rational. Objective. Methodical.

This opposition is powerful because it is simple. But it hides the actual labour of both practices.

An artist working in a studio is not simply “being intuitive”. She is making decisions. She tests materials. She compares versions. She observes what happens when a line shifts, when a surface changes, when a colour becomes too strong, when a form collapses.

A scientist working in a laboratory is not simply “being objective”. She has to decide what to measure, which instrument to trust, how to define a category, how to interpret an anomaly, when data are sufficient, and when a model no longer holds.

So the opposition between intuition and rationality is misleading.

A good artist needs discipline.
A good scientist needs imagination.

A good artist tests.
A good scientist interprets.

A good artist works with judgement.
A good scientist also works with judgement.

What falls apart is not the difference between art and science, but the cliché that one side feels while the other side thinks.

***

Slide 4 — What both practices share

Five trainable operations

The lecture is structured around five operations.

Questioning.
Seeing.
Imagining.
Testing.
Sustaining.

This sequence is not meant as a rigid method. It is not a recipe. It is not a universal model of creativity.

It describes an attitude of thought.

To question means to interrupt what appears self-evident.
To see means to perceive beyond habitual recognition.
To imagine means to create spaces of possibility.
To test means to put an idea into contact with reality, material, data or form.
To sustain means to remain with uncertainty without closing it too early.

These five operations are trainable. They are not reserved for geniuses. They can be cultivated through practice.

This is why drawing is so important for us today. Drawing is not only a way to represent something. It is a way to train questioning, seeing, imagining, testing and sustaining uncertainty.

***

Slide 5 — 1 | Curiosity

Curiosity begins with a gap

The first operation is curiosity.

Curiosity begins with a gap. But not every gap is productive.

The productive question is not simply: What is missing?

The deeper question is: Which assumption holds the field together?

George Loewenstein describes curiosity as a response to a perceived knowledge gap. When attention becomes focused on such a gap, tension arises. This tension motivates inquiry.

But in both art and science, the most powerful curiosity does not only ask for more information. It asks why a certain frame exists in the first place.

In art, this might mean asking:

Why must a drawing describe an outline?
Why must a painting represent a scene?
Why must a work be finished?
Why must a material obey the artist’s intention?

In science, it might mean asking:

Why do we assume this model is correct?
Why do we classify the phenomenon this way?
Why do we ignore this anomaly?
Why does this method define what counts as evidence?

Curiosity is therefore not only a desire to know. It is also the ability to destabilise what we think we already know.

***

Slide 6 — Curiosity as paradigm break

The strongest questions attack the frame

This becomes especially visible in moments of paradigm break.

Modern art asked one of the most radical questions in the history of visual culture:

Why must art represent anything at all?

This question changed painting, sculpture and the entire concept of the artwork. Impressionism, Cubism, abstraction and Conceptual Art did not simply introduce new styles. They attacked the frame of what art was expected to do.

Something similar happens in science.

Thomas Kuhn describes scientific revolutions as moments in which a paradigm is not simply improved but replaced. The strongest scientific transformations occur when the existing framework no longer explains what needs to be explained.

Heliocentrism, relativity, quantum mechanics and plate tectonics are not merely additions to existing knowledge. They reorganise the field.

The key point is this:

In both art and science, major change often begins when someone asks a question that the existing system is not prepared to ask.

Curiosity becomes radical when it attacks the frame.

***

Slide 7 — 2 | Observation

Seeing is not recording. Seeing is interpretation

The second operation is observation.

We often think of seeing as something simple. The eyes receive information; the brain processes it.

But perception is not passive recording. Seeing is interpretation.

A trained drawer knows this immediately.

When we draw a bowl, a chair, a hand or a plant, we realise that we do not simply see what is there. We see through concepts: bowl, chair, hand, plant.

Drawing interrupts that quick recognition.

A trained drawer can shift between different modes of seeing.

The object as a thing.
The surface as a flat arrangement.
The tonal value.
The edge.
The negative shape.
The relation between parts.

This is a trained perceptual skill.

Observational drawing connects visual analysis, mental image processing and motor control. Studies such as Chamberlain and colleagues show that drawing ability is connected to artistic training and to brain structures involved in fine motor control and procedural memory.

But the important point for us is not only neurological. It is epistemological.

Drawing teaches us that seeing is something we can learn.

***

Slide 8 — Predictive processing

The brain sees through expectations

Contemporary theories of perception, such as predictive processing, help us understand why this matters.

The brain does not simply receive the world. It constantly predicts the world.

Perception can be understood as a process of comparison:

Expectation.
Sensory input.
Prediction error.
Adaptation.

Andy Clark and Karl Friston describe perception as active and prediction-based. We do not see from nowhere. We see through expectations.

This is exactly where art can intervene.

Art can irritate a habit.
It can delay certainty.
It can interrupt recognition.
It can force perception to reorganise.

A drawing that remains open, a painting that refuses stable space, a sculpture that shifts as we move around it — all these practices disturb prediction.

They make us see again.

This is also why sketching is such a powerful research practice. Sketching slows perception down. It allows prediction errors to remain visible long enough to become productive.

***

Slide 9 — 3 | Imagination

Before a theory can be tested, it must become thinkable

The third operation is imagination.

We often place imagination on the side of art and rational testing on the side of science. But this separation is too simple.

Before a scientific theory can be tested, it must first become thinkable.

Einstein worked with thought experiments. He asked what one would see when travelling alongside a beam of light.

That question is not yet an experiment in the laboratory. It is an imaginative construction. It creates a mental space in which a new theory can become possible.

Artistic work functions analogously.

A painting, a drawing, a building or a performance begins as a possibility. It asks: What would happen if the world were organised like this? What if a body were seen from several perspectives? What if colour created space? What if a line did not close a form but opened it?

In that sense, an artwork can be understood as a hypothesis made visible.

Not a hypothesis in the narrow scientific sense, but a proposal:

This is how the world could be seen.
This is how experience could be organised.
This is how a relation could become visible.

Einstein called it play. Picasso called it research.

Both describe the same movement: creating spaces of possibility.

***

Slide 10 — Creativity in the brain

Neither dreaming alone nor control alone

Creativity is often misunderstood as pure freedom.

But creative cognition is not dreaming alone. It is also not control alone.

Research by Beaty and colleagues shows that creative cognition emerges through the dynamic cooperation of large-scale brain networks.

The Default Mode Network is associated with imagination, simulation, memory and possible futures.

The Executive Control Network is associated with selection, evaluation, direction and revision.

Creative work requires both.

If there is only free association, ideas may remain diffuse.
If there is only control, nothing new can emerge.

This is important for our teaching and learning at the University of Zurich.

Exercises should contain openness and revision.

You students, need moments where you can generate without knowing too early. But you also need moments where you return, compare, select, refine and structure.

This is true in art.
It is true in science.
It is true in design.
It is true in writing.
It is true in research.

Creativity is not the absence of discipline. Creativity is the movement between openness and discipline.

***

Slide 11 —  4 | Experimentation

Studio and laboratory think both iteratively

The fourth operation is experimentation.

No artwork emerges fully formed.
No experiment emerges fully formed.
No theory emerges fully formed.

Knowledge emerges in loops, iterations.

In the studio, these loops appear as sketches, prototypes, discarded versions, colour tests, material trials, unfinished forms.

In the laboratory, they appear as measurement series, failed trials, adjusted protocols, recalibrated instruments, revised hypotheses and new interpretations.

Donald Schön calls this reflection-in-action. Professional thinking happens in the midst of doing.

This is crucial.

We often imagine that thinking happens first and doing comes afterwards.

But in many practices, thinking happens through doing.

A line is drawn. Then it is seen. Then it changes the next line.
A measurement is taken. Then it raises a new question.
A model is built. Then it reveals its own limitation.

Studio and laboratory are both spaces of iterative intelligence.

They do not merely execute ideas.
They generate ideas.

***

Slide 12 — Ann C. Thresher

Science is not a machine

At this point, I introduce an additional theoretical layer: Ann C. Thresher.

Thresher criticises the cultural image that science is exact, objective and algorithmic, while art is creative and subjective.

Her argument is very helpful for this lecture because it shifts the discussion.

We do not only say: art has something in common with science.
We also say: science itself is much more creative than the popular image suggests.

In actual practice, science requires situated judgement.

Scientists must make decisions when designing experiments, reading data, choosing models and deciding what counts as evidence.

This does not weaken rigour. It explains how rigour is produced.

Good science is not mechanical. It is a tangle of judgement and creativity.

This is a powerful statement.

It means that creativity does not stand outside method. Creativity belongs inside the method.

The scientific method is not an algorithm that removes human judgment. It is a disciplined practice that trains judgment.

***

Slide 13 — Objectivity | Rigour | Method

Objectivity requires judgement

This is especially important when we speak about objectivity.

Objectivity is often imagined as the absence of judgment. The scientist is thought to be neutral, detached, without perspective.

But Thresher helps us see that objectivity requires judgement.

Objectivity is not one universal checklist.

What it means to be objective in climate science is not exactly the same as what it means in psychology or materials research.

Each field must ask:

What does avoiding bias mean here?
What counts as a valid measurement?
How much should participants know?
When is a survey systematic?
How is “healthy” defined?

These questions cannot be answered mechanically. They require expertise, context and judgement.

This is where art and science touch.

Both require decisions about viewpoint, framing, material, scale and relevance.

An artist deciding how to depict a horse must decide from which angle, at which scale, in which material, with which emphasis.

A scientist deciding how to study a phenomenon must also decide what counts, what is excluded, which instrument is appropriate, and which model is useful.

Objectivity is therefore not the absence of judgement.
It is a trained judgement under methodological conditions.

***

Slide 14 — The scientific method

Not a straight line, but a tangle

The scientific method is often taught as a straight line:

Question.
Hypothesis.
Experiment.
Result.
Conclusion.

This model is useful as a simplification. But actual scientific practice is more tangled.

Science consists of many interacting parts:

Questions.
Theories.
Data.
Instruments.
Classifications.
Models.
Methods.
Interpretations.
Publications.

These elements influence each other.

A new instrument can change what becomes measurable.
A classification system can change what becomes visible.
A model can shape the interpretation of data.
A publication format can influence how findings are presented.

AI finds new patterns.

Following Thresher, there is no mechanical process that can capture all the nuances of scientific practice.

This does not mean science is weak. It means science is complex.

Science produces reliability
not by eliminating judgement,
but by organising judgement through method, criticism, repetition, comparison and public scrutiny.

The method is not a straight line.
It is a tangle that must be handled carefully.

***

Slide 15 — Nature is an artful modeller

Nancy Cartwright via Thresher

Thresher also brings in Nancy Cartwright, who describes nature as an artful modeller.

This is a beautiful and important idea.

The world does not reveal its rules mechanically.

Nature is complex, situated and model-like. It does not present us with simple instructions printed on its surface.

This is why science needs creativity.

Researchers reconstruct recipes.

They use models, analogies, measurements, experiments and revisions.

If nature mixes, models and hides rules,
then research must also model, test and compose.

This is a useful bridge to field research, botany, drawing, material practice and Sketch&Draw we are focusing on in our course.

Anyone who has worked with plants, soils, weather, growth, decay or material transformation knows that the world does not behave like a simple diagram.

The researcher must observe, compare, wait, draw, measure, adjust.

In this sense, science and drawing share a deep relation to complexity.

Both are practices for approaching a world that does not immediately give itself away.

***

Slide 16 — Error as discovery

Expertise means being able to read deviations

The next operation is error.

In school, error is often treated as a deficit. It means that something went wrong.

But in art and research, error can become a moment of insight.

Fleming reads contamination as a clue.
The artist reads the stain as a new form.
The drawer reads the wrong line as direction.

The difference is not making no mistakes. It is recognising mistakes as information.

This is central to expertise.

Experts are not people who never fail. Experts are people who can read deviations.

They can ask:

Is this simply noise?
Or is this a signal?

Is this mistake irrelevant?
Or does it reveal something about the system?

Should I correct it?
Or should I follow it?

In Sketch&Draw this is especially clear.

A wrong line can remain on the page. It becomes part of the visual field. It may guide the next line. It may open a new relation.

The mistake does not disappear. It becomes material for thinking.

***

Slide 17 — 5 | Ambiguity

Not wanting to know too soon

The fifth operation is ambiguity.

To be uncertainty is not a deficiency. It is the working space of knowledge.

Artistic processes often begin before it is clear what the work will become.

Basic research often begins before it is clear whether a question is solvable.

Tolerance for ambiguity means dealing multiplicity without simplifying too soon.

This is difficult, because we tend to want closure. We want the answer, the category, the conclusion, the result. The publication.

But both art and science often require a slower pace.

The artist must stay with a form before it stabilises.
The scientist must stay with a problem before it becomes clear.
The drawer must stay with a line before the image appears.

Ambiguity tolerance is not passivity. It is active patience.

It is trainable, especially through open, iterative practice. And here Artful thinking plays an important role. We train this in our course here.

Even drawing with the method of Sketch&draw trains this very directly. An open line is not yet a conclusion. A bundle of lines is not yet a fixed form. A crossing is a moment of decision.

The page becomes a space where uncertainty is not eliminated but worked with.

***

Slide 18 — Body and material

Thinking does not stay in the head

The hand draws.
The eye probes.
The body measures distance.
The material responds.

In art, this is obvious. But it is also true in science.

Laboratory work, field research, microscopy, preparation, mapping and model-making are embodied practices.

The researcher does not think from nowhere. She thinks with instruments, with gestures, with samples, with notebooks, with bodily routines, with attention trained by practice.

Merleau-Ponty reminds us that perception is embodied and anchored in the world.

Ingold and Sennett help us understand material and craft as forms of thinking.

This matters because it challenges a purely mental image of knowledge.

Knowledge is not simply an idea inside the head. It is often distributed across hand, eye, surface, instrument, material, body and environment.

A line on paper is part of thinking.

***

Slide 19 — Models, diagrams, sketches

External thinking tools

A diagram does not merely explain.
It thinks along.

This is true for scientific diagrams, artistic sketches, maps, models, storyboards and visual notations.

Scientific models reduce the world in order to make relations testable.
Artistic models open perception in order to make relations experienceable.

Both are external tools for thought.

Barbara Tversky shows how movement, spatial arrangement and diagrams shape thinking. Root-Bernstein and Root-Bernstein describe how creative people across disciplines use similar tools: pattern, analogy, model, play.

This is important for visual research.

A sketch is not merely a draft of an image.
A model is not merely a representation.
A diagram is not merely a communication tool.

These forms allow us to think what we could not think without them.

They externalise relations.
They stabilise complexity.
They allow comparison.
They allow revision.

They make thought visible enough to be worked on.

***

Slide 20 — Regimes of truth

Related does not mean identical

At this point, we need to protect the difference.

Related does not mean identical.

Science asks:

Is it testable?
Is it reproducible?
Does it explain more than before?

Art asks:

What becomes visible?
What becomes experienceable?
Which perception shifts?

These are different questions.

Science has a strong obligation to evidence, method and public justification.

Art may work through ambiguity, affect, material presence, atmosphere, contradiction and experience.

This difference is not a problem. It makes the dialogue productive.

If we collapse art and science into one thing, we lose the richness of both.

The goal is not to say that art is science.
The goal is not to say that science is art.

The goal is to understand how different practices of inquiry can illuminate each other.

Science can learn from art about perception, ambiguity, materiality and imagination.

Art can learn from science about method, precision, modelling and critical testing.

Their dialogue is strongest when their differences remain visible.

***

Slide 21 — Historical perspective

The divide is younger than the practice

Historically, the strict divide between art and science is younger than the practices themselves.

Leonardo da Vinci did not separate drawing, anatomy, mechanics, botany and aesthetics in the way modern institutions often do.

For him, drawing was not an illustration after knowledge. It was a way of knowing.

He drew bodies, machines, plants, water, bones, muscles, proportions and movements.

The modern divide between art and science is strongly produced by institutions:

Academies.
Laboratories.
Journals.
Museums.
Funding systems.
Disciplines.

These institutions are important. They organise knowledge. But they also create boundaries that can make older continuities invisible.

If we return to practice, we see that drawing, observing, modelling and experimenting have long belonged together.

The divide is not natural.
It is historical.
And because it is historical, it can be rethought.

And at last, AI asks for changes in creativity and genuine thinking.

***

Slide 22 — Sketch&Draw as a bridge

Drawing trains movements of knowledge

This brings us to Sketch&Draw.

Sketch&Draw can be understood as a bridge between art and science because it trains movements of knowledge.

The method works with visual noise:

Fluttering lines.
Line bundles.
Crossings.
Open lines.
Highlights.

These are not only aesthetic features. They are cognitive operations.

The brain must decide, search and compare. A sketch becomes controlled uncertainty.

This is very close to the structure of research.

You begin before you know.
You produce traces.
You compare possibilities.
You strengthen some lines.
You leave others open.
You allow the image to emerge through interaction.

The line is not only the result of a thought. It is a place where thoughts emerge.

This is the key application thesis.

Drawing trains the capacity to remain
in the unfinished
long enough for perception  …. to become more precise.

***

Slide 23 — Mini exercise for the lecture

3 minutes of perceptual shifting

At this point in the lecture, I suggest a short exercise.

Please choose one object in the room.

It can be a chair, a cup, a bag, a hand, a lamp, a bottle, anything simple.

We will draw for three minutes.

First minute: draw it as a thing.
Do not worry about quality. Just draw what you recognise.

Second minute: draw only surfaces and spaces in between.
Forget the object name. Look for shapes, spaces, edges and relations.

Third minute: draw only energy, weight and direction.
Where does the object lean? Where is the tension? Where does the eye move?

After three minutes, we ask ourself:

What became visible when the category disappeared?

This small exercise demonstrates the main argument of the lecture.

Seeing is not fixed.
Seeing can shift.
Drawing can train that shift.

***

Slide 24 — For discussion

Three productive questions

I would like to open three questions for discussion.

First:

Where does knowledge begin: in the head, in the hand or in the material?

This question challenges the assumption that thinking happens only mentally. It invites us to consider knowledge as embodied, material and situated.

Second:

When does an error become a discovery?

This question asks us to rethink failure. An error becomes a discovery when it is read differently, when it is not immediately erased, when it is allowed to reveal something.

Third:

What kind of knowledge does a drawing produce?

This is perhaps the central question for artistic research and for Sketch&Draw.

A drawing may not produce knowledge in the same form as a scientific paper. But it can produce perceptual knowledge, relational knowledge, embodied knowledge, spatial knowledge and procedural knowledge.

The aim is to understand art not as an illustration of science, but as its own form of research.

***

Slide 25 — Closing image

A shared orientation towards the unknown

Let me return to the beginning.

Art and science are different cultures of questioning.

They differ in institutions, methods and regimes of truth.

But both interrupt certainties.
Both make models.
Both test reality.
Both require imagination, discipline and patience.

The difference is not that one side feels while the other thinks.

The difference lies in how thinking becomes visible, testable and experienceable.

Science makes thinking testable through method, evidence and public criticism.
Art makes thinking experienceable through form, material, image, sound, body and perception.

Both are ways of approaching the unknown.

And both remind us that knowledge does not begin with certainty.

It begins with a gap.
A question.
A deviation.
A line.
A material response.
A model that does not yet fit.
A perception that shifts.

So the final thesis is this:

Art and science are not opposites. They are two different ways of staying with the unknown long enough for new knowledge to appear.

***

Slide 26 — Sources

Closing spoken sentence

The sources behind this lecture come from several fields: neuroscience, philosophy of science, creativity research, phenomenology, drawing research and material practice.

This is important because the topic itself is interdisciplinary.

To understand the relation between art and science, we need more than one discipline.

We need theories of perception.
We need theories of method.
We need studies of creativity.
We need histories of art and science.
We need embodied practice.
And we need drawing itself.

Thank you.

 

Very short final closing version

Art and science do not share the same truth procedures.
But they share a deep orientation towards the unknown.

They ask, see, imagine, test and sustain uncertainty.
They use models, diagrams, materials, bodies and errors.
They both transform not-knowing into a practice.

That is why drawing matters.
Drawing is not only a skill.
It is a training in how knowledge begins.

 

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