07 September 2026

A Clinostat — Can You Confuse a Plant About Which Way Is Down?

Put a plant on its side and something rather remarkable happens.

It does not simply continue growing sideways.

Within hours, the shoot begins to curve upwards while the root turns in almost exactly the opposite direction.

The plant has no eyes.

It has no ears.

It has no brain.

And yet somehow it appears to know which way is up.

That raises a wonderful biological question:

How does a plant know which way gravity is acting?

One of the classic ways of investigating this is with a wonderfully simple piece of scientific apparatus called a clinostat.

A clinostat slowly rotates a plant so that the direction of gravity is continually changing relative to the plant's tissues.

Gravity has not disappeared.

Instead, from the plant's point of view, there is no longer one consistent direction that remains "down".

Can we confuse the plant's normal gravitational response?

That makes the clinostat a fascinating experiment for anyone interested in plant biology, hormones, tropisms or even how scientists investigate plants in space.

Plants Are Constantly Sensing Their Environment

Plants may look passive, but biologically they are anything but.

They constantly respond to their surroundings.

Among the most familiar responses are:

phototropism — growth in response to light;
gravitropism — growth in response to gravity;
hydrotropism — growth in response to water;
thigmotropism — growth in response to touch.

Gravitropism is particularly interesting because gravity is always present.

A seed germinating underground cannot necessarily use light to decide which direction its new root should grow.

Yet its first root generally grows downwards while its shoot grows upwards.

That is enormously important.

Roots growing downwards are more likely to enter the soil where they can obtain water and mineral ions.

Shoots growing upwards are more likely eventually to reach the light required for photosynthesis.

Plants therefore show two different gravitational responses.

Roots generally have positive gravitropism because they grow towards the direction of gravity.

Shoots generally have negative gravitropism because they grow away from it.

But how can we demonstrate this?

Experiment One: Put the Seedlings on Their Side

The first experiment does not need a clinostat at all.

Grow several seedlings vertically until their young roots and shoots are clearly visible.

Suitable plants might include:

broad beans;
peas;
cress;
radish;
wheat;
oats;
mung beans.

Seeds can be germinated between moist paper, in transparent bags, on agar or in suitable growing medium.

Once the roots and shoots have developed, turn the seedlings through approximately 90 degrees so that they are growing horizontally.

Then watch.

A useful experiment might photograph the seedlings every hour or every few hours.

Over time the shoot should begin curving upwards.

The root should begin curving downwards.

The interesting part is that the plant has not been physically bent by gravity like a piece of soft wire.

Instead, different parts of the growing region have grown at different rates.

The curvature is being produced biologically.

Now Introduce the Clinostat

A clinostat changes the experiment.

Instead of leaving the seedling in one position, attach it to a slowly rotating platform.

The rotation needs to be slow and steady.

The aim is not to spin the plant rapidly.

A simple educational clinostat might rotate at only a few revolutions per minute.

As the plant rotates, gravity is always pulling vertically downwards relative to the room.

But relative to the plant, the apparent direction of gravity continuously changes.

At one moment one side of the plant faces downwards.

Half a rotation later, the opposite side faces downwards.

Over time the plant receives no persistent gravitational direction from one side.

This provides a fascinating comparison.

A Simple Experimental Design

You could prepare three groups of similar seedlings.

Group A — Normal vertical seedlings

Leave these growing normally.

They provide a reference showing ordinary root and shoot development.

Group B — Horizontal stationary seedlings

Place these on their sides and leave them stationary.

These should demonstrate the normal gravitropic response.

The shoots should curve upwards.

The roots should curve downwards.

Group C — Horizontal seedlings on the clinostat

Place comparable seedlings horizontally on the rotating clinostat.

Now photograph and measure their growth.

The question becomes:

Will they curve in the same way as the stationary seedlings?

Ideally the clinostat seedlings should show much less consistent curvature because the gravitational stimulus is continually being reoriented.

That difference is the heart of the experiment.

What Should We Measure?

Simply looking at the seedlings is interesting.

Measuring them turns the demonstration into an investigation.

Photograph each seedling from the same position at regular intervals.

You could record:

shoot length;
root length;
angle of shoot growth;
angle of root growth;
time before curvature becomes visible;
amount of curvature after 12, 24, 48 or 72 hours.

A printed grid placed behind the seedlings can make measurements easier.

Even better, take photographs with the camera fixed in the same position.

Students could then use image-analysis software to estimate the angle through which the root or shoot has curved.

For example, the original direction of growth could be defined as 0 degrees.

If the shoot eventually bends upwards through approximately 70 degrees, that can be compared quantitatively with a clinostat-grown shoot that perhaps changes direction only slightly.

Suddenly a plant on a rotating disc has become a proper experimental investigation.

Keep the Variables Under Control

Clinostat experiments also provide an excellent lesson in experimental design.

If we are investigating gravity, we do not want another directional stimulus dominating the experiment.

Light is the obvious problem.

Shoots also respond strongly to directional light.

If your seedlings are illuminated strongly from one side, you may think you are observing gravitropism when you are actually observing phototropism.

Ideally the illumination should therefore be:

diffuse;
symmetrical;
from directly above where appropriate;
or excluded during the relevant part of the experiment.

Temperature should also be similar between the rotating and stationary seedlings.

The seedlings should ideally be:

the same species;
approximately the same age;
at similar stages of germination;
supplied with similar amounts of water;
exposed to similar temperatures.

The only major difference should be the rotation.

This is precisely the sort of thinking that turns an interesting demonstration into good science.

But How Does the Plant Detect Gravity?

This is where the experiment becomes even more interesting.

Inside certain specialised plant cells are structures containing dense starch-filled organelles called amyloplasts.

When they are involved in gravity sensing, these structures are often described as statoliths.

Because they are relatively dense, they tend to settle towards the lower part of the cell under gravity.

Imagine a snow globe.

Turn the globe sideways and the particles eventually settle towards the new bottom.

Something conceptually similar occurs inside gravity-sensing cells in plants.

The position of these sedimenting statoliths provides information about the direction of gravity.

Specialised gravity-sensing cells are known as statocytes.

In roots, particularly important statocytes occur in the root cap.

In shoots, gravity sensing involves specialised tissues including cells associated with the endodermis.

The movement of the statoliths appears to initiate signalling processes that eventually affect growth.

Detecting Gravity Is Only the Beginning

Knowing which direction gravity acts is not enough.

The plant must somehow turn that information into directional growth.

This brings us to the plant hormone auxin.

When a plant organ is placed horizontally, gravity sensing contributes to an unequal distribution of auxin between the upper and lower sides.

The effects differ between roots and shoots.

In shoots, increased auxin on the lower side generally promotes greater cell elongation.

The lower side therefore grows faster than the upper side.

The shoot curves upwards.

In roots, higher auxin concentrations on the lower side inhibit elongation more strongly.

The upper side therefore elongates faster.

The root curves downwards.

That difference is worth emphasising.

Students sometimes learn the oversimplified rule:

"Auxin makes plants grow."

The real biology is more interesting.

The effect of auxin depends upon:

its concentration;
the plant tissue;
developmental conditions;
interactions with other signalling systems.

The same redistribution of a hormone can therefore contribute to opposite-looking responses in roots and shoots.

What Is the Clinostat Actually Doing?

There is an important scientific caution here.

A clinostat does not switch gravity off.

Gravity is still acting on the plant.

The Earth has not stopped pulling on it.

Instead, the rotation continually changes the direction from which the plant experiences the gravitational stimulus.

If the rotation is appropriate, there is no persistent gravitational direction relative to the plant.

Scientists sometimes describe this as gravity-vector averaging.

That distinction matters because clinostats are sometimes loosely described as producing "zero gravity".

They do not.

Real microgravity requires very different conditions, such as those experienced aboard an orbiting spacecraft.

Clinostats can nevertheless be extremely useful for investigating how organisms respond when they are denied a stable gravitational direction.

More sophisticated research may use devices such as random positioning machines or specialised centrifuge systems.

But the basic scientific idea can be explored with a remarkably simple rotating platform.

Could You Build Your Own Clinostat?

Yes.

A basic educational clinostat does not have to be an expensive scientific instrument.

The essential requirement is a slowly rotating mounting system.

Possible approaches include:

a geared low-speed electric motor;
a small turntable mechanism;
a modified rotating display stand;
a motor controlled using an Arduino or Raspberry Pi;
a 3D-printed support attached to a suitable low-speed motor.

The seedling container needs to be held securely so that it rotates with the axis of the clinostat.

It is important that the seedling does not repeatedly fall or move around inside the container.

The rotation should also be reasonably smooth.

Very rapid rotation creates an additional problem: centrifugal effects.

The objective is therefore not:

Spin the plant as fast as possible.

It is:

Change its orientation slowly enough that gravity does not remain acting in one consistent direction relative to the plant.

That is a much more subtle experiment.

An Excellent Use for Time-Lapse Photography

This experiment is almost perfect for time-lapse photography.

Plants move too slowly for us to appreciate their behaviour easily in real time.

Take one photograph every few minutes and combine the images into a video.

A process taking two days can then be compressed into perhaps 20 or 30 seconds.

The stationary horizontal seedling may appear dramatically to sweep its shoot upwards.

The root moves in the opposite direction.

The clinostat-grown plant may behave very differently.

Time-lapse transforms something that appears static into something almost animal-like.

It is one of the best ways of reminding students that plants are actively responding organisms.

Take the Experiment Further

Once the basic experiment works, there are plenty of possible extensions.

Does the speed of clinostat rotation matter?

Try several rotation speeds.

At what point is the gravitational response most effectively disrupted?

Be careful: very high speeds may introduce centrifugal effects and other mechanical stresses.

Do roots and shoots respond equally quickly?

Measure how long it takes before curvature becomes visible.

Does the root begin responding before the shoot?

Do different plants respond differently?

Compare:

peas;
beans;
cereals;
cress;
radish.

Are the rates of gravitropic response similar?

Does seedling age matter?

Compare very young seedlings with slightly older plants.

What happens after removing the plant from the clinostat?

Allow a plant to rotate for perhaps 24 or 48 hours.

Then stop the clinostat and leave the plant horizontally.

How quickly does gravitropic curvature return?

This gives a wonderfully clear demonstration that the plant's gravity-sensing mechanism is still functioning.

Gravity and Plants in Space

The clinostat experiment naturally leads to a much bigger question.

What happens to plants in space?

If humans are ever to live for long periods:

aboard space stations;
on the Moon;
on Mars;
or during long journeys through the Solar System,

growing plants may become extremely important.

Plants could provide:

food;
oxygen;
carbon dioxide removal;
water recycling;
psychological benefits for crews.

But plants evolved under Earth's gravity.

Remove or greatly reduce that familiar gravitational cue and their normal growth patterns may change.

Space biology therefore asks questions remarkably similar to those we are investigating with our small rotating seedlings:

How important is gravity to plant development?

Can other environmental signals take over?

How do roots decide where to grow when "down" is no longer obvious?

A small clinostat on a classroom or laboratory bench therefore connects remarkably well with experiments conducted in space.

The Bigger Lesson: Plants Are Not Passive

One reason I like experiments such as this is that they change the way we look at plants.

A seedling sitting in a pot can appear to be doing almost nothing.

In reality it is continually:

detecting light;
detecting gravity;
responding to water;
regulating hormones;
changing patterns of cell growth;
directing roots and shoots towards favourable environments.

The plant has no nervous system telling it what to do.

Instead, environmental information is translated into chemical and cellular responses.

That makes a simple question such as:

"Which way is down?"

far more biologically interesting than it first appears.

A Three-to-Seven-Day Experiment That Opens Up a Huge Area of Biology

The clinostat experiment does not need spectacular chemicals, expensive sensors or complicated preparation.

You need seedlings, some careful controls and a slowly rotating platform.

Yet from that simple equipment you can explore:

gravitropism;
positive and negative tropisms;
plant hormones;
auxin redistribution;
differential cell elongation;
root and shoot physiology;
statocytes;
statoliths;
amyloplasts;
experimental controls;
time-lapse photography;
and even plant biology in space.

Most importantly, it encourages exactly the sort of question that good science should encourage.

Put a seedling on its side and it turns.

Rotate it continuously and its behaviour changes.

So the question is no longer simply:

"Do plants respond to gravity?"

We know that they do.

The more interesting question is:

How can an organism with no brain, no eyes and no sense of balance work out which way is down?

Sometimes an experiment does not need to produce an unexpected result to be fascinating.

Sometimes the fascinating part is discovering just how much biology is hidden inside something we normally take completely for granted.

06 September 2026

Identical Twins, Different Families: Can Twin Studies Separate Nature from Nurture?

Few topics in A Level Psychology capture the nature-nurture debate as neatly as twin studies.

Take two ordinary siblings. If they have similar intelligence, personalities or interests, what caused the similarity?

They share some genes.

They probably grew up in the same home.

They may have attended similar schools, eaten similar food, been read similar books and been encouraged by the same parents.

Immediately, genetics and environment become tangled together.

Now consider twins.

And then consider something much rarer and scientifically fascinating:

identical twins who were separated when very young and raised in different families.

Suddenly we have something approaching a natural psychological experiment.

The twins have extremely similar genetic inheritance, but parts of their environments are different.

So if they grow into remarkably similar adults, does that demonstrate the power of genes?

And if they become very different, does that demonstrate the importance of environment?

As is so often the case in Psychology, the real answer is much more interesting than either of those simple conclusions.

First, Why Are Twins So Useful to Psychologists?

There are two main types of twins relevant to behavioural genetics.

Monozygotic twins

Monozygotic twins, usually called identical twins, develop when a single fertilised egg divides.

They therefore share virtually all their DNA sequence.

Dizygotic twins

Dizygotic twins, often called fraternal or non-identical twins, develop from two different eggs fertilised by two different sperm cells.

Genetically, they are approximately as similar as ordinary brothers and sisters, sharing on average about 50% of their segregating genetic variants.

That difference gives psychologists an extremely useful comparison.

Suppose we measure intelligence in large numbers of twins.

If identical twins are considerably more similar in intelligence than non-identical twins, one possible explanation is that genetic differences contribute to differences in intelligence.

That is the basic logic behind the classical twin study.

Correlation Is the Key

Psychologists usually do not expect two twins to receive exactly the same IQ score.

Instead, researchers look at correlations.

Imagine we tested hundreds of pairs of twins.

If Twin A scored highly and Twin B also tended to score highly, while lower-scoring Twin A partners tended to have lower-scoring Twin B partners, the correlation would be positive.

If identical twins produced a correlation of, say:

r = 0.80

while non-identical twins produced:

r = 0.45

the greater similarity between the identical twins would suggest a genetic contribution.

A simplified estimate sometimes introduced when explaining the classical twin method is:

Heritability = 2 x (rMZ - rDZ)

where:

rMZ = correlation between monozygotic twins

rDZ = correlation between dizygotic twins

However, real behavioural-genetic research uses much more sophisticated statistical modelling than this simple calculation.

And there is another extremely important warning.

Heritability is about variation within a population. It is not a percentage describing an individual person.

If a study estimates the heritability of intelligence at 60%, it does not mean that 60% of your intelligence was produced by your genes and 40% by your environment.

That interpretation is wrong.

Then Comes the Fascinating Case: Identical Twins Raised Apart

Identical twins raised together share two very important things:

extremely similar genes;
much of their childhood environment.

That creates a problem.

Perhaps they are similar because of their genes.

But perhaps they are similar because their parents treated them similarly.

Or perhaps both are involved.

This is why monozygotic twins reared apart are so valuable.

Imagine identical twins separated shortly after birth.

One grows up in London.

The other grows up in Edinburgh.

They have different parents, different schools, different friends, perhaps different family incomes and very different childhood experiences.

Thirty years later psychologists locate them and test them.

If their intelligence remains strongly correlated, shared upbringing becomes a much less convincing explanation.

Their genetic similarity becomes much more interesting.

The Famous Minnesota Twins Reared Apart Study

One of the best-known investigations was the Minnesota Study of Twins Reared Apart, associated particularly with Thomas Bouchard and colleagues.

Beginning in 1979, researchers studied more than 100 sets of twins or triplets who had been separated and raised apart, subjecting participants to extensive psychological and physiological testing.

Bouchard and colleagues reported that approximately 70% of the variance in IQ in their sample was associated with genetic variation. Identical twins raised apart also showed striking similarities across several other psychological characteristics.

This is powerful evidence for a genetic contribution to intelligence.

But notice the wording:

a genetic contribution.

It does not demonstrate that intelligence is fixed genetically.

The twins were not identical in intelligence.

That difference is important too.

Where identical genes are associated with similarity, psychologists investigate genetic influences.

Where genetically identical individuals differ, psychologists have evidence that genes cannot be the whole explanation.

Why “Raised Apart” Does Not Mean “Environment Removed”

This is one of the most important evaluation points for an A Level student.

It is tempting to imagine that identical twins raised apart have:

Same genes + completely different environments

Unfortunately, real life is rarely that tidy.

Twins raised apart may still share environmental characteristics.

For example:

adoption agencies may place children into broadly similar families;
both families may have similar socioeconomic backgrounds;
both twins may grow up within the same culture;
both may receive similar levels of education;
the twins shared the same prenatal environment before birth;
twins may discover one another and have contact later;
adoptive families are not randomly selected from every possible environment.

This creates the problem of selective placement.

Suppose both children are deliberately placed into stable, relatively well-educated homes.

Their environments may be more similar than the phrase “raised apart” suggests.

Therefore:

similarity between separated twins cannot automatically be attributed entirely to genetics.

And Twins Raised Together Create Another Problem

Traditional twin studies also make what is known as the equal environments assumption.

The argument goes something like this:

Identical twins share more genes than non-identical twins.

If identical twins are more psychologically similar, the difference can therefore be attributed partly to greater genetic similarity.

But what if identical twins are also treated more similarly?

Parents may:

dress identical twins similarly;
encourage the same activities;
put them in the same classes;
buy them the same toys;
expect them to behave similarly;
encourage a shared identity.

Other people may treat them more similarly simply because they look alike.

If the environments experienced by identical twins are more similar than those experienced by non-identical twins, some of the higher identical-twin correlation could reflect environment as well as genetics.

Again, nature and nurture become difficult to separate.

Van Leeuwen and the Twin-Family Study of Intelligence

An especially useful study for taking this topic further was conducted by Marieke van Leeuwen, Stéphanie van den Berg and Dorret Boomsma.

Rather than studying only pairs of twins, their research used an extended twin-family design.

Their sample involved 112 families, including twins, their siblings and their parents.

This is clever because researchers gain more comparisons.

They can examine similarities between:

identical twins;
non-identical twins;
ordinary siblings;
children and parents;
husbands and wives.

That gives researchers much more information than simply comparing identical and non-identical twins.

But How Do You Measure “Intelligence”?

This question deserves far more attention than it sometimes receives.

We talk casually about someone being “intelligent”, but psychologists need an operational definition.

They need something they can actually measure.

Van Leeuwen and colleagues used the Raven Progressive Matrices.

Participants are presented with patterns containing a missing element and have to determine which option correctly completes the pattern.

A simple example might look conceptually like this:

Triangle -> Square -> Pentagon -> ?

The participant has to identify the underlying rule rather than simply recall a fact they have learned.

Real Raven questions are considerably more sophisticated and use visual patterns rather than little sequences like this.

The test is particularly associated with abstract and non-verbal reasoning.

Van Leeuwen's team used performance on Raven matrices and estimated general intelligence using a Rasch measurement model.

That point matters.

The researchers did not somehow observe “intelligence” directly.

They observed performance on psychological tasks from which intelligence was estimated.

Is an IQ Test Really Measuring Intelligence?

This provides an excellent evaluation question.

Raven's matrices have advantages.

Because they rely less heavily on vocabulary than some intelligence tests, they may reduce some effects of language and formal knowledge.

But “less dependent on education” does not mean “independent of environment”.

Performance can still potentially be affected by:

schooling;
familiarity with tests;
concentration;
motivation;
anxiety;
fatigue;
understanding instructions;
experience solving abstract puzzles;
health;
nutrition;
developmental opportunities.

So when we say that researchers are investigating the inheritance of intelligence, we need to be more precise.

They are studying individual differences in measured cognitive performance.

That is not quite the same thing as discovering a single biological quantity called intelligence.

What Did Van Leeuwen's Study Find?

The researchers found that identical twins resembled one another more strongly in IQ than first-degree relatives such as non-identical twins, siblings and parent-child pairs.

After controlling for unreliability in the measurement scale, the model estimated that additive genetic effects accounted for around 67% of population variance in measured IQ.

They also reported a correlation of about r = 0.33 between spouses' IQ scores. Their modelling favoured the idea of phenotypic assortment — people with similar intelligence tending to partner with one another — rather than similarity arising purely because people from similar social backgrounds meet each other.

That is important because the classical twin model can become more complicated if mating is not random for the characteristic being studied.

But perhaps an even more interesting result was evidence suggesting that genetics and environment did not simply operate as two independent forces.

The researchers estimated that a further portion of variation was associated with gene-environment interaction.

And this leads us beyond the simple nature-versus-nurture argument.

Perhaps Nature Versus Nurture Is the Wrong Question

Students often begin this topic imagining two competing explanations.

Nature

Your genes determine your intelligence.

Nurture

Your upbringing determines your intelligence.

But modern behavioural genetics suggests something much more complicated.

Genes can influence how people respond to environments.

And environments can influence how genetic differences are expressed.

This is called gene-environment interaction.

A Practical Example: Same School, Different Effect

Imagine two children attend exactly the same mathematics lesson.

Same teacher.

Same textbook.

Same classroom.

Same explanation.

We might describe that as a shared environment.

But it does not follow that the lesson has the same psychological effect on both children.

One student may understand the pattern quickly, become interested, attempt harder questions and receive positive feedback.

The other may struggle initially, become frustrated and avoid the subject.

One shared environmental event has produced two different experiences.

This is why simply listing “genes” and “environment” as separate causes can be misleading.

Genes Can Also Help Create Environments

Consider another possibility.

A child who finds reading relatively easy may start reading voluntarily.

Because they read more, they develop a larger vocabulary.

That makes reading increasingly enjoyable.

They start choosing more difficult books.

Teachers notice their ability and recommend additional material.

The original differences may have contained a genetic component.

But that genetic influence has helped produce a particular environment.

The environment then strengthens the behaviour.

This is an example of why psychologists discuss gene-environment correlation.

Genes do not operate in isolation from experience.

They can partly influence the experiences people seek, receive and create.

An Important Thought Experiment for Students

Imagine we discovered two genetically identical babies.

We raise Baby A in an environment containing:

excellent nutrition;
stimulating conversation;
books;
good healthcare;
high-quality schooling;
opportunities to explore;
adults who encourage curiosity.

Now imagine Baby B experiences:

severe nutritional deprivation;
little stimulation;
interrupted schooling;
chronic stress;
serious illness;
few learning opportunities.

Would anyone seriously expect their cognitive development to be identical simply because their genes were identical?

Of course not.

Genes affect development within an environment.

This is why high heritability does not mean that environmental interventions are useless.

Bouchard and colleagues themselves explicitly noted that evidence for substantial heritability did not reduce the importance of education and other interventions.

Environment Can Have Measurable Effects

Adoption research gives us another way of approaching the question.

A large Swedish study compared siblings where one child had been raised by biological parents while another had been adopted into a different family.

The researchers found that adoption into more advantaged socioeconomic circumstances was associated with higher measured cognitive ability at age 18.

This provides evidence that rearing environment can influence cognitive outcomes, even when genetic effects are substantial.

So the evidence does not support the simplistic conclusion:

“Intelligence is genetic.”

Nor does it support:

“Intelligence is produced entirely by upbringing.”

Instead, intelligence appears to emerge from a complicated developmental system involving both.

Another Surprise: Heritability Can Change with Age

There is another finding that often surprises students.

The estimated heritability of intelligence does not necessarily remain constant throughout life.

A longitudinal Dutch twin study found that estimated heritability of full-scale IQ increased from about 34% at ages 9-11 to around 65% at ages 12-14, while estimated shared environmental influence decreased.

At first this can sound extraordinary.

Surely we accumulate more environmental experiences as we get older?

Yes.

But as children gain independence, genetically influenced preferences may increasingly affect which environments they choose.

The child interested in music practises more music.

The child fascinated by numbers chooses mathematical activities.

The strong reader reads more.

Small initial differences can become amplified through experience.

Once again, genes and environment are interacting rather than taking turns.

Why Twin Studies Are Powerful

Twin studies have several major strengths.

They provide naturally occurring comparisons

It would obviously be completely unethical to deliberately separate identical twins merely to conduct an experiment.

Researchers instead study naturally occurring circumstances.

They allow genetic similarity to vary systematically

Identical and non-identical twins provide different degrees of genetic relatedness while often having broadly similar family backgrounds.

They can produce quantitative evidence

Researchers can calculate correlations and construct statistical models rather than relying simply on anecdotal similarities.

Reared-apart twins are particularly informative

When identical twins remain similar despite different childhood homes, purely shared-family explanations become less convincing.

But Twin Studies Have Important Limitations

Good Psychology requires evaluation, not simply memorising a result.

1. Twins raised apart are extremely rare

Large representative samples are difficult to obtain.

Researchers may therefore work with unusual groups of participants.

That raises questions about generalisability.

2. “Apart” does not mean completely different environments

Adoption placement, social class and culture may make the homes more similar than expected.

3. Prenatal environment is shared

Identical twins raised in different homes still spent their prenatal development together.

4. Identical twins may be treated unusually similarly

This can complicate comparisons with non-identical twins.

5. Intelligence itself is difficult to operationalise

An IQ score is a measurement obtained from particular psychological tasks.

It should not automatically be treated as a complete measurement of every aspect of human intellectual ability.

6. Correlations do not prove individual causation

A high correlation between identical twins does not identify particular genes or explain the biological mechanism producing a behaviour.

Do Not Confuse Heritability with Inevitability

This is probably the single most important sentence in this whole topic:

A heritable characteristic can still be strongly influenced by environment.

Height provides an obvious analogy.

Human height is substantially heritable.

But serious childhood malnutrition can reduce adult height.

Genes influence the developmental range.

Environment affects how development actually proceeds.

Intelligence is considerably more complicated than height, but the same basic warning applies.

A population-level heritability estimate tells us something about why people differ under the conditions in which that population was studied.

It does not tell us what a particular individual could achieve under different circumstances.

Why I Think Twin Studies Are Such a Good A Level Psychology Topic

What I like about twin research is that it initially appears to offer a beautifully simple experiment.

Identical twins have the same genes.

Separate them.

See what happens.

But the more carefully we examine the idea, the more complicated it becomes.

What counts as the environment?

Are two adoptive families really independent environments?

How do we measure intelligence?

Does a test score represent intelligence itself?

Can genes influence which environments a person experiences?

Can environmental effects depend upon genotype?

Suddenly a simple nature-nurture comparison has become a lesson in research methods, correlations, operationalisation, validity, ethics, biological psychology and individual differences.

That is exactly what makes Psychology interesting.

Turning This into an A Level Exam Argument

Suppose the question asked:

“Discuss the contribution of twin studies to our understanding of intelligence.”

A strong argument might develop like this:

Point: Twin studies provide evidence for genetic influences on intelligence.

Evidence: Identical twins share virtually all their genes, whereas non-identical twins share about half of their segregating genetic variation. Greater similarity in identical twins therefore supports genetic influence.

Example: Bouchard's study of twins raised apart reported substantial similarity in intelligence despite separate rearing environments.

Evaluation: However, twins raised apart may still experience similar cultural and socioeconomic environments through selective placement, so environmental similarity cannot be eliminated completely.

Further evidence: Extended twin-family research such as Van Leeuwen et al. found substantial additive genetic influence on measured IQ.

Further evaluation: Nevertheless, evidence for gene-environment interaction suggests that separating behaviour neatly into genetic and environmental percentages may oversimplify development.

That is much stronger than writing:

“Bouchard proved intelligence is inherited.”

He did not.

The Bigger Lesson: Genes Are Not a Script

Perhaps the greatest contribution of twin research is not that it finally settles the nature-nurture debate.

It is that it shows why the original debate was too simplistic.

Identical twins raised in different families can sometimes remain remarkably similar.

That tells us that genetic variation matters.

But identical twins are never psychologically identical.

That tells us something else matters too.

Environment matters.

Individual experience matters.

Development matters.

And increasingly, research suggests that genes and environments continually influence one another.

So instead of asking:

“Is intelligence inherited or learned?”

perhaps the better psychological question is:

“How do genetic differences and environmental experiences interact during development to produce the differences in intelligence that we observe?”

That is a much harder question.

But it is also a much better one.

And that is often where the most interesting Psychology begins.

05 September 2026

Beyond Windows and Mac — Why Every Computing Student Should Spend Some Time Using Linux

For many GCSE and A Level Computer Science students, the phrase operating system really means one of two things:

Windows or macOS.

They may know, because the specification tells them, that an operating system manages memory, processes, files, peripherals and the user interface. They may be able to answer an examination question about multitasking or virtual memory.

But there is a considerable difference between learning that an operating system can work in different ways and actually experiencing one that does.

That is why I think one of the most useful things a computing student can do is spend some time with Linux.

Not because everybody should abandon Windows.

Not because Linux is automatically “better”.

And certainly not because using a terminal somehow makes somebody a more serious computer scientist.

The value is much simpler.

Linux makes you realise that many things you thought were fundamental features of a computer are actually just choices made by the designers of the operating system you normally use.

And once students understand that, their view of computing changes.

An Operating System Is More Than the Desktop You See

One misconception I frequently encounter is the idea that the graphical desktop is the operating system.

Students see:

the Start menu;
the taskbar;
File Explorer;
windows;
icons;
menus;
desktop backgrounds.

Because these are the parts of Windows they interact with every day, it is easy to assume that an operating system naturally looks something like Windows.

Linux quickly challenges that assumption.

Strictly speaking, Linux is the kernel, the central part of the operating system that communicates with the hardware and manages resources.

What people commonly call “Linux” is normally a complete Linux distribution containing the kernel together with utilities, libraries, applications, package-management software and usually a graphical desktop environment.

Examples of Linux distributions include:

Ubuntu;
Debian;
Fedora;
Linux Mint;
Arch Linux;
openSUSE;
Raspberry Pi OS.

Already, that gives students something interesting to think about.

There isn't simply one Linux operating system that looks the same on every machine.

Different distributions can be designed for different purposes.

Then You Discover the Desktop Can Change Too

Install Windows and you largely receive Microsoft's Windows interface.

Install macOS and you receive Apple's desktop environment.

Linux can be rather different.

A Linux installation might use GNOME, which presents one style of graphical interface.

Another could use KDE Plasma, which can feel much closer to the traditional desktop environment familiar to Windows users.

Others might use:

Xfce;
Cinnamon;
MATE;
LXQt.

This is a fascinating discovery for students because the same underlying operating system can be presented through substantially different graphical environments.

It immediately raises a useful computing question:

Where does the operating system finish and the user interface begin?

That is much more interesting than simply memorising:

“The operating system provides a user interface.”

Linux allows students to see that separation in practice.

Your First Linux Surprise: The Terminal

Then comes the part that can initially look slightly frightening.

The command line.

Open a terminal window and instead of icons and folders you might see something such as:

student@computer:~$

At first it can appear as though computing has suddenly travelled backwards 40 years.

Where have all the windows gone?

Where is File Explorer?

Why would anybody want to type commands when they can click on something?

Spend a little time with it, however, and something interesting happens.

You begin to understand why command-line interfaces have survived.

They are extraordinarily powerful.

Your First Few Linux Commands

A beginner does not need hundreds of commands.

Start with a handful.

To find the current directory:

pwd

To display its contents:

ls

To change directory:

cd Documents

To move up one level:

cd ..

To create a directory:

mkdir experiments

To copy a file:

cp results.txt backup.txt

To rename or move one:

mv results.txt oldresults.txt

To display a text file:

cat results.txt

Within quite a short period of time, students are navigating the filesystem without touching the mouse.

And something more important is happening.

They are beginning to understand the filesystem rather than merely operating a graphical representation of it.

Suddenly File Paths Make More Sense

Students learning programming regularly encounter file paths.

On Windows they might see:

C:\Users\Philip\Documents\program.py

On Linux they might encounter:

/home/philip/Documents/program.py

Linux introduces the idea of the root directory:

and beneath it directories such as:

/home
/etc
/bin
/var
/tmp

You certainly don't need a GCSE student to memorise the entire Linux filesystem hierarchy.

What is useful is seeing that there are alternative ways of organising storage.

It also makes absolute and relative paths far less abstract.

One of Linux's Best Lessons: Permissions

Suppose a student tries to modify a system file and Linux replies:

Permission denied

Initially this can be annoying.

Educationally, it is wonderful.

Who owns this file?

Which user is the program running as?

Who is allowed to read it?

Who is allowed to modify it?

Who is allowed to execute it?

A command such as:

ls -l

might display something resembling:

-rwxr-xr--

Those apparently mysterious characters introduce a very real implementation of:

read permissions;
write permissions;
execute permissions;
users;
groups.

Suddenly access rights are no longer just another paragraph in an operating-systems textbook.

They are controlling what you can actually do.

Installing Software Works Differently Too

For many students, installing software means:

search for a website;
download an installer;
double-click it;
click Next several times;
hope you downloaded the genuine version.

Linux distributions commonly use package managers instead.

On a Debian or Ubuntu-based system, for example, software can often be installed using something like:

sudo apt install python3

There is a lot contained in that single command.

The computer:

contacts configured software repositories;
identifies the requested package;
checks dependencies;
downloads the required files;
installs them;
registers the software with the package-management system.

Students can then discover that updating installed packages can also be centrally managed.

This leads to excellent discussions about:

software repositories;
dependencies;
version control;
trusted sources;
security updates;
system administration.

Then You Discover Pipes

One of my favourite ideas to show students is the Unix philosophy of combining small tools.

Imagine one command produces some information.

Instead of building an enormous application that does everything, Linux encourages us to take the output of one program and give it to another.

The vertical bar symbol:

is called a pipe in this context.

For example:

ls | sort

The first command generates a list.

The second sorts it.

You can begin building increasingly powerful combinations:

ls | sort | head

The concept is remarkably elegant:

take several simple programs and combine them to solve a more complicated problem.

That is a very computer-science way of thinking.

Searching Becomes Extremely Powerful

Suppose we have a large text file and want every line containing the word "error".

Instead of opening the document and searching visually, we might use:

grep "error" logfile.txt

Now imagine searching hundreds of log entries.

Or thousands.

Or searching the output from another program.

That is when the command line begins to stop looking old-fashioned and starts looking efficient.

Students begin to appreciate that graphical interfaces and command-line interfaces are not competing generations of computing.

They are tools suited to different tasks.

Processes Stop Being an Abstract Diagram

GCSE and A Level students study processes.

They learn that programs executing in memory become processes and that an operating system schedules processor time between them.

Linux gives them opportunities to observe this.

Commands such as:

ps

can display running processes.

Programs such as:

top

allow users to watch processor and memory usage changing in real time.

A student can start a program, observe the process appear, terminate it and watch it disappear.

Now an examination question about process management has a physical experience attached to it.

That matters.

Linux Is Particularly Good for Learning Networking

Networking is another subject that can become rather theoretical at school.

Linux provides an extraordinary collection of tools for exploring networks.

Students can investigate concepts using commands such as:

ping

and tools for examining:

IP addresses;
routing;
DNS;
active connections;
network interfaces.

For example:

ping bbc.co.uk

does much more educationally than simply demonstrate whether a website responds.

It raises questions.

How did the computer convert the domain name into an IP address?

What route did the packet take?

Why does latency vary?

What happens if a host does not respond?

What exactly is being transmitted?

A networking specification suddenly becomes a network you can interrogate.

Linux and Python Work Beautifully Together

Linux is also a natural environment for programming.

Suppose we create a simple Python file:

hello.py

containing:

print("Hello from Linux")

We can run it directly from the terminal:

python3 hello.py

Now consider writing a Python program that:

reads command-line arguments;
processes a text file;
creates a log;
communicates across a network;
monitors a sensor;
controls a Raspberry Pi.

The relationship between software, filesystem and operating system becomes much more visible.

This is especially valuable for students moving beyond very simple programming exercises.

From Commands to Shell Scripts

Eventually students discover that if they keep typing the same commands, they can put them into a file and run them automatically.

That introduces shell scripting.

A very simple script might perform several operations sequentially.

Perhaps it:

creates a backup directory;
copies important files;
adds the date to a log;
reports whether the operation completed successfully.

Now we have moved from merely using the operating system to programming the operating system's environment.

Concepts such as:

sequence;
selection;
variables;
loops;
input;
output;
automation

suddenly appear somewhere other than Python or another classroom programming language.

That reinforces the underlying principles.

Linux Quietly Runs Much of the Computing World

Another reason students should encounter Linux is that desktop market share gives a rather misleading impression of its importance.

Walk around homes and schools and Windows, macOS, Android, iOS and ChromeOS may appear dominant.

Look behind the scenes and Linux becomes extremely important.

Linux-based systems are widely found in:

web servers;
cloud computing;
supercomputers;
networking equipment;
embedded systems;
development environments;
containers;
scientific computing;
Raspberry Pi projects.

Android itself uses the Linux kernel.

So even a student who never intends to replace Windows on their laptop is likely to encounter Linux somewhere if they continue into computing.

You Probably Already Own a Linux Computer

There is an especially easy route into all this.

A Raspberry Pi.

Raspberry Pi OS gives students a graphical desktop that feels reassuringly familiar while providing the full Linux terminal underneath.

It is an excellent combination.

Use the desktop to browse the web or edit a Python program.

Open a terminal and investigate:

ls

or:

python3

Then connect an LED, sensor or other device and Linux suddenly becomes part of a physical-computing system.

The computer is no longer simply something that runs applications.

It becomes something you control.

You Don't Need to Replace Windows

This is important.

I would not recommend that a student unfamiliar with Linux immediately erase their main computer and install an unfamiliar operating system.

There are much safer ways to experiment.

Option 1 — Use a Raspberry Pi

Probably the most enjoyable educational route.

You have a completely separate Linux computer to experiment with.

If you make a mess of it, you can reinstall the operating system.

That freedom encourages experimentation.

Option 2 — Use a Virtual Machine

Programs such as virtualisation software allow Linux to run as a computer inside your existing computer.

You can experiment without changing your normal Windows installation.

Option 3 — Use a Live USB

Many Linux distributions can run from a USB drive without immediately installing anything on the computer.

Option 4 — Windows Subsystem for Linux

Windows users can also explore many Linux command-line tools through WSL.

For a student primarily interested in programming and command-line skills, this can provide a useful introduction.

A Practical Linux Challenge for Computing Students

Rather than simply installing Linux and looking around, I would turn the exercise into a challenge.

See whether you can complete all of these.

Challenge 1

Find your current directory.

Challenge 2

Create a directory called:

linuxchallenge

Challenge 3

Move into that directory.

Challenge 4

Create a text file.

Challenge 5

Copy the file.

Challenge 6

Rename the copy.

Challenge 7

Display the contents of the directory.

Challenge 8

Run a Python program from the command line.

Challenge 9

Find your computer's IP address.

Challenge 10

Use ping to test another computer or suitable internet host.

Challenge 11

Look at running processes.

Challenge 12

Investigate the permissions on one of your files.

A student who completes those twelve tasks has probably learned more about operating systems than somebody who has merely revised several pages of notes describing them.

A More Ambitious Project: Build a Tiny Linux Server

For an A Level student, I would go further.

Take a Raspberry Pi or an old suitable computer and turn it into a simple server.

It might host:

a web page;
files;
a small Python web application;
sensor readings;
a database;
a home dashboard.

Now the student has to think about:

IP addressing;
users;
permissions;
processes;
services;
ports;
files;
software installation;
security;
networking.

Almost an entire section of the Computer Science curriculum begins joining together.

That interconnected understanding is far more valuable than learning each specification point independently.

The Mistakes Are Part of the Lesson

Anyone learning Linux will make mistakes.

You will type a command incorrectly.

You will try to access a directory that does not exist.

You will wonder why a file cannot be executed.

You will receive:

Permission denied

You will search the internet for an explanation.

You may spend 20 minutes discovering that Linux filenames are case-sensitive and that:

Program.py

and:

program.py

are not necessarily the same file.

This can occasionally be frustrating.

But it is also how a great deal of genuine computing is learned.

You form a hypothesis.

You try something.

It fails.

You read the error.

You investigate.

You modify your approach.

You try again.

That is computational thinking in a much more authentic environment than simply answering a multiple-choice question about operating systems.

Linux Also Teaches an Important Lesson About Choice

There is another reason I like students encountering Linux.

It makes computing feel less fixed.

You can change:

the desktop;
the terminal shell;
the file manager;
the text editor;
the programming tools;
the services running in the background.

You begin to realise that a computer does not have to operate in exactly the way its manufacturer originally presented it.

For somebody interested in Computer Science, that is quite an empowering discovery.

But Is Linux Better Than Windows or macOS?

That is probably the wrong question.

Windows has enormous software compatibility and is familiar to vast numbers of users.

macOS provides an extremely polished environment tightly integrated with Apple's hardware and ecosystem.

Linux provides exceptional flexibility, transparency and access to powerful development and administrative tools.

All three have strengths.

The educational value comes from experiencing the differences.

A student who has only ever used one operating system may mistake its conventions for universal computing principles.

A student who has used several begins separating the concept from the implementation.

That is exactly what Computer Science education should encourage.

Final Thought — Stop Simply Using the Computer and Start Investigating It

There is a moment when learning computing changes.

At first, the computer is something that provides applications.

You open Word.

You launch a browser.

You run a game.

You write a Python program.

But eventually you start wondering what is underneath all of that.

Where are the files actually stored?

Which processes are running?

Who owns them?

How does the machine know where another computer is?

What happens when software is installed?

How can one program send its output into another?

What exactly is the operating system doing?

Linux provides a wonderful environment in which to start asking those questions.

So my challenge to GCSE and particularly A Level Computer Science students is simple:

Spend a little time outside Windows or macOS.

Install Linux in a virtual machine, try it on a Raspberry Pi, experiment with WSL or boot a suitable live Linux system.

Open the terminal.

Type:

ls

and begin exploring.

You may discover that the most valuable thing Linux gives you is not another operating system.

It is a different way of looking at the computer you thought you already understood.