“I think I can safely say that nobody really understands quantum mechanics,” said Richard Feynman in 1964. One year before he got the Nobel prize for his work in quantum mechanics.

What was he referring to and do we still not understand it?

Let’s briefly explore the key ideas of quantum mechanics to set the context and the language for diving deeper.

In quantum mechanics, we need two kinds of information to describe the fundamental particles that make up our universe:

1. Fundamental properties: Intrinsic characteristics that do not change with time – mass, charge, etc.
2. Quantum states: Characteristics of the particle that change with time – position, momentum, etc.

Whenever we interact with a particle in the real world, we always find it in a definite and singular quantum state. This is important to state since there is a lot of confusion about this on the internet.

However, if we want to predict which of the many possible states it will be in, we find it impossible to do so with 100% accuracy. The equations of quantum mechanics can only tell us the probability of finding it in each possible state.

This isn’t strange. There are examples like this even in classical physics. If we want to predict a coin toss before it lands, the best we can do is calculate the probability of getting heads and tails – 50% each.

However, in classical mechanics, this uncertainty comes from not knowing all the information required to predict a coin toss. If we know all the information, it is possible to predict the outcome of a coin toss with 100% accuracy.

This is not the case in quantum mechanics.

In quantum mechanics, we use a mathematical quantity called wave function to calculate the probability of finding a particle in one state or another. Most of our difficulty in understanding quantum mechanics comes from not knowing how to interpret what this wave function means.

But what’s the big difficulty? Why don’t we interpret it the same way as we interpret probability in classical mechanics?

Because some experiments, like the famous double-slit experiment, showed us that different possible states of a particle can interact with each other to change how the particle behaves. In the case of a coin toss, it’s like the “possibility of heads” interacting with the “possibility of tails”, to give you new answers for the probability of getting heads or tails when the coin lands.

But wait, you might say, it’s just a mathematical possibility, not two separate real coins – one head, one tail – how will they interact with each other?

That’s the puzzle.

Almost all the strangeness of quantum mechanics comes from this interaction between “mathematical possibilities” that are not even supposed to be real in classical mechanics.

Why and how do they interact? Does this mean all mathematical possibilities are real in some sense or some other universe?

If they are real, why don’t other limitations that apply to real things apply to them? Like the limitation Einstein discovered – “no information can travel faster than the speed of light between two real particles” which is broken when entangled particles instantly exchange information even if they are light years away.

If they are neither real nor purely mathematical, are they something in between?

I can summarize the answer to all these questions by borrowing Feynman’s words from 1964 – “I think I can safely say that nobody really understands.”

Enter QBism

For the longest time, I struggled to find a clear enough articulation of what QBism says. Eventually, as I understood it better, I came up with one:

QBism argues that a wave function represents the real information we have, as observers, about various possible states of a particle.

To truly understand QBism, we must remind ourselves that no particle (or person) ever finds any other particle in a superposition of multiple possible states in the real world. It only happens in the “mathematical model” we must construct to predict what we may observe in the real world. It’s as if they exist in a superposition of multiple states before we interact with them. As-if.

Most particles in the universe do not go around predicting each other. As far as they are concerned, there’s no strangeness or spookiness in our universe. Every particle always exists in definite and singular states whenever they observe each other.

The only entities that encounter spookiness are the ones trying to predict future outcomes – like us. To do this, they must store and manipulate information somewhere – brains, computers, something else – making it real.

If quantum mechanics describes this real information about particles and NOT the particles themselves, then all the spookiness disappears!

Superposition? Of course, our information about a particle can exist in a superposition of two states. The contradiction disappears.

Faster-than-light communication between entangled particles? No matter how far two particles travel, our information about them remains within us. So, there’s no need for faster-than-light communication to update our information about the two entangled particles. Contradiction disappears.

All we have to give up in exchange is our belief that physics describes objective reality, not just our information about it.

Not Again!

“Not again! 🙄” This is probably what you’re thinking if you’ve read my previous article about Predictive Processing – an emerging view of our brain as a prediction machine that perceives its own predictions as reality.

Well, what can I say? I am obsessed with ideas that question the very nature of reality we perceive (neuroscience) or live in (physics).

If we look at history, every time science seemed stuck and unable to progress, it was eventually set free by ideas that questioned and altered our understanding of the very nature of reality. Why should it be different this time around?

As always, I hope this post sparks enough excitement in you to explore this beautiful idea on your own. And if you ever need another curious mind to give you company on this journey – hit me up! ✨

What happens in our brains when we interact with the world around us? Let’s say when we see an apple in front of our eyes.

In the traditional view, light from the apple produces various sensations in our eyes. Our eyes then send these sensations to different layers of neurons within our brain. Each layer identifies different features of these sensations – colour, shape, movement, etc. Finally, all these individual features are integrated into one singular experience of “seeing an apple”. This is called perception.

Predictive Processing (PP) flips this model. It reimagines our brain as a prediction machine, always predicting its own future. But its future is influenced by various sensations produced in our sense organs when we interact with reality. So, to predict its own future, it must also predict how the outside world will interact with it.

Every time it generates these predictions, it sends them to all the relevant neurons connected to our sense organs. And every time we interact with reality, these neurons check whether the predictions they receive match the sensations produced by reality.

If they match, these neurons stay silent and our brain preserves its beliefs about external reality. These beliefs are called priors. If they don’t match, these neurons send an error signal to the neurons above them in the hierarchy. This is called prediction error. Our brain uses these prediction errors to update its priors and checks if the new priors result in accurate predictions. This process goes on over and over again until all the prediction errors become small enough to be ignored.

This is the view presented by Predictive Processing.

Why is it Revolutionary?

If you compare the two views I shared, you’ll notice something missing in the second picture: perception.

You’ll see that in this new view, our brain can’t perceive any sensations produced by our interactions with reality. Because when our predictions are right, these sensations are not even passed on to other neurons in our brains. And when they are incorrect, only the resulting prediction error is passed on.

So, most regions of our brain only receive two kinds of information:

1. Predictions generated by neurons above them in the hierarchy
2. Prediction errors sent by neurons below them

So our perception can only come from one of these.

Predictive Processing argues that all we ever perceive in life – colours, tones, scents – are the predictions generated by our own brains and NOT sensations produced by the outside world.

It’s like living in a Matrix-style simulation generated by our own brains. A simulation whose purpose is not to give us an accurate picture of our reality, but to correctly predict what sensory inputs are generated when we interact with it.

This is a revolutionary idea with, as we shall see later, far-reaching consequences. But first, how do we know it’s the right idea?

The Clues

Well, we don’t. But several clues are pointing us in this direction. Let’s look at a few interesting ones.

#1 The mystery of our brain’s energy efficiency

You might have heard of AI systems consuming enormous computing power and energy as they are beginning to get smarter. Yet, we all have a brain doing far more complex tasks with just a fraction of the energy.

How is our brain pulling this off? What makes it SO energy efficient?

In 2021, a team of researchers decided to investigate this question by making a small change to a class of neural networks called Recurrent Neural Networks (RNN).

In our brain, the firing of neurons consumes the most energy. So, to minimize energy consumption, we’d have to minimize the amount of firing. In RNNs (and in all neural networks), the amount of firing is influenced by the strength of the connection between its neurons – also called weights. So the researchers forced the RNN to perform its task by simultaneously trying to achieve the smallest possible weights between its neurons.

With just this one extra constraint, the network started organising itself into an architecture where some of its neurons began predicting the signals received by other neurons, which then learnt to fire only if these predictions were wrong. Initially, when the RNN had no data to make accurate predictions, it generated a lot of prediction errors, resulting in a lot of firing. However, as its predictions improved, the firing rates decreased, minimising the energy consumed.

This is a pretty significant clue – forcing regular neural networks to conserve energy naturally results in a predictive processing architecture.

#2 A brain slice that learnt to play a videogame

In a fascinating experiment from 2022, now popular as “Dishbrain”, researchers connected a bunch of neurons grown in a petri dish to a simple video game – Pong, where you control a paddle to hit a ball back and forth.

One part of the brain tissue was fed signals about the ball’s position and the signals from a different part of the tissue were used to move the paddle. In Pong, every time you miss the ball, it resets from a random location. To simulate this, researchers sent random unpredictable signals to the brain tissue whenever it missed the ball.

At first, the brain tissue was moving the paddle in random directions. After all, it was just a small bunch of neurons without the rest of the brain to tell it what to do.

However, over the next few minutes, the brain tissue slowly learned to coordinate its internal electrical activity to avoid missing the ball – sustaining longer and longer rallies with time.

But how? It’s just a slice of brain tissue in a petri dish!

Predictive processing has no issues explaining the results of this experiment. Each layer of neurons is always trying to predict the electrical activity of the neurons in the layer below. Every time the paddle misses the ball, it receives a random unpredictable signal that generates a prediction error. In response, the neurons reorganize their connections to minimize prediction errors in the future. Many rounds of reorganization later, the brain tissue ends up getting connected in a way that minimizes missing the ball.

Now, alternatives to predictive processing can explain the results of the Dishbrain experiment only if we assume that random unpredictable signals somehow count as “punishment” and predictable signals count as “reward”. Yet, they offer no real reason for why this should be the case without invoking predictive processing.

#3 Natural emergence of Self and subjective experience

One of the biggest mysteries of neuroscience is explaining how a bunch of neurons can give rise to the rich subjective experiences of our mind.

Let’s take the colour red. Physics tells us that each colour is related to the wavelength of light that hits our eye. A camera and our eye will both receive the same wavelength.

Yet, we perceive “something more” when we perceive red. After all, a colour-blind person sees the same wavelength but perceives a “redness” that is different from what others perceive. This “something more” is called qualia. It’s the redness of red and the sweetness of sugar. Qualia forms the building blocks of our subjective inner reality.

While many efforts are underway to understand how our brain produces qualia, predictive processing offers the simplest explanation among its rivals.

In predictive processing, the actual wavelength of red light is immaterial to our perception since we never directly perceive the sensations produced by it. Instead, our perceptions come from our brain’s predictions. These predictions are inherently subjective since the brain activity that helps me predict the sensation of red light might differ from the brain activity that helps you predict the same sensation. Thus, qualia.

By the same logic, our brains don’t directly perceive the signals from our own bodies either. Instead, we only perceive the models or simulations our brain creates to predict incoming signals from our bodies. The same goes for our brains. Some regions predict the electrical activity of others. These predictions become the basis for our subjective sense of “self.”

So much explanatory power emerges from a simple assumption: our neurons don’t transmit raw signals, but only the prediction errors that arise from those signals.

Of course, this simplicity alone doesn’t make it right, but it does make it an appealing idea to explore, no?

The Challenges

As you would’ve guessed, we’d already be hailing predictive processing as the new default of neuroscience if it didn’t have challenges waiting for a fix. Let’s look at them in this section, hoping that they may inspire us to attempt solving them.

⚠ Lack of testable predictions

The biggest challenge and opportunity for predictive processing is to define clear, testable predictions that uniquely arise from predictive processing, distinguishing it from other theories. Without such predictions, critics argue that it is still too broad and general, maybe even unfalsifiable in its current form.

⚠ Anatomical implementation

While we now have computational models to implement predictive processing, we still lack a clear explanation for how it may be implemented within the brain. Most of its explanatory power comes from a “black box algorithm” level of understanding.

In its defence, none of its alternatives are any closer.

⚠ Mechanism for Active inference

Active inference, a key component of predictive processing, suggests that organisms don’t just update their priors to reduce prediction errors but also take actions to fulfil predictions (e.g., moving a finger to confirm a prediction that “my finger will move now”). How this can happen remains unanswered.

⚠ Integration with well-established cognitive principles

Predictive processing is yet to be integrated with other well-established cognitive principles, such as reinforcement learning (learning by trial and error with rewards), Hebbian learning (neurons that fire together, wire together) and embodied cognition (cognition is not just something the brain does, it’s something the whole body does).

The Possibilities

Despite its challenges, predictive processing holds the potential to one day become the default theory of how our brains work. Here are the possibilities I’m most excited about –

✦ Unified Framework for Cognition

Starting with just one assumption that our brain operates on the principle of minimizing prediction error, it provides a unified account of how various features of our mind emerge – from perception and action to learning and even higher-level processes like decision-making and consciousness.

If we can develop this into a full-fledged testable theory, it might someday emerge as the “theory of everything” of neuroscience.

✦ A Fundamental Connection with the Physics of Life

The predictive processing framework aligns with the Free Energy Principle, a mathematically grounded theory that explains how biological systems maintain order and resist entropy. This alignment gives it a solid theoretical foundation and connects it to broader principles of thermodynamics and information theory, giving it a strong headstart in explaining why it evolved in the first place.

✦ A New Way to Understand Neurological Conditions

Predictive processing provides new ways to model and simulate several neurological disorders by framing them as disruptions in the brain’s predictive mechanisms. For example, conditions like schizophrenia, autism, and anxiety disorders can be understood as resulting from imbalances in prediction and error correction, offering new avenues for treatment and intervention.

✦ A Gateway to Artificial Sentience

By providing a computational model for how subjective experiences could emerge as a natural side-effect of minimizing prediction errors generated by signals from objective reality, predictive processing opens the door for creating artificial systems with subjective experiences, a sense of self, and possibly even consciousness.

Closing Thoughts

Over the years, I have implemented several forms of predictive processing models: black box algorithms, neural networks, models with altered parameters that successfully reproduce several symptoms of autism and schizophrenia, models that failed at reproducing symptoms of ADHD, anxiety and depression…

It has been an exciting journey, peppered with moments of deep frustration when facing its limitations. But I can confidently say that the deeper I’ve dived into this framework, the more I’ve come to bet on its potential.

So, I hope this post sparks enough excitement in you to explore this beautiful idea on your own. And if you ever need another curious mind to give you company on this journey – hit me up! ✨

Historically, researchers believed that our eye temporarily stops processing visual signals when we move our eyes (our eye moves in rapid jerks called ‘saccades‘) to avoid seeing blurry images of the world around us every time we shift our gaze. This was called ‘saccadic blindness‘. However, a clever experiment showed that we indeed process retinal images even during a saccade. A series of vertical lines on a screen was made to move horizontally at speeds fast enough to make them invisible to the naked eye. However, when the viewer moves the eye in the same direction as the movement of the lines, they can temporarily see the lines since the relative speed between the moving eye and the moving lines is reduced. This experiment showed that the brain is processing the images falling on our retina even during a saccade, but somehow suppressing them unless they’re clear – ‘saccadic suppression’. If the brain’s intent is to avoid processing blurry images, then why don’t we suppress blurry images resulting from external motion? When we’re looking out of a moving train, for example.

In a 2016 paper titled ‘Neural mechanism of saccadic suppression’, the authors discuss their experiment to identify how our brain’s perception of retinal images change during a saccade, specifically in the middle temporal (MT) and the middle superior temporal (MST) cortical areas of the brain. They found that about 66% to 68% of the neurons showed significant differences in the way they process retinal images from a saccade-induced motion vs externally-induced motion (like a train journey). The remaining neurons either didn’t respond to high-velocity image motion at all or responded equally well in both cases. What surprised them is a saccade-induced reversal in the behaviour of a significant percentage of neurons from the former category. Neurons that lit up when they identified left-to-right motion of an externally-induced image were lighting up when the images moved right-to-left during a saccade! Further, this reversal started about 70ms before the saccade began, indicating that there’s some top-down intervention responsible for saccadic suppression that is linked with the brain’s decision to initiate a saccade in addition to whatever effects the actual movement of the eye may be contributing.

What could be influencing this alteration in the way our brain processes the signals from our eye during a saccade? The clue lies in the observation that this alteration starts about 70ms before the actual saccadic movement begins. What comes just before a saccadic movement? To answer this question, let’s take a small detour into the fascinating world of electric fish.

Mormyrid fish detect their prey by using electroreceptors on their body to sense small electric fields generated by their prey. However, this is a tricky business for the Mormyrids because they themselves repeatedly generate large electric pulses for navigation and communication (known as Electric Organ Discharges or EODs). These EODs activate its electroreceptors, interfering with the much weaker electrical fields generated by their prey. How then do they go about detecting their prey without confusing themselves all the time? The authors investigate this question using an elegant and elaborate setup.

First, they figure out how to record electrical activity from the neurons in the fish’s electrosensory lobe (ELL), the region within the fish brain where signals from its electroreceptors first get processed. Then, they manage to mimic the fish’s EODs by placing a small electric dipole within the water and near the electroreceptors on its scales. Then, they paralyse the muscles which generate EODs without interfering with the fish’s ability to send commands to these muscles. They also figured out how to tell when a fish was sending a command to discharge EODs. Lastly, they generate fake prey-like electric fields within the water to see how it’s processed by the Mormyrid ELL.

What they found is that the fish’s ability to detect the fake prey reduced drastically whenever they sent an artificially generated EOD that was not synced with the fish’s command. However, the fish regained its ability for prey detection the moment they synced their EODs to predictably follow the fish’s command. Their best guess? Whenever the fish sends out a command to generate an EOD, it also tells the neurons in its ELL to filter out the electrical signature of its own EOD from the total signal received at its electroreceptors. Think of it as a negative image of its own EOD’s electrical signature that gets sent to the neurons in its ELL. When this negative image gets added to the total signal coming from its electroreceptors, it filters out the EOD from the electrical signature generated by external electrical activities in its environment (like the presence of a prey). This is akin to how your active-noise cancellation headphones work.

If this is how electric fish cancel out the sensory signals resulting from their own actions, could this be something our brain does too? The answer is yes.

Every time we initiate a movement of any kind, our brain sends a copy of that command (called an “efference copy“) to all the relevant regions of the brain that are involved in sensory perception. Why? To help them predict and adapt to the incoming signals generated from our own actions. Such information about our own commands for action plays a different role in prediction-based models when compared to extraction-based models of the brain.

In extraction-based models, such information helps our brain identify which component of the incoming sensory signal must be attributed to the consequences of one’s own actions. The primary benefit of this information is accurate attribution. This, as we’ll see later, is the precursor to our sense of self, at least of one kind (there are several kinds of self we possess).

On the other hand, in prediction-based models, such information helps our brain predict future sensory signals that are a direct consequence of our own actions. The primary benefit of this information is the accurate processing of cause and effect. This is the precursor to our sense of agency. The feeling of being the cause of actions or free will.

Does this mean our sense of self is stronger in systems of the brain that employ extraction-based models? Conscious thinking, for example. On the same lines, is our sense of agency stronger in systems that have prediction-based models (like perception)? Is it possible to have one without the other if we can find an activity that exclusively uses systems that are extraction-based or prediction-based? How can we test these predictions? More on these later.

In predictive coding (PC), the primary role of our brain is not extraction of information from external signals, but to predict the information in future signals. In this model, our brain first tries to predict exactly what signal might be received by its neurons. Then, it uses the actual signals from our sense organs to validate or invalidate this prediction. If the prediction turned out to be correct, it’ll strengthen the assumptions on which the prediction was made. If the prediction was incorrect, it’ll weaken those assumptions. The collection of assumptions based on which we make our predictions can be called our brain’s internal model of the world. As we interact more and more with the world around us, this internal model becomes better and better at predicting the features of our world.

This distinction between extraction-based and prediction-based models of the brain might seem trivial and mechanical at first. However, it makes a world of difference once we start digging deeper. The root of all those differences is this insight:

An extraction-based model will be limited to perceiving what is already there in the signals coming from the outside world. However, a prediction-based model can generate perceptions that have no basis in reality as long as they help us predict the observable consequences of reality.

Let me give you an example –

In this image, you can see a bunch of 2D circles that appear either to be bulging out of the paper or to be dented inwards. If you observe them closely, the dented circles are simply an upside-down version of the bulging-out circles. Why should our brain perceive a shape drawn on a 2D paper to extend into the third dimension? Why should this extension into the third dimension change from outwards to inwards upon rotation? More interestingly, why can’t we unsee this projection into the third dimension despite knowing that the shape is drawn on 2D paper?

We’ll not see such illusions in a purely extraction-based model of processing signals from the external world. These are the side-effects of a prediction-based model. The prediction of a bulged or dented 3D object in front of our eyes will NOT get invalidated by the signals falling on our eyes. At the same time, since we live in a world where we’re more likely to see 3D objects than 2D objects, such a prediction is less likely to get invalidated by future experiences than the prediction that the signals are coming from a 2D shape artificially darkened in just the right way to produce this illusion. So our brain picks this generic and more likely prediction over a specific and less likely prediction even when we intellectually know that such a prediction happens to be wrong in this case.

But wait, you might stop me, our intellectual knowledge is also produced by the same brain that produces perception. Then, why isn’t it influenced by these quirks of a prediction-based brain design?

It is this question that led me away from pure models of the brain to impure ones – where our brain is neither purely extraction-based nor purely prediction-based, but a combination of systems. Some of these systems happen to be purely prediction-based – all systems involved in perception, for example. Other systems like conscious thinking seem to be purely extraction-based. Further, it seems, that these two kinds of systems don’t talk to each other as often as they talk to systems of the same kind. Are there systems that are partly prediction-based and partly extraction-based? They are beginning to pop up. The first such theory was published in April 2022.

Despite countless hints that our brain is an amalgamation of both these kinds of systems, almost all researchers are focusing their attempts on pure models. This is probably stemming partly from their need to keep our models of the brain simple and clean. However, if there’s one thing we know about evolution – it’s messy, it’s hacky and it’s opportunistic. Simplicity might be too lofty a burden to place on a brain that is a product of such evolutionary processes. Borrowing Einstein’s words to frame my criticism of this bias – “Everything should be made as simple as possible, but no simpler.” A second, stronger reason for ignoring hybrid models might be that there’s still so much left to be understood in each of these models that it’s still too early to dive into hybrid models. To someone holding this point of view, a hybrid model might seem too lazy. It might seem like settling for a hacky explanation before testing the limits of more elegant alternatives. To be honest, I see the merit in this point of view. However, I’m beginning to silence my own criticisms in this matter since I believe at the end of the day, nature doesn’t care about elegance any more than it cares about simplicity.

In the following series of posts, we’ll dive deeper into both these kinds of models of the brain as well as explore what hybrid designs of the brain might look like. Let’s get started!

• Genetics: which genes are present
• Epigenetics: which of the present genes are switched on/off 
• Cellular health and nutrition: which raw materials required to build proteins are present in the cell

One such gene, the DRD4, codes for proteins that sit on the cell membrane of a neuron and influence how the neuron interacts with dopamine that is released into their synapse (the narrow space between the communicating ends of two neurons) by another neuron or by itself. Such proteins are called dopamine receptors and they can be excitatory (makes the neuron more likely to send out a signal) or inhibitory (the opposite). The DRD4 gene codes for D4 – an inhibitory receptor.

Sidenote: The most common dopamine receptor in the brain is D1, which is excitatory. It is followed by D2, D3 (both inhibitory), D5 (excitatory), and lastly, D4.

Wait, you might ask, isn’t the whole function of a neuron to transmit signals it receives from other neurons? What’s the point of receiving a signal if it is not going to be transmitted further? Such a question will eventually lead you to a fundamental discovery about neurons:

A neuron’s primary job is not to transmit signals, but to decide if a signal must be transmitted.

There are 2 sources of information (or electrical inputs) a neuron can use to make such a decision:

1. Real-time stimuli from the outside world, relayed via our sense organs
2. Stored memories from past experiences

In the simplest version of how a neuron makes this decision, it simply adds up all the signals telling it “fire!” as well as the signals telling it “don’t fire!”. If the resultant signal says “fire!” strongly enough to initiate an outgoing electrical signal, it fires. Else, it doesn’t.

Now, coming back to the DRD4 gene – it comes in various flavours. Each flavour, called an allele, differs in how many times the specific sequence of nucleotides (that are the signature of DRD4) are repeated within the gene. Most people have the 4R allele in which the sequence repeats 4 times. However, 7R and 2R alleles have also been growing in popularity over the last 40,000 years of human evolution.

Curiously, the 7R allele is twice as common in individuals with ADHD when compared with the overall population, at least amongst white people.

This has led to a lot of attention being given to the role of the 7R allele in explaining the symptoms of ADHD. The 7R allele has also been shown to have a higher prevalence in people with substance abuse issues, novelty-seeking behaviour as well as antisocial personality traits (again, the studies were mostly focused on white people).

Is there any merit to these associations or are they just a case of correlation being confused with causation? Let’s try to predict how the 7R allele might affect a person’s day-to-day life so that we can come to our own conclusions on this matter.

Sidenote: Interestingly, while 48% of Americans have DRD4-7R, only 2% of Asians have this allele.

The 7R allele, by virtue of a small change in the spatial geometry of the D4 receptors it produces, happens to be less efficient in its task of inhibiting a neuron than the 4R allele (only half as efficient in lab conditions). Let’s first look at how it affects the individual neurons carrying the D4 receptors.

Normally, when the D4 receptor binds with dopamine released in its vicinity, it ends up inhibiting the action of another enzyme connected to the neuron’s cell-membrane – Adenylyl cyclase, which converts ATP (your cell’s energy reserves) into a messenger chemical called cyclic AMP or cAMP. As a result, whenever dopamine arrives at the D4 receptor, it reduces the number of cAMP molecules within the cell body of the neuron (in comparison with normal levels).

But what does cAMP do?

1. Neuronal firing: Loosely put, more cAMP = more likelihood of the neuron firing. i.e. more excitable neurons. (That’s why D4 receptors reduce cAMP levels to inhibit the neuron)
2. Processing glucagon and adrenaline: It helps cells process hormones like glucagon (converts glycogen and fat into glucose, increasing the amount of energy available for burning. i.e. metabolism) and adrenaline (prepares the body for rapid response to threats by activating our fight-or-flight response or stress response).
3. Facilitating long-term changes to the neuron: It helps in the transcription of a bunch of other proteins within the cell that aid in a number of processes including long-term memory formation, synaptic plasticity as well as time-keeping.

Since the 7R allele is less effective in reducing the number of cAMP molecules within the neuron, all of these processes become amplified compared to neurons with the 4R allele. i.e.

1. Neurons with D4 receptors become more excitable than they should be under the inhibitory action of the D4 receptors. In other words, less inhibited than they should be – under-inhibited.
2. Dopamine-based control of metabolism works less efficiently, making cells process more than normal amounts of glucagon. This might lead to neurons with D4 receptors running out of energy quicker than usual.
3. Dopamine-based control of stress response works less efficiently, making cells stay in stress response for longer than normal. This might lead to stress-response mediating neurons with D4 receptors over-activate stress induced behaviours.

Sidenote: There is an exception to the general trend. In some neurons, the D4 receptors are present on the presynaptic neurons too (in much lesser numbers than on the postsynaptic neurons) to help them self-regulate the amount of dopamine they release by inhibiting their own continued activation. In such cases, the neurons end up releasing more dopamine than normal due to inefficient self-inhibition. Depending on whether the postsynaptic neurons interacting with such neurons have excitatory or inhibitory dopamine receptors, the end result on them varies from over-excitation to over-inhibition instead of under-inhibition.

If there’s one thing our brains are really great at, it’s adapting to new challenges – both in the external environment as well as in our internal environment. Naturally, all of these changes brought about by the 7R allele are met by adaptation processes that try to counter these effects. This adaptation can happen locally at the level of individual neurons or globally at the level of the individual’s behaviour.

Local adaptation can happen in 2 broad ways that compensate for the inefficiency of the D4 receptors:

1. Increase the amount of dopamine available to the D4 receptors: This involves epigenetic changes to the presynaptic neurons that might make them –
   • Release more dopamine than normal, and/or
   • Slow down the rate at which neurons reabsorb the dopamine released by them (dopamine reuptake), thereby prolonging the duration for which it stays in the synapse
2. Increase the number of D4 receptors on the postsynaptic neurons: This involves epigenetic changes to the postsynaptic neurons that might increase the number of D4 receptors present on their membrane. If there is a frequent occurrence of more dopamine in the synapse than what the receptors can process, then this triggers an epigenetic process that produces more receptors in response to this excess dopamine. (However, if the excess dopamine exceeds a particular threshold and stays high for extended periods of time, then the postsynaptic neuron ends up going through epigenetic changes that reduce the number of receptors on its surface – creating the opposite adaptation.)

Sidenote: Since the inefficiency arising from the 7R allele primarily affects the postsynaptic neurons, they are more likely to go through these epigenetic changes than the presynaptic neuron.

Global adaptation, on the other hand, can occur in ways as diverse as the individuals possessing the 7R allele. These arise from the brain changing its overall behaviour and personality to compensate for the inefficiencies brought about by the 7R allele. We can bucket them broadly into 2 categories:

1. Strong preference for being in environments that are rich in dopamine-triggering stimuli. While novelty and rewards (especially social rewards since they are inherently variable in nature and variable rewards trigger more dopamine release than predictable rewards) are probably the most popular dopamine-triggering stimuli, risk and uncertainty are equally powerful in triggering dopamine release. This might be the reason why novelty-seeking and risk-taking traits are more common in individuals with the 7R allele. They are also more likely to thrive in environments that offer frequent opportunities for social interactions – making them prefer such contexts.
2. They are more likely to develop an attraction to substances that increase the amount of dopamine available to the postsynaptic neurons. Most addictive drugs come under this category – increasing the prevalence of substance abuse in such individuals. Interestingly, even most ADHD medications work either by artificially increasing the amount of dopamine released by the presynaptic neurons or by blocking dopamine reuptake to various levels.
3. Greater affinity to glucose-generating sources of food, from sugars to simple carbs, to compensate for glucose overconsumption by cells. This might manifest as frequent cravings for high-carb foods. This also suggests that maintaining a regular inflow of glucose-rich foods in tiny quantities might help improve mental performance.
4. They’re oversensitive to long-term effects of stress and might display symptoms of burnout and chronic stress even under moderate levels of stress. This might lead them to avoid stress-inducing experiences at all cost. At the same time, they are better at dealing with stressful situations in the short-run since they can mobilize their stress-response systems faster than others. This might make them chase stressful situations if they are guaranteed to be short-lived.

Which of these local and global adaptations manifest in an individual is largely determined by their life experiences, especially from childhood and adolescence. In fact, studies have shown that kids and teenagers who have access to healthy sources of dopamine – like positive social interactions, warmth and affection from parents – tend to exhibit less of these adaptations. Further, since every individual with the 7R allele is likely to evolve a unique combination of these adaptations based on their unique life experiences, the impact of this allele will also be very different in different individuals.

What happens when these adaptations fail? How will the 7R allele affect an individual’s day-to-day life? We can anticipate some of these effects using what we’ve discussed so far about the role of D4 receptors:

1. Dopamine-mediated processes show various levels of dysregulation based on the extent to which D4 dopamine receptors are involved in them. This includes attention, motivation, cognitive control, working memory, learning, and motor functioning. Dysregulation doesn’t necessarily mean a deficit. Instead, it fluctuates between hyper and hypo states in a way that’s not under the control of the individual. Hyper-performance in these areas generally requires an environment rich in dopamine-inducing stimuli – novelty, risk, uncertainty, and rewards.
2. Because of cAMP’s effect on glucose processing, they are more likely to have higher than average levels of metabolism. They can lose weight easily and struggle to gain weight. They are also more likely to be physically active, which in turn increases their likely lifespan (by as much as 10% in some studies). They may also be more likely to chase sugar or simple carbs since they are easy sources of glucose. This craving combined with poor impulse control may make them susceptible to overeating disorders – which might affect their metabolism and weight.
3. They’re likely to have a faster response to adrenaline-inducing experiences due to increased sensitivity to glucagon and adrenaline, making them better at dealing with such situations for short durations. However, if the effects of adrenaline extend longer, the negative effects might accumulate very quickly. This might result in a bipolar pursuit of stress-inducing experiences – alternating between chasing them actively and avoiding them at all costs.
4. Their long-term memory is biased strongly in favour of storing and recalling negative experiences. Even individuals with the 4R allele display the same bias, but it is further enhanced in those with 7R. Further, owing to difficulties with working memory and attention, they may prefer simplistic interpretations of the world around them as opposed to complex ones. This may come across as seeing the world in black and white, with black being more prominent than white.
5. Dopamine-rich, low-stress environments prove highly advantageous to individuals with the 7R allele, especially during childhood and adolescence. Low-dopamine, high-stress environments, on the other hand, end up being more detrimental than it is for individuals with the 4R allele. This amplifies the consequences of both good and bad upbringing on their adult life. They’re also more likely to chase dopamine-rich environments or avoid high-stress environments even when the costs seem high to those with the 4R allele. As a result, they’re more prone to novelty-seeking, substance-abuse as well as quitting stressful situations.
6. Rewards/threats need to be very high in order to result in effective learning of associated behaviours. This is because the strength of learning needs to be strong enough to regulate future behaviour despite a weaker inhibitory action of D4 receptors. On the other hand, low rewards/threats are practically the same as no rewards/threats since the level of neural activity produced by them may not be sufficient to cross the threshold for inhibitory action. As a result, they may be bad at picking up social norms and practices with low consequences compared to their peers with the 4R allele (a lot of adulting depends on such practices). At the same time, they may be better than their peers at picking up social norms and practices that have very high consequences, resulting in a “sharp-around-the-edges and flat-everywhere-else” kind of personality. They may also have difficulty mustering enough motivation to function normally in low-reward or even average-reward environments. This is very similar to Dr. Russel Barkley’s views on ADHD –
   

   “If you want to make an individual with ADHD fail, keep them in an environment with no (or low) consequences”

7. In the romantic domain, reducing the number of D2-like inhibitory dopamine receptors (of which D4 is one) before two people have fallen in monogamous love (or to use the technical term, pair-bonding) reduces the chances of falling in love i.e. D2-like receptors are involved in forming pair-bonds. On the other hand, reducing D2-like receptors after they have fallen in love makes them less likely to cheat (probably because it reduces the chances of falling in love with someone else). At the same time, increasing D1-like receptors (excitatory) after they have fallen in love increases the likelihood of positive, pleasurable associations with the relationship and hence increases the chances of sustaining their love (might also be the mechanism behind the love haze period of love where everything about the other person appears amazing). As an extrapolation of this study, an already inefficient D4 receptor system might make it harder for people to fall in monogamous love. Further, a higher ratio of D2 to D1 receptors, in some studies, has shown a greater likelihood of stable relationships. This suggests that it might be harder for 7R allele carriers to sustain stable relationships.

Lastly, we’re gonna look at the applications of all this knowledge about the DRD4 gene and its 7R variant. Is there anything individuals with the 7R allele can do to enhance its advantages as well as protect themselves from its disadvantages? It’s hard to answer this question since genes are influenced by a million other things in the real world – from life experiences to diet and lifestyle. However, we can evolve a broad framework to experiment with likely hacks that may work well for us – healthy ways of maintaining optimum levels of dopamine as well as stress:

1. Ensure we have access to enough healthy sources of dopamine – so that the temptation to seek out unhealthy sources in times of need is reduced. A few common healthy sources:
   • Dopamine rich foods – eggs, lean meat, diary, almonds, walnuts, salmon, mackerel, bananas, dark chocolate, berries
   • Positive social interactions (since every interaction with real people is unpredictable, it usually triggers dopamine more reliably than non-social activities)
   • Harmless avenues for exploration like travelling or frequently exploring new projects, books, events
   • Positive channels of risk, uncertainty and rewards like social games (boardgames, mafia, chess, etc)
   • Frequently learning something new whenever your motivation is high enough to make an effort (learning something new is usually accompanied by dopamine release)
   • Physical activity, even short and mild versions of it can give a temporary boost of dopamine
2. Ensure we have access to healthy sources of stress – they usually tend to be short-lived, moderate and occur in environments where we feel safe to reduce the temptation to chase unhealthy sources. A few examples:
   • Games and sports
   • Performance arts
   • Adventurous activities
   • Meeting new people in safe settings
   • Competitions and contests we find interesting

As is often the case in matters that involve dopamine or stress, the hardest trick to learn is saying no. Saying no to experiences that we know are unhealthy is as important as being aware of the healthy alternatives – saying no to unhealthy sources of food, sex, social interactions, risky gambles or substances.

Finally, one big disclaimer: a single gene can never define who you are. So I urge you to take everything I have discussed about the DRD4 gene with a pinch of caution. Although I’ve tried to be comprehensive in my analysis of how the 7R allele of this gene might influence an individual, the extent of influence in different aspects of life can vary a lot from one individual to another. The best use of the information discussed in this article is not to evaluate whether you have the 7R allele or not, but to understand how various dopamine-dependent systems affect our life so that we can deal with any ill-effects with greater awareness and confidence.

Good luck! 🙂