Chapter 38: Evolution of Cognition: A 4E Perspective

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Welcome back to the Deep Dive, where we sift through the most dense, complex ideas and extract the crucial mind -expanding nuggets you need to be truly well -informed.

Today we are undertaking a truly fundamental task.

We are not just analyzing the mind.

No, not at all.

We are challenging the very definition of what the mind is and I guess where it even resides.

That's exactly right.

This deep dive focuses on the evolution of cognition, but specifically through the lens of the 4E framework.

It's embodied, embedded, inactive, and extended cognition.

Precisely.

Our source material for this is a chapter that delivers a really radical critique.

It argues that for a long time, traditional science has been trapped in an anthropocentric or a human -centered idea of intelligence.

So the mission today is pretty clear.

We need to understand exactly why that traditional computation -based model just falls apart when you confront it with the full spectrum of evolutionary history.

And then once we've done that, we're going to build up a new model, a powerful bottom -up model.

What the chapter calls a biogenic framework.

Right.

One that understands cognition as arising not from our massive human brains, but from the most fundamental organization of life itself.

The core problem we're facing seems really simple on the surface.

How and why did cognition evolve?

But it's deceptive.

You really can't answer that question until you first decide what even qualifies as cognition.

Sure, because if your starting line is, say, human language and symbolic reasoning.

Then you're only ever going to find intelligence in humans or the species that look the most like us.

So we need to find the minimal criteria,

the lowest possible bar you have to clear to be considered a functioning cognitive system.

Exactly.

Where does mine begin?

OK, so let's unpack that classical view first, because it's the foundation that this whole 4E perspective is, well, rebelling against.

It's a view that really took shape during the cognitive revolution back in the mid -20th century.

It was formalized by thinkers like Ulrich Neisser in 1967.

And what was his definition exactly?

Well, Neisser defined cognition as the processes by which sensory inputs are transformed, manipulated, reduced, elaborated, stored, recovered, and eventually lead to motor outputs.

OK, that sounds pretty comprehensive.

It is.

But the critical and often totally unstated assumption was that all of these processes take place solely inside the brain.

Right.

And you can't separate that from the historical context, can you?

The cognitive revolution was happening at the same time as the rise of computers.

Exactly.

The brain became the hardware and cognition was the software.

It was seen as a series of algorithms or, you know, computational transformations being applied to data.

This analogy, this metaphor, it made the brain the undisputed solitary seat of intelligence,

everything else, the rest of the body, the world.

They were just relegated to being mere inputs and outputs.

The body was just a sensor suite and a motor for the real show happening in the head.

And because this whole model was based on understanding or replicating human intelligence with computers, it's inherently anthropocentric, places human capabilities at the very center of the universe of thought.

So when we talk about truly cognitive processes in that classical sense, what are we actually talking about?

We're talking about a very specific and I'd say human peculiar list of abilities, things like reasoning, concept formation, perspective planning, language,

and crucially, the ability to represent objects or ideas in their absence.

This creates a huge bias, which the source material calls the anthropogenic approach to evolution.

Right.

You start by looking at your current complex human self.

You decide that things like theory of mind or language are the absolute hallmarks of cognition.

And then you work backward.

You start hunting through the biological past, trying to find the story that accounts for these human traits, basically looking for little versions of yourself and other species.

And this just causes complete intellectual chaos in fields like comparative psychology.

How so?

Well, the entire discipline gets stuck fighting this endless battle to sort the animal kingdom into two camps.

You have the sophisticated creatures, the ones capable, flexible, human -like thought.

And then everyone else, the simple creatures only capable of what instinctive or associative behavior.

Exactly.

And the core issue, as one author noted, is that there's just no consensus on what criteria actually lets you call something a cognitive process.

We point to the human list logic language, but we struggle to draw a clear line.

We can't explain why those processes count, but other complex adaptive behaviors like, say, a bee's navigation don't.

It creates this continuous,

exhausting debate.

Is this complex behavior we're seeing in an animal a result of a true cognitive process, or is it merely associative learning?

And the source points out that even the leading textbook definitions undermine this whole distinction.

They do.

Someone like Shuttleworth, for instance, defined cognition really broadly as any process by which animals acquire, process, store, and act on information from the environment.

Well, if that's your definition, then the whole debate is kind of pointless, isn't it?

It really is.

That definition captures everything from a simple Pavlovian reflex all the way up to abstract reasoning.

So the traditional framework just doesn't have the vocabulary.

It can't discuss the adaptive abilities of simple creatures without forcing them into these awkward, human -like mental boxes.

And since defining these processes is so difficult, evolutionary accounts often take a shortcut, an easy way out.

The proxy.

The proxy.

Brain size.

Researchers focused on this metric because, well, it's easy to measure, and you can even apply it to fossils.

And it feels intuitive, right?

We look at the animals we think are obviously cognitive,

humans, apes, dolphins, and we see they have large brains relative to their body size.

So the intuition goes, brain size must be an accurate proxy for cognitive abilities.

It feels logical.

By this definition, a bacterium with no nervous tissue at all simply cannot be cognitive.

Case closed.

But this simple equation, brain equals cognition.

It gets really unstable when you test it against the sheer ingenuity of natural selection.

It completely demolishes the metric.

Think about single cell bacteria.

They have no nervous tissue whatsoever, yet they display this remarkable behavioral flexibility.

They adapt to complex environments.

They find solutions.

They avoid threats.

This is where we get to my favorite counter example.

Yeah.

The salt acid spiders, the jumping spider.

Ah, yes.

They have brains the size of a poppy seed.

I mean, truly minuscule.

And yet there's research showing they execute these incredibly complex, flexible hunting tactics.

And we're not talking about simple leaps here.

Not at all.

They engage in deceptive mimicry to distract their prey and they perform these long complex detours.

Can you explain what a detour involves in this case?

It's amazing.

The fighter will move completely away from its visible prey, sometimes losing sight of it entirely.

It will follow a complicated route over several obstacles, only to reappear much later in the absolute perfect position for an ambush.

To do something like that, you have to hold the goal in your memory.

You have to plan a complex path that actually moves away from the goal, execute all these intermediate steps, and maintain focus without continuous sensory input.

The fact that a brain the size of a poppy seed can manage that degree of flexible, forward looking behavior,

it just completely invalidates the idea that brain size is a necessary or reliable proxy for advanced cognition.

And even with the big brain species, the evidence for mapping smart behavior onto specific brain structures is incredibly complex and contested.

The central assumption might hold, but the details are a mess.

So if the classical view anthropocentric computational brain, if it fails the test of evolutionary reality, we have to shift our entire foundation.

And this is the exact moment the 4E framework embodied, embedded, and active extended steps in to offer a coherent alternative.

The 4E view forces us to acknowledge that the brain can't be divorced from the body or the environment.

Exactly.

It's not a remote CPU operating in a vacuum.

This shift offers what we call the biogenic perspective.

We stop searching for human precursors and we start focusing on the core principles of biological organization and how they link to fitness and survival.

The question fundamentally shifts from is this creature a simple version of a human to what is cognition at its most fundamental life sustaining level?

It's an approach that tries to see life and mind from the ground up.

So when we move away from that brain in a VAT computation model and focus instead on functional definitions, what cognition actually does in the world, we immediately start to blur that sharp line between cognitive and non -cognitive systems.

OK, let's nail down some of those functional definitions.

One author, Leon, defined cognition as the capacity to infer relations between external circumstance and internal need to facilitate agency.

Which, to put it simply, just means enabling successful action in your little corner of the world, your niche.

Another definition from Anderson is even simpler.

Cognition is just situated activity.

And crucially, neither of those definitions requires a complex brain or internal symbolic thought.

Not at all.

And this brings us to the really monumental work of the robotics pioneer, Rodney Brooks.

Brooks famously rejected the idea that you need representation and reason for intelligent behavior.

Yeah.

That was the cornerstone of classical AI.

Right.

And his work in robotics was the proof.

He showed that robust adaptive behavior could emerge from very simple, tightly coupled perception action mechanisms.

They were directly linked to the environment.

His robots didn't have a central processing unit.

No complex internal maps of the world.

And yet they were adaptive and functional.

This concept, often called intelligence without representation, is profoundly important.

Because it reframes what intelligence even is.

Exactly.

It argues that intelligence is a relational property.

It's not something stored in you.

It arises from the dynamic relationship between your physical body and its operation within a specific environment.

The intelligence emerges from the interaction.

And Brooks put this fascinating evolutionary spin on this that really challenges our deepest biases.

He did.

He argued that the so -called primitive, non -representational forms of intelligence, the basic perception and action loops refined over four billion years of life, those must have been the hardest things to evolve.

That's a massive evolutionary hierarchy reversal.

It really is.

We're conditioned to see language and logic and abstract planning as the pinnacle, the hardest skills to achieve.

And Brooks is saying no, those human specific, highly representational processes must have been relatively easy to set in place later.

Because they were built on this incredibly robust, resilient foundation of non -representational sort of insect level intelligence.

Hold on.

I need to press on that.

Is he really saying that building a self -repairing adaptive bacterium is fundamentally harder, evolutionarily speaking, than, say, writing a Shakespearean sonnet?

How do you even quantify that hardness?

You quantify it by looking at time and foundation, the computational complexity required to achieve just basic biological self -maintenance, navigation and repair, the functions that let an organism persist that took billions of years to perfect.

So Brooks was trying to get AI to move beyond what he called the final 3 % of human cognition, the language and logic.

And to understand the foundational 97 % of basic real time existence.

His argument was if we can't even mimic a cockroach successfully, we can't pretend to understand human intelligence.

That reframing is essential.

But this shift, especially towards what's called radical inactivism, it creates a new problem, doesn't it?

The inactivist boundary problem.

It does.

That's when you push the radical position a little too far.

Radical inactivism argues that all life is inherently cognitive, as one author put it.

Mind is lifelike and life is mindlike.

But if mind or cognition just becomes another word for life, then the concept loses all its useful meaning.

Right.

You need a way to clearly differentiate the cognitive processes of the rabbit and those of the carrot.

You have to define a minimal boundary for what counts as a functionally cognitive system.

Traditionally, that boundary was the nervous system.

Some argued that without one behavior is just an extension of metabolic processes.

They required what they called meta -metabolic function systems that go beyond simple metabolic upkeep to create an autonomous sensor motor domain.

But the 40 perspective argues, and I think correctly, that the nervous system isn't some cognitive Rubicon.

It just augments abilities that were already present in simpler life forms.

And to prove this, we can dive into the functional genius of the single -celled bacterium E.

coli and its chemotaxis system.

E.

coli uses a remarkably efficient system to find food and avoid poison.

It's called two -component signal transduction, or TCST.

And it's a perfect example of sensorimotor coordination without a single neuron in sight.

Let's break down that mechanism to show why it's truly meta -metabolic.

The system has two interacting pathways that work on different time scales.

First, you have the FAST pathway, operating in milliseconds.

Resectors on the surface detect chemicals, which triggers a signal inside the cell that directly controls the rotation of its flagella.

Those are the little tails that propel it.

Right.

And the flagella rotation dictates its movement.

One direction causes a directed run toward a goal.

The other causes a random direction -changing tumble.

So the FAST pathway is pure immediate perception and action.

Yes.

But the real genius is in the slower pathway, which operates over several seconds.

This system involves chemically modifying or methylating the receptors that are occupied.

Because that chemical process is slower than the immediate perception.

The number of methylated receptors essentially logs the concentration of chemicals from a few moments ago.

It functions as a form of molecular memory.

And the cognitive output comes from the comparison between these two pathways.

Exactly.

The bacterium doesn't register the absolute concentration of chemicals.

It registers the rate of change.

So if it's running and the chemical concentration is increasing, it keeps running.

But if the concentration plateaus or decreases, its molecular memory registers that lag and it triggers a tumble to reorient and try a new direction.

So you think of it like this.

The FAST pathway is like a stopwatch reading the current situation and the slow pathway is like a calendar recording the recent past.

And the E.

coli is constantly comparing the stopwatch to the calendar to decide what to do next.

Which fulfills that metabolic requirement.

The physical change moving through a medium is distinct from the underlying metabolism that benefits from the movement.

It's actively manipulating its environment.

And it's a powerful illustration of embodiment.

The bacterium is too small to use a spatial sensor.

It can't measure the difference in concentration between its front and its back at the same time.

So its physical size forces it to use a temporal system, that molecular memory, to gauge the gradient over time.

Its body's limitations dictate its form of intelligence.

Which is why sensor motor coordination, grounded in embodiment and environment, is a much clearer starting point for minimal cognition than just looking for a nervous system.

OK, so if we accept this radical idea that bacteria can be functionally smart without a nervous system, we are left with a huge evolutionary puzzle.

Why did nervous systems evolve at all?

Right.

If minimal intelligence was achieved four billion years ago, what was the point of the neuron?

This is the exact question that the SkimBrain thesis, or SBT, addresses.

The researchers who proposed it argue that nervous systems evolved not primarily for more intelligence.

That basic capacity was already there.

But what was it for?

It was to enable a completely new mechanical ability,

muscular behavior in large multicellular forms.

I like the analogy the chapter uses here.

It's easy to build a structure with Lego blocks.

Which are like the light independently moving cilia or flagella of single cells.

A multicellular life required moving massive stone blocks muscles to create locomotion.

And that requires a completely different and more complex means of coordination.

So the SBT re -frames the earliest nervous systems.

They weren't complex input output systems for abstract calculation.

They were coordination systems for muscle contraction across a wide surface.

Like the diffuse nerve nets you see in a jellyfish.

Exactly.

They enable powerful massive contraction.

So let's trace the proposed evolutionary steps.

Phase one is the hypothetical initial transition.

The evolution of this excitable tissue called myoepithelia.

Right.

This is basically epithelial tissue skin that gained contractile properties and could conduct electrical signals but only to its immediate neighbors.

These unbroken sheets of proto -nervous and muscle tissue were called Panton surfaces.

And they produced patterning self -organized waves of coordinated contraction.

What's so critical here for the 4E perspective is that this patterning inherently reflects a specific structure of the animal's body.

The implication for embodiment is profound.

The movement isn't being commanded by a central brain.

The movement is the action of the organ.

There is no controller separate from the thing being controlled.

The intelligence is inherent in the physics of the coordination which is shaped by the body's form.

The movement of the jellyfish is a self -organized dynamic pattern across its Panton surface.

And this dynamic nature offers a new view of sensing.

For this patterning to remain functional for the organism to keep moving coherently it must be incredibly sensitive to any disturbances.

Disturbances from the inside like growth or from the outside like being bumped by a predator.

Right.

So the organism senses the environment via the skin brain.

The body's ongoing dynamics the patterns of activation they become the primary sensing device.

So an external disturbance interrupts the internal pattern forcing the system to restore function and in doing so the organism registers the environmental change.

It's sensing through its own physical state.

Phase two then brought specialization to coordinate larger more complex bodies specialized signaling cells the neurons evolved.

They had long processes to signal non adjacent cells forming a diffuse nerve net.

But the function of this net wasn't to provide specific input output reflexes like the classical view would assume.

No it was to add flexibility and long range control to that self -organizing Panton surface.

It enabled wave like patterns across much larger bodies which removed the physical constraints on body size and form.

So you can think of this diffuse nerve net as an extra loop of control sort of like the molecular memory pathway in E.

Coli but scaled up to a macroscopic level.

Exactly.

It regulates and modifies the primary self -organized movement pattern.

And once that net was in place the contractile tissue could differentiate.

It could split off from the skin and move internally to become muscle tissue.

And that general differentiation eventually gave rise to the complex centralized nervous systems we're familiar with today.

So the SBT just completely shifts the narrative.

It gives us a purely embodied and biogenic perspective on nervous system origins.

And it argues that the first purpose of the neuron wasn't abstract computation.

It was the production of concrete dynamical patterns of activation needed for large scale muscular movement.

This emphasis on dynamical patterns and coordination fits perfectly with the broader theme of cognitive evolution.

Right.

Which is that organisms are in a progressive process of becoming better equipped to track and deal with unpredictable things in changeable environments.

This helps explain why we see these massive central nervous systems in certain lineages.

Larger long -lived species just encounter a wider variety of conditions over their lifetime.

And longevity increases the pressure for behavioral flexibility.

You need complex nervous systems to manage that.

When we talk about these complex systems we really have to emphasize the nervous system as a whole not just the brain mass.

It's so easy to assume brain tissue just equals thinking tissue.

But a huge often overlooked portion of neural tissue is required just to control and coordinate complex sensor motor systems.

Our large brains for instance are necessary to manage the complexity of our motor system.

Balancing on two legs coordinating two hands controlling two eyes on a movable neck.

Attached to a highly flexible spine.

This is what allows us to actively explore and sample the environment.

We aren't passive recipients of input.

We're active environmental interrogators.

And that increasing sophistication facilitated by the nervous system as a whole is what expands an organism's umwelt.

This is one of the most beautiful philosophical concepts in this whole deep dive.

It really is coined by Jakub von Uxgel.

Umwelt refers to the subjective world an organism inhabits.

It's the idea that an organism is only sensitive to aspects of the environment that hold direct significance or relevance for its survival and reproduction.

So a tix umwelt might be defined by just a few things temperature butyric acid from mammal sweat and hair.

Whereas a dog's olfactory umwelt is just astronomically rich.

So when an organism evolves these elaborate sensory motor systems it's not just getting smarter in some abstract way.

It's physically expanding its umwelt.

It's gaining the capacity to detect and interact with relevant aspects of its niche that were previously invisible to it.

And that dramatically increases its problem solving capacity.

And this expansion isn't limited to just neural tissue.

An embodied perspective fundamentally shows that non -neural anatomy the sheer morphology of the animal contributes directly to successful problem solving.

It acts as a crucial component of the cognitive system itself.

And this is where we find these incredible examples from natural history that serve as philosophical proof points.

Like the new Caledonian crows famous for their tool use.

They outperform other large brain corvids in complex foraging tasks.

Why?

Well research found it wasn't just raw intelligence it was their morphology.

Specifically they have greater binocular vision greater convergence between the eyes and straighter beaks.

And that physical configuration makes the visual guidance of the stick tools they use far easier than it is for their cousins who might have curved beaks or less effective stereoscopic vision.

So their superior performance isn't just mind over matter.

No it's body enabling mind.

The body is part of the cognitive loop.

I found that seahorse example equally fascinating.

The architecture of the animal's body its geometry is solving a coordination problem.

That implies the body is part of the neural network.

Absolutely seahorses have square tails not the typical cylindrical shape.

And this unique morphology significantly improves their ability to maintain a controlled grasp on coral reefs which is essential for stability and currents.

The square tail increases the surface area contacts.

It aids in grasping and it limits torsional rotation.

It's a structural solution to a physical control problem that enhances fitness.

And once you start seeing these morphological solutions you realize how widespread the principle of extension is.

The cognitive labor is being offloaded onto the body or the environment.

Take the planhoppers to synchronize their powerful jumping movements they use a microscopic gearing mechanism in their legs.

A mechanical gear.

A mechanical gear and it synchronizes the movements more precisely than neural control alone ever could.

The physical gear is the coordinator.

It's a non -neural solution to a timing problem.

Where the mudskippers

the fish that feed on land.

They've evolved the ability to create a hydrodynamic tongue by rapidly ejecting a mouthful of water to capture prey, which mimics the sticky tongue of a newt.

Their unique ability to manipulate a medium water outside their body acts as an extended sophisticated tool.

So the Fourier approach powered by these examples just shifts our foci dramatically.

We stop asking how much like us are they?

And we start asking how does this organism's specific embodied form operating within its specific environment successfully solve the fundamental problem of living?

OK, so we've established the problems with the classical view and the strength of this embodied biogenic approach.

We've shown the body and environment are integral to intelligence.

And now the chapter takes its final and most radical step.

It distinguishes between two different types of embodied thought.

We move from what's called conservative embodied cognitive science, CEC.

Represented by thinkers like Andy Clark.

To the radical embodied in an activist positions or REC championed by people like Kimero, Hutto and Manin.

Both views include the body and environment, but they differ fundamentally on the role of internal computation.

So the conservative view CEC it acknowledged the importance of action, but it still maintained a kind of revised computational model.

It often used the concept of action oriented representations.

Right.

They believe the body was still running on some sophisticated internal software that helped guide its actions.

But the radical view, REC,

embraces a strong anti -representational stance, at least for what they call basic minds.

Any non or pre -linguistic minds.

REC critiques CEC directly, calling it a watered down version because it still clings to that computational theory of mind.

And given that computational theory has these deep anthropocentric origins, the argument is that the burden of proof is on the conservative side to justify why we need internal representations for basic minds.

If we accept the REC stance and reject contentful representation for basic minds, we can clear out a lot of the intellectual clutter that has held back comparative psychology.

But this puts us in direct conflict with the logic of evolutionary continuity, which is one of the most deeply ingrained ideas in traditional psychology.

Right.

The continuity logic says that since complex human knowledge, like knowing that something is true, didn't just appear from nowhere, its evolutionary precursors, its proto representations must be found in other species.

And this forces comparative psychologists into these intellectual gymnastics to try and find human like internal thought in animals.

The chapter uses the classic work on baboons and vervet monkeys to illustrate the flaw in this logic.

These researchers characterize primate social knowledge as having a hierarchical structure similar to human language.

They even suggested their alarm calls were equivalent to a proposition.

But the authors themselves noted this critical mismatch.

Their monkeys appeared unaware of their effects on their audience's knowledge.

They didn't exhibit what we call knowing that the crucial representational truth of valuable component of human knowledge.

So they acted strategically, but without apparently understanding the content of their action or its effect on the listener.

So to maintain this idea of evolutionary continuity in the face of that mismatch, they were forced to water down the concepts.

They had to define a proposition, for example, without requiring any truth preserving component.

It was just a thought with a subject and a predicate completely stripped of his actual philosophical meaning.

And when you have to strip a concept of its core characteristics, its content and truth value, just to force it to fit onto a non -human animal, you have to start questioning the utility of that entire intellectual framework.

The commitment to continuity under the computational theory forces this flawed premise that at the evolutionary base of human knowledge is just

more knowledge.

And REC argues we need to get our continuity the right way around.

And here the chapter draws inspiration from Wittgenstein, who is often seen as an early and activist thinker.

What was his argument?

Wittgenstein argued that our most basic beliefs, what he called hinge certainties, are non -epistemic and non -propositional.

OK, what does that mean in plain language?

It means they are not things we think about or hold to be true.

They are the bedrock framework of our actions, the very structure of reality that we simply act into.

He said that it is action that lies at the base of our knowledge, not contentful thought.

So if Wittgenstein is right, then the precursors of human linguistic thought are not found in the supposed proto -thought processes of primates.

No, they're found in the non -representational ways that all living beings act and coordinate their behavior.

That basic sensory motor coordination we saw even in E.

coli.

That forms the genuine, deep evolutionary common ground.

And this stance allows REC to be pluralist.

It accepts that linguistic, socioculturally scaffolded human minds, our minds,

clearly use rules and representations.

That's how we're communicating right now.

But it rejects the idea that this highly complex, contentful thought must characterize cognition across the entire animal kingdom.

Critics of this view often argue that it fails to bridge the gap between basic non -representational minds and inculturated representational minds.

They say it denies true psychological continuity.

But there's a counter argument.

There is.

Huddo and Satney counter by asking a crucial question.

Must evolutionary continuity require psychological continuity?

They argue that the human species occupies a unique sociocultural cognitive niche.

Complex content and representations don't start inside our heads.

They emerge publicly and externally first within that niche.

And then these contents are internalized through a mastery of sociocultural practices like language and teaching.

Which perfectly validates Rodney Brooks's original argument.

The evolution of basic minds was the hard part, the foundational part.

So REC gets continuity, right, by building complex content involving human abilities on top of a non -representational foundation rather than forcing abstract thought deep into the evolutionary past where the evidence just doesn't support it.

It gives us a framework to appreciate both the fundamental genius of minimal life and the truly unique nature of the human niche.

We have covered a massive amount of territory today.

Fundamentally reorienting our understanding of mind.

We have.

Let's quickly synthesize the chapter's five main contributions.

OK.

First, we established why the classical anthropocentric computational definition of cognition just failed when confronted with the vast variety of adaptive life.

It was simply too narrow, too rooted in human self -reflection.

Second, we move to defining minimal cognition functionally as sensor -motor coordination.

We saw this evidence by the complex meta metabolic processes of the E.

coli TCST system, which uses time instead of space to solve its navigational problems.

Third, we explored the skin brain thesis, the SBT, which provided a radically embodied account of nervous system origins.

It shifted the primary function of the neuron from abstract computation to the production of concrete dynamical patterns of activation necessary for coordinated muscular movement.

Fourth, we saw that behavioral flexibility is driven by the nervous system as a whole, including all the elaborate sensor -motor systems, which expand the organism's umlaut.

And critically, we saw how morphology itself acts as a cognitive solution, perfectly demonstrated by the specialized beaks of New Caledonian crows and the stabilizing geometry of the seahorse's square tail.

And finally, we concluded with the radical and activist position.

To properly achieve evolutionary continuity, we have to accept that non -representational action, Wittgenstein's hinge certainties, forms the common ground of all living beings.

With complex, contentful human minds emerging very late in the game through the mastery of a unique sociocultural niche.

So the main takeaway for you, the listener, is this profound shift in perspective.

Understanding the evolution of mind requires letting go of the question, how much like us are they?

And instead, you have to ask, how does this specific organism's body, operating within its specific environment, successfully solve the fundamental problem of living and persistence?

And this leaves us with our final thought for exploration.

If the highly complex human mind emerged relatively easily, constructed upon four billion years of foundational, non -representational sensorimotor refinement,

what are the basic, deeply shared abilities, the implicit bodily know -how we share with an E.

collie that we still rely on every single second to maintain our balance, navigate a crowded room or simply grasp a cup without ever pausing to represent that process consciously?

We are constantly acting based on non -representational mastery.

It really makes you wonder how much of your intelligence is simply the genus of your embodied systems.

We'll leave you with that thought.

Thank you for joining us for this deep dive.

We hope this has provided you with a powerful biogenic foundation for understanding cognitive evolution.

We will catch you next time.