In 1637, Descartes framed the issue of how many senses unite into one mental experience as the mind-body problem. His answer was that sight, sound, smell, thoughts, and feelings all clear through the pituitary gland, and this lets an incorporeal mind observe it. The unification of brain functions is then done by the mind, like a little man watching a movie.

But by the same logic, that little man also needs another viewer in his head to integrate what he saw and so on, in an infinite regress (Dennett, 1991) (Figure 6.34). For this and other reasons, science rejected Descartes mind-body dualism to conclude that only the physical brain exists, so each neuron:

“… doesn’t ‘know’ it is creating you in the process, but there you are, emerging from its frantic activity almost magically.” (Hofstadter & Dennett, 1981), p352.

Yet that nerves with no ability to observe just act, then there you are, as if by magic, makes even less sense than dualism so the mind-body problem of centuries ago lives on today in neuroscience as the binding problem:

“One of the most famous continuing questions in computational neuroscience is called ‘The Binding Problem’. In its most general form, ‘The Binding Problem’ concerns how items that are encoded by distinct brain circuits can be combined for perception, decision, and action.” (Feldman, 2013), p1.

For example, the occipital lobes at the back of the cortex analyze visual data, while sound data is handled by the temporal lobes at the side, so how do vision and sound combine into one perception? This would need another area to process both outputs, so the cortex should be wired like a computer motherboard with many lines to a central processor, but it isn’t. Each neural hierarchy processes its own data but no central area binds them together, so how do sight and sound combine into one experience? The binding problem is that we recognize objects by sight, sound, touch, smell, memories, and thoughts, but the neural circuits that generate them aren’t centrally connected. Different areas evolved to process sight, smell, sound, thoughts, feelings, touch, and memory but there is no area that binds them all together. 

The problem is that brain hierarchies can’t talk to each as global workspace theory claims (6.1.6) because when a visual cortex nerve fires to report a line, it doesn’t say “I saw a line” like a little person. It just fires a yes-no response like any other neuron, so to bind that response to another feature like redness needs higher processing in the same hierarchy. At each step in the hierarchy, a nerve can fire to trigger a motor response, but it can’t add to an experience because it doesn’t know why it fired. The six-layered visual cortex can process lines, shapes, colors, and textures but the last nerve to fire in a sequence knows no more than the first, so how these features combine into a single experience is a mystery. 

  It follows that different brain hierarchies are encapsulated, so even if they did experience their content, they couldn’t exchange it with another:

“Because of the principle of encapsulation, conscious contents cannot influence each other either at the same time nor across time, which counters the everyday notion that one conscious thought can lead to another conscious thought … content generators cannot communicate the content they generate to another content generator. For example, the generator charged with generating the color orange cannot communicate ‘orange’ to any other content generator because only this generator (a perceptual module) can, in a sense, understand and instantiate ‘orange’.” (Morsella et al., 2016), p12.

The binding problem then isn’t just that brain activities combine in a way that its wiring doesn’t support, but that they do so in a way that no wiring could support, so the unification of sensations, feelings, thoughts, and actions we experience should be impossible.

For example, consider the fact that we see one visual field but two different areas generate it. The brain divides up the work of vision, as nerves from the right side of both eyes go to the left hemisphere, and from the left side go to the right, so each only analyzes half the visual field, but what puts it together? Apparently, the corpus callosum that connects the hemispheres does, as when it is cut, split-brain studies confirm that each hemisphere only responds to its half of the visual field. Do the nerves that connect the hemispheres then let them exchange data? Encapsulation doesn’t allow that but if it did, cutting those nerves should give a sense of loss, but it doesn’t:

“… despite the dramatic effects of callosotomy, W.J. and other patients never reported feeling anything less than whole. As Gazzaniga wrote many times: the hemispheres didn’t miss each other.” (Wolman, 2012).

   Why don’t split-brain patients know that their hemispheres are disconnected? If an injury cuts the optic nerve, we know we are blind as no data comes from the eyes, and if it cuts the spinal cord, we know we are paralyzed as no data comes from the legs. But when the millions of nerves joining the hemispheres are cut, both carry on as if nothing had happened! If the verbal hemisphere normally sees the entire visual field using data from the other hemisphere, it should report being half blind, but it doesn’t. It follows that there is no sense of loss because the corpus callosum doesn’t transmit any data between the hemispheres.

Instead of data loss, dividing the hemispheres just divides consciousness. One patient couldn’t smoke because when the right hand put a lit cigarette in his mouth, the left hand removed it, and another found her left hand slapping her awake if she overslept (Dimond, 1980) p434. Conflicts made simple tasks take longer – one patient found his left hand unbuttoning a shirt as the right tried to button it. Another found that when shopping, one hand put back on the shelf items the other had put in the basket. One patient struggled to walk home as one half of his body tried to visit his ex-wife while the other wanted to walk home. These extraordinary but well documented cases show that cutting the corpus callosum gives two hemispheres with different experiences and opinions about what the body should do.

   If the left hemisphere only analyzes data from the left visual field, our experience of a single visual field must arise in some other way. It is now proposed that the hemispheres synchronize their electromagnetic fields into one consciousness by means of the eight million nerves linking them (Pockett, 2017). The answer to the binding problem is then that consciousness causes integration not the reverse, where consciousness is the ability to integrate information to yield adaptive action (Morsella, 2005). 

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The human brain doesn’t come with an instruction manual but if it did, it would stress that many ways to control a feedback loop is a feature not a bug. An airline cockpit is full of knobs and dials, so international flights have two pilots and an autopilot for routine tasks, giving a backup in case one pilot is indisposed and also more options in an emergency. If airliners have three control centers, it isn’t surprising that our body evolved a brain cockpit that is the same (Figure 6.33).    

The forebrain, midbrain, and hindbrain also specialized to handle sense input, body state, and movement sequences respectively, just as tank crews specialize in planning, loading, and driving. People can run, blush, and talk at the same time because the brain can move, set body states, and communicate simultaneously.

The brain distributes control as the Internet does, to let each party act of its own accord as it sees fit. Special air service (SAS) teams illustrate this, as when facing a cliff, the climbing expert takes charge, and at sea the naval expert does, and this helps the team survive. The brain shares control in the same way, between specialists that each have their own input, processing, and output so they can act as the situation requires. Our brain is then a team that shares control rather than a neural dictatorship.

But what happens if these brain centers conflict? They can’t talk as people do because they in effect have different languages, and each is too busy with its own input to try. For example, a person with a spider phobia can discuss their fear to accept that a harmless spider isn’t a threat, but put a little spider on the table and they jump in fear! The intellect knows the spider isn’t dangerous but the emotions still press the panic button when one appears. There is no knowledge transfer because the midbrain learns in its own way, by experience not thought. Yet three centers doing their own thing is also useful, as I recall once talking to someone when an object fell from a shelf above and my hand reached to catch it without a thought. This response wasn’t a reflex, as the hindbrain had to constantly monitor space, calculate a trajectory, and direct a coordinated catch movement.

How we respond to situations is then complex. For example, an accidental fall on a hard surface can cause serious injury, so what decides whether we break a bone and end up in hospital or just get back up? It happens in less than a second so a cortical voluntary response is usually too slow. The midbrain is fast enough but its response of tensing all the muscles can injure bones or joints worse than the fall itself. In most cases, the best tactic is to relax and let the hindbrain handle the fall, but it needs practice, so parachutists have to learn to fall and roll in a relaxed way to avoid injury. Using the right center for the job isn’t easy, as while the cortex can prime a roll left or right, and fear can avoid the accident in the first place, only the hindbrain can handle the moment of falling. As someone who rode a motorcycle for many years, and fell off it regularly, I can confirm that learning to relax in a fall isn’t easy.

We then have a three-in-one brain that supports fast responses, emotional directives, and complex thoughts, as illustrated by the following story:

Once upon a time there were three brothers who delivered goods in a city to earn money by flying a small plane. Elder brother Hunter flew the plane by manually manipulating its lower flight controls, and the others tagged along. Middle brother Milo was still a child, so he sat in the cockpit to adjust the aircon knob and monitor the fuel gauge. Baby brother Finn just looked out the window commenting on what he saw, occasionally shouting Watch Out!

And so it went for years, until one day they bought a bigger plane that could travel between cities. Hunter still flew it as before, but Milo got more buttons, like the throttle he used to boost engine performance in takeoff. It also came with a camera so while Hunter just memorized his maneuvers, Milo stored time-tagged photos in an album of memories, marking the important ones and those that gave reward or were dangerous. He then took over scheduling where to go and when, as he knew the benefits and dangers, including when to refuel the plane or stop for maintenance. The plane also had pre-set buttons for common situations, like a white button that readied it to refuel, and a red button to prepare for action, so Milo used them to set up the plane in advance. Meanwhile Finn was still figuring out what was out there so he used symbols to represent features like red. His warnings about unexpected events helped Hunter to adapt, but the others still mostly ignored him as they ran the plane.

The brothers made more money and eventually acquired an intercontinental jet with a state-of-the-art computer. Hunter already had a simple radar that showed objects as a dot location and direction, so he stuck with that. Milo also had the pictures he needed to manage his dials and knobs, so fell to Finn to use the computer. He used it to control the plane, not as fast as Hunter, but enough to demonstrate new flight maneuvers to add to Hunter’s repertoire. He also used it communicate with other planes using symbols, so he took charge of that. His role also changed from just observing events to predicting them by analyzing trends and causes using Milo’s memory albums. Finn then focused on planning, as he could simulate the potential outcomes of different actions before deciding on one, though this could take a while.

The brothers worked as a team by dividing up the work as follows: Hunter handled the action details, Milo managed scheduling priorities, and Finn planned ahead and communicated with other planes. They then dominated the airways because three pilots are better than one when each does what they are good at.

Evolution produced a brain with three centers of control for good reasons, but why then do we experience one person? We assume one decision maker but neuroscience assures us that it isn’t so:

“In contrast to this first-person experience of a unified self, modern neuroscience reveals that each brain has hundreds of parts, each of which has evolved to do specific jobs – some recognize faces, others tell muscles to execute actions, some formulate goals and plans, and yet others store memories for later integration with sensory input and subsequent action.” (Nunez, 2016), p55.

This contrast between our unified self-experience and brain decentralization then raises the problem of how sensations, memories, and goals generated in distant brain areas are integrated into a single object experience.

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In humans, the forebrain expanded to form cortical hemispheres whose language and thought abilities set us apart from other species. The hindbrain cerebellum packs about 80% of the brain’s nerves into 10% of its volume, but forebrain nerves occupy about 75% of its volume because they are larger and need more support cells. The cortex is a 2-5mm thick folded sheet with six layers while the midbrain hippocampus only has three layers. It was the last part of the brain to evolve and is the last to mature in children but if other parts ran the feedback loop before it, how can it direct behavior? The answer, it seems, is with difficulty.

By studying children Piaget concluded that thought and language develop in four stages:

1.             Sensorimotor (0 to 2): Babies and toddlers think in sensorimotor terms.

2.    Preoperational (2 to 7): Children begin to think symbolically and learn language.

3.            Concrete operations (7 to 11): Children think logically about concrete events.

4.             Formal operations (12+): Abstract thought emerges.

These stages suggest that the human ability to think matures as the brain does, which by its evolution was first the hindbrain, then the midbrain, and finally the forebrain cortex.

In the sensorimotor stage (0-2), the hindbrain directs activities like reaching and standing, so it also tries to speak as those around it do. The result is infant babbling, which can sound like speech but is actually just the copying of sounds with no attached meaning. Even so, it lets an infant learn all the phonemes of its language, including intonations, before it learns its first word at about one. At this point, the midbrain can’t lay down memories, so before two years or later we have childhood amnesia, a period we can’t remember because there are no long-term memories, and animals are the same (Feigley & Spear, 1970).

In the preoperational stage (2-7), the midbrain matures enough to associate sounds with sensations, to give them meaning. At the same time, the child learns syntax, the rules of word order in sentences, possibly using the hindbrain. Yet it still thinks emotionally, that everyone sees the world as it does. For example, a five-year-old shown a chocolate box will say it contains chocolates, but if it is shown to contain pencils, and is asked what another child will think is in the box, they say pencils not chocolates, so they can’t project how others see the world yet.

The first speech is then egocentric, as the child keeps up a running commentary on what they do, even when alone. At first, they comment after an action, so a four-year-old may stroke a teddy bear then say “Good boy”, but at five the same child says “Good boy” as they stroke it, and at six they say “Good boy” then stroke it. It is as if part of the brain first observes what happens after-the-fact, then makes current comments, and finally predicts what will happen, so:

“One area of the brain and mind may initiate a behavior, which is witnessed or experienced by other (disconnected) brain areas, only as it occurs outside the brain and body.” (Joseph, 2017a), p442.

Cortical language is then initially an observer rather than a player, so it comments on what the body does but can’t change it.

In the concrete operations stage (7-11), the midbrain still dominates, so behaviors change by reward and punishment not logic, but the cortex learns to apply concepts like number to concrete things. For example, given two equal length lines of checkers, spacing one line more makes it look bigger, so a child under 7 may say it has more checkers, but by 9 they know that number doesn’t depend on size (Figure 6.30). They can apply concepts like number to concrete images but can’t connect one abstraction to another.

Only in the formal operations stage at about twelve can the child connect abstract concepts, to see speed for example as distance divided by time. In effect, the forebrain intellect matures enough to allow genuine thought. Prior to this, children can rote learn history dates by repetition, or associate concrete sensations like similar faces, and even recall entire textbook pages, but still struggle with abstract mathematics. For example, consider the following problem:

Bob rides his bicycle to pick up his motorbike from the repair shop at 10 mph. How fast must he ride his motorbike back to average 20 mph for the whole trip?

Emotional thinking based on associations suggests 30mph as 30 plus 10 averages 20, but this isn’t right. To see this, if the shop is 10 miles away, at 10mph it takes an hour to get there, so he must instantly return to average 20mph! The answer then is that it’s impossible because doubling a distance only doubles the speed if time doesn’t change for the return journey, which is impossible. Problems like this are hard because speed as distance over time is an abstract ratio that only the forebrain intellect can handle. A child can’t understand the dx/dt ratios of calculus until their intellect matures, so training a six-year-old in calculus has little effect because midbrain learning isn’t forebrain learning.

If the above example was a struggle don’t worry about it, as real thought always is. The brain must change its locus of control from hind/midbrain rote or associative thinking to intellectual thought, to work in a new way. Even after twelve the cortex still needs another decade to learn to think formally and it continues to mature into the mid-twenties. The forebrain then changes its role, from backward thinking to justify existing knowledge, to forward thinking to discover new knowledge. Cherry-picking the Internet to find facts that confirm preconceptions is then backward thinking, while impartially using facts to form conclusions is forward thinking, which underlies the scientific method. Western science traces back to Socrates, who essentially invented forward thinking, but two thousand years later we still struggle to follow his example because thought hurts. 

Does the forebrain control the feedback loop as it matures? It has the links to do so as the frontal-lobe motor cortex (Figure 6.31) projects nerves to body muscles based on their importance (Figure 6.32). A voluntary act like raising a hand is accompanied by motor cortex activity, and supplementary and pre-motor areas light up before that to prepare the action. They activate even at the thought of moving so experiments can predict an action long before the muscles move (Nachev et al., 2008). Are the frontal lobes then the long-sought senior executive of the brain? If so:

“… the “will” to move begins in the SMA and medial frontal lobes and exerts executive control over the secondary, primary and subcortical motor areas which then perform these “willed” actions.” (Joseph, 2017b), p151.

Motor cortex damage should then produce paralysis but rats with no motor cortex still move normally but struggle with unexpected events, so it is required to learn a motor skill but not to execute it (Kawai & et al., 2015). Even with no cortex at all, rats, cats, and dogs can run a treadmill and monkeys can peel a banana, so what does the motor cortex do? The current view is that it evolved to handle unexpected events like a slip or fall, which requires a rich analysis of sense input that only the cortex can provide (Lopes & et al., 2023). The motor cortex role is then to increase flexibility, also called robust dexterity (Petryński, 2005), as system performance depends on surviving external as well as internal changes.

The picture that emerges from neuroscience is that multiple systems drive bodily actions:

 “Figuratively speaking, the skeletomotor output system is akin to a single steering wheel that is controlled by multiple drivers …” (Morsella et al., 2016), p6.

The body is then like a car with three drivers, except that each has their own dedicated steering wheel and controls, map of the outside world, and memory of past events. But if a car with three drivers meets an obstacle, what stops one driver turning left as another turns right, so they cancel and crash into it? How can three brain control centers effectively share control of the same body?

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The midbrain section of the neural tube that human brains develop from grows to become what has been called the limbic system (Figure 6.28), which is generally described as including the:

·      Thalamus. A sensory relay station for sight, sound, and touch input.

·      Hypothalamus. Connects to the peripheral nervous system that controls body states.

·      Hippocampus. Acts to lay down memories.

·      Amygdala. Analyzes sense and body state input to generate emotions.

·      Cingulate gyrus. Links to the cerebral cortex.

These structures handle emotions, motivation, and memory in humans and other animals in ways that are still being understood but clinical studies give some clues. Hippocampus damage can result in amnesia, the inability to recall memories or lay down new ones. Cingulate gyrus damage is involved in depression and schizophrenia. Amygdala damage reduces the ability to process emotions based on facial and other signals, and is a neural marker of autism. The hypothalamus links to the peripheral nervous system, a second brain of a 100 million nerves outside the head that handles body hormones and digestion. The limbic system then relates body states to sensory situations, lays down memories of them, and generates emotions that motivate behavior, so it can be called a feedback control center in its own right.

Locus of control theory describes the midbrain as a control center specializing in body states, with its own sensory-visceral input, learning, memories, and state output. For example, while hindbrain muscle memory remembers motor tasks like riding a bicycle, midbrain memories recall past events like whether we left a stove on. This episodic memory includes time and space tags plus any feelings at the time, so remembering a funeral can re-experience its sadness. The past event timeline of midbrain memories allows emotional learning that links situations to good or bad consequences, to allow cause and effect conclusions. 

Like the hindbrain, the midbrain limbic system has its own dedicated nerve paths, so it can react to facial data in less than a tenth of a second by a subcortical visual path before the cortex can respond (Adolphs, 2008). Sense data from the thalamus goes direct to the amygdala by a short route, as well as taking a longer route to the cortex (Figure 6.29), so the amygdala can initiate emotional responses like sweaty hands, dry mouth, and tense muscles before the visual cortex can identify seen objects. The thalamus still passes data to the cortex for a more complex but slower decision, but the two paths are separate. Like the hindbrain, the midbrain has its own neural links, that evolved long before the cortex developed the ability to think, which it can use to initiate responses.

The limbic system also has its own space apart from the cerebellum. The hindbrain provides a navigational map based on vector directions and distances between key points, but the midbrain map gives a visual record of location details, including what is there, when it was accessed, and the likely benefits or dangers. Two maps are better than one, so navigation maps help hunters in featureless terrains like deserts and oceans, while location maps help food gatherers remember what is where, and when it is available. This midbrain map provides the details needed to return to a point in space (O’Keefe & Nadel, 1978), so birds like nutcrackers can hide about 30,000 seeds at locations over a 200 square mile area and still recover them six months later.

The limbic system then isn’t a brain that takes over but an emotional control center, just as the cerebellum is a movement control center, so they operate in parallel rather than overlay. For example, the hindbrain can initiate a fight response that the midbrain prepares the body for, or the midbrain can initiate a desire to flee which the hindbrain then responds to. Each represents the world differently, as one reveals what we can do based body position and response options, while the other reveals what we want to do based on past experiences and what the body needs.  

If the midbrain directs emotional responses, what then is an emotion? An emotion can be seen as a neural representation of a body state that can associate with situations. For example, fear is the state of increased heart rate, breathing, adrenaline, blood pressure, and blood sugar that arises in a threat situation. Having experienced this visceral state, for any reason, the limbic system can recall and re-activate it, to prepare the body for fight or flight, so fear evolved over time because being primed to act increases survival.

Applying this logic to other basic emotions suggests they also evolved to support survival. Emotions like lust, anger, and greed now seem unnecessary urges but anger is useful in a fight, lust benefits the species, and greed lets us feast to survive a later famine. Dependence might not help an adult but children who stay by their parents survive more, and even laziness has a value, to motivate tired animals to rest and recover. All emotions have survival value in the right situations.

Midbrain emotions are then a tool kit of body-state responses that help survival when tailored to situations. Their evolution was a big advance at the time but like all toolkits, one must use the right tool for the job. If a carpenter uses a hammer to shorten a plank instead of a saw, it isn’t the toolkit’s fault, and likewise, what we now call negative emotions are only so if used incorrectly. 

The limbic amygdala is the key processing center for emotions, that manages detection of danger, emotional learning, and interprets facial expressions like anger (Hooker et al., 2006). For example, a skin sensation that is like a crawling insect can produce a sudden emotional and physical response initiated by the amygdala. It also allows the brain to assess another’s intent, to project what I would do on another. For example, many birds cache their food to hide it for use later, but if they see another bird watching them do it, they return later to re-hide it (Clayton et al., 2007). In humans, this ability to understand another allows higher emotions like empathy, the ability to feel what another feels, which could be the basis of what we call love.

The hindbrain evolved because doing something is better than doing nothing, but if a predator is nearby, it’s often better to stay still. As the emotional center evolved to manage threat responses, it had to override the tendency to move to effect this response. When a mammal sees a predator, the instinct to run away is stopped by the emotion of fear, as the amygdala activates its connections to the brainstem and cerebellum to freeze an animal in its tracks (Ressler, 2010). Mammals have this paralysis by fright response but fish don’t because their midbrain is less evolved. One center can then override another, but by suppressing rather than replacing it.

The natural next step of brain evolution was to develop the forebrain control center to give humans language and thought. We experience one being but our brain evolved in stages, so the forebrain can represent reality using thoughts as well as actions and feelings. The classical division of human nature into will, emotions, and intellect then began when brains specialized in output, state, and input functions, so the next section addresses how our intellect develops.

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The human brain grows from a neural tube whose top, middle, and rear areas become the forebrain, midbrain, and hindbrain respectively (Figure 6.24). They then specialize in three basic brain functions:

       1. Input. What is out there? (forebrain)

       2. State. What is the body state? (midbrain)

       3. Output. What actions can be done? (hindbrain)

Life requires all three, as animals must sense food or danger, know if the body is hungry or tired, and direct actions like biting or chewing, to survive. If early growth follows the same path as evolution, brains began as simple neural tubes that then specialized in analyzing sense input, evaluating body state, and directing muscle activity, so our brain has a cerebrospinal hindbrain, a limbic midbrain, and a cortical forebrain.

An engineer might design a feedback system to analyze input, assess internal state, and direct responses then add a control center but evolution doesn’t design ahead, so it just evolved three functions in parallel and didn’t bother with a control center. The basic brain-environment feedback loop then runs through three processing centers that specialized in sensory, state, and motor processing, as shown in Figure 6.25, any of which can drive the loop.

Locus of control theory is that the hindbrain, midbrain, and forebrain all evolved to control the feedback loop, so its locus of control can change as individuals evolve, grow, or live. The evolutionary advantage is flexibility, as what directs the body can shift as circumstances require to allow:

1. Sensory control: Based on sense input patterns.

2. State control: Based on body state feelings.

3. Movement control: Based on sensorimotor patterns.

Hence in life, an animal can respond differently according to the feedback locus of control. For example, given a chance to bite, it might do so under movement control, or it might freeze in place from fear under state control, or it might ignore the situation because sensory analysis suggests that there is no danger. Our brain then isn’t three overlaid sub-brains as Triune theory proposed, but three feedback-control centers, any of which can control behavior by itself alone. 

Fish brains are then partitioned as ours are, as they have forebrain optical and olfactory areas to process sense data, a midbrain amygdala and pituitary to manage endocrine tasks, and a hindbrain cerebellum to direct movement (Figure 6.26). All three functions exist, but the fish cerebellum is far more evolved than its cortex (Montgomery et al., 2012), so it can control the feedback loop using data from the more primitive forebrain and midbrain. This is possible because the cerebellum projects excitatory and inhibitory nerves to the motor cortex, even in humans (Daskalakis et al., 2004).

The hindbrain control center then advanced before the other two because in evolution, actions are more critical to survival than sensations. It is better to act blindly than to see but be unable to act, so single-celled protozoa evolved the ability to move before they could see. Human embryo growth supports this conclusion, as motor nerves develop before sensory ones, so babies in the womb kick long before their eyes start working.

Locus of control theory suggests that as brains evolve or grow, the hindbrain runs the feedback loop while the midbrain and forebrain develop. In humans, the hindbrain bulges out from the base of brain as the cerebellum (Figure 6.27). The cerebellum doesn’t look big but it actually contains more nerves than the rest of the brain put together! Its two cross-linked hemispheres are about 80% of all nerves, so some call it the little brain. People with cerebellum damage struggle with walking, reaching, speaking, gaze, and balance, and their staggered walk, poor eye-control, slurred speech, and other features makes them appear drunk. They lose the hindbrain ability to handle fast sensorimotor sequences, so when a gymnast does a back-flip on a balance beam, it is managed by the super-fast processing of the hindbrain, not the slower motor cortex.

Our brains then contain a movement control center that once ran the body entirely, so can it still do so today? When we sleep, parts of our brain shut down to allow recovery but in parasomnia, people sleepwalk, to get up, walk, eat, and even cook a meal. In one case, a sleepwalker got up, rode her motorbike for 20 minutes, returned and parked it, then went to bed to wake up later with no memory of it at all. How then can sleepwalkers carry out complex tasks while still being largely asleep? Locus of control theory proposes that while the cortical and midbrain systems that govern rational thought and memory were dormant, the hindbrain woke up to control the body using sensorimotor connections that it evolved long ago. Clearly sleepwalking isn’t just reflexes, as cooking a meal and riding a motorbike are purposeful acts that use tools and navigation. 

But how can the brain navigate if the visual cortex is dormant? Monkeys with no visual cortex can’t discern a circle from a triangle, as expected, but still move around like normal monkeys using vision (Humphrey, 1992). Humans also exhibit this blindsight, as people with visual cortex damage report seeing nothing but can still catch a ball, or insert a coin into a tilted slot whose angle they say they can’t see (Goodale & Milner, 2004). Hence if cortical vision fails, the hindbrain can still navigate in space by what is called implicit perception (Hannula et al., 2005), so the hindbrain has its own sensory input paths distinct from the midbrain and forebrain.

If advanced systems fail, older ones take over, so subjects who can’t speak due to cortical damage can still swear and sing! Amnesic patients given the same jigsaw every day say: “I have never seen this before” but still solve it faster each day, so these older systems can also learn. For example, tasks like riding a bike are done badly by the voluntary motor cortex become until they automatic, and then they are easy. Essentially, the cerebellum learns a sensorimotor schema to ride a bike and remembers it, so when you get on a bike, it triggers to control movement and balance, like a sort of autopilot.

Note that the cortex doesn’t control the cerebellum but just lets it act, just as when driving we let a car’s automatic transmission monitor events and change gear as needed. How then do we know if we remember how to ride a bike if it is unconscious? The only way is to get on one and push off, and let the cerebellum take over to handle the body and balance as only it can. The hindbrain is then a movement control center with its own dedicated sensorimotor input-output, learning, and memories, that can act independently if needed because long ago, it was the senior brain system. Other parts of the brain can interfere with it, but they can’t do what it does, and have no need to.

We tend to forget that the evolutionary path to our brains was long and arduous. For example, in infant swimming, babies instinctively hold their breath underwater thanks to a diving reflex, and move their arms and legs to propel them through the water by an amphibian reflex that flexes same-side hip and knee kicks. These instinctive actions disappear later, as the child learns to swim as people do, by moving limbs alternately. That babies swim like frogs but lose the ability after four months suggests that our brain retraces its evolutionary ancestry as it grows.    

   To call the hindbrain primitive because it isn’t conscious is like calling a jet engine primitive because it has no video feed, when given what it does, that’s unlikely. Just as modern jets have the latest engines, our brain has the latest movement control that evolution can provide, so we don’t have an old reptile brain but a state-of-the-art movement center. It acts implicitly without fuss, so it’s easy to ignore, but the midbrain emotions of the next section are anything but unseen.

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A brain that analyzes input but can’t affect output doesn’t help survival, as an eye seeing danger isn’t useful if the muscles don’t move the body away from it. Given that every creature is in a basic feedback loop with its environment (Figure 6.20), the brain must control this loop to survive. but last century psychology split into two camps on the issue of what controls the feedback loop:

a. Behaviorism argued that people are machines driven by outside events, so stimuli and responses entirely define the loop and what the brain does.

b. Constructivism argued that the brain controls the loop by actively constructing reality, hence it can produce more sentences than we could ever learn from stimulus-response associations (Chomsky, 2006).

They differed on what controls the basic feedback loop, as in behaviorism it was driven by input but in constructivism, it was driven by the brain (Figure 6.21). Since a circular process can be initiated by a choice at any point, logic lets both be true, so the loop can be driven by external events or by brain events. Behaviorism then just reinvents Newton’s mechanistic universe, as did Dawkin’s selfish gene (5.7.2), and Crick’s pack of neurons (6.1.1). However biological evolution requires choice, and in psychology learning requires it, so brains must control the feedback loop to learn or evolve.

The brain isn’t input-driven as computers are but both face similar problems when they update. For example, when Microsoft upgraded the DOS operating system to Windows, it just replaced it, so decades of user learning suddenly became obsolete, as copying files by complex commands like xcopy now just needed a simple mouse drag and drop. If nature worked like this, the mammal brain would have replaced the reptile brain, making it obsolete, along with hundreds of millions of years of evolutionary experience!  

That nature doesn’t discard advances but builds on them led an American Institute of Mental Health physician to propose Triune theory, that our brain is a reptile brain overlaid by a mammal brain overlaid by a human brain (MacLean, 1990) (Figure 6.22). He linked the reptile brain to the hind-brain cerebellum, the mammal brain to the mid-brain limbic system, and the human brain to the neocortex of higher thought. Autism was then just the reptile brain taking over, and anxiety was the mammal brain taking charge. In this view, evolution first evolved a reptile brain to handle movement, then a mammal brain to handle emotions, and finally a neocortex for human thought, so our brain was three brains in one, each overlaying the last.

The triune brain explained autism and animals, so Temple Grandin, an authority on animal psychology who is also autistic, wrote:

“To understand why animals seem so different from normal human beings, yet so familiar at the same time, you need to know that the human brain is really three different brains, each one built on top of the previous at three different times in evolutionary history. And here’s the really interesting part: each one of those brains has its own kind of intelligence, its own sense of time and space, its own memory, and its own subjectivity. It’s almost as if we have three different identities inside our heads, not just one.” (Johnson & Grandin, 2006).

However the triune model was rejected by biologists because evolution doesn’t deposit layers like geological strata, so as one critic put it: Your brain isn’t an onion with a tiny reptile inside. Evolution didn’t build a reptile brain, then a mammal brain, then a human cortex because it isn’t a linear production line. Nor did it add new brains without precedent, as bat wings are modified forelimbs that existed before, so even reptiles have a primitive cortex that lets them care for their young and solve problems (Patton, 2008).

Triune theory also didn’t explain how warm-blooded birds evolved from reptilian dinosaurs over millions of years. Birdbrain is a term of ridicule but they aren’t dumb, as crows can bend a wire into a hook to get food their beak can’t reach (Weir et al., 2002) (Figure 6.23), while children can’t use tools like this until about eight, and even then only half succeed (Cutting et al., 2014). Birds then are more like feathered apes than reptiles. For example:

 “On a university campus in Japan, crows and humans line up patiently, waiting for the traffic to halt. When the lights change, the birds hop in front of the cars and place walnuts, which they picked from the adjoining trees, on the road. After the lights turn green again, the birds fly away and vehicles drive over the nuts, cracking them open. The birds wait patiently with human pedestrians for a red light before retrieving their prize. If the cars miss the nuts, the birds sometimes hop back and put them somewhere else on the road.” (Earthfire Institute).

Birds then are smart, but while the mammal cortex is folded, their cortex is smooth because it evolved differently (Jarvis & et al., 2005). Evolution then wasn’t just building a human brain, but rather bird and mammal brains evolved from the basic reptile design by different paths, that later converged to achieve equivalent functional benefits (Lefebvre et al., 2004). Triune theory doesn’t allow this, as it proposes that the hind, mid and forebrains evolved in sequence, so an alternative that allows them to evolve simultaneously is now explored.

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Evolution develops forms gradually over time, in a step-by-step fashion (Figure 6.9), so brains had to evolve by a long series of modifications pruned by natural selection. This descent with modification is known to produce modular hierarchies (Gao, 2025), as regions of the brain fine-tune to a function by increasing their internal connections, and older structures aren’t discarded but become the foundation for later developments. 

Motherboards are first designed then built, but nature had to in effect design as it built, so it produced one module, tested it for a few million years, then added another on top of it. The evolutionary advantage was less risk of functional failure, as adapted modules could operate while others evolved. Module hierarchies let evolution make brain design decisions one at a time, giving common ancestors that led to the next step.

A transistor can’t grow into a motherboard, but a cell can grow into a brain because evolution found the way, and embryos follow that path as they grow. In growth and evolution, the brain hierarchy started with one cell, the neuron, so we begin there. 

A nerve is a cell whose body receives electrical input from dendrites and projects electric pulses down an axon to other nerves (Figure 6.10). When the dendrite strength passes an input threshold, the nerve fires, to trigger other nerves. For example in Figure 6.11, inputs B and D are strong enough to fire neuron A, but B and C aren’t, as they don’t reach its threshold of four. Nerves are then devices that selectively pass on electrical impulses.

Embryonic nerves grow from the brain to form the retina, so light entering the eye touches the brain directly. It is estimated that each eye inputs about 8.75 Megabits a second, and the brain total is over 20 Mbps, so a baby’s first impression of the world is probably information overload:

““The baby, assailed by eyes, ears, nose, skin, and entrails at once, feels it all as one great blooming, buzzing confusion” (James, 1890).

The baby’s brain has to transform the data from millions of optic nerves into objects, like a cup, to interact with the world, but how? Computers do it by identifying features like shape, so videos can be compressed by keeping key features and discarding the rest, and the brain the does the same. Essentially, it handles information overload by reducing the incoming sense data to key features, like borders and shapes, that let us recognize objects.

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The first step of vision then is to differentiate black from white. Photo-electric cells do this by firing when there is light, so in information terms, black is 0 and white is 1, as set by its designer. But brains had no designer so evolution, as it always does, took both options, as one type of retinal cell responds to light and another to light (Figure 6.12). Instead of defining data absolutely, the retina responds to both light and dark, which lets it identify the next key feature, of borders. 

Retinal cells then interact, to excite or inhibit each other, to enhance the borders that define object shapes. The next step in object recognition is to make one side of the border the object, and the other its background, but which? The visual data is ambiguous, so vision assumes as best it can which side is figure and which is ground. For example, in Figure 6.13, making white the background just gives black blobs, but making black the background lets you read MAIL BOX.

Vision then isn’t a simple input-output process because it must make assumptions that could be wrong. Visual processing begins when the eye detects light, and this data is then subject to layer upon layer of processing modules that detect relevant key features. For example, some visual cortex nerves fire for different line angles (Figure 6.14) and others detect other features.

Computer processing tends to be a simple one-way flow, but brain nerves have bottom-up, lateral, and top-down links, so processing can be top-down and sideways as well as bottom-up. Bottom-up paths analyze data as computers do, but lateral paths enhance results, and top-down paths prime, interrogate, and check the result for consistency or errors (Dehaene, 2014) p139. Higher modules of the hierarchy can then re-run the process with different assumptions to get an entirely different result. 

For example, is Figure 6.15 a young lady or an old one? If you see a young lady, can you see an old one? To do this you must rerun your visual processing, to make the ear of the young lady the eye of an old one. The visual system makes a best guess, but you can ask for a redo because nerves in the visual hierarchy project down as well as up. Note that the change in what you see is sudden, and you can see one or the other but not both at once. Lower processing may be out of sight and out of mind, but it can still be redone by top-down control. This is necessary because all perception is just a hypothesis of an ambiguous world.

   Subconscious processing is often assumed to be primitive but the spinning ballerina illusion (Figure 6.16) suggests otherwise. Click on the link to see a ballerina spinning but the rotation is ambiguous, so you might see her spin clockwise or anti-clockwise. Try to see her spin the other way. If you can’t, pause the video and if you see an extended leg at the front, imagine it at the back, or vice-versa. Restart the video and if she spins the other way, you just reprogrammed some very complex unconscious visual processing. This ability lets higher conscious modules reconfigure lower ones to see the world in a new way.

The cortex is then a nested hierarchy of nerve modules that evolved to process sense data in various ways. But while the optic nerve has about a million neurons, the auditory nerve only has about 50,000, so the visual hierarchy base is much broader. As shown in Figure 6.19, for the same processing capacity, a broad input base has less processing depth, while a narrow base has more. The narrow base of sound allows deeper processing than vision, so language began with speech because sound allows the deep analysis that language needs. Given the trade-off between processing breadth and depth, the hemisphere better adapted to deep processing specialized in language, and the other specialized in the broad processing of spatial analysis.

Sensory hierarchies let lower modules process their data to identify key features that pass on to higher ones, but how the results combine into the unified experience we call consciousness is unclear. What is clear is that this unified view can be checked and rerun, as illusions show, so consciousness lets us revise our reality assumptions to see it in a new way. For example, this book challenges the assumption that objects exist by pointing out the factual inconsistencies that follow, so we see quantum-generated events not self-existing particles. But if nested module hierarchies run the brain, how does it maintain control?

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Our computer networks were centrally controlled until we discovered that decentralized networks like Ethernet are ten times faster, and degrade gracefully under load instead of crashing suddenly. Yet when the Internet was first proposed, pundits still expected it to collapse in chaos without central control but it didn’t, because decentralization allowed it to survive.

Likewise, early brain theories still expected central control, like Schacter’s model (Figure 6.3) where a central executive inputs conscious awareness of sense data, memory, and thought to respond (Schacter, 1989). It was then assumed that the cerebral cortex, the folded outer layer of the brain (Figure 6.4) that handles thought and language, contained the executive unit.

Yet the cortex, the most advanced part of the brain, is divided into hemispheres that share the work between them. The left hemisphere controls the right side of the body and the right directs the left, but which then is the executive?

One hemisphere was assumed to dominate the other via the corpus callosum, the 800 million nerve bridge that connects them (Figure 6.5). But then surgeons treating epilepsy, an electrical malfunction that spreads from one hemisphere to the other, tried cutting the corpus callosum as doing this in animals didn’t seem to harm them. The treatment worked, but while serious side-effects were expected, the patients seemed to speak and act normally, so some even wondered if the corpus callosum was just a structural support!

To investigate further, researchers devised the split-brain experiment. They knew that the left hemisphere controls the right side of the body and the right controls the left, and for vision, the left hemisphere receives nerves from the right side of both eyes while the right receives from the left side (Figure 6.6). Hence, the left hemisphere processes the right visual field and controls the right hand, while the right hemisphere processes the left visual field and controls the left hand. 

The split-brain experiment shown in Figure 6.7 then worked as follows.

Subjects were asked to point to the picture that matched what they saw on the screen, which unknown to them was split into two pictures. The result was that if the left of the screen showed snow and the right showed a claw, the left hand chose a shovel to match the snow, and the right chose a chicken to match the claw! Subjects asked what they saw just said a claw, because the left hemisphere that controls speech only saw that. With the corpus callosum cut, each hemisphere acted like a brain in itself, and neither seemed aware of the other’s choice, so there was no conflict. Rather than one hemisphere dominating the other, both just analyzed their visual data and responded, so no central executive was needed.

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If a subject was specifically asked why his left hand chose a shovel, the reply might be “you need a shovel to clean up after chickens”. Yet researchers knew that the verbal left hemisphere didn’t know why the shovel was chosen because it didn’t see the snow, so it invented a reason! They concluded that it was just trying to explain events as best it could:

“These findings all suggest that the interpretive mechanism of the left hemisphere is always hard at work, seeking the meaning of events. It is constantly looking for order and reason, even when there is none – which leads it continually to make mistakes. It tends to overgeneralize, frequently constructing a potential past as opposed to a true one.” (Gazzaniga, 2002), p30.

In interpreter theory, the left hemisphere’s language and thought are more servant than master, so if the brain is a federation of agents (Minsky, 1986), it is like a diplomat whose job is to explain the decisions of others, not the CEO as some suggest (Kaku, 2014). Given that the animal most likely to harm a human is another human, perhaps our intellect grew as our societies did because those who can justify their actions survive. Science then, probably isn’t what our intellect originally evolved to do.

But is the right hemisphere really conscious if it can’t speak? It doesn’t specialize in language but that doesn’t make it illiterate. In one study, a split-brain boy was asked “Who is your favorite?” but the left of the screen showed “Who is your favorite girlfriend?” (Wolman, 2012). There was no verbal reply, as the left hemisphere didn’t see the word girlfriend, but a nervous giggle revealed that his right hemisphere understood fully what it saw. The boy was then able to use his left hand to select scrabble tiles to spell out L-I-Z, a cute girl in his class. It followed that the right hemisphere could read and spell, so it was conscious in any way you care to define it:

“Everything we have seen indicates that that the surgery has left these people with two separate minds, that is, two separate spheres of consciousness. What is experienced in the right hemisphere seems to lie entirely outside the realm of awareness of the left hemisphere. This mental division has been demonstrated in regard to perception, volition, learning and memory.” (Sperry, 1966), p299.

This discovery, that dividing our brains at the highest level produces in effect two people, was surprising. Yet in evolutionary terms, it favors survival, because if one hemisphere is damaged, the other can carry on. For example, take the case where an iron rod pierced the middle and left cortical lobes (Figure 6.8) of a railway worker called Phineas Gage, who soon after walked off, conscious and speaking. He showed disturbed behavior but lived for 13 more years and died of unknown causes. If you bang a nail through a mother-board, it stops working, but brains aren’t that fragile, as Von Neumann observed:

“How could a mechanism composed of some ten billion unreliable components function reliably while computers with ten thousand components regularly fail?” (von Neumann, 1948).

The answer is that the brain duplicated functions and decentralized control to survive, so if the body is a ship, the brain crew that runs it has no captain, even at the highest level:

“Studies of the structural and functional organization of the brain have shown that this organ is, to a large extent, decentralized, and processes information in parallel in countless sensory and motor subsystems. In short, there is no single homunculus in our brains that controls and manages all these distributed processes.” (Singer, 2007).

We experience the world as observed sense input that we respond to, so why not peel away the brain layers to find the observer? Unfortunately, doing this is like searching the Internet for a center that it doesn’t have. Neuroscience is clear, that the brain is a decentralized collective with no center:

“In contrast to this first-person experience of a unified self, modern neuroscience reveals that each brain has hundreds of parts, each of which has evolved to do specific jobs – some recognize faces, others tell muscles to execute actions, some formulate goals and plans, and yet others store memories for later integration with sensory input and subsequent action.” (Nunez, 2016), p55.

There are then two facts, that we experience one observer, and that our brain is a decentralized collective with no central processing unit (CPU). Some conclude that a brain with no center can’t experience one, so conscious states must be delusions (Dennett, 1991), but delusions vary while our experience of being one observer runs across all cultures. Given two contradictory facts, science doesn’t cherry-pick one and explain away the other but tries to reconcile them. The question of how decentralized brains produce a unified consciousness is addressed later (6.3) but for now, the conclusion is that brains are decentralized because evolution required it, so what else did it require? 

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Superficially, brains are like computers as both use electricity to run on/off units, and neuron logic gates process data as transistors do (McCulloch & Pitts, 1943). Also, sensorimotor nerves mirror computer input-output channels, so it was proposed that the brain processes the senses to give motor output as computers process input to give output (Churchland & Sejnowski, 1992). Yet the comparison ends there because building a processor and evolving one present different challenges (Whitworth, 2008).

For example, we build computers to be efficient but brains evolved to be reliable. Most modern computers use the Von Neumann design shown in Figure 6.2, with a central processing unit (CPU) controlling everything, but if it fails, so does the whole system. However this design doesn’t work for evolution because biological parts fail regularly, so a brain that stops when one part does is too fragile to survive. Evolution needed a better way, so our brain doesn’t have a central processing unit.

We build computers at leisure then switch them on but evolution couldn’t do that because life never stops. It is like building a computer where the first part has to work or you don’t get to add the second! The bee’s neuron sliver then has to work for it just as our brain works for us. We don’t work on live computers because it is too dangerous but evolution had to, because brains are always plugged into life. The answer was a hierarchy of layers, where one layer runs things while another is evolving, and it was also a backup if the new one failed.

Finally, to survive, brains must add value, say by moving a creature towards light, as a sense like light detection is useless if it doesn’t cause action. Hence evolution let the brain direct the feedback loop between sensory input and muscle output in a way that computers don’t.

Our computers are centralized, single-layered (on a motherboard), and input-driven, but brains are decentralized, multi-layered, and direct feedback. Hence, decentralization, hierarchies, and feedback are evolutionary principles that explain the brain better than any computer design.

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The human brain seems to be the most complex structure in the known universe for its size (Figure 6.1). It has more nerves than there are people in the world (or devices on the Internet), but it took hundreds of millions years to evolve.

The brain of a bee is just a sliver of neurons, but it lets them fly, form colonies, and communicate with each other better than any robot of that size. Yet nature didn’t stop there because our brains came from brains like theirs, so what is difference between evolving a brain and building a computer? 

QR6.2.1 Is The Brain A Computer?

QR6.2.2 Decentralization

QR6.2.3 Hierarchies

QR6.2.4 Feedback

QR6.2.5 Three-Center Theory

QR6.2.6 The Emotional Center

QR6.2.7 The Intellectual Center

QR6.2.8 Sharing Control

QR6.2.9 The Binding Problem

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