Determined: A Science of Life without Free Will

by Robert M. Sapolsky

Introduce a smidgen of a difference in this nonviable starting state, namely that the open/filled status of just one of the twenty-five boxes differs—box 20 is filled instead of open:

And suddenly, life erupts into an asymmetrical pattern (see figure below). Let’s state this biologically: a single mutation, in box 20, can have major consequences.

Let’s state it in a way that is ultimately most meaningful: a butterfly in box 20 either did or didn’t flap its wings.

you can’t predict the mature state from the starting state—you have to simulate every intervening step; you can’t predict the starting state from the mature state because of the possibility that multiple starting states converged into the same mature one

unexpectedly, the concepts of chaos theory became really popular, sowing the seeds for a certain style of free-will belief.

Part of the neglect reflected the fact that chaos theory is a horrible name, insofar as it is about the opposite of nihilistic chaos and is instead about the patterns of structure hidden in seeming chaos.

chaoticism lurked behind the most interesting complicated things. A cell, a brain, a person, a society, was more like the chaoticism of a cloud than the reductionism of a watch.[1]

determinism and predictability are very different things. Even if chaoticism is unpredictable, it is still deterministic. The difference can be framed a lot of ways. One is that determinism allows you to explain why something happened, whereas predictability allows you to say what happens next.

To say that chaotic systems are unpredictable is not to say that science cannot explain them.”

“When Good Theories Make Bad Predictions,” describe chaotic systems as “deterministically unpredictable.”[10]

We do something, carry out a behavior, and we feel like we’ve chosen, that there is a Me inside separate from all those neurons, that agency and volition dwell there. Our intuitions scream this, because we don’t know about, can’t imagine, the subterranean forces of our biological history that brought it about. It is a huge challenge to overcome those intuitions when you still have to wait for science to be able to predict that behavior precisely.

It’s more when two very different sorts of species have converged on the same solution to the same sort of selective challenge.[*6] Among analytical philosophers, the phenomenon is termed overdetermination—when two different pathways could each separately determine the progression to the same outcome.

So you can’t do radical eliminative reductionism and decide what single thing caused the fire, which button presser delivered the poison, or what prior state gave rise to a particular chaotic pattern. But that doesn’t mean that the fire wasn’t actually caused by anything, that no one shot the bullet-riddled prisoner, or that the chaotic state just popped up out of nowhere.

Instead, put enough of the same simple elements together, and they spontaneously self-assemble into something flabbergastingly complex, ornate, adaptive, functional, and cool. With enough quantity, extraordinary quality just…emerges, often even unpredictably.[*2]

Now let’s consider a growing neuron. It extends a projection that has branched into two scout arms (“growth cones”) heading toward two neurons. Simplifying brain development to a single mechanism, each target neuron is attracting the growth cone by secreting a gradient of “attractant” molecules. One target is “better,” thus secreting more of the attractant, resulting in a growth cone reaching it first—which causes a tubule inside that growing neuron’s projection to bend in that direction, to be attracted to that direction. Which makes the parallel tubule adjacent to it more likely to do the same.

The cortex is a six-layer-thick blanket over the surface of the brain, and cut into cross section, each layer consists of different types of neurons

The first step in cortical development is when a layer of cells at the bottom of each cross section of cortex sends long, straight projections to the surface, serving as vertical scaffolding. These are our ant scouts, called radial glia

You know what’s coming next. Newly born neurons wander randomly at the base of the cortex until they bump into a radial glia. They then migrate upward along the glial guide rail, leaving behind chemoattractant signals that recruit more newbies to join the soon-to-be mini column.[*20],[17] Scouts, quality-dependent broadcasting, and rich-get-richer recruiting, from insects and slime molds to your brain. All without a master plan, or constituent parts knowing anything beyond their immediate neighborhood, or any component comparing options and choosing the best one.

Each cell in your body is at most only a few cells away from a capillary, and the circulatory system accomplishes this by growing around forty-eight thousand miles of capillaries in an adult. Yet that ridiculously large number of miles takes up only about 3 percent of the volume of your body.

iterative bifurcation—something grows a distance and splits in two; those two branches grow some distance and each splits in two; those four branches…over and over,

genes (we only have about twenty thousand).

How can your genes code for something abstract like “grow four times the diameter and then split, regardless of scale”?

the membrane is made of two layers, and in between the layers is some Growth Stuff (hatched), coded for by a gene. The Growth Stuff triggers the area of the neuron just below to start constructing a trunk that will rise from there

How much Growth Stuff was there at the beginning? 4Zs’ worth, which will make the trunk grow 4Z in length before stopping.

What has this section provided us? The same themes as in the prior section about pathfinding ants, slime molds, and neurons—simple rules about how components of a system interact locally, repeated a huge number of times with huge numbers of those components, and out emerges optimized complexity. All without centralized authorities comparing the options and making freely chosen decisions.[*28]

Take a beaker full of neurons. They’re newly born, so no axons or dendrites yet, just rounded-up little cells destined for glory. Pour the contents into a petri dish filled with a soup of nutrients that keep neurons happy. The cells are now randomly scattered everywhere.

No committee, no planning, no experts, no choices freely taken. Just the same pattern as for the planned town, emerging from some simple rules:

—Each neuron that has been thrown randomly into the soup secretes a chemoattractant signal; they’re all trying to get the others to migrate to them. Two neurons happen to be closer than average to each other by chance, and they wind up being the first pair to be clumped together in their neighborhood. This doubles the power of the attractant signal emanating from there, making it more likely that they’ll attract a third neuron, then a fourth…Thus,

—Each clump of neurons reaches a certain size, at which point the chemoattractant stops working.

start secreting a molecule that inactivates chemoattractant molecules.

It’s only when enough neurons have migrated into an optimally sized cluster that there is collectively enough of the stuff to prompt the neurons in the cluster to start forming dendrites, axons, and synapses with each other. —Once this local network is wired up (detectable by, say, a certain density of synapses), a chemorepellent is secreted, which now causes neurons to stop making connections to their neighbors, and to instead start sending long projections to other clusters,

attractant and repellent signals. This is the fundamental yin/yang polarity of chemistry and biology—magnets attracting or repelling each other, positively charged or negatively charged ions, amino acids attracted to or repelled by water.[*30]

Most neurobiologists spend their time figuring out minutiae like, say, the structure of a particular receptor for a particular attractant signal.

“The Simple Rules That Can,” has shown things like the three simple rules needed for neurons in the eye of a fly to wire up correctly. Simple rules about the duality of attraction and repulsion, and no blueprints.[*32]

This is the 80:20 rule—approximately 80 percent of interactions occur among approximately 20 percent of the population.

a power-law distribution is when the substantial majority of interactions are very local, with a steep drop-off after that, and as you go out further, interactions become rarer.

Most proteins in our bodies are specialists, interacting with only a handful of other types of proteins, forming small, functional units. Meanwhile, a small percentage are generalists, interacting with scores of other proteins (generalists are switch points between protein networks—for example, if one source of energy is rare, a generalist protein switches to using a different energy source).[*36]

Writ large, this explains “brain-ness,” a place where the vast majority of neurons form a tight, local network—the “brain”—with a small percentage projecting all the way out to places like your toes.[33]

brains have evolved patterns that balance local networks solving familiar problems with far-flung ones being creative, all the while keeping down the costs of construction and the space needed.

A random bunch of neurons, perfect strangers floating in a beaker, spontaneously build themselves into the starts of our brains.[*42]

These adaptive systems emerge from simple constituent parts having simple local interactions, all without centralized authority, overt comparisons followed by decision-making, a blueprint, or a blueprint maker.[*43]

Compatibilists and free-will-skeptic incompatibilists agree that the world is deterministic but disagree about whether free will can coexist with that.

The chaos chapter showed how you get there by confusing the unpredictability of chaotic systems with indeterminism.

List, a philosophy heavyweight who made a big splash with his 2019 book, Why Free Will Is Real. As noted, List readily recognizes that individual neurons work in a deterministic way, while holding out for higher-level, emergent free will. In this view, “the world may be deterministic at some levels and indeterministic at others.”[3]

This raises an important dichotomy. Philosophers with this interest discuss “weak emergence,” which is where no matter how cool, ornate, unexpected, and adaptive an emergent state is, it is still constrained by what its reductive bricks can and can’t do. This is contrasted with “strong emergence,” where the emergent state that emerges from the micro can no longer be deduced from it, even in chaoticism’s sense of a stepwise manner.

You can be out of your mind but not out of your brain; no matter how emergently cool, ant colonies are still made of ants that are constrained by whatever individual ants can or can’t do, and brains are still made of brain cells that function like brain cells.[6]

Sit a subject down and show them three parallel lines, one clearly shorter than the other two. Which is shorter? Obviously that one. But put them in a group where everyone else (secretly working on the experiment) says the longest line is actually the shortest—depending on the context, a shocking percentage of people will eventually say, yeah, that long line is the shortest one.

In this circumstance, there is activation of the amygdala, reflecting the anxiety driving you to go along with what you know is the wrong answer. The second type is “private conformity,” where you drink the Kool-Aid and truly believe that somehow, weirdly, you got it all wrong with those lines and everyone else really was correct.

But the whole point of emergence, the basis of its amazingness, is that those idiotically simple little building blocks that only know a few rules about interacting with their immediate neighbors remain precisely as idiotically simple when their building-block collective is outperforming urban planners with business cards. Downward causation doesn’t cause individual building blocks to acquire complicated skills; instead, it determines the contexts in which the blocks are doing their idiotically simple things.

And the core belief among this style of emergent free-willers is that emergent states can in fact change how neurons work, and that free will depends on it. It is the assumption that emergent systems “have base elements that behave in novel ways when they operate as part of the higher-order system.” But no matter how unpredicted an emergent property in the brain might be, neurons are not freed of their histories once they join the complexity.[9]