Who’s In Charge? Free Will and the Science of the Brain by Michael S. Gazzaniga
On the evening of October 10th 1769, in one of his typically curt dismissals of a philosophical problem, Dr Johnson silenced Boswell, who wanted to talk about fate and free will, by exclaiming: “Sir…we know our will is free, and there’s an end on’t.”
That anecdote is related by Anthony Gottlieb, who in a recent essay reminds us that the history of those who would deny free will is long and varied. The latest, and certainly not the last, attack is from neurologists and academics who think they understand neurology. These folks hold with strict determinism, a position which argues that we should “not hold people accountable for their actions or anti-social behavior.” After all, their genes—no, I mean brains—made them do “it”, whatever it is. David Stove called “selfish genes,” the penultimate theory of how mysterious entities secretly rule our lives, “genetic Calvinism.” Following this, we can label brain science Neurological Predestination.
Nevertheless, there are still some of us who cannot but agree with Dr Johnson, who believe that observation trumps theory. Michael Gazzaniga is one of the rare neurologists who isn’t ready to abandon the idea that we have minds and responsibility. He made his fame by thinking up slicing through the corpus callosum to cure epilepsy patients. He gives the full history of this procedure and its consequences, which is fascinating. But we needn’t dwell on that here.
Think of the corpus callosum as a telephone trunk or bundle of wires which carries signals from the left to the right halves of the brain and back again. Notice this is “signals” and not “all signals.” Chopping through this trunk cured, or mostly cured, the epileptics who consented to have their heads cracked open. But that didn’t mean the separated brain didn’t find ways of talking to its severed self.
That’s because it’s strictly wrong to think of the brain as being an isolated organ that sits encased in a skull. It’s also the spinal cord and the nerves that snake out of it and all throughout the body. It’s even, if you want to think of it this way, the whole body, because those nerves that terminate out to pinkies and toes communicate with the tissue surrounding them.
The start of the idea of the brain as a deterministic machine comes from observations that prove that the mind constructs a view of reality as a function of various stimula. Now, there are many of examples in the book, and doubtless the interested reader knows plenty, so I won’t repeat all of them. Besides, Gazzaniga provides the best and clearest example I’ve seen. Touch your nose. You feel the pressure on your nose and finger simultaneously, yet the signal has to travel a lot farther from your finger to your brain than from your nose. That you “feel” the two at once means your mind is piecing together the two events separated in time. What becomes aware—your mind—is different than a machine itself, which first knows of the nose and only much later of the finger.
Deterministic Gedanken Experiment
Gazzaniga helpfully reminds us that the brain is a “vastly parallel and distributed system, each with a gazillion decision-making points and centers of integration.” A gazillion is a lot, so here is my example. Suppose instead there is just one point, one neuron which accepts inputs and has one output. These inputs can be as many as you like (but finite); they are simple chemical channels the body uses to talk to the neuron. The output behaves similarly.
The neuron takes the different inputs and, via some internal logic, combines them into an output. In other words, the output is a (non-linear) function of the input. This is how statisticians and computer scientists model neurons, anyway. That makes this neuron fully deterministic. If we know the inputs exactly, we can predict the output exactly, as long as we know the form of the function. And if we don’t know the form, given enough input and output data, we can do a pretty good job guessing it.
Now, since this is a real neuron we’re imagining, things won’t always work as they would in a mathematical model. The same inputs might produce different outcomes at different times, possibly because of quantum mechanical effects, or maybe because of other vagaries in the chemistry, or say because of changes in the structure of the neuron (nothing contingent lasts forever). And it could also be that we can’t measure the inputs to sufficient precision. If the algorithm governing the neuron is chaotic, it could appear that the “same” inputs generate different outputs, even widely different outputs. But that flexibility in the output doesn’t mean the workings of the neuron aren’t deterministic: they still are; we just can’t figure out the form of the determinism.
So far, we are in a fully determined world, even though we might not be able to measure it perfectly. Now add in neuron number two and wire it to the first. What changes? The number of connections, for one, and our difficulty in measuring. But that still does not change the deterministic nature of this two-neuron “brain.”
You can see where this is going: add in as many neurons as you want, and even add in neurons of different “ability” or function. It becomes an impossible mess to track and measure practically, of course, but a “massively parallel” deterministic system is just as deterministic as a non-massive, single neuron. To repeat: our ability to measure, model, or predict the form of this determinism is irrelevant to our understanding that the system is deterministic. Why, the brain in the sense we have been describing is just like a big computer.
But our minds are not computers. There are two simple ways to prove this. Here’s the first. Take out the cheapest pocket calculator you can find and use it to calculate digits of π. It will be a fairly slow process, and one which will not fool anybody into thinking the calculator is conscious. Now transfer your algorithm to a “super computer”, perhaps a “massively parallel” one. The only thing that will change is that the digits of π will come faster. More calculations per second does not a mind make. That super computer will also not know what it is doing. This realization was what dashed the hopes of early “artificial intelligence” researchers.
The second example was given to us by Searle1. You’re in a room with a rulebook and box of cards on which Chinese characters are written. You can’t speak or read Chinese. There are two holes in the walls, one for input, one for output. When a string of Chinese symbols comes as input, you look up in the rulebook (written in English) that says, “with this string of input, send that output.” And so you send that output. The input is then switched to output and vice versa. You then wait for new input via the old output, and send the new output via the old input. Get it?
Chinese speaking (reading) people outside the room will be under the impression you/the room speak (read) Chinese, when as stated you haven’t a clue. You as “the room,” or computer, pass the Turning test with respect to those outside the room. But just because you have followed a rulebook, i.e. that you have behaved deterministically, it does not mean, and it is not true, that you understand Chinese as a human mind understands Chinese. Understanding requires more than determinism, it requires a mind.
Gazzaniga does not hold with the strict determinism of the “causal chain gang” which so infatuates many of his colleagues. We have minds and free will. Even though some of what we feel is certainly determined for us—unless your name is Shadrach, Meshach, or Abednego, you can’t will the feeling of cold by being tossed into a furnace—we still have the ability to make choices. Perhaps choices more limited than has previously been allowed, but choices just the same.
So what are his candidates for a mind? He does not argue for dualism. He has a short go at chaos theory and quantum mechanics, but never develops those themes beyond stating that measurement is difficult. He does not appear to be aware of Roger Penrose’s arguments2 about the inability of quantum mechanics to explain the mind. And we saw above that difficulty of measurement is not a proof against (nor for) determinism.
Next time: measurement, emergence, and why people hold with determinism.
1Best short treatment (I left out many details) outside Searle himself is Ed Feser’s Philosophy of Mind pp. 155-159. If you’re looking for just one book by Searle, get Philosophy in a New Century: Selected Essays. This book is also highly, very highly, recommended for statisticians with an interest in philosophy. Next in line is his The Rediscovery of the Mind.
2See his Shadows of the Mind: A Search for the Missing Science of Consciousness, or even better his The Emperor’s New Mind: Concerning Computers, Minds, and the Laws of Physics. This latter book also contains the best description of the EPR paradox I’ve ever seen.
I’ll be away from the computer until late Thursday.