Chapter 3    Part E 

             As an acceptable alternative to brain structure and chemistry, scientists interested in thought also study patterns of behavior in organisms like rats, pigeons, and people that are being stimulated in controlled, replicable ways. We can, for example, try to train rats to work for wages. This kind of study is the focus of Behavioral Psychology. (See Baum’s 2004 book “Understanding Behaviorism”.) (7)

               

        As a third alternative, we can even try to program computers to do things very similar to the things that humans do. Then if the computers do behave in human-like ways, we should be able to infer some tentative, testable conclusions about what human thinking and knowing are from the programs that enabled these computers to behave like humans. This kind of research is done in a branch of Computing Science called "Artificial Intelligence" or A.I.

     To many empiricist philosophers and scientists, A.I. seems to offer them their best hope of defining once and for all a base for their way of thinking, a base that can explain all of human thinking’s so-called “abstract processes” and that is also materially observable. A program either runs or it doesn’t. One that made computers imitate humans so well that we couldn’t tell which was the computer answering us and which was the human would arguably have encoded what thinking is. At last, a beginning point beyond the challenges of the critics of Empiricism and their endless counter-examples. (A layman’s view on how A.I. is doing is in Meltzer’s article in The Guardian, 17/4/2012.) (8)   

               

         Testability and replicability of the tests, I repeat, are the characteristics of modern Empiricism and of all Science. All else, to modern empiricists, has as much reality and as much reliability to it as fantasy creatures in a fantasy novel ... amusing daydreams, nothing more.

     Kurt Godel   

             

          The most optimistic of the Empiricists for years were looking to A.I. for models of thinking that would work in the real world. Their position has been cut down in several ways since those eager, early days. What exploded it for many was the proof found by Kurt Godel, Einstein’s companion during his lunch hour walks at Princeton. Godel showed that no rigorous system of symbols for expressing some of the most basic of human thinking routines can be a complete system. (In Godel's proof, the ideas that he analyzed were basic axioms in Arithmetic.) Godel's proof is difficult for laymen to follow, but non-mathematicians don't need to be able to do that formal logic in order to grasp what Godel’s proof implies about everyday thinking. (See Hofstader for an accessible critique of Godel.) (9.)

     Douglas Hofstadter           

          If we take what it says about Arithmetic and extend that finding to all kinds of human thinking, then what Godel's proof says is that there is no symbol system for expressing our thoughts that will ever be good enough to express and discuss all of the new ideas that the human mind can dream up. Furthermore, in principle – in other words at the roots of human thinking itself – there can’t be any such system of expression. (O.W. Holmes said: “No generalization is worth a damn, including this one.” If this statement is true, it makes itself false. If false, it makes itself true. Our problem occurs because the statement is self-referencing. It is a rough example in ordinary English of what Godel is talking about.)  

               

            What Godel's proof implies is that no way of modeling what the human mind does will ever adequately model or explain that very thing. Not in English, Logic, French, Russian, Chinese, Java, C++, music, or Martian. We will always be able to generate thoughts, questions, and statements that we can't express in any one symbol system. If we find a system that can be used to encode some of our favorite ideas really well, we will only discover that no matter how well the system is designed, no matter how large or subtle it is, we will have other thoughts that, in that system, we can't express at all. Yet we have to make statements that at least attempt, however inadequately, to communicate our ideas. Science, like almost all activities of human life, is communal. It has to be shared in order to advance.   

               

           The further conclusion to be drawn from Godel is that researchers in sciences like Physiological Psychology, Behavioral Psychology, Computer Science, Sociology, etc., may all study what thinking and knowing are, with each discipline approaching the phenomena from its perspective, but no single one of them, and no set of them taken together will ever completely define what human thinking and knowing are. No system for representing and expressing the thoughts generated in the mind will ever be capable of fully defining or describing the mind or what it is doing as it does that generating.

               

        Other theorems in Computing Science seem to offer fascinating support to Godel's theorem. For example, in the early days of the development of computers, programmers over and over were creating programs with loops in them. After a program had been written, it would be run and then, sometimes, the program would get stuck in a sub-routine that kept going over one sequence of steps from, say, line 193 to line 511 then back to line 193, again and again. Whenever a program contained this kind of flaw, a human being had to stop the computer, go over the program, find why the loop was occurring, then either re-write the loop or write around it. The work was frustrating and very time consuming.

               

           Soon, a few programmers got the idea of writing a kind of meta-program that they were hoping would act as a "check" program. It would scan other programs, find their loops, and fix them, or at least point them out to the programmer so that she could fix them. The programmers knew that writing such a program would be difficult, but once it was written, it would save so many people so much time.

     However, progress on the writing of this "check" program seemed to be running into difficulty after difficulty. Eventually, someone really good with computer languages (Alan Turing) published a proof which showed that writing a check program was, in principle, not possible. A foolproof algorithm for checking other algorithms is, in principle, not possible. (See “Halting Problem” in Wikipedia.) (10) 

               

         This finding in Computing Science, the science which many people see as the bridge between the abstractness of thinking and the concreteness of material reality, is, I believe, Godel all over again. In another kind of proof, it confirms our deepest feelings about Empiricism. It is doomed to remain incomplete. No completely effective check program has ever been found. Some check programs which are able to catch the simpler mistakes that beginning programmers make have been written, but no foolproof one has ever been created in any of the many programming languages that have evolved in the field over the years.  

               

           There are some computer scientists who believe that by using math theorems not even known yet, one day it may be possible for programmers to write computer programs that keep iterating closer to doing what human programmers do, even checking other programs. But they admit that no program so far can do what a human programmer does when she fixes a faulty program … or writes a song.    

               

          The possibilities are fascinating, but for our purposes in trying to find a base for a philosophical system and a moral code, the conclusion is much simpler. The more we study both the theoretical points and the real world evidence, including evidence from Science itself, the more we are driven to conclude that the Empiricist way of seeing or understanding what thinking and knowing are will probably never be able to explain itself. If Godel's proof is right, and nearly everyone in Math and Computing Science thinks it is, and if it is extended to human thinking in general, Empiricism's own methods have ruled out the possibility of an unshakable Empiricist beginning point for epistemology.

     If I think that I have found a way to describe what thinking is, then I will have to express what I want to say about the matter in a language of some kind … English, Russian, C++ or some other sort of language for encoding thoughts. But there is not, nor can there be, a code that is capable of capturing and communicating what the thinker is doing as she is thinking about her own thinking. It is a mental conundrum with no solution. (What is the meaning of the word “meaning”?)

           

 Diagram of the human brain

      A single neuron, showing its branching structure

   

           Of course, the last few paragraphs are only describing the dead ends that have been hit in A.I., but other sciences searching for this same holy grail – a clear, evidence-backed model of human thinking – haven’t fared any better. Neurophysiology and Behavioral Psychology also keep striking out.

     If a neurophysiologist could set up an MRI or some other similar imaging device, then predict in advance which networks of neurons in his own brain would be active when he turned the device on and watched the machine give its information on his own brain activities as he was studying them himself, in real time, then he and his science could say that they had formulated a model of what consciousness is. But on both the theoretical and practical sides, neuroscience is not even close to being so complete.

    Patterns of neuron firings that are mapped on one occasion when a subject is performing even a very simple task unfortunately can’t be counted on. We find different patterns of firings, apparently, depending on what we assume consciousness is and different researchers’ definitions differ widely. A human brain contains one hundred billion neurons, each one capable of connecting to as many as ten thousand others. Philosophers looking for a solid base for Empiricism are disappointed if they go to neuro-physiology for that base. (11)  

  Similar problems beset Behavioral Psychology. The researchers can condition rats and predict what they will do in controlled experimental situations, but endless ad hoc add-ons and exceptions have to be made to their explanations of what humans in everyday life do.

     In a simple example, alcoholics who say that they truly want to get sober for good can be given a drug that makes them violently, physically ill if they imbibe even very small amounts of alcohol, but that does not affect them as long as they do not drink alcohol. This would seem to be a behaviorist’s solution to alcoholism, one of society’s most intractable problems. But alas it doesn’t work. Thousands of alcoholics have kept on with their self-destructive ways while on disulfiram. (12) What is going on in these cases is obviously much more complex than any explanation given by behaviorism’s best theories can account for. And this is but one simple example.

     I, for one, am not disappointed to learn that the human animal turns out to be an enormously complex piece of work, no matter the model under which we analyze it.


Notes 

7. Baum, William; “Understanding Behaviorism: Behavior, Culture, and Evolution”; Blackwell Publ.; 2005

8. Meltzer, Thomas; “Alan Turing’s Legacy: “How Close Are We To Thinking Machines?”; The Guardian, June 17, 2012.

9. Hofstader, Douglas; “Godel, Escher, Bach: An Eternal Golden Braid”; Basic Books; 1999.

10. “Halting Problem”; Wikipedia; 2012.

11. Noe, Alva and Evan Thompson; “Are There Neural Correlates Of Consciousness?”; available online at http://selfpace.uconn.edu/class/ccs/NoeThompson2004AreThereNccs.pdf  

     12. Fuller, Richard K.; “Does Disulfiram Have A Role In Alcoholism Treatment Today?”; Addiction; Dec.