Chapter 5                      The Joys and Woes of Empiricism

                 

                John Locke, empiricist philosopher (credit: Wikimedia Commons)

                                        

                      

                   David Hume, empiricist philosopher (credit: Wikimedia Commons)

Empiricism is a way of thinking about thinking and what we mean when we say we “know” something. It is the logical base of Science, and it claims it begins only from sense data, i.e. what we touch, see, hear, taste, and smell.

Empiricism assumes that all we know is sensory experiences and memories of them. This includes even the concepts that enable us to sort and save those experiences and memories, plan responses to events in the world, and then enact the plans. For empiricists, concepts are labels for bunches of memories that we think look alike. Concepts enable us to sort through, and respond to, real life events. We keep and use those concepts that have reliably guided us in the past to less pain and more joy. We drop ones that have proved useless.

According to Empiricism, our sense organs are continually feeding bits of information into our minds about the sizes, textures, colours, shapes, sounds, aromas, and flavors of things we encounter. Even when we are not consciously paying attention, at other, deeper levels our minds are taking in these details. “The eye – it cannot choose but see. We cannot bid the ear be still. Our bodies feel where’er they be, against or with our will.” (Wordsworth)

For example, I know when I hear noises outside of a car approaching or a dog barking. Even in my sleep, I detect gravel crunching sounds in the driveway. One spouse awakes to the baby’s crying; the other dozes on. One wakes when the furnace is not cutting out as it should; the other sleeps. The ship’s engineer sleeps through steam turbines roaring and props churning, but she wakes up when one bearing begins to hum a bit above its normal pitch. She wakes up because she knows something is wrong. A bearing is running hot. Empiricism is a modern way of understanding our complex information-processing system – the human body, its brain, and the mind that brain holds.

In the Empiricist model, the mind notices how certain patterns of details keep recurring in some situations. When we notice a pattern of details in encounter after encounter with a familiar situation or object, we make mental files – for example, for round things, red things, sweet things, or crisp things. We then save the information about that type of object in our memories. The next time we encounter an object of that type, we simply go to our memory files. There, by cross-referencing, we get: “Fruit. Good to eat.” Empiricists say all general concepts are built up in this way. Store, review, hypothesize, test, label, repeat.

Scientists now believe this Empiricist model is only part of the full picture. In fact, most of the concepts we use to recognize and respond to reality are not learned by each of us on our own, but instead are concepts we were taught as children. Our childhood programming teaches us how to cognize things. After that, almost always, we don’t cognize things, only recognize them. (We will explore why our parents and teachers program us in the ways that they do in upcoming chapters.) Also note that when we encounter a thing that doesn’t fit any of our familiar concepts, we grow wary. (“What’s that?! Stay back!”)

But, empiricists claim that all human thinking and knowing happens in the experienced-based way. Watch the world. Notice patterns that repeat. Create labels (concepts) for the patterns that you keep encountering, especially those that signify hazard or opportunity. Store them up in memories. Pull the concepts out when they fit, then use them to deal with life events. Remember what works. Keep trying.

For individuals and nations, according to the empiricists, that’s how life goes. And the most effective way of life for us, the way that makes this common-sense process rigorous, and that keeps getting good results, is Science.

There are arguments against the empiricist way of thinking about thinking and its model of how human thinking and knowing work. Empiricism is a way of seeing ourselves and our minds that sounds logical, but it has its problems.

                   

 

             Child sensing her world (credit: Sheila Brown; Public Domain Pictures) 

Since Locke, critics of Empiricism (and Science) have asked, “When a human sees things in the real world and spots patterns in the events going on there, what is doing the spotting? The human mind and the sense data-processing programs it must already contain to be able to do the tricks empiricists describe obviously came before any sense-data processing could be done. What is this equipment, and how does it work?” Philosophers of Science have trouble explaining what this “mind” that does the “knowing” is.

Consider what Science is aiming to achieve. What scientists want to discover, come to understand, and then use in the real world are what are usually called “laws of nature”. Scientists do more than just observe the events in physical reality. They also strive to understand how these events come about and then to express what they understand in general statements about these events, in mathematical formulas, chemical formulas, or rigorously logical sentences in one of the world’s languages. Or, in some other system used by people for representing their thoughts. (A computer language might do.) A natural law statement is a claim about how some part of the world works. A statement of any kind – if it is to be considered scientific – must be expressed in a way that can be tested in the real, physical world.

Put another way, if a claim about a newly discovered real-world truth is going to be worth considering, to be of any practical use whatever, we must be able to state it in some language that humans use to communicate ideas to other humans, for example, mathematics or one of our species’ natural languages: English, Russian, Chinese, etc. A theory that can be expressed only inside the head of its inventor will die with her or him.

Consider an example. The following is a verbal statement of Newton’s law of universal gravitation: “Any two bodies in the universe attract each other with a force that is directly proportional to the product of their masses and inversely proportional to the square of the distance between them.”

The mathematical formula expressing the same law is: 

   

 

                           (credit: dna-Dennis, Wikimedia Commons) 

                          

                           

Now consider another example of a generalization about human experience:

                                                      

                                                        

                                   

                              Pythagoras' Theorem illustrated (credit: Wikimedia)

In plain English, this formula says: “the square on the hypotenuse of a right triangle is equal to the sum of the squares on the two adjacent sides”.

The Pythagorean Theorem is a mathematical law, but is it a scientific one? In other words, can it be tested in some unshakable way in the physical world? (Can one measure the sides and know the measures are perfectly accurate?)

The harder problem occurs when we try to analyze how true statements like Newton’s Laws of Motion or Darwin’s Theory of Evolution are. These claim to be laws about things we can observe with our senses, not things that may exist – and be true – only in the mind (like Pythagoras’ Theorem).

Do statements of these laws express unshakable truths about the real world or are they just temporarily useful ways of roughly describing what appears to be going on in reality – ways of thinking that are followed for a few decades while the laws appear to work for scientists, but that then are seriously revised or even dropped when we encounter new problems that the law can’t explain?

Many theories in the last 400 years have been revised or dropped totally. Do we dare to say about any natural law statement that it is true in the way in which “5 + 7 = 12” is true or the Pythagorean Theorem is true?

This debate is a hot one in Philosophy, even in our time. Many philosophers of Science claim natural law statements, once they’re supported by enough experimental evidence, can be considered to be true in the same way as valid mathematical theorems are. But there are also many who say the opposite – that all scientific statements are tentative. These people believe that, over time, all natural law statements get replaced by new statements based on new evidence and new models or theories (as, for example, Einstein's Theory of Relativity replaced Newton's Laws of Motion and Gravitation). 

If all natural law statements are seen as being, at best, only temporarily true, then Science can be seen as a kind of fashion show whose ideas have a bit more shelf life than the fads in the usual parade of TV shows, songs, clothes, makeup, and hairdos. In short, Science’s law statements are just narratives, not true so much as useful, but useful only in the lands in which they gain some currency and only for limited time periods at best.

The logical flaws that can be found in empiricist reasoning aren’t small ones. One major problem is that we can’t know for certain that any of the laws we think we see in nature are true because even the terms that we use when we make a scientific law statement are vulnerable to attack by the skeptics.

When we state a natural law, the terms we use to name the objects and events we want to focus on exist, the skeptics argue, only in our minds. Even what makes a thing a “tree”, for example, is dubious. In the real world, there are no trees. We just use the word “tree” as a convenient label for some of the things we encounter in our world and for our memories of them.

A simple statement that seems to us to make sense, like the one that says hot objects will cause us pain if we touch them, can’t be trusted in any ultimate sense. To assume this “law” is true is to assume that our definitions for the terms hot and pain will still make sense in the future. But we can’t know that. We haven’t seen the future. Maybe, one day, people won’t feel pain.

Thus, all the terms in natural law statements, even ones like force, atom, acid, gene, proton, cell, organism, etc. are labels created in our minds because they help us to sort and categorize sensory experiences and memories of those experiences, and then talk to one another about what seems to be going on around us. But reality does not contain things that somehow fit terms like “gene” or “galaxy”. Giant ferns of a bygone geological age were not trees. But they would have looked like trees to most people from our time who use the word “tree”. How is a willow bush a bush, but not a tree? If you look through a powerful microscope at a gene, it won’t be wearing a tag that reads “gene.” 

In other languages, there are other terms, some of which overlap in the minds of the speakers of that language with things that English has a different word for entirely. In Somali, a gene is called “hiddo”. And the confusions get even trickier. German contains two verbs for the English word “know”, as does French.  Spanish contains two words for the English verb “be”.

We divide up and label our memories of what we see in reality in whatever ways have worked reliably for us and our ancestors in the past. And even how we see simple things is determined by what we've been taught by our elders. In English, we have seven words for the colors of the rainbow; in some other languages, there are as few as four words for all the spectrum’s colors.  

Right from the start, our natural law statements gamble on the future validity of our human-invented terms for things. The terms can seem solid, but they are still gambles. Some terms humans once confidently gambled on turned out later, in light of new evidence, to be naïve and inadequate.                               

                                              

                     

             Isaac Newton (artist: Godfrey Kneller) (credit: Wikimedia Commons)

Newton’s laws of motion are now seen by physicists as being approximations of the relativistic laws described by Einstein. Newton’s terms body, space, and force once seemed self-evident. But it turned out that space is not what Newton assumed it to be.

A substance called phlogiston once seemed to explain all of Chemistry. Then Lavoisier did experiments which showed that phlogiston doesn’t exist.

On the other hand, people spoke of genes long before microscopes that could reveal them to the human eye were invented, and people still speak of atoms, even though nobody has ever seen one. Some terms last because they enable us to build mental models and do experiments that get results we can predict. For now. But the list of scientific theories that “fell from fashion” is long.

                                                                

                          

                                      Chemists Antoine and Marie-Anne Lavoisier 

                                                   (credit: Wikimedia Commons) 

Various further attempts have been made in the last 100 years to nail down what Science does and to prove that it is a reliable way to truth, but they have all come with conundrums of their own.

Now, while the problems described so far bother philosophers of Science a lot, such problems are of little interest to the majority of scientists themselves. They see the law-like statements they and their colleagues try to formulate as being testable in only one meaningful way, namely, by the results shown in replicable experiments done in the lab or in the field. Thus, when scientists want to talk about what “knowing” is, they look for models not in Philosophy, but in the branches of Science that study human thinking, like neurology for example. However, efforts to find proof in neurology that Empiricism is logically solid also run into problems. 

The early empiricist John Locke basically dodged the problem when he defined the human mind as a “blank slate” and saw its abilities to perceive and reason as being due to its two “fountains of knowledge,” sensation and reflection. Sensation, he said, is made up of current sensory experiences and current reviews of categories of past experiences. Reflection is made up of the “ideas the mind gets by reflecting on its own operations within itself.” How these kinds of “operations” got into human consciousness and what is doing the “reflecting” that he is talking about, he doesn’t say.¹

Modern empiricists, both philosophers of Science and scientists themselves, don’t like their forebears giving in to even this much mystery. They want to get to definitions of what knowledge is that are solidly based in evidence.

Neuroscientists who aim to figure out what the mind is and how it thinks do not study words. They study physical things, like electro-encephalographs of the brains of people working on assigned tasks. 

For today’s scientists, philosophical discussions about what knowing is are just words chasing words. Such discussions can’t bring us any closer to understanding what knowing is. In fact, scientists don’t respect discussions about anything we may want to study unless those discussions are based on a model that can be tested in the real world.

Scientific research, to qualify as “scientific”, must also be designed so it can be replicated by any researcher in any land or era. Otherwise, it’s not credible; it could be a coincidence, a mistake, wishful thinking, or simply a lie. Thus, for modern scientists, analysis of physical evidence is the only means by which they can come to understand anything, even when the thing they are studying is what’s happening in their brains while they are studying those brains.

The researcher sees a phenomenon in reality, gets an idea about how it works, then designs experiments that will test his theory. The researcher then does the tests, records the results, and reports them. The aim of the process is to arrive at statements about reality that will help to guide future research onto fruitful paths and will enable other scientists to build technologies that are increasingly effective at predicting and manipulating events in the real world.

For example, electro-chemical pathways among the neurons of the brain can be studied in labs and correlated with subjects’ descriptions of their actions. (The state of research in this field is described by Delany in an article that is available online and also by Revonsuo in Neural Correlates of Consciousness: Empirical and Conceptual Questions, edited by Thomas Metzinger.^2,3)

Observable things are the things scientists care about. The philosophers’ talk about what thinking and knowing are is just that – talk.

As an acceptable alternative to the study of brain structure and chemistry, scientists interested in thought also study patterns of behavior in organisms like rats, birds, and people, behavior patterns elicited in controlled, replicable ways. We can, for example, try to train rats to work for wages. This kind of study is the focus of Behavioural Psychology. (See William Baum’s 2004 book Understanding Behaviorism.⁴)

As a third alternative, we can even try to program computers to do things as similar as possible to things humans do. Play chess. Write poetry. Cook meals. If the computers then behave in human-like ways, we should be able to infer some testable theories about what thinking and knowing are. This research is done in a branch of Computer Science called “Artificial Intelligence” or “AI”.

Many empiricist philosophers see AI as our best hope for defining, once and for all, what human thinking is. AI offers a model of our own thinking that will explain it in ways that can be tested. A program written to simulate thinking either runs or it doesn’t, and every line in it can be examined. When we can write programs that make computers converse with us so well that, when we talk to them, we can’t tell whether we’re talking to a human or a computer, we will have encoded what thinking is. Set it down in terms programmers can explain with algorithms. Run the program over and over and observe what it does. 

With the rise of AI, cognitive scientists felt that they had a real chance of finding a model of thinking that worked, one beyond the challenges of the critics with their counterexamples. (A layman’s view on how AI is doing can be found in Thomas Meltzer’s article in The Guardian, 17/4/2012.⁵)

Testability in physical reality 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 creatures in a fantasy novel. Ents. Orcs. Sandworms. Amusing daydreams, nothing more.

                                                                     

                  

                                                              

                                      Kurt Gödel (credit: Wikimedia Commons)

For years, the most optimistic of the empiricists looked to AI for models of thinking that would work in the real world. Their position has been cut down in several ways since those early days. What exploded it for many was the proof found by Kurt Gödel, Einstein’s companion during his lunch hour walks at Princeton. Gödel showed that no rigorous system of symbols for expressing human thinking can be a complete system. Thus, no system of computer coding can ever be made so that it can adequately refer to itself. (In Gödel’s proof, the ideas analyzed were basic axioms in Arithmetic.) Gödel’s proof is difficult for laypersons to follow, but non-mathematicians don’t need to be able to do formal logic in order to grasp what his proof implies about everyday thinking. (See Hofstadter for an accessible critique of Gödel.⁶)

                     

                                        Douglas Hofstadter (credit: Wikipedia)

If we take what it says about Arithmetic and extend that finding to all kinds of thinking, Gödel’s proof says no symbol system for expressing our thoughts will ever be powerful enough to enable us to express all the thoughts about thoughts that human minds can dream up. In principle, there can’t be such a system. In short, what a human programmer does as she fixes flaws in her programs is not programmable.     

What Gödel’s proof implies is that no way of modelling the human mind will ever adequately explain what it does. Not in English, Logic, French, Russian, Chinese, Java, C++, 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 express some of our ideas really well, we discover that no matter how well the system is designed, no matter how large or subtle it is, we have other thoughts that we can’t express in that system at all. Yet we must make statements that at least attempt to communicate all our ideas. Science is social. It has to be shared in order to advance.

Other theorems in Computer Science offer support for Gödel’s theorem. For example, in the early days of the development of computers, programmers were frequently creating programs with “loops” in them. After a program had been written, when it was run, it would sometimes become stuck in a subroutine that would repeat a sequence of steps from, say, line 79 to line 511 then back to line 79, 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 rewrite the loop or write around it. The work was frustrating and time consuming.

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

However, progress on the writing of this check program met with problem after problem. Eventually, Turing published a proof showing that writing a check program isn’t possible. A foolproof algorithm for finding loops in other algorithms is, in principle, impossible. (Wikipedia, “Halting Problem”.⁷) This finding in Computer Science, the science many see as our bridge between the abstractness of thinking and the concreteness of material reality, is Gödel all over again. It confirms our deepest feelings about Empiricism. Empiricism is useful, but it is doomed to remain incomplete. It can’t explain itself.

Arguments and counterarguments on this topic 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 theoretical models and real-world evidence, including evidence from Science itself, the more we are driven to conclude that the empiricist way of understanding what thinking is will probably never explain its own method of reaching that understanding. Empiricism’s own methods have ruled out the possibility of it being a base for epistemology. (What is the meaning of meaning?) (Solve x² + 1 = 0).

My last few paragraphs describe only the dead ends that have been hit in AI. Other sciences searching for this same holy grail – a clear, evidence-backed model of human thinking – haven’t fared any better. Neurophysiology and Behavioural Psychology also keep striking out.

If a neurophysiologist could set up an MRI or similar imaging device and use his model of thinking to predict which networks of neurons in his brain would be active when he turned the device on and studied pictures of his own brain activities, then he could say he had set down a reliable working model of what consciousness is. ("Those are my thoughts: those neuron firings right there.") But neuroscience is not even close to being that complete.

Patterns of neuron firings mapped on one occasion when a subject performs even a simple task can’t be counted on. We find different patterns every time we look. A human brain contains a hundred billion neurons, each one capable of connecting to as many as ten thousand others. Infinite possibilities. And the patterns of firings in that brain are changing all the time. Philosophers who seek a base for Empiricism strike out if they look for it in Neurophysiology.⁸

                           

                      Diagram of a Skinner Box (credit: Wikimedia Commons) 

Problems similar to those in AI and Neurophysiology also beset Behavioral Psychology. Researchers can train rats, pigeons, or other animals and predict what they will do in controlled experiments, but when a behaviorist tries to give behaviorist explanations for what humans do, many exceptions have to be made. A claim like: "There's the mind: a set of behaviors we can replicate at any time." isn't even close for Behavioral Psychology yet. 

In a simple example, alcoholics who say they truly want to get sober for good can be given a drug that makes them violently ill if they imbibe even small amounts of alcohol, but that does not affect them as long as they do not drink alcohol. This would seem to be a behaviourist’s solution to alcoholism, one of society’s most painful problems. But it doesn’t work. Thousands of alcoholics in early studies kept their self-destructive ways while on disulfiram.⁹ What is going on in these cases is obviously much more complex than Behaviorism can account for. And this is but one commonplace example.

I am not disappointed to learn that humans turn out to be complex, evolving, and impossible to pin down, no matter the model we analyze them under.

At present, it appears that Science can’t provide a rationale for itself in theory and can’t demonstrate the reliability of its methods in practice. Could it be another set of temporarily effective illusions, like Christianity, Communism, or Nazism once were? Personally, I don’t think so. The number of Science’s achievements and their profound effects on our society argue powerfully that Science works in some profound way, even though it can’t explain itself.

Do Science’s laws sometimes fail glaringly in the real world? Yes. Absolutely. Newton’s Laws of Motion turned out to be inadequate for explaining data drawn from more advanced observations of reality. The mid-1800's brought better views of the universe provided by better telescopes. These led Physics past Newton’s laws, and on to the Theory of Relativity. Newton’s picture turned out to be too simple, though it was useful on the everyday scale.

Thus, considering how revered Newton’s model of the cosmos once was and knowing that it gives only a partial, inadequate picture of the universe can cause philosophers – and ordinary folk – to doubt Science. We then question whether Empiricism can be trusted as a base to help us design a new moral code.  Our survival is at stake. Science can’t even explain its own thinking.

As we seek to build a moral system we can all live by, we must look for a way of thinking about thinking based on stronger logic, a way of thinking about thinking that we can believe in. We need a new model, built around a core philosophy that is different from Empiricism, not just in degree but in kind.

Empiricism’s disciples have achieved some impressive results in the practical sphere, but then again, for a while in their heydays, so did Christianity, Communism, and Nazism. They even had their own “sciences,” dictating in detail what their scientists should study and what they should conclude.

Perhaps the most disturbing example of a worldview that seemed to work very well for a while is Nazism. The Nazis claimed to base their ideology on Empiricism and Science. In their propaganda films and in all academic and public discourse, they preached a warped form of Darwinian evolution that enjoined and exhorted all nations, German or non-German, to go to war, seize territory, and exterminate or enslave all competitors – if they could. They claimed this was the way of the real world. Hitler and his cronies were gambling confidently that in this struggle, those that they called the “Aryans” – with the Germans in the front ranks – would win.  

                          

                                Nazi leader Adolf Hitler (credit: Wikimedia Commons)

“In eternal warfare, mankind has become great; in eternal peace, mankind would be ruined.”                    (Mein Kampf)

Such a view of human existence, they claimed, was not cruel or cynical. It was a mature, realistic acceptance of the truth. If people calmly and clearly look at the evidence of History, they can see that war always comes. Mature, realistic adults, the Nazis claimed, learn and practice the arts of war, assiduously in times of peace and ruthlessly in times of war. According to the Nazis, this was merely a logical consequence of accepting that the survival-of-the-fittest rule governs all life, including human life.

Hitler’s ideas about race and about how Darwinian evolution could be applied to humans, were, in the real science of Genetics, unsupported. Hitler’s views of “race” were silly. But in the Third Reich, this was never acknowledged.

                            

                        

                                 Werner Heisenberg (credit: Wikimedia Commons)

And for a while, Nazism worked. The Nazi regime rebuilt what had been a shattered Germany. But the sad thing about the way smart men like physicist Werner Heisenberg, biologist Ernst Lehmann, and chemist Otto Hahn became tools of Nazism is not that they became its tools. The really disturbing thing is that their worldviews as scientists did not equip them to break free of the Nazis’ distorted version of Science. As I pointed out earlier, their religion failed them. But Science failed them too.

                                

             

                                       Otto Hahn (credit: Wikimedia Commons)

There is certainly evidence in human history that the consequences of Science being misused can be horrible. Nazism became humanity’s nightmare. Some of its worst atrocities were committed in the name of Science.¹⁰ Under Nazism, medical experiments especially passed all nightmares.

For practical, evidence-based reasons, then, as well as for theoretical ones, millions of people around the world today have become deeply skeptical about all systems of thought and, in moral matters, about the moral usefulness of Science in particular. At deep levels, we are driven to wonder: Should we trust something as critical as the survival of our culture and our grandchildren, even our Science itself, to a way of thinking that, first, can’t explain itself, and second, has had horrible, practical failures in the past? Science can put men on the moon, grow crops, and cure diseases. But as a moral guide, even for its own activities, so far it looks very unreliable.  

In the meantime, we must get on with trying to build a universal moral code. Reality won’t let us procrastinate. It forces us to think, choose, and act every day. To do these things well, we need a comprehensive moral guide, one that we can refer to in daily life as we observe our world and choose our actions.

As a base for that guide, Empiricism looks unreliable. Is there something else to which we might turn?

Notes

1. John Locke, An Essay Concerning Human Understanding (Glasgow: William Collins, Sons and Co., 1964), p. 90.

2. Donelson E. Delany, “What Should Be the Roles of Conscious States and Brain States in Theories of Mental Activity?” PMC Mens Sana Monographs 9, No. 1 (2011): 93–112.http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3115306/.

3. Antti Revonsuo, “Prospects for a Scientific Research Program on Consciousness,” in Neural Correlates of Consciousness: Empirical and Conceptual Questions, ed. Thomas Metzinger (Cambridge, MA, & London, UK: The MIT Press, 2000), pp. 57–76.

4. William Baum, Understanding Behaviorism: Behavior, Culture, and Evolution (Malden, MA: Blackwell Publishing, 2005).

5. Tom Meltzer, “Alan Turing’s Legacy: How Close Are We to ‘Thinking’ Machines?” The Guardian, June 17, 2012.

http://www.theguardian.com/technology/2012/jun/17/alan-turings-legacy-thinking-machines.

6. Douglas R. Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid (New York, NY: Basic Books, 1999).

7. “Halting Problem,” Wikipedia, the Free Encyclopedia. Accessed April 1, 2015. http://en.wikipedia.org/wiki/Halting_problem.

8. Alva Noë and Evan Thompson, “Are There Neural Correlates of Consciousness?” Journal of Consciousness Studies 11, No. 1 (2004), pp. 3–28.

http://selfpace.uconn.edu/class/ccs/NoeThompson2004AreThereNccs.pdf.

9. Richard K. Fuller and Enoch Gordis, “Does Disulfiram Have a Role in Alcoholism Treatment Today?”Addiction 99, No. 1 (Jan. 2004), pp. 21–24. http://onlinelibrary.wiley.com/doi/10.1111/j.1360-0443.2004.00597.x/full.

10. “Nazi Human Experimentation,” Wikipedia, the Free Encyclopedia.

Accessed April 1, 2015.

http://en.wikipedia.org/wiki/Nazi_human_experimentation.