Chapter 5.                        (continued) 

                                        IBM supercomputer "Blue Gene"

   

                         (credit: Argonne National Laboratory, via Wikipedia) 

Various further attempts were made in the last one hundred 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 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 in the first place, 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 knowing 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.^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 Behavioral Psychology. 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”.