What is computational neuroscience? (VII) Incommensurability and relativism

I explained in previous posts that new theories should not be judged by their agreement with the current body of empirical data, because these data were produced by the old theory. In the new theory, they may be interpreted very differently or even considered irrelevant. A few philosophers have gone so far as to state that different theories are incommensurable, that is, they cannot be compared with each other because they have different logics (e.g. observations are not described in the same way in the different theories). This reasoning may lead to relativistic views of science, that is, the idea that all theories are equally “good” and that their choice are a matter of personal taste or fashion. In this post I will try to explain the arguments, and also to discard relativism.

In “Against Method”, Feyerabend explains that scientific theories are defined in a relational way, that is, elements of a theory make sense only in reference to other elements of the theory. I believe this is a very deep remark that applies to theories of knowledge in the broadest sense, including perception for example. Below, I drew a schematic figure to illustrate the arguments.

Theories are systems of thought that relate to the world. Concepts in a theory are meant to relate to the world, and they are defined with respect to other concepts in the theory. A given concept in a given theory may have a similar concept in another theory, but it is a different concept, in general. To explain his arguments, Feyerabend uses the analogy of language. It is a good analogy because languages relate to the world, and they have an internal relational structure. Imagine theories A and B are two languages. A word in language A is defined (e.g. in the dictionary) by using other words from language A. A child learns her native language by picking up the relationship between the words, and how they relate to the world she can see. To understand language A, a native speaker of language B may translate the words. However, translation is not definition. It is imprecise because the two words often do not have exactly the same meaning in both languages. Some words may not even exist in one language. A deeper understanding of language A requires to go beyond translation, and to capture the meaning of words by acquiring a more global understanding of the language, both in its internal structure and in its relationship with the world.

Another analogy one could make is political theories, in how they view society. Clearly, a given observation can be interpreted in opposite ways in conservative and liberal political views. For example, the same economic crisis could be seen as the result of public debt or as the result of public cuts in spending (due to public acquisition of private debt).

These analogies support the argument that an element of a new theory may not be satisfactorily explained in the framework of the old theory. It may only make full sense when embedded in the full structure of the new theory – which means that new theories may be initially unclear and that the concepts may not be well defined. This remark can certainly make different theories difficult to compare, but I would not conclude that theories are incommensurable. This conclusion would be valid if theories were closed systems, because then a given statement would make no sense elsewhere than in the context of the theory in which it is formulated. Axiomatic systems in mathematics could be said to be incommensurable (for example, Euclidian and non-Euclidian geometries). But theories of knowledge, unlike axiomatic systems, are systems that relate to the world, and the world is shared between different theories (as illustrated in the drawing above). For this reason, translation is imprecise but not arbitrary, and one may still assess the degree of consistency between a scientific theory and the part of the world it is meant to explain.

One may find an interesting example in social psychology. In the theory of cognitive dissonance, new facts that seem to contradict our belief system are taken into account by minimally adjusting that belief system (minimizing the “dissonance” between the facts and the theory). In philosophy of knowledge, these adjustments would be called “ad hoc hypotheses”. When it becomes too difficult to account for all the contradictory facts (making the theory too cumbersome), the belief system may ultimately collapse. This is very similar to the theory of knowledge defended by Imre Lakatos, where belief systems are replaced by research programs. Cognitive dissonance theory was introduced by a field study in a small American sect who believed that the end of the world would occur at a specific date (Festinger, Riecken and Schachter (1956), When Prophecy Fails. University of Minnesota Press). When the said date arrived and the world did not end, strangely enough, the sect did not collapse. On the contrary, it made it stronger, with the followers more firmly believing in their view of the world. They considered that the world did not end because they prayed so much and God heard their prayers and postponed the event. So they made a new prediction, which of course turned out to be false. The sect ultimately collapsed, although only after a surprisingly long time.

The example illustrates two points. Firstly, a theory does not collapse because one prediction is falsified. Instead, the theory is adjusted with a minor modification so as to account for the seemingly contradicting observation. But this process does not go on forever, because of its interaction with the world: when predictions are systematically falsified, the theory ultimately loses its followers, and for a good reason.

In summary, a theory of knowledge is a system in interaction with the world. It has an internal structure, and it also relates to the world. And although it may relate to the world in its own words, one may still assess the adequacy of this relationship. For this reason, one may not defend scientific relativism in its strongest version.

For the reader of my other posts in this blog, this definition of theories of knowledge might sound familiar. Indeed it is highly related to theories of perception defended by Gibson, O’Regan and Varela, for example. After all, perception is a form of knowledge about the world. These authors have in common that they define perception in a relational way, the relationship between the actions of the organism in the world (driven by “theory”) and the effects of these actions on the organism (“tests” of the theory). This is in contrast with “neurophysiological subjectivism”, for which meaning is intrinsically produced by the brain (a closed system, in my drawing above) and “computational objectivism”, in which there is a pre-defined objective world (related to the idea of translation).

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