It occurs to me as I read Rosenberg’s Philosophy of Science (2005), that we tend in that field to classify epistemic activities into two kinds: induction (about which we have many arguments as to its warrantability) and deduction (with many arguments about its applicability). But I believe there is something else that we do to learn about what exists in the world. In my forthcoming book, The Nature of Classification, coauthored with Malte Ebach, I argue that this is classification, but typically classification is seen as either of the other two kinds of inference. I think it is a third kind.
So what happens when we classify in the absence of theory? We aren’t yet inductively constructing theory, and we aren’t able to deduce from theory (since there isn’t any yet) the classes of objects in the domain we are investigating. We argue that what is happening here is pattern recognition (Bishop 1995). We are classifier systems. It is one of the distinguishing features of neural network (NN) systems such as those between our ears that they will classify patterns. They do so in an interesting fashion. Rather than being cued by theory or explanatory goals, NNs are cued by stereotypical “training sets”. In effect, in order to see patterns, you need to have prior patterns to train your NN.
Where do these come from? I think that there are several sources. One is evolution: we are observer/classifier systems of a certain kind. This gives us a host of cue types to which we respond by training our stereotype classifier system. For example, we respond to movement of large objects, to differences in colour and shade, and so on, in our optical system. Quine (1953) referred to this as our “quality spaces” – these are fields of discriminata, to which we (in Quine’s view, behaviouralistically) react. They are adaptations to the exigencies of survival for organisms of the kind that we are. The problem is that so long as our survival and reproductive success is ensured, evolution cannot guarantee us access to the way things “really” are. At best it gives us a good balance between false positives and false negatives. It is good enough, as it were, for government work (Godfrey-Smith 1991). But is it good enough for science and metaphysics?
One of the standard accounts of the success of science is that it increasingly approaches the truth. This is called the Ultimate Argument for Scientific Realism by van Fraassen (1980) and the Miracle Argument by Putnam (1975, that unless science does converge on reality, science would be a miracle). It is quite clear that the received dispositions evolution has bequeathed our cognitive capacities is not enough. While one might reject the Plantingan argument against all naturalism based on this insufficiency of our evolved cognitive powers (Plantinga 2002), there is a problem. How do we come to identify aspects of the world reliably and properly?
Science proceeds by refining its categories of what exists in the world based on two main sources. These are evidence, and explanatory force. In the case of a domain of investigation for which there is as yet no explanation, all we have is evidence, but apart from our evolved dispositions to respond to certain stimuli, how do we identify the salient aspects of evidence? There is an almost infinite amount of possible information we might make use of, and so we must glean the right sources of information. One source is economic necessity. Over time, farmers and hunters will tend to respond to the features of the things they are engaged in acquiring and using that are more or less important for success, because those features which are not salient will impose a cost of time and effort that tends to reduce success. This is a process very like natural selection, and has been the basis for what came to be known as evolutionary epistemology, in which a parallel process to biological evolution occurs in the domain of knowledge. Cognitive traditions become better at acquiring reliable knowledge because ideas and approaches that do not aid this goal are costly and are abandoned.
However, we have a superfluity of cognitive and conceptual resources. We can retain ideas and practices that are not really relevant for social reasons, such as rituals and “explanations” that have no counterpart with the reality being dealt with. So the fact that a particular culture is successful at farming by relying upon a ritual calendar (as in pre-Indonesian Bali) doesn’t warrant belief in Hindu gods. The functional aspects of the rituals acts to transmit the information even if nobody in the culture (or in Western agribusiness) fully understands why those rituals make farming successful (Lansing 2007).
So when a classifier recognises patterns in economic circumstances, what counts is not the conceptual superstructure, the theories and ideologies, but the categories of what matters – in this case of water, soil, and landscapes. How might this explain the success of science?
Taxonomists are classifiers in a particular economic situation: professional science. When a taxonomist encounters organisms in the wild, they are in the same situation as when a hunter hunts in that ecology. To succeed at taxonomy, as to succeed at hunting, the agent must know the right things about the target objects. A hunter that doesn’t know what different species of bird look like and how they behave and where they live is in exactly the same economic conditions as a taxonomist who also lacks knowledge. Neither will end up with dinner on the plate (qua hunter or taxonomist). In the case of the taxonomist, the gap between failure and hunger is somewhat more distal than for the hunter (but hunters typically get most of their food from foraging rather than hunting anyway, courtesy of the non hunters, mostly women, in their village), but ultimately economic success depends directly upon correct pattern recognition.
Ernst Mayr was fond of telling the story of how when he visited Papua in the 1930s, he and the local hunters identified the same species of bird, with an exception where western ornithologists also disagreed, and he used this as justification for the reality of those (and all) species. He made the inference that science was able to discover kinds of things that were real in the world, and he may have been right (many biologists and philosophers believe species are not real), but it was not, I think, because of the pattern recognition abilities of humans per se to see species. When Ed Wilson tried the same experiment about ants, a subject he knows intimately, instead of the locals counting the same species he did (several dozens) he got something like “the black ones, the bitey ones and the red ones”. Why did Mayr’s informants know their birds while WIlson’s did not know their ants? The answer is that birds, but not ants, were of economic importance to the locals, while ants were of economic importance to Wilson and entomologists only.
By “economic” I do not mean fiscal, but the acquisition of resources, success at which gives the person investigating a living. What distinguishes scientific success is a unique socioeconomic system of professionalism, credit in society, and access to funds and resources like labs, students, and equipment. The motivations of the individuals concerned are several, often (but not always) based on personal curiosity, but curiosity is not enough if you don’t get the resources to do the work.
So we are very good at turning our perceptual pattern recognition systems to scientific work. What evolution provides, science refines. It happens that pattern recognition and the subsequent classificatory activities can deliver reliable knowledge of the world when it matters. But being as it is parasitic upon those evolved capacities, and being as scientists are social organisms, this is not without its failures. Social influences, particularly the inherited traditions of ritual and conception that history bequeaths, can skew and bias our categories about the world. This is where theory and experiment come in.
Science, by way of its historical accidents, also seeks to explain things in ways that can be tested. Here the ordinary philosophical issues come into play – we inductively generalise based on the patterns we have recognised, and form hypotheses, and from those hypotheses we derive deductive consequences, which we can test in ways that are not circular, which do not rely upon our original observations. As T. H. Huxley once said, nature whispers yes, but shouts NO! And so we can eliminate hypotheses that are not fit to the facts, more or less. This is what the evolutionary epistemologists, and philosophers like Popper, built their views upon. What evolutionary epistemology never explained, nor Popper, was how we came up with those hypotheses in the first place. Pattern recognition does.
For a half century or more we have had the view that observation is theory laden. As I have argued before (and which is part of our forthcoming book), observation need not be laden with theory of the domain under investigation. And what evolution has bequeathed need not be in the slightest theoretical, nor even reliable (as the massive literature on illusions shows us). We can naively observe things that we know little about, but we never start knowing, or at least being disposed to know, nothing.
So can we say that science is adequate to tell us the true nature of the world? Putnam’s miracle argument indicates a reason for thinking the world is knowable. If we could not know the world, there would be no reason to think that success indicated anything. And while success is not a guarantee of truth, it is as good as fallible knowers can ever achieve. In the end, I think that truth is, as the pragmatists said, what works. More than that is restricted to gods, demons and mathematicians.
Bishop, Christopher M. 1995. Neural networks for pattern recognition. Oxford, New York: Clarendon Press; Oxford University Press. [Sorry, I forgot to put this in]
Van Fraassen, Bas C. 1980. The scientific image. Oxford: Clarendon Press.
Godfrey-Smith, Peter. 1991. Signal, decision, action. Journal of Philosophy 88:709-722.
Lansing, J. Stephen. 2007. Priests and programmers: technologies of power in the engineered landscape of Bali. Princeton NJ: Princeton University Press.
Plantinga, Alvin. 2002. The Evolutionary Argument against Naturalism. In Naturalism Defeated? Essays on Plantinga’s Evolutionary Argument against Naturalism, edited by J. K. Beilby. Ithaca, NY: Cornell University Press:1-13.
Putnam, Hilary. 1975. Mind, language, and reality, His Philosophical papers v. 2. Cambridge Eng. ; New York: Cambridge University Press.
Quine, Willard Van Orman. 1953. From a logical point of view: 9 logico-philosophical essays. Cambridge MA: Harvard University Press.
Rosenberg, Alexander. 2005. Philosophy of science: a contemporary introduction. 2nd ed, Routledge contemporary introductions to philosophy. New York; London: Routledge.