Species Realism 351Pattern Recognition and Abduction
We tend 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). In recent years, the notion of abduction, or Inference to the
Best Explanation (IBE) has been added to the list. But there is something else that we
do to learn about what exists in the world. In my book with Malte Ebach, I argue that
this is classification, but classification is typically regarded as one of the other kinds
of inference.^36 Instead, we believe, it is a new kind, similar to abduction, but distinct.
When we classify in a theory-lacking domain we are not yet inductively construct-
ing theory, and we are not able to deduce from theory (since there is not any yet) the
classes of objects in the domain we are investigating, nor are we able to abductively
explain the classes as such; we merely recognize them. What is happening here is
pattern recognition.^37 We are classifier systems. It is one of the distinguishing fea-
tures 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 training sets come from? There are several sources. One is evolu-
tion itself: 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 color and shade,
and so on, in our optical system. Quine referred to this as our “quality spaces”—
fields of discriminata, to which we (in Quine’s view, behavioralistically) react.^38
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.^39 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 and the [No] Miracle[s] Argument by Putnam—that unless sci-
ence does converge on reality, science would be a miracle.^40 It is quite clear that
the received dispositions evolution has bequeathed our cognitive capacities are not
enough. While one might reject Plantinga’s argument against all naturalism based on
this insufficiency of our evolved cognitive powers,^41 there remains a problem. How
do we come to identify aspects of the world reliably and properly?^42
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
(^36) Wilkins and Ebach 2013.
(^37) Bishop 1995.
(^38) Quine 1953.
(^39) Godfrey-Sm ith 1991.
(^40) Putnam 1975, Van Fraassen 1980.
(^41) Plantinga 2002.
(^42) Griffiths and Wilkins 2014.