s of disagreement do not spoil the point of agreement. You can have a compelling reason to say that a claim is true even before you light upon a clear understanding of what that claim means about the world.
Often, we get our hooks into some general phenomenon and hold on for dear science
This position isn’t so uncommon in science. The concept of temperature could be put to good scientific service even before we had any real conception of what it was – a conception that was developed through a lot of labour and ingenuity. In the early 1960s, the American physicist Richard Feynman said that it ‘is important to realise that in physics today, we have no knowledge of what energy is’, and it’s debated how much better off we are now. And while Albert Einstein offered a detailed theory of gravity, we don’t have a full-enough understanding of what gravity is to reconcile it with the other fundamental forces. See also: quantum mechanics (I won’t say any more than that because I don’t know any more). Scientific hypotheses have often yielded good predictions and insightful explanations, often by applying mathematical principles to natural systems, even though, when it comes to saying what it really means for the world to be as the hypothesis says… that takes some figuring-out.
Throughout the history of science, we have often started by knowing just enough to be able to point to an important thing, though we didn’t grok it well enough to paint the full picture. We do not need to start with a fixed, fully fleshed-out conception of some deep feature of the world and then see whether it fits our observations. Often, we get our hooks into some general phenomenon as we try to solve specific problems, and we have to drag ourselves closer and closer to the phenomenon underlying those problems, developing our understanding over the course of generations, sometimes withstanding fierce swings and great setbacks as we hold on for dear science. We’re on to something when we say the brain is a computer, but it might not be clear for a while what exactly it is that we’re on to.
How will we come to make sense of the general claim that the brain is a computer? I think we have to resist talking solely in abstractions. We have to keep ourselves firmly rooted in the specific hypotheses that the brain computes X, Y and Z. What do these specific hypotheses have in common? What makes them tick? How do they give rise to systematic predictions and why do they seem to provide insightful explanations? What would we lose if, instead of saying that the brain computes depth from binocular disparity, we adopted a totally undefined term, ‘jorgmute’ (I know not what it is to ‘jorgmute’), and said that the brain jorgmutes depth from binocular disparity?
These questions are, in fact, just the sorts of questions you find philosophers of science regularly asking. To its credit, cognitive science (unlike other sciences, recently) has always welcomed and acknowledged the contributions of philosophers. As the late American philosopher Jerry Fodor wrote in The Language of Thought (1975):
One wants to say: ‘If our psychology is, in general, right then the nature of the mind must be, roughly, this …’ and then ﬁll in the blank. … [T]he experimentalist can work the other way around: ‘If the nature of the mind is roughly …, then our psychology ought henceforth to look like this: …’, where this blank is filled by new first-order theories. We ascend, in science, by tugging one another’s bootstraps.
The brain is almost certainly partly a computer. We still have to uncover what that means about our brains and about ourselves.