A new comment in Scientific American (here) discusses the challenges facing the newly hyped brain mapping project that seems ready to get scads of funding. The writer, Partha Mitra, is the Crick-Clay professor at the Cold Spring Harbor lab, a pretty fancy place for brain research I am told. The comment is interesting for several reasons and I urge you take a look. It’s pretty short so it won’t cut into your weekend relaxation time much. Here are three things that caught my attention.
First, he offers a shout out to the (often maligned) competence-performance distinction within linguistics. As Mitra notes “from a scientific perspective we want to know what the brain is capable of doing in principle, not what it actually does in a specific instance. In other words, we want to understand the laws of brain dynamics, not the details of brain dynmaics.” I couldn’t agree more. Same within linguistics. The aim of studying what people actually do linguistically is in service of figuring out the underlying capacity. As noted here, this is the essence of the rationalist perspective: what something is does not reduce to what something does.
Second, Mitra points out that getting more and more data, more and more careful measurements of more and more neurons is likely a pointless exercise as this project suffers from conceptual incoherence: “the proposal does not stand up to theoretical scrutiny.” There is a disease that Big Data Science is susceptible to: viz. if its collectible, collect it! As Mitra points out, before vacuuming up every stray data point “we need a theoretical question of what we gain by recording every neuron.” Amen. There is no substitute for thought and big data by itself cannot substitute for theory. With theory in place data becomes comprehensible. Without it, more data is as likely to mislead as to enlighten. As Mitra poetically puts it: without theory “we risk the fate of the naturalist Stapelton as he rushed across the great Grimpen Mire at the conclusion of the Hound of the Baskervilles: even with all his knowledge, he stepped into a bog, and was not heard of again.”
Third, Mitra makes a very useful analogy between earlier work in physics and current work in neuroscience. Mitra first observes is that in many domains of physics the “phenomena were first discovered at the macroscopic level, studied at the macroscopic level, and even the theoretical framework was established at the macroscopic level; the microscopic measurements and statistical mechanical theories entered at a later stage to refine the understanding already established.” He then unpacks the fable to reveal the following moral: “Animal behavior provides a close analog of the macroscopic behaviors of physical systems…The study of psychological pehnomena in terms of constructs like memory, attention, language and affect also get at the macroscopic properties of nervous system dynamics…”(my emphasis, NH). A least for Mitra, the biolinguistic program is in no need of justification. It provides the necessary Marrian “ “computationalist” perspective” that biology using “engineering principles and evolution” can aim to explain. In other words, generative theories of linguistic competence are targets of neuroscientific explanation, the payoff being a deeper understanding of how evolution and good design “apply to brains.”
All of this should sound pretty familiar, but it is nice to hear the standard biolinguistic viewpoint endorsed with so little methodological angst. Mitra takes it as obvious that cognitive theories are ultimately biological theories and that they are necessary parts of the neuroscience program of understanding how brains work. Indeed, for him linguistic theory is to brain science what thermodynamics is to statistical mechanics. I’m perfectly happy with this company. If this attitude spreads we may one day be spared the captious remarks of the philosophically sophisticated. One can only hope.