A “Real” Science of Mind: Learning?

There’s an unusually relevant article up at the NYTimes Opinionator blog by Tyler Burge titled “A Real Science of Mind.” Burge takes issue with some of the popularized findings from cognitive neuroscience which triumph scientific “advances” by locating various aspects of our mental lives in the brain using fMRI or other non-invasive imaging methods. Everyone has probably run into a news article describing how researchers have located the “I want a candy bar” part of the brain or the “I don’t want to do my homework” part of the brain. It would seem to many that the field is on a quest to locate every possible thought one might have in a particular part of the brain.

However, it might have occurred to you (oh savvy blog reader) that coming up with a big list of where particular thoughts occur in the brain is unlikely to be that helpful. (In fact, this was exactly what phrenologists aimed to do in the early 1800s.) Maps of our thoughts and ideas are not a bad starting place for a rigorous science of mind and behavior, but scientific progress ultimately demands prediction. We don’t want to simple know where ideas occur in the brain, but how they occur, why they occur, and when they will occur. Given the wide-spread attention that thought “localizing” garners in the news, are attempts to localize mental function really the best “psychological science” that we currently have?

A real science of mind?

Burge argues no (and I tend to agree). In particular, the main thesis of his article is that a “real” science of mind has emerged over the last 40 years called “vision science.” Being fortunate to work at one of the premier research institutions in the world for the study of vision (NYU), this comes as no surprise really. Perceptual science (the field which includes vision science but also other forms of sensory perception) has a strong intellectual tradition which values detailed, quantitative approaches to the study of perception. Perhaps if a non-vision scientist were to look at the latest issue of Journal of Vision (aside from noticing the beautiful graphics) they might not recognize much that could be identified as a theory of human behavior.

However, unlike the “neurobabble” (Burge’s term for shoddy neuroscience that makes it into the news), vision scientists aim to develop detailed, testable theories about how people visually perceive their environment. In place of folk psychological theories or even intuitively specified scientific theories, such approaches offer rigorous, mathematically grounded theories that can predict (and explain) behavior across a wide range of situations. The theories specifically try to understand the stages of information processing and representations that people use to interact with the visual world. Since the theories are detailed enough to be expressed mathematically (or as computer programs) there is much less disagreement in the scientific community about what they predict, and it is easier to falsify theories when they are incorrect. It is the kind of rigor which led to important advances in the physical and biological sciences, and is doing the same for the psychological and brain sciences.

What else is there?

Burge’s article made me think about what other areas of behavioral or social science have made progress in developing rigorous testable theories of psychological phenomena. In general, I might argue that the field I work in (cognitive science) meets a similar standard (with a number of extra challenges that vision scientists fortunately don’t have to grapple with), but in fact, I think an even better example is the learning sciences**. The learning sciences may be one of the best examples of a rigorous, mathematical science of behavior that is not primarily perceptual or sensory in nature.
Learning is one of the oldest and most successful areas of psychological science. The pioneering work of Pavlov, Thorndike, Skinner, Hull, Tolman, Rescorla, and others laid the foundation for a rigorous understanding of learning behavior. Much of this early work was done under the (now tarnished) name of Behaviorism, but the ideas still hold considerable influence in the field. Indeed, learning science today focuses on a wide set of issues include how people learn from examples, how they learn to imitate one another, and how they learn to adapt their behavior to changing circumstances.

In fact, I would argue that the learning sciences may be of the best examples of a rigorous, mathematical science of behavior that is not primarily perceptual or sensory in nature. Such work speaks to fundamental questions about human thought (e.g., nature versus nurture), is validated and refined through cross-species comparisons, and is backed up with in detailed, quantitative modeling. There is even a growing understanding of the neural mechanisms supporting learning (including high quality fMRI research finding parts of the brain responsible for adapting behavior). What’s particularly interesting about the learning sciences is that they have a strong potential for immediate impact on a number of important societal issues (e.g, How best do we structure learning environments to help children learn? What role does nature or nurture play in learning? How can we build machines that can learn as well as people do? What is the role of learning in addiction?).

Anyway, I’ve always admired the precise and careful control that vision scientists adopt in their stimuli, tasks, and theories. However, relative to flashier imaging findings, this work is often overlooked (particularly in media reports about psychological science). That’s why it is great to have someone like Burge highlighting this work in a high-profile outlet. I guess I’d just like to add the learning sciences as another example of a “real” science of mind.

** My definition of “learning sciences” is considerably more broad that the intuition given here which focuses on cognitive-educational theories. My view is that these areas form a contiguity with the more general psychological study of learning and memory (from multiple perspectives including neuroscience, cross species work, etc…) and with machine learning/artificial intelligence. It’s a big tent, but as all the features of a emerging, quantitative, and socially relevant science. A good summary was published by Meltzoff et al. in Science recently.

  1. […] science that we will be talking about in future generations will come from further development of a detailed, quantitative, and mathematically-based science of human behavior, exactly of the type exemplified by the baby naming […]

    thinking about thinking» Blog Archive » From under the microscope: FAQs about Sen. Coburn’s report on “frivolous” research at NSF