Archive for the ‘Regularity Theory’ Category

Cognitive Regularities 1

August 30, 2015

My work on the cognitive foundations of mind is guided by the underlying intuition that the study of the mind is at a stage similar to chemistry in the late nineteenth century – on the one hand large amounts of new data are being collected that point to underlying principles, and on the other hand conceptual problems are being raised about the relationship between the mind and other natural entities. As we know now, chemistry was incompatible with nineteenth century physics; it took the quantum mechanical revolution to bring chemistry and mechanics into one theoretical structure. I believe the same is true of the study of the mind now.
A tremendous amount of new data is being generated from neuroscience and cognitive science experiments as well related social science disciplines like economics. One can think of the new data as the counterparts of chemical reactions; we are getting a sense for what happens when two mental entities interact with each other. However, at the same time, conceptual problems (such as that of intentionality and consciousness) are being raised about how to reconcile these phenomena with what we know of the physical world. I believe that regularity theory is a good lens through which we can view these new developments in the mind sciences; these notes are a first attempt to summarise past work and lay out a research agenda.

To push the analogy between chemistry and the mind sciences further, while we are not ready for the full fledged ‘quantum mechanics of the mind’, I do think we are ready for the Plank and Bohr model of the mind sciences, i.e., a halfway stage that integrates both the experimental and the conceptual problems into one framework. These notes are an introduction to a regularities approach to cognition with the intent of grounding knowledge itself in cognition, in the spirit of the classical Indian pramana theorists.


Understanding Regularities 1: Some examples

August 30, 2015

One problem with the regularities framework is that, like other frameworks, it is an interlocking set of conceptual intuitions and hypotheses that do not lead to an easy definition. It is almost OK to say that regularities are not definable but we know one when we see one. I don’t quite agree with that conclusion, but let us first see if we can agree about some phenomena being regularities, so that we can at least say that we know one when we see one. Here are a few examples of what I would call regularities:

  1. The size of an animal predicts the pitch of it’s voice. Mice squeak and lions roar and not vice versa
  2. Clouds are puffy while water is runny.
  3. More controversially, the size of an animal predicts how smart it is. A bacterium can never be as smart as a dophin.

These three examples are all related to each other though not in any obvious way. The underlying mechanisms for mice squeaking, clouds puffing and dolphins thinking are all different. Even the evolutionary histories are different. However, at a thermodynamic level, we can see that all of them have to do with how energy and information flow through the respective systems. Physicists talk about “universality” i.e., that the macroscopic properties of a system can often be independent of it’s microscopic origins. The regularity approach takes this one step further, that the regularities of a system are not only independent of the underlying mechanism or causal features, they are the real thing. Especially when it comes to biological processes we can hypothesize that it is regularities and their graspability that is being selected for in natural selection, not the underlying mechanism. I see this as a biologically grounded version of the hardware/software distinction well known in AI and cognitive science. Just as an earlier generation of theorists argued that the same software can be instantiated in different hardware, we can argue that the same regularity can be instantiated via different mechanisms while remaining the same.