Mechanistic Models of the Brain

I'm getting increasingly interested in neuroscience, and how to make it ecological. I also think that the ecological approach is capable of supporting mechanistic explanatory models of behaviour and is the correct scale* at which to ground these models. This means that my current plan is to find a way to add neuroscience as a lower scale part of a model grounded at the scale of the organism-environment system. 

There's a lot going on in that sentence, though, so I want to unpack it a bit to lay out the rules. and the things I currently don't know. 

(*NB I am using scale rather than level throughout because the concept of a level is complicated and currently, I am convinced that scale is a better term. The argument continues, however). 

The Task Dynamics of Angiogenesis

In the last two posts, I have laid out the proposal that endothelial cells seem to actively perceive their environments, and set out the details of the argument in favour of explicitly taking an ecological approach to understanding why they do what they do during angiogenesis. It's now time to develop that analysis more explicitly.

To do this, I will apply the 4 questions we proposed in Wilson & Golonka (2013) to the question of the endothelial cell behaviour.  These are

1. What is the task to be solved? 
2. What are the resources the organism has access to that might solve the task?
3. How might these resources get assembled so as to solve the task?
4. Do organisms actually do what you describe in Q3?

We gave some worked examples of this analysis in the 2013 paper, and have described how it drives my work on coordinated rhythmic movement (Golonka & Wilson, 2012, 2019). This will hopefully serve as another example.

Free Energy: How the F*ck Does That Work, Ecologically?

Karl Friston has spent a lot of time recently developing the free energy principle framework as a way to explain life, behaviour and cognition; you know, biology, and it's become the cool kid on the block in fairly record time. 

Crudely, the basic idea of the FEP is that living organisms need to operate within a range for a given process, or else they will be malfunctioning to some extent and might suffer injury or death. Being within the relevant range across all your processes means you are alive and doing well, and so for an organism that has made it this far in evolution those states must be highly probable. Being outside those ranges is therefore less probable, and so if you find yourself outside a range you will be surprised. Your job as a self-sustaining organism can therefore be described as 'work to minimise surprise'.

There is a problem with this formalisation though. The information-theoretic term that formalise 'surprise' is not a thing that any organism can access, so you can't work to control it. Luckily, there is another formal quantity, free energy, that is related to surprise and is always higher than surprise. Free energy is therefore the upper bound on surprise and minimising that upper bound can reduce surprise as well. 

All this is currently implemented in an inferential, Bayesian framework that aligns, at least on the surface, with modern representational cognitive science. Andy Clark thinks this is the future, and Jakob Howhy has worked hard to nail this connection down so it won't move. If this is all right, and if the FEP is being successful, perhaps non-representational, non-inferential accounts like ours are going to lose.

A recent paper (Bruineberg, Kiverstein & Rietveld (2016) tries to wedge the FEP and Bayesian psychology apart to allow room for an ecological/enactivist take on the FEP. To be honest, I found the paper a little underwhelming, but it did get me thinking about things, and two questions have emerged.

Before we worry about an ecological account of the FEP, we need to know 1) whether such a thing makes any sense and 2) whether it adds anything new to the proceedings. All comments welcome - these are genuine questions and if there are answers we would love to know.

Ecological Mechanisms and Models of Mechanisms (#MechanismWeek 4)

Mechanistic models are great, but so far cognitive science doesn't have any. We have functional models (of, for example, memory or categorisation) and dynamical models (of, for example, neural networks) but none of these can support the kind of explanations mechanistic models can. Is that it for psychology, or can we do better?

Here we propose that it's possible to do psychology in a way that allows for the development of explanatory, mechanistic models. The trick, as we have discussed, is to identify the correct level of analysis at which to ground those models. These models will definitely end up being multi-level (Craver, 2007), but the form of these final models will be dictated and constrained by the nature of the real parts and operations at the grounding level.

The correct level of analysis, we propose, is the ecological level. Specifically, ecological information is going to be the real component whose nature will place the necessary constraints on both our empirical investigations of psychological mechanisms as well as the mechanistic models we develop.

Let's see how this might work.

Mechanisms and Models of Mechanisms (#MechanismWeek 1)

In this first #MechanismWeek post, I will define a mechanism and briefly describe the kind of models of mechanisms you can build. I begin with various kinds of functional models (Cummins, 1975, 2001; Weiskopf, 2011). These either break capacities of systems into more coherent, easily studied sub-capacities (think of breaking memory into long term memory and short term memory as a simple example) or model them with components that may or may not be really implemented in the organism (e.g. geons, exemplars).

I will then introduce the idea of a dynamic causal mechanistic model (Bechtel & Abrahamsen, 2010) which attempt to map model components directly onto the real parts and processes of the mechanism at hand. The argument is that while functional models provide useful descriptions of mechanisms, they do not provide an explanation of that mechanism, and that only mechanistic models can explain.