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.

Endothelial Cells are Intelligent, Perceiving-Acting Agents

In my last post, I laid out a new project I'm working on about the perceptual life of cells. I spent the day at the Crick Institute recently to move the project forward, and this post is about developing the perception-action analysis in more detail. The goal in this post is to address the first question that needs an answer, specifically, are the cells perceiving-acting agents, or just doing something more mechanical?. In the next post, I will apply our task dynamical analysis to frame the project (from Wilson & Golonka, 2013). 

To cut to the chase, I'm now pretty happy that a perception-action analysis is appropriate at this particular cellular level. I set a high bar for this (mostly by reading Turvey & Carello papers, which should illustrate that height pretty clearly :) but it seems clear the cells are behaving with respect to information, and not simply being buffeted by forces. Applying some key criteria, and resting on the hard work of Turvey & Carello showing that intelligence isn't about brains but about behaviour, I will claim here that Bentley's endothelial cells are agents that exhibit intelligent behaviour, and there is a clear need for a behavioural scale contribution to any explanation of that behaviour.

The Perceptual Life of Cells

Over the summer, at ICPA 2019, I met a computational biologist called Katie Bentley. She is interested in angiogenesis, the cellular level process of new blood vessel growth. She was at ICPA because she has been developing a more dynamical systems approach to understanding how angiogenesis works. More specifically, her work suggests that the cells involved behave in very active, exploratory ways that are very analogous to the kinds of perception-action systems ecological psychologists study, and she was looking for people to help bring our insights to her work. We've been chatting, and the project of connecting our work is now on the go. 

The basic idea is simple. Mainstream cell biology is currently very gene-centric, in almost exactly the same way as mainstream cognitive science is very brain-centric. Like cognitive science, this has been methods driven - it's been hard to study cells in action, and imaging techniques have been static and structural. In general, why biologists see a cell doing something, the research goes looking for the genes that are making it do that thing. Bentley's work has instead started asking questions about time - how long does something take to happen? How are the component processes organised in time? What happens if that timing is perturbed? She's now in a good position to say that one of the key regulators of the dynamics of angiogenesis is timing, that this timing is not centrally controlled by a genetic clock, and that a temporal perspective should therefore move front and centre of the research on the process.

The analogy between the debates between information processing and ecological accounts of cognition and behaviour are uncanny, and her move is a very ecological one. The question we are now discussing is whether it makes sense to start considering this process as not just a dynamical process, but a perception-action process, which has become an option given her temporal perspective on things. 

In this post I'm going to discuss some of the specific empirical findings reviewed in her paper The Temporal Basis of Angiogenesis, which is a good read even if you aren't a cell biologist. I'm getting ready to go hang out in her lab this week and I'm working on figuring out the best questions to be asking to move this project forward in the best way. Remember, Gibson's theory is about what happens at the ecological scale of organisms like us, and there's no guarantee that it will have much to say about the perceptual life of cells. However, there is definitely enough evidence to make it worth checking out, and that's what I'll be up to for the next little while. 

Show Me the TALoN! (Thoughts on Raja & Anderson, 2019)

There is a special issue of Ecological Psychology out with contributions from lots of people (including us) on what a Gibsonian neuroscience might look like. I'll work my way through the papers over the next few weeks - today, we read Raja & Anderson's contribution for our lab meeting, and I wanted to write about where we ended up with this paper. The upshot is that the paper is clear and the basic ideas of neural reuse and Transiently Assembled Local Neural Subsystems (TALoNs) really do match up nicely to the perception-action scale explanations in the ecological approach. However, it's just not yet clear how much value is added to the ecological approach by these concepts; neural reuse is perhaps not that radical a notion, and there isn't yet any good evidence that TALoNs are a good account of actual neural architecture. As a functional level description of an ecological approach to brains, it seems quite nice, but there isn't anything convincing in here that this is actually how brains work. Show me that, then let's see what happens.

Can the Free Energy Principle be made ecological? (Bruineberg et al, 2018)

Everyone loves Friston's free energy principle (FEP), and everyone wants it for their own. Not everyone can have it, though (well, at least not if it's going to mean anything) and so there's a spirited fight about who's theory it best fits in the literature. 

Bruineberg, Kiverstein & Rietveld (2018) argue two points in an effort to win the fight for the good guys. First, they want to show that inferential, representational takes on the FEP end up in an unworkable place. Second, they want to show that an ecological/enactivist analysis works much better. Overall I think they take a solid swing at both, so it will be interesting to see the responses this sparks. Here I want to review their arguments.

To unbury the lede, I like this paper a lot. It's really long and repetitive, but in here is an excellent ecological analysis of the free-energy principle that also works to explicitly rule out the competition. I am obviously biased, but their work pointing out the flaws of Hohwy's account all make good sense to me, not least because these flaws show up in all kinds of places in the representational ontology. Hohwy fails for the reason interface theory does, in my view, and it's nice to see separate analyses end up in the same place as me. 

For what it's worth, I am not yet convinced that the FEP is the way we need to go. However, if it ends up being a good idea, Bruineberg et al have done sterling work in showing how we should go about it. 

Where is the Haptic Information? (A Purple Peril)

Haptics (or proprioception) is the sensory modality built into our bodies; it's provides constant information about the state of the body and things it is in mechanical contact with, such as tools. Many ecological psychologists (myself included) have investigated haptic perception and it's role in the control of action, but unlike the optic array, we have basically zero work identifying what the relevant information variables look like. 

I first investigated haptic perception in the context of coordinated rhythmic movements (Wilson, Bingham & Craig, 2003). Geoff had run studies showing that visual judgements of different relative phase varied in stability in the same way that the production of those relative phases does. This suggested that the movement phenomena were being caused by the way relative phase is perceived. This was vision, however, and the movement phenomena obviously involve motion of the body and the haptic system. This involvement was typically explained in terms of muscle homology and neural crosstalk effects. Our study had people track manipulanda that moved up and down one of three mean relative phases with various levels of phase variability added, and had them make judgements of that variability (replicating the visual studies). We found haptic perception of relative phase, as measured by those judgements, behaved just like visual perception of relative phase - we inferred that the information, the relative direction of motion, can be detected by both systems and has the same effects. 

I am moving back into the haptic information world for two related reasons. 

First, I want to replace the muscle homology/neural crosstalk stories with a haptic perception story. The effects these theories account for are very large and reliable, and Geoff's perception-action model currently only applies to visual information. Specifically, muscle homology applies to relative phase defined in an egocentric (body centred) frame of reference, while Geoff's model applies to relative phase defined in an allocentric (external) frame of reference. Relative phase is clearly detected in both frames of references; when they are pitted against one another experimentally, both matter and the egocentric effects dominate (e.g. Pickavance, Azmoodah & Wilson, 2018).

Second, I have become interested in individual variation in the variables used to perceive relative phase. Based on his data, Geoff's model predicts relative phase is perceived via the information variable relative direction of motion, the detection of which is modified by the relative speed of the oscillators. In Wilson & Bingham (2008; blog post), we showed this was true in 7 out of 10 untrained participants judging 0° and 180°. The other three became unable to judge these phases when we perturbed another candidate variable, relative position. This experiment also showed that people trained to perceive 90° had improved because they had switched to this variable, but we were not expecting people at the other relative phases to be using this variable. I'm finally getting back into experiments probing the prevalance of this individual difference in visual information use and the consequences for perception-action stability (briefly: there's a lot of variation and it matters!). As part of the above project, I want to do the same kinds of studies on haptic perception too. 

My problem here is, there is essentially no information in the literature on the nature of haptic information variables. This Peril lays out my current hypothesis about where to look; please, dear God, come help me!

Tolerance, Noise and Covariation in Skilled Action

The field of motor control has been recently steadily moving towards the idea that there is no such thing as an ideal movement. The system is not trying to reliably produce a single, stable, perfect form, and movement variability has gone from being treated as noise to being studied and analysed as a key feature of a flexible, adaptive control process. This formalises Bernstein's notion of 'repetition without repetition' in movement, and recognises that the redundancy in our behavioural capabilities relative to any given task allows for multiple solutions to that task being legitimate options. 

There are many new analysis techniques within this 'motor abundance' framework, and I've reviewed most of them already; uncontrolled manifold analysis, stochastic optimal control theory and goal equivalent manifolds are the three big ones, as well as nonlinear covariation analysis. The essence of all these methods is that they take variability in the execution or outcome of a movement, and decompose that variability into variability that does not interfere with achieving the outcome and variability that does. 

This post will explain the variability decomposition process in Sternad & Cohen's (2009) Tolerance, Noise and Covariation (TNC) analysis, which my students and I are busily applying to some new throwing data from the lab. I have talked a little about this analysis here but I focused on the part of the analysis that involves a task dynamical analysis identical to the one I did for my throwing paper in 2016. In this post, I want to explain the TNC analysis itself. I will be relying on Sternad et al, 2010, which I've found to be a crystal clear explanation of the entire approach; you can also download Matlab code implementing the analysis from her website.