Mechanisms for Cognitive and Behavioural Science (#MechanismWeek 5)

This week we have reviewed what a mechanism is, various ways to model mechanisms, and talked about the kinds of functional and dynamical models cognitive science currently relies on. We then rejected the argument that cognitive science cannot have causal mechanistic models that refer to real parts and processes. We claim that if we ground our models at the ecological level of perceptual information, a truly mechanistic analysis is possible, and we walked through a causal mechanistic model of a perception-action task as proof of concept for our claims. Sabrina then presented these ideas this week at a mechanism conference in Warsaw to an audience that included Bechtel and Craver (with great success, hurray! :)

The message we want you to go home with after #MechanismWeek is this:

Despite the fact that psychology has been trucking on very-nicely-thank-you developing various kinds of functional models, these remain extremely limited in their explanatory scope and they are not moving us towards explanatory mechanistic models. We have demonstrated that explanatory, causal mechanistic modelling of cognitive and behavioural systems is possible, so long as that analysis is grounded at the level of ecological information. These models are powerful scientific tools for exploring and understanding the behaviour of systems, and if we can get them, we should definitely be trying to. 

The research programme for getting mechanistic models is that laid out in Bechtel & Abrahamsen (2010) who used the development of mechanistic models of circadian rhythms as an exemplar for cognitive science. That programme involves spending time empirically decomposing and localising the real parts and processes of that actual mechanism. This requires going into the mechanism at a useful level of analysis; if you are struggling to find real parts and processes, you might be working at the wrong level. Only once you know the composition and organisation of the mechanism do you try to model it, typically using dynamical equations containing terms serving as representations of each component, placed in the appropriate relation to one another.

We have risen to their challenge by identifying the ecological level of analysis as the correct place to ground our work, and by identifying a cognitive science model that parallels the biological exemplar. It is our hope that this work will help others move in the direction of mechanistic research and modelling in the cognitive and behavioural sciences, so that we all gain the many benefits of causal mechanistic explanations.

This work will form the centre piece of a large scale paper we are currently writing. We've posted this part of the work on the blog in part to stake a claim to this analysis, but also to try and garner useful feedback from interested parties. If you have questions, comments or feedback, please contact us by commenting on any relevant post (we'll see it, even if it's on an older post), emailing us or finding us on Twitter. 

Thanks for reading along with us! We hope you enjoyed #MechanismWeek :)

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.

Do Dynamic Models Explain? (#MechanismWeek 3)

So far we have learned what a mechanism is, two ways of modelling mechanisms (functional and mechanistic) and we've identified that cognitive science is currently dominated entirely by functional models which will never actually turn into mechanistic models without a change in our research priorities.

Bechtel & Abrahamsen (2010) challenged cognitive science to make the move to mechanisms. They laid out the form of the necessary research programme (empirically decompose and localise the actual mechanism, and then model those real components), and they described all the benefits of mechanistic models we might really want to have. The main benefit is a move from mere description to proper explanation of the mechanisms we study, and this certainly seems like something we would want.

But can we do the work needed to get mechanistic models? Can we decompose cognitive systems into sensible components and model the result?  Chemero & Silberstein (2008) and Silberstein & Chemero (2010) argue we can't, because cognitive systems are nonlinear and therefore non-decomposable; it makes no sense to break them down into parts because the behaviour of the system is more than the sum of those parts. They do argue, though, dynamical models are appropriate and count as proper explanations. This post will review but ultimately reject this argument.

Cognitive Models Are Not Mechanistic Models (#MechanismWeek 2)

So far we have talked about what mechanisms are and what sort of model counts as properly mechanistic. The next question is to have a look in more detail at the models of cognitive science and see how far they can take us towards mechanistic explanations.

Last time I discussed the examples of research on memory, visual object recognition and categorisation. This kind of functional modelling work is the rule, not the exception in cognitive science - it's how we're taught to work and how the field moves along.

@PsychScientists A mechanism is a graph with at least three boxes and two arrows.

— Tim van der Zee (@Research_Tim) May 24, 2016

This kind of program feels like it's heading towards mechanism . Every division into new sub-capacities comes from work showing the two sub-capacities function differently and are therefore the result of different mechanisms. Every new representational model adds a new component (part or process) that handles another part of the capacity. There is one basic problem, however. None of these models make any explicit reference to any real parts or processes that have been empirically identified by other work - for example, 'working memory' still refers to a capacity, not a component. This means there is no reason to think this new capacity maps onto any particular parts and processes or if it does, to which parts and processes.

We are, in effect, doing science backwards: modelling first, running experiments later, and the result is that we are not actually on a trajectory towards mechanistic models, just better functional ones. This is a problem to the extent you want access to the many real benefits mechanistic models offer, in particular the ability to explain rather than simply describe a mechanism (see Bechtel & Abrahamsen, 2010 and the last post). This post reviews whether functional models explain or whether they can be part of a trajectory towards an explanation. The answer, unsurprisingly, will be no.

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.

Ecological Representations

Funny story. One day, I got a text from Sabrina that said "Holy crap. I think ecological information is a representation." "Uh oh", I thought - "Twitter is gonna be maaaaad". Then we thought, "we should probably write this idea down, see if we can break it". So we wrote, and the funnier thing was....it all just got stronger. The result is a paper we call "Ecological Representations", which we have just uploaded as a preprint to BioRxiv.

In it, we

1. argue that Gibsonian, ecological information meets the criteria to be a representation, then
2. predict that this information leads to neural activity that preserves its structure and that also meets the criteria to be a representation, and
3. we argue that these two ecological representations (informational and neural representations) can address the three core reasons cognitive science wants representations (getting intentionality/aboutness from a physical system, solving poverty of stimulus and enabling higher order cognition) while 
4. avoiding the two big problems with mental representations trying to address those motivations (symbol grounding and system-detectable error). 
5. We then spend a bunch of time getting serious about higher order cognition grounded in information (see, I told you we were working on it!)

We submitted this to a good cognitive science journal and got the reviews back last week. We were rejected after hitting a wall of confusion from two reviewers who got distracted by side issues and one who just didn't quite get it. No-one gave us much in the way of specific actionable things that need fixing, nor did anyone actually say our analysis of information as a representation was wrong. We remain unimpressed with the quality of the reviews (although the editor has been very clear and generous with his time in replying to us querying the rejection). 

That said, we are taking one hint from the process before submitting elsewhere, and that is we are clearly having trouble articulating the argument, in part I think because comes out of left field and we're tripping a lot of different knee-jerk reactions. We think the story makes sense but then we're us, so what we need is some fresh eyes. This is where you lovely people come in.

I have made some minor structural revisions to the version that got reviewed to address some of the issues that came up and I have uploaded it as a pre-print to BioRxiv.org. Now, we want your help.

#MechanismWeek (a week of posts commencing June 20th 2016)

Cognitive science is, in principle, the search to understand the mechanisms that cause our behaviour to look the way that it does. We run experiments designed to figure out the form of the behaviour to be explained, and we propose models that try to account for the behaviour. But how well are we doing, and can we do better?

It turns out that there is a rich and extremely useful philosophical literature about mechanisms. Specifically, there is a lot of clear and accessible work describing what mechanisms are, and, more importantly, how science can go about modelling those mechanisms. This literature has provided us with a wonderfully useful central focus for our ongoing work, and I wanted to walk through the key issues here. (We have covered this topic in a couple of posts - here and here - but there are several interlocking issues that I want to spell out one at a time).

Sabrina is in Warsaw June 23-25th attending the Mechanistic Integration and Unification in Cognitive Science conference, where she will present on how ecological information provides the key to mechanistic explanations in psychology.

To celebrate, there will be a new post from her every day of the week commencing June 20th 2016 on the topic of mechanisms, specifically cognitive mechanisms, and how to model them. Below is a list describing the upcoming posts and providing links to some useful reading.

We'd like to invite you all to play along, either in the comments or on Twitter (where we live as @PsychScientists; suggested hashtag #MechanismWeek). I've sketched out the week below, with recommended readings so yo can play along.