Thursday, 21 July 2016

Framing the Debate

In 2014 we published a book chapter with Eric Charles in which we argued that the most important thing psychology and neuroscience needed from people like us was a new language in which to talk about the problems we are trying to solve. Our Ecological Representations paper is part of this, and we have a much larger paper in development laying out the more complete set of conceptual tools needed to do ecological psychology across a wider range of problems.

One reason why this is important is a simple fact; we are asking psychology to change and it is up to us to clearly articulate what we want it to change into, or else nothing can happen. A related reason is that without a clear framework, we can't reformulate the questions in a useful way and we're left stuck because we can't explain something like 'theory of mind' because the actual solution is that ToM doesn't exist or need explaining. Ecological neuroscience, for example, will look very different to cognitive neuroscience.

A final reason is that the language in which psychology frames it's understanding of behaviour drives popular understanding of behaviour too. I recently came across my favourite example of this in a tweet by Alice Dreger;
Dreger, for some reason, spends most of her life only using her right eye, even though her left is perfectly functional. She blogged about it here. Every now and again, something makes her left eye kick in and she suddenly has stereo vision.

What caught my eye here is her description of her experience is grounded in the myth that you need two eyes in order to perceive in 3D (I bug my students about this in class every year too). The myth is based in the standard image-based analysis of vision which I'll lay out below; but the point I want to make here is that people still describe their experience of monocular vision as 'not being able to see 3D/depth' even though this is inarguably, demonstrably not what is happening in their visual experience. It's like blind echolocators talking about how the sound creates 'an image in their minds'; this is just not the case, but this is the language psychology has provided them for talking about the perceived experience of spatial layout. What fascinates me is that it's trivial to demonstrate that monocular vision allows for 3D perception, but everyone lets the framing override their own experience. This, to me, is a big part of why our work right now is important - we will never make progress until we can reframe the debate.

Tuesday, 19 July 2016

Reply to Hamlyn: In detail

This is a detailed reply to the critique Jim Hamlyn wrote critiquing our Ecological Representation preprint (and related blog post). If you aren’t him, then you might want to visit his blog to read his full review before proceeding…

First, Jim, thank you very much for providing such a thorough review of our paper. I hope this lengthy reply can be a part of an ongoing dialogue about these ideas. 

I’ve written this the same way I did the one for Sergio Graziosi (here) – replying to each point you make without reading ahead. 

Quotations are text taken directly from Hamlyn’s critique and are presented in the order in which they appear in his blog post.

“Hopefully they can be persuaded that this is only true if the required representations are of the fully public and intentional sort and not the neural and non-intentional sort that they seem to have embraced.”

As this sentence comes at the very beginning of your critique, let me take a second to say that I think what we propose here is consistent with a conception of public and intentional representations. The notion of informational representations is both public (in that they are external physical structures) and inherently intentional (in that they specify biologically relevant and, frequently, dispositional properties and, as such, coordinating with respect to this information is fundamentally FOR or ABOUT something, which satisfies most people’s construal of intentionality). 

Monday, 18 July 2016

Reply to Graziosi: In detail

This post is a detailed reply to Sergio Graziosi's useful critique of our Ecological Representation pre-print. As such, it's specific to his particular concerns about our argument, but I'm putting it here so that others can join in the discussion. If you are reading this and are not Sergio, you might want to head over to his blog and read the critique first. 

First, thank you very much for your detailed critique of the paper. It is incredibly useful and we are sincerely grateful for the time you've taken to comment on our crazy ideas. 

A quick note to start with. I am largely writing this reply reading a little bit of what you’ve written at a time because I want to respond to each point you make and to keep myself honest in evaluating your later proposed solutions to some of the problems you’ve identified in the paper (so I can’t shift my positions on basic issues!). So, apologies if my responses to these points aren’t relevant because of something you say later in your reply. 

Friday, 24 June 2016

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 :)

Thursday, 23 June 2016

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.

Wednesday, 22 June 2016

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.

Tuesday, 21 June 2016

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.
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.