Sunday, 18 October 2015

Dynamic Mechanistic Explanations in Radical Embodied Cognitive Science

I'm on my way back from an enactivist/embodiment conference in Warsaw. I gave a talk (slides) in which I argued that in order to make theories of distributed/embodied cognition work, you have to have something like a theory of ecological information as the glue to hold it all together. All the talks I saw that discussed any kind of plan for distributing cognition were missing this piece and desperately needed it, so I'm hoping the talk will make people realise this tool exists and can help. Drop me a line if you would like any help!

I argued specifically that information lets us propose mechanistic explanations for distributed cognitive systems. We recently came across the philosophical literature on what mechanisms are and how to make them, and it seemed immediately clear that we should be doing this (and that we already are; see below). 

It turns out, though, that some of the radical camp (specifically Chemero and Silberstein) don't think we can have distributed cognition mechanisms, but that this is ok because we still get explanations out of our dynamical models. 

This post will briefly review what dynamic mechanistic explanation is and why they are so useful (Bechtel & Abrahamsen, 2010). I'll briefly summarise the radical opposition to mechanisms, point out their answer doesn't work, then talk an example that shows we can have radical dynamic mechanistic explanatory models without giving anything up. The trick, as ever, will be to rely heavily on information as the component part that allows cognitive mechanisms to extend out over body and environment. 

Monday, 28 September 2015

The Interface Theory of Perception - The View from Ecological Psychology

Psychonomic Bulletin & Review has released, with much fanfare and a hashtag, an article called 'The Interface Theory of Perception' (Hoffman, Singh & Prakash, 2015). From the website description:
In a nutshell, interface theory postulates that our perception operates like a species-specific desktop: We perceive the world in representations that do not represent the “truth” about the world as it actually is, but that are useful “icons” which represent fitness-relevant information about the world. To illustrate, imagine a world in which red and green berries are nutritious but blue and yellow berries make you sick. Will your perceptual system differentiate red from green and blue from yellow? According to interface theory, the answer is no—the organism will have evolved to differentiate between only two colors, namely gred and byellue.
The meat of the paper is a series of evolutionary simulations that pit various perceptual strategies against one another. These strategies vary in how veridical they are, and the key result is that interface strategies, in which perception codes things in a way that bears no resemblance to the world, wins every time. We do not perceive the world as it really is. 

This seems to go against people like Gibson, who argue that perception is of a real world and real properties of things, like affordances. These simulations seem to show that 'realist' perceptual strategies are evolutionarily unsustainable. 

The devil, as always, is in the details, and having read the paper I am now pretty sure that Gibson is quite safe, and that information offers a path out of the weirdness Hoffman conjures.

Friday, 21 August 2015

From Specification to Convention (A Purple Peril)

I previously laid out how specification works and why it's important to the ecological approach. Read that first, because I build on it a lot here. I also laid out the corollary of specification, that it allows that information to come to be something an organism can actually use to coordinate and control functional behaviour. Here, I think out loud about how convention might be able to do similar work, because of Sabrina's work (here, and published now here; read that paper for the extended detail on this) expanding ecological information to handle tasks such as language where specification is not always an option.

This is in part an attempt for me to get my head around some implications of Sabrina's analysis. My plan here is to develop an analogy to specification. This analogy will detail the work specification does to make information informative, then try to lay out how conventions fills this role. The goal is to see if conventions can support behaviour without needing representational help. The answer will be yes, because all the differences between law based and convention-based information are 'behind the curtain', only visible from the third-person perspective. From the first person perspective of the organism, all it gets is structures in energy arrays it can try to use to organise behaviours. Conventions place no special learning burden on the organism (Golonka, 2015) and this means that convention can support behaviour with any representational enrichment the same way specification can. (The hidden differences do have consequences, however, so I will map that out a bit.)

There are many things I have not attempted to explain and as usual this reflects my current thinking, not necessarily my final thinking. I look forward to hearing what questions this leaves unanswered for the reader as a way to move this discussion forwards.

Tuesday, 7 July 2015

Brains Don't Have to be Computers (A Purple Peril)

A common response to the claim that we are not information processors is that this simply cannot be true, because it is self-evidently the case that brains are transforming and processing information - they are performing computations. Greg Hickok throws this ball a lot, and his idea is clear in this quote from his book 'The Myth of Mirror Neurons':
Once you start looking inside the brain you can’t escape the fact that it processes information. You don’t even have to look beyond a single neuron. A neuron receives input signals from thousands of other neurons, some excitatory, some inhibitory, some more vigorous than others. The output of the neuron is not a copy of its inputs. Instead its output reflects a weighted integration of its inputs. It is performing a transformation of the neural signals it receives. Neurons compute. This is information processing and it is happening in every single neuron and in every neural process whether sensory, motor, or “cognitive.”
Hickok, pg 256.
There are two claims here. First, neurons are processing information because their input is not the same as their output; they are transforming the former into the latter. Second, this process is computational; 'neurons compute'.

This is a widely held view; psychologist Gary Marcus even wrote about this in the NYT saying 'Face it, your brain is a computer'. In response, Vaughn Bell at Mindhacks posted about this op-ed and this issue in a nicely balanced piece called 'Computation is a lens'. He sums up the issue nicely by asking 'Is the brain a computer or is computation just a convenient way of describing its function?'. The answer, I propose here, is that computation is a fantastically powerful description of the activity of the brain that may or may not be (and probably isn't) the actual mechanism by which the brain does whatever it does. This is ok, because, contra Hickok,  not every process that sits in between an input and a different output has to be a computational, information processing one

Tuesday, 30 June 2015

What Would It Take to Refute Radical Embodied Cognition?

People often send us papers and data via Twitter that they believe rule out a radical, non-representational theory of cognition. Because I have yet to agree about any of these studies, these people then often ask in exasperated tones 'well, what would you accept as evidence?'. 

My current best answer is "about 20 years of hard work". 

Friday, 26 June 2015

The Perturbation Experiment as a Way to Study Perception

When you study perception, your goal is to control the flow of information going into the system so that you can measure the resulting behaviour and evaluate how that information is being used. There are two ways to do this, one (sometimes) used by me, one used by, well, everyone else. In this post I'm going to compare and contrast the methods and describe why the perturbation method is what we should all be doing.

The standard method is to present experimentally isolated cues and test whether people can detect those cues. The perturbation experiment presents a 'full cue' environment but selectively interferes with the link between a single variable and the property it might be information about. These two different methods lead to very different ways of thinking and talking about perceptual abilities. 

Monday, 4 May 2015

Is Autism a Deficit in Invariance Detection?

If ASD is a problem detecting invariants, the world would remain a 'blooming buzzing confusion' and lead to the behaviours we see in children with ASD, claims a new paper. 
A new paper in Frontiers in Psychology (Hellendoorn, Wijnroks & Leseman, 2015) has proposed that autistic spectrum disorders might be the developmental consequence of a low level, domain general perceptual deficit, specifically the detection of invariants. They explicitly ground this hypothesis in Gibson's ecological approach and theories of embodied cognition that emphasise the key role perception plays in behaviour. This seemed like something I should evaluate, so thanks to Jon Brock for sending this my way on Twitter.

While I am very sympathetic to the basic idea, this particular implementation is too flawed to get off the ground. The authors make a critical conceptual confusion. They mix up invariant features of the world with invariant features of perceptual arrays that might serve as information for the world, and this stops the paper in it's tracks. I think an interesting exercise might be to fix this problem and then simply repeat the paper with the more careful grounding to see where you end up. 

In this post I've briefly reviewed the claims in the order in which they came up in the paper. I've focused my attention on the central hypothesis about invariant detection because that underpins everything else. I've also briefly summarised some of the cited evidence and implications as laid out by the authors, and commented on any issues I saw. This bit is briefer, because my knowledge of the specifics of ASD are limited. I am also considering a comment to Frontiers on this paper, so feedback on this welcome. If you want in on a comment or reworking of the paper, let me know!