Lecture 17: Pattern Recognition and Representation Bearers (Turvey, 2019, Lectures on Perception)

Last Lecture Turvey reviewed the basics of the computational approach, and highlighted again how it is just the latest iteration of the Cartesian programme. In this Lecture, he explores the specific topic of pattern recognition, which has been a major topic in the computational approach and exemplifies many of the major problems. These problems primarily boil down to systems requiring loans of intelligence to even come close to working.

Lecture 16: The Computational-Representational Perspective: Preliminaries (Turvey, 2019, Lectures on Perception)

The next four Lectures are specifically focused on the modern form of the Cartesian programme - the computational-representational approach. It's important to spend some time here, because this is the ecological approach's current opposition, and because the fact it is just yet another Cartesian programme matters, and is at the heart of most of our objections to it. 

In Lecture 6 we learned about the 3 grades of sense. First, there is reflex. The second is limited awareness of secondary qualities. The third is full mental awareness of what it all means. The proposed solution (Hobbes) for building the third grade out of the other two is the manipulation of symbols to do inference. In modern times, this is implemented as computation, and implemented in a representation.

The Ecological Approach, Explained to an 8 Year Old

About 3 weeks ago I got an email from a person who had found our blog via Robert Epstein's piece 'The Empty Brain'. The email said

I've had a good read this afternoon, and it has been informative to some degree, however ...
I have an 8 year old son, and due to questions we both have, we have had some very interesting laypeople's conversations about the nature of experience and "the mind" (is it a thing, a physical thing, a process?) as well as such things as memory, embodiment and perception.
It seems it would be really helpful for us (and by extension, possibly many others?) if you could summarise the broad strokes of your theory in some way in which an intelligent 8 year old (and his father!) could understand.
Would this be possible?

Ed Yong has taught me that good science communication doesn't have to be dumbed down, it just has to be pitched right, and while I am no Ed Yong, I say, challenge accepted! Let me know how it goes!

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. 

What else could it be? The case of the centrifugal govenor.

Previously, I’ve dismissed the idea of mental representation because 1) no one knows what a representation is and 2) the arguments for representation tend to be pretty weak. Now, I’d like to spend a bit of time discussing a possible alternative – a dynamical systems approach to cognition. To frame this discussion, I’m going to summarise a very handy philosophy paper by Van Gelder (1995) in which he distinguishes between a computational and dynamical solution to a particular problem (see also Andrew's post on the polar planimeter). Van Gelder has clearly picked a side - that cognition emerges from dynamical systems and that cognitive processes are evolutions in the state-space within these systems. One of the main arguments for computation is that it’s difficult to imagine what else could be going on (see footnotes p. 346 for references for this argument). So, Van Gelder wrote this paper,  not to decisively rule out computation, but to provide an answer to the question “what else could [cognition] be?”

In which I finish talking about discrete computational representations

In a previous post, I summarised Dietrich & Markman’s definition of representations and ideas about how representations get their content. While there are many flavours of representation, D&M subscribe to the discrete computational (DC) variety. To summarise the previous post: According to D&M, representations are internal mediating states that govern behaviour. Representations have relations to both the external and internal (i.e., other representations) environment. They acquire content in two ways. The first way is through correspondence, where some internal state connects to some external state. The second is through functional relations with other representations. Representations are transformed via computations.

The main purpose of this post is to summarise D&M's main arguments in favour of discrete representations so that I can refer to these in other posts. I make several comments about the quality of these arguments, but this is in no way meant to be a systematic response to their paper.