What is a “mental” process? The stuff we’re conscious of or a limbo between real, wet, neural processes and observable behavior?
A well known analogy is the computer. The hardware stuff you can kick is analogous to the brain; the stuff you see on the screen is, I suppose, the phenomenology; then the software, all of which correlates with processes you could detect in the hardware if you looked hard enough, some but not all of which affects the screen, is cognition.
Forget for a moment about minds and consider the engineering perspective; then the point of the levels is clear. When you want, say, to check your email, you probably don’t want to fiddle around directly with the chips in your PC. It’s much less painful to rely on years of abstraction and just click or tap on the appropriate icon. You intervene at the level of software, and care very little about what the hardware is doing being the scenes.
What is the point of the levels for understanding a system? Psychologists want to explain, tell an empirically grounded story about, people-level phenomena, like remembering things, reasoning about things, understanding language, feeling and expressing emotions. Layers of abstraction are necessary to isolate the important points of this story. The effect of phonological similarity on remembering or pragmatic language effects when reasoning would be lost if expressed in terms of (say) gene expression.
I don’t understand when the neural becomes the cognitive or the mental. There are many levels of neural, not all of which you can poke. At the top level I’m thinking here about the sorts of things you can do with EEG where the story is tremendously abstract (for instance event-related potentials or the frequency of oscillations) though dependent on stuff going on in the brain. “Real neuroscientists” sometimes get a bit sniffy about that level: it’s not brain science unless you are able to talk about actual bits of brain like synapses and vesicles. But what are actual bits of brain?
Maybe a clue comes from how you intervene on the system. You can intervene with TMS, you can intervene with drugs, or you can intervene with verbal instructions. How do you intervene cognitively or mentally? Is this the correct way to think about it?
The mainstream media is notoriously rubbish at explaining the relationships between brain, feelings, and behaviour. Those of a suspicious disposition might argue that the scientists don’t mind, as often the reports are very flattering — pictures of brains look impressive — and positive public opinion can’t harm grant applications.
The Socialist Worker printed a well chosen and timely antidote: an excerpt of a speech by Steven Rose about levels of description.
… brains are embedded in bodies and bodies are embedded in the social order in which we grow up and live. […]
George Brown and Tirril Harris made an observation when they were working on a south London housing estate decades ago.
They said that the best predictor of depression is being a working class woman with an unstable income and a child, living in a high-rise block. No drug is going to treat that particular problem, is it?
Many of the issues that are so enormously important to us—whether bringing up children or growing old—remain completely hidden in the biological levels.
You can always find a brain “correlate” of behaviour, and what you’re experiencing, what you’re learning, changes the brain. For instance becoming an expert London taxi driver — a cognitively extremely demanding task — is associated with a bit of your brain getting bigger (Maguire et al, 2000). These kinds of data have important implications for (still laughably immature) theories of cognition, but, as Steven Rose illustrates with his example of depression, the biological level of analysis often suggests misleading interventions.
It’s obvious to all that would-be taxi drivers are unlikely to develop the skills they need by having their skull opened by a brain surgeon or by popping brain pills. The causal story is trickier to untangle when it comes to conditions such as depression. Is it possible that Big Science, with its fMRI and pharma, is pushing research in completely the wrong direction?
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. and Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97, 4398-4403
Some careful philosophical discussion by Monti, Parsons, and Osherson (2009):
There may well be a “language of thought” (LOT) that underlies much of human cognition without LOT being structured like English or other natural languages. Even if tokens of LOT provide the semantic interpretations of English sentences, such tokens might also arise in the minds of aphasic individuals and even in other species and may not resemble the expressions found in natural language. Hence, qualifying logical deduction as an “extra-linguistic” mental capacity is not to deny that some sort of structured representation is engaged when humans perform such reasoning. On the other hand, it is possible that LOT (in humans) coincides with the ‘‘logical form’’ (LF) of natural language sentences, as studied by linguists. Indeed, LF (serving as the LOT) might be pervasive in the cortex, functioning well beyond the language circuit […].
Levels of analysis again. Just because something “is” not linguistic doesn’t mean it “is” not linguistic.
This calls for a bit of elaboration! (Thanks Martin for the necessary poke.) There could be languages—in a broad sense of the term—implemented all over the brain. Or, to put it another way, various neural processes, lifted up a level of abstraction or two, could be viewed linguistically. At the more formal end of cognitive science, I’m thinking here of the interesting work in the field of neuro-symbolic integration, where connectionist networks are related to various logics (which have a language).
I don’t think there is any language in the brain. It’s a bit too damp for that. There is evidence that bits of the brain support (at the personal-level of explanation) linguistic function: picking up people in bars and conferences, for instance. There must be linguistic-function-supporting bits in the brain somewhere; one question is how distributed they are. I would also argue that linguistic-like structures (the formal kind) can characterise (i.e., a theorist can use them to chacterise) many aspects of brain function, irrespective of whether that function is linguistic at the personal-level. If this is the case, and those cleverer than I think it is, then that suggests that the brain (at some level of abstraction) has properties related to those linguistic formalisms.
Monti, M. M.; Parsons, L. M. & Osherson, D. N. (2009). The boundaries of language and thought in deductive inference. Proceedings of the National Academy of Sciences of the United States of America.
Found this paper by Edwards, Ashmore, and Potter (1995) amusing as recently I tapped a table to make a point about different levels of analysis. From the intro:
When relativists talk about the social construction of reality, truth, cognition, scientific knowledge, technical capacity, social structure, and so on, their realist opponents sooner or later start hitting the furniture, invoking the Holocaust, talking about rocks, guns, killings, human misery, tables and chairs. The force of these objections is to introduce a bottom line, a bedrock of reality that places limits on what may be treated as epistemologically constructed or deconstructible. There are two related kinds of moves: Furniture (tables, rocks, stones, etc. — the reality that cannot be denied), and Death (misery, genocide, poverty, power — the reality that should not be denied). Our aim is to show how these “but surely not this” gestures and arguments work, how they trade off each other, and how unconvincing they are, on examination, as refutations of relativism.
And the point about levels is made:
It is surprisingly easy and even reasonable to question the table’s given reality. It does not take long, in looking closer, at wood grain and molecule, before you are no longer looking at a “table”. Indeed, physicists might wish to point out that, at a certain level of analysis, there is nothing at all “solid” there, down at the (most basic?) levels of particles, strings and the contested organization of sub-atomic space. Its solidity is then, ineluctably, a perceptual category, a matter of what tables seem to be like to us, in the scale of human perception and bodily action. Reality takes on an intrinsically human dimension, and the most that can be claimed for it is an “experiential realism”
Edwards, D., Ashmore, M. and Potter, J., (1995). Death and furniture: The rhetoric, politics and theology of bottom line arguments against relativism, History of the Human Sciences, 8, 25-49.
When reading these kinds of articles, I look for a couple of things: (a) discussion of the importance of different levels of description and that they may be mapped onto each other; (b) clear language separating personal and sub-personal level descriptions.
It’s not bad. He notes for instance Smolensky’s arguments that “certain types of higher-level patterns of activity in a neural network may be roughly identified with the representational states of commonsense psychology”. BUT two issues to be separated here: classical notions of representation and how these relate to connectionist representations—and models even closer biologically; and also how phenomenology could arise from, e.g., connectionist networks.
Worth a read.
David Marr (1982) is often cited for his theory of levels of explanation. The three levels he gives are (a) the computational theory, specifying the goals of the computation; (b) representation and algorithm, giving a representation of the input and output and the algorithm which transforms one into the other; and (c) the hardware implementation, how algorithm and representation may be physically realised. I sometimes wonder how ideas from computer science related to levels of analysis could map across to the cognitive and brain sciences and perhaps generalise or make more concrete Marr’s three levels. This is already being done, mostly notably by researchers who investigate the relationship between logics and connectionist networks (see this earlier posting for a recentish example). But how about deeper in computer science, well away from speculation about brains?
There is a large body of work on showing how an obviously correct but inefficient description of a solution to a problem may be transformed into something (at one extreme) fast and difficult to understand. One particularly vivid example is given by Hutton (2002) on how to solve the Countdown arithmetic problem. Here follows a sketch of the approach.
In the Countdown problem you are given a set of numbers, each of which you are allowed to use at most once in a solution. The task is to produce an expression which will evaluate to a given target number by combining these numbers with the arithmetic operators +, -, /, * (each of which may be used any number of times), and parentheses. For instance from
1, 5, 6, 75, 43, 65, 32, 12
you may be asked to generate 23. One way to do this is
((1 + 5) – 6) + 20 – (32 – 35)
Hutton begins by producing a high-level formal specification which is quite close to the original problem. This requires specifying:
- A method for generating all ways of selecting collections of numbers from the list, e.g. , , , …, [5,6], … [1,5,75,43], …
- A method for working out all ways to split a list in two so you’ve got two non-empty lists, e.g. for [1,5,75,43] you’d get
- A method which given a couple of lists of numbers gives you back all the ways of combining them with arithmetical operators.
- A method which evaluates the expression and checks if what pops out gives the right answer.
When carried through, this results in something executable which can relatively easily be proved equivalent to a formalisation of the problem description. The downside is that it’s slow. One of the reasons for this is that you end up generating a bucketload of expressions which aren’t valid. The method for solving the various elements described above are too isolated from each other. Hutton gives the example of finding expressions for the numbers 1, 3, 7, 10, 25, and 50. There are 33,665,406, but only 4,672,540 are valid (around 14%); the others fail to evaluate because of properties of arithmetic, e.g. division by zero. His solution is to fuse the generation and evaluation stages, thus allowing cleverer generation. He proves that the new version is equivalent to the previous version. Next he takes advantage of other properties of arithemetic, e.g. commutativity of addition, x + y = y + x, which again reduces the search space. More proofs prove equivalence. This process continues until you’re left with something less obvious, but fast, and with explanations at each stage showing the correspondences between each refinement.
Why is this relevant to brain stuff? I’m not suggesting that people should try to use refinement methods to relate stuff measurable directly from the brain to stuff measurable and theorised about in psychology. The relevance is that this is an excellent example of levels of description. There may be many levels and they’re relatively arbitrary, guided by ease of explanation, constrained by ease of execution. Presumably the ultimate goal of brain research is to relate feeling and behaviour down through dozens of levels to the physics, but the journey is going to require many fictional constructions to make sense of what’s going on. Naively mapping the constructs to, e.g., areas of the brain seems likely to bring much misery and despair, as does arguing about which fiction is correct.
Hutton, G. (2002). The Countdown Problem. Journal of Functional Programming, 12(6), 609-616.
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman.