“Are thoughts just a bunch of electrical and chemical signals being tossed around inside the brain, or is there more to it than that?”

“In our world,” said Eustace, “a star is a huge ball of flaming gas.”

“Even in your world, my son, that is not what a star is but only what it is made of…”

The Voyage of the Dawn Treader, CS Lewis

I really like the quote above, which is from the Chronicles of Narnia. It raises a neat little metaphysical question:

Why do we assume that what a thing is made up of is what a thing is?

As a neuroscientist I have to point out that no one really knows what a thought is from a scientific perspective. This means that we don’t know what we would need to measure in order to ‘decode’ a person’s thoughts. For the foreseeable future, I cannot look at a brain scan and say, “This person is definitely thinking about pineapples!”

Of course, thoughts seem to be closely linked with neural patterns in the brain, and those patterns are clearly linked with electro-chemical signaling. Tinkering with the signaling clearly tinkers with the thinking. Otherwise the effects of drugs such as alcohol and coffee on thought would be a mystery. Perhaps some day we will have a scanner that tells us what a person is thinking of.


Matter and form

While I admit that electro-chemical signals being tossed about is a necessary precondition for thinking — no phenomenon that lacks such tossing will be unanimously labeled as thinking — I think that material constituency is a less than stellar guide for thinking about what something is.

Consider charcoal, diamonds, graphite, and graphene. These are made up of carbon. But is that all there is to the story of what they are? I hope the answer is an emphatic no, since they all have radically different properties. Charcoal is black and relatively soft. Diamonds are transparent and exceptionally hard. Graphene and graphite conduct electricity whereas other forms do not.

What explains the differences between the various allotropes of carbon? Clearly it isn’t what they are made of — it’s the same stuff in each case.

Eight allotropes of carbon: a) diamond, b) graphite, c) lonsdaleite, d) C60 buckminsterfullerene, e) C540, Fullerite f) C70, g) amorphous carbon, and h) single-walled carbon nanotube. Source: Wikipedia

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What differs among the allotropes is the arrangements of carbon atoms. In other words, form is as important as ‘content’. Depending on how you arrange carbon atoms, you will end up with something soft and opaque or hard and transparent. Clearly the properties of the substances are not to be found in the properties of the atoms.

This is generally true for most complex and interesting objects and processes. You can boil them down to some set of elements — and these elements maybe subatomic particles, atoms, molecules, genes, cells, neurotransmitters — but some defining feature of the overarching process will be missing from the constituent parts considered in isolation, just as transparency or opaqueness are missing from individual carbon atoms.

In complex systems theory and in condensed matter physics, the word emergence is often used to describe the phenomenon by which collections of matter acquire new properties as a result of arrangement or sheer scale. Chemistry is full of examples. Oxygen is a gas. Hydrogen is a gas. But when they combine together in the right way, they produce water, which is a liquid — and one with all kinds of properties that can’t be predicted from first principles through analyzing the constituent parts.


Are thoughts emergent?

Since we don’t know exactly what thoughts are, we cannot say for sure whether they are emergent phenomena or not. But we can indirectly infer that they are by considering properties of thoughts and comparing them with properties of chemicals being tossed around in the brain.

A hallmark of thoughts is that they are about things. When you are thinking about a pineapple, there is an “aboutness” relation between the thought and the pineapple. Thoughts refer to things — which may be real things in the world, or imaginary things like dragons. This is a distinctive feature of mental phenomena, and the philosophers call it intentionality. (Note that intentionality has nothing to do with intentions or motivations — it’s not the best term, but that’s where you’ll find the relevant writings.)

This “aboutness” or “intentionality” is not a feature of chemical tossing patterns. A pattern is a pattern is a pattern, and isn’t intrinsically about any other pattern. At the very least, we can say that modern physics and chemistry have had no reason to invent an “aboutness” concept so far. In other words, there is no purely physical theory of reference.

So it seems reasonable to at least consider the possibility that the property of “aboutness” emerges when matter is arranged in just the right way.


“Is there more to it?”

This admittedly abstract concept is not really going to satisfy people who were hoping that thoughts were actually composed of “magic dust”, as Sam Moss quite rightly termed it. Thoughts are not “made up” of some special secret sauce. If you look at a brain — or any other tissue — under a microscope, all you see are cells. And cells are made up of atoms — mostly carbon, hydrogen and oxygen, with some crucial cameos by nitrogen, calcium, phosphorus, sulfur, sodium, potassium, magnesium and choride.

So is that all a brain or a body is? A stew of a dozen elements? If you followed the story with carbon, then you’ll know that the answer is no. The arrangement of the atoms makes all the difference in the world.

But does this mean that “there is more to it”? If “more” implies a substance of which thoughts are made, then the answer is most likely no.

In any case, given that matter makes up everything, saying that something is “just” matter seems a bit unfair to matter — it’s about as magical a dust as you could possibly hope for!

Matter gives you the universe and you ask if there is more to it?! 😉

If you like, you can call arrangement or form the special “something more”. Arrangement is the “something more” that distinguishes charcoal from diamonds, and thought from nonsense.

But arrangement will not fulfill all the duties of magical dust. It is not the same as the traditional notion of a soul. A soul can live on without a body. But a form has no meaning without the constituent matter that is arranged.

So here’s the compromise: thoughts are made up of electro-chemical signals tossing around, but that is not what they are, since this definition does not distinguish in any useful way between thoughts and perceptions, feelings, moods, emotions or sensations — or even unconscious neural processes for that matter — all of which are also made up of electro-chemical signals.

So saying thoughts are electro-chemical signals is about as useful as saying diamonds are carbon. It’s true, but not in an especially interesting or informative sense.


Notes

If you’ve made it this far, well done! I guess I was having a slow Friday evening! 🙂

I know that what I’ve written is quite abstract, but that goes with the territory if you are thinking about thoughts.

Here are some answers that may be of interest:

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This post was originally a Quora answer.

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Are mental disorders the same as brain disorders? Maybe not!

I am currently reading an excellent paper that will be published in Behavioral and Brain Sciences soon. It raises some very important issues with popular conceptions of mental illness.

Brain disorders? Not really… Why network structures block reductionism in psychopathology research

These two figures capture some of the key points:

Here is the abstract:

“In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. However, the intense search for the biological basis of mental disorders has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this paper, we show that this conceptualization can help understand why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition, because in symptom networks there is no such common cause. Second, symptom network relations depend on the content of mental states and as such feature intentionality. Third, the strength of network relations is highly likely to partially depend on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett’s (1987) notion of real patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.”

Behavioral and Brain Sciences is one of the premier journals for “big thinking” in cognitive science and neuroscience, so it’s great to see these ideas there.

Why an organism is not a “machine”

I just came across a nice article explaining why the metaphor of organism as machine is misleading and unhelpful.

The machine conception of the organism in development and evolution: A critical analysis

This excerpt makes a key point:

“Although both organisms and machines operate towards the attainment of particular ends that is, both are purposive systems the former are intrinsically purposive whereas the latter are extrinsically purposive. A machine is extrinsically purposive in the sense that it works towards an end that is external to itself; that is, it does not serve its own interests but those of its maker or user. An organism, on the other hand, is intrinsically purposive in the sense that its activities are directed towards the maintenance of its own organization; that is, it acts on its own behalf.”

In this section the author explains how the software/hardware idea found its way into developmental biology.

“The situation changed considerably in the mid-twentieth century with the advent of modern computing and the introduction of the conceptual distinction between software and hardware. This theoretical innovation enabled the construction of a new kind of machine, the computer, which contains algorithmic sequences of coded instructions or programs that are executed by a central processing unit. In a computer, the software is totally independent from the hardware that runs it. A program can be transferred from one computer and run in another. Moreover, the execution of a program is always carried out in exactly the same fashion, regardless of the number of times it is run and of the hardware that runs it. The computer is thus a machine with Cartesian and Laplacian overtones. It is Cartesian because the software/hardware distinction echoes the soul/body dualism: the computer has an immaterial ‘soul’ (the software) that governs the operations of a material ‘body’ (the hardware). And it is Laplacian because the execution of a program is completely deterministic and fully predictable, at least in principle. These and other features made the computer a very attractive theoretical model for those concerned with elucidating the role of genes in development in the early days of molecular biology.”

The machine conception of the organism in development and evolution: A critical analysis

I’ve actually criticized the genetic program metaphor myself, in the following 3QD essay:

3quarksdaily: How informative is the concept of biological information?

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Image source: Digesting Duck – Wikipedia

What is a biological model? Here’s a useful categorization system for people interested in neuroscience, cognitive science, and biology

I found an excellent classification of models in a paper on neurogenesis: Using theoretical models to analyse neural development.

I think this should be illuminating for anyone interested in theoretical, mathematical and/or computational approaches in neuroscience, cognitive science, and biology.

There are several ways in which models of biological processes can be classified. 

Formal or informal models

Informal models are expressed in words or diagrams, whereas formal models — which this Review is concerned with — are described in mathematical equations or computer instructions. Using formal language forces a model to be precise and self-consistent. The process of constructing a formal model can therefore identify inconsistencies, hidden assumptions and missing pieces of experimental data. Formal models allow us to deduce the consequences of the postulated interactions among the components of a given system, and thus to test the plausibility of hypothetical mechanisms. Models can generate new hypotheses and make testable predictions, thereby guiding further experimental research. Equally importantly, models can explain and integrate existing data.

 Phenomenological or mechanistic models 

Most formal models lie on a continuum between two extreme categories: phenomenological and mechanistic. A phenomenological model attempts to replicate the experimental data without requiring the variables, parameters and mathematical relationships in the model to have any direct correspondence in the underlying biology. In a mechanistic model, the mathematical equations directly represent biological elements and their actions. Solving the equations then shows how the system behaves. We understand which processes in the model are mechanistically responsible for the observed behaviour, the variables and parameters have a direct biological meaning and the model lends itself better to testing hypotheses and making predictions. Although mechanistic models are often considered superior, both types of model can be informative. For example, a phenomenological model can be useful as a forerunner to a more mechanistic model in which the variables are given explicit biological interpretations. This is particularly important considering that a complete mechanistic model may be difficult to construct because of the great amount of information it should incorporate. Mechanistic models therefore often focus on exploring the consequences of a selected set of processes, or try to capture the essential aspects of the mechanisms, with a more abstract reference to underlying biological processes. 

Top-down or bottom-up models 

Formal models can be constructed using a top-down or a bottom-up approach. In a top-down approach, a model is created that contains the elements and interactions that enable it to have specific behaviours or properties. In a bottom-up approach, instead of starting with a pre-described, desired behaviour, the properties that arise from the interactions among the elements of the model are investigated. Although it is a strategy and not a type of model, the top-down approach resembles phenomenological modelling because it is generally easier to generate the desired behaviour without all of the elements of the model having a clear biological interpretation. Conversely, the bottom-up approach is related to mechanistic modelling, as it is usual to start with model elements that have a biological meaning. Both approaches have their strengths and weaknesses.

(I removed citation numbers for clarity.)

One point might be relevant here: a model is neither true nor false — ideally it’s an internally consistent mini-world. A theory is the assertion that a model corresponds with reality.