“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?
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.
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?
Image source: Digesting Duck – Wikipedia
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.