In an eclectic discipline such as Neuroscience, models are built using many different research paradigms. In Chapter 8 of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’, he writes about the response of the scientific communities to crises in science. Kuhn suggested that a paradigm was either successful in which case there would be an opposing paradigm (or paradigms) or else the paradigm was static and became a research tool. If we consider a Neuroscience model which borrows from several paradigms, how will Kuhn’s insights influence our understanding of this? Kuhn’s insights can be restated as
‘If a research paradigm is successful it will face competing research paradigms otherwise the unopposed paradigm will become an inactive science‘
If the Neuroscientist constructs an interdisciplinary model, then the Neuroscientist will borrow from several research paradigms. This leads to several possibilities according to the above statement. The model may incorporate a combination of active and inactive research paradigms. For the active paradigms, the Neuroscientist will need to choose one of the competing paradigms. In contrast if the model borrows from inactive research paradigms then no choice is needed as the dominant paradigm is unopposed. This latter possibility is more straightforward in terms of model building.
However if we return to the first example, what happens when a Neuroscience model borrows from active paradigms? Firstly the Neuroscientist must choose between competing paradigms and validate this choice. Secondly the validity of this model will be contingent on the paradigm debate within the research community. If the opposing paradigm prevails then the model becomes invalidated. Contrasting again with the second example of a model which borrows from inactive sciences – this model is more robust because the state of flux in the research community is absent.
In practical terms however the research paradigms more relevant to Neuroscience are numerous and we can ask what can we properly consider as a research paradigm. If we look at the actions of Serotonin on mood in the Limbic System, the phenomenon can be broken down into several components. The question of whether Serotonin is a Neurotransmitter that acts on neurons relates to a paradigm which can be considered as inactive. The ability of Serotonin to act as a neurotransmitter is not seriously challenged. A Medline search using the term “Serotonin Neurotransmitter” returns 100, 379 articles. Searching through the first 20 abstracts, none of the papers challenged the basic assertion that Serotonin is a neurotransmitter. Restricting the search to reviews using the term “Serotonin Neurotransmitter Review” retrieved 12, 319 articles. Looking at the first 20 abstracts, this paper suggests additional roles for Serotonin in platelets and via an action on Liver Serotonin receptors. However this does not challenge the theory that Serotonin acts as a Neurotransmitter.
We can easily find contemporary studies that support the theory that Serotonin acts as a neurotransmitter. Searching Medline using the term “Serotonin receptor depression” retrieves this paper in which Positron Emission Tomography was used. The researchers show that 5HT1 A receptor binding changes after treatment with a medication that increases Serotonin levels in the intracellular space – the Serotonin Reuptake Inhibitors. The central assumptions in this study are fairly straightforward including Serotonin’s action as a neurotransmitter. At this stage it is not too far fetched to say that researchers take it as read that Serotonin is a neurotransmitter and have moved on with their inquiries which in terms of the broader literature on Serotonin have become ever more esoteric.
Turning next to the relationship between Serotonin and the brain’s emotional centre – the Limbic System, this paper looks at the research evidence which shows that almost every type of Serotonin receptor is present in the Hippocampus. This discussion occurs in the field of Histology, the study of the microscopic properties of cells and tissues. This in turn borrows from a number of other research paradigms in order to build working models that are used to interpret the data. There are a large number of papers retrieved using the search term “Hippocampus Serotonin Receptor” although the central question of whether there are Serotonin receptors reliably found in the human Hippocampus is less clear without a more detailed analysis of the abstracts and papers.
Finally what can we say about the relationship between Serotonin, Mood and the Hippocampus? (limiting the Limbic system question to the Hippocampus). Using the search term “Serotonin Receptor Hippocampus Mood” did not retrieve any studies. However the PubMed interface automatically generated an alternative search term which utilised other terms as well as the OR operator to yield 403 results with varying degrees of relevance. These papers used a variety of different models. Again a superficial examination of the results did not show a clear answer to the question of whether mood was related to the Serotonin receptors in the Hippocampus. In addition, the research studies were complex and some were in vitro which meant that limited conclusions could be made regarding mood.
The analysis of one simple example above shows that the complex theoretical problems in understanding the psychopharmacological aspects of mood in relation to the Limbic Cortex are not resolved by simply considering the debate between two or more opposing research communities with different research paradigms. Instead there are many research paradigms. The central theories in these paradigms are robust and the the research (perhaps the ‘normal science’ that Kuhn refers to) becomes increasingly esoteric. By combining these research paradigms it becomes difficult to establish a clear causal pathway between receptor activation in one brain region and changes in mood.
The problem is that science works best when it takes a small part of the world under carefully controlled conditions and the scientist is able to manipulate a few variables leaving all other conditions invariant. In this regards physicists have had it lucky! The question of whether we can relate mood to changes in Serotonin in the Hippocampus is partly a ‘real world question’. To understand the relation to mood we must measure the person’s mood and how it changes over time. We cannot isolate a few molecules or a tissue. We must see the whole person. As soon as that is done, it becomes very difficult to produce controlled conditions. Ecologically valid studies require that the person is evaluated in the natural environment. Under those conditions there are large numbers of other factors that may influence mood. For instance there may be changes in the activity of Serotonin or other neurotransmitters in other areas of the brain, the optimal time period for evaluation may be unclear, there may diurnal changes in mood, physical activity levels may alter, hormonal changes, dietary changes, the metabolism of Serotonin may fluctuate due to various factors, relationships with other people may influence affect and mood and so on.
Perhaps it is the question of ‘real world evaluation’ which is the central problem for Neuroscience research and indeed for Psychiatric Research. Nevertheless when significant results are found this means that the observed effects are being seen despite this ‘real world’ problem. That in turn means that despite such challenges the scientists have been able to reliably identify real and important phenomenon. If we take the analogy of science as a magnifying glass looking at nature however the more esoteric studies are probably testing the resolution of the magnifying glass. Sometimes they exceed the resolution and produce artefacts while at other times they get it just right.
Appendix 1 – Review of Chapter 8
In Chapter 8 of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’ is titled ‘The Response to Crisis’. Whereas in Chapter 7, Kuhn focuses on how the crisis in science arises in this chapter he elaborates on how the scientific community responds to this crisis. He makes the interesting point that in criticising one theory the scientist must propose an alternative otherwise this is not the pursuit of science. What is also interesting is that he suggests that when this competitive process ends, the branch of science becomes static and in the example he gives it becomes a ‘research tool’. Kuhn suggests that there are always discrepancies even in the most successful of paradigms. With a move towards crisis there are increasingly divergent explanations and there is a loss of identity within the field. Indeed Kuhn maintains that all crises involve a blurring of paradigms. The crises are closed in one of three ways. In the first case, the crisis is handled. In the second scenario there is a resistance to radical approaches. In the final scenario the crisis leads to the emergence of a new candidate for paradigm.
Kuhn then goes onto discuss commentators on the field who refer to Gestalt theory in which a visual perception is dependent on the whole rather than part of an object. So if the reader looks at the cube below, the lower square face can be interpreted either as sitting at the front of the cube or the back of the cube. In both cases the square takes on a different meaning within the whole object that is perceived. In the same manner Kuhn suggests that new paradigms lead to a different way of seeing a body of empirical facts. He is quick to point out however that this is a crude analogy and that scientists do not quickly switch back and forth between paradigms. Nevertheless it illustrates the essence of his arguments well.
Alan De Smet, ‘Multistability‘ (Public Domain)
Kuhn then goes on to say that the scientist having identified the anomaly central to a crisis will go on to explore the anomaly and to better characterise it. In crisis, speculative theories multiply and increase the chance of a successful paradigm being reached. He also suggests that philosophical inquiry into assumptions can challenge some of the tenets of the current paradigm. Finally Kuhn finishes by commenting that many scientists leading to scientific revolutions are deeply immersed in crisis and they are either very young or new to the field in change which he interprets to mean that there thinking has not been shaped by the component rules of a paradigm. However Charles Darwin would be a notable exception having published ‘On the Origin of Species’ at a mature age and with a comprehensive knowledge of the related fields in biology. Nevertheless there are numerous counterexamples and the main result of this chapter is that Kuhn provides the reader with very effective tools for thinking about science in transition.
* One thought I had here was that in the very early stages of a science there must be a lot of theories that are initially developed but which are quickly shaped by the experimental facts. In this way many theories would exist before quickly falling to experimental findings in which case there would be a ‘survival of the fittest’ theories which are tested against each other. This has a number of implications.
Firstly that a philosophical system might define this pre-science phase in which a large number of theories exist without being tested against the experimental facts. The brain’s analytical and other abilities are used as an alternative to hypothesis testing in the real world in order to generate ‘realistic’ solutions based on experience and intuition. As time proceeds and assuming the system has an efficient or effective ‘memory’ and scientific inquiry produces a growing body of empirical facts the competitive process in which proponents of different models challenge each other’s models and refine their own leads to ‘fitter’ models (using evolutionary terms). However these models are adapted to the empirical facts which in turn are a byproduct of the initial inquiries in this area.In this manner, mathematics might offer the best ‘starting conditions’ for this philosophical inquiry as these starting conditions give philosophical inquiry the least opportunity for diverging from reality using such an approach.
Secondly fitter theories might well diverge significantly from an explanation of reality depending on their starting conditions although there might be other phenomenon which curtail that line of inquiry as this divergence becomes more evident. What this would also mean is that the development of the most effective scientific theories is not only a measure of how effectively a theory fits with the empirical data but is also a marker of how effectively a theory keeps the focus on the empirical data in which the theory initially flourished as well as a measure of how effectively the theory recruits and retains proponents.
Appendix 2 – Resources on this Site
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