‘The Interpretation of Scientific Revolutions’ is a response to Thomas Kuhn’s ‘The Structure of Scientific Revolutions’ and follows on from an earlier review of this work. This was originally written as a series of earlier posts and is a look at how Kuhn’s work relates to Neuroscience.
The following is an attempt to interpret Thomas Kuhn’s ‘The Structure of Scientific Revolutions’ using the framework of a discipline which is eclectic, pragmatic and empirical in approach. The starting point of this interpretation is a review of ‘The Structure of Scientific Revolutions’ which the interested reader will find via the link in the Appendix. This first part is a response to the introduction. Kuhn writes in the introduction that the social sciences produce a response different to that of Chemistry, Astronomy, Biology and Mathematics. Interestingly one fundamental difference between the social sciences and the latter group is that they become possible only when there are sufficient numbers of people to form a society and that they have a well-developed language for communication. In evolutionary terms, the understanding of the former group of sciences has an immediate adaptive value that can be used by small groups. More explicitly a knowledge of chemistry can occur preverbally and enables the so-called hunter-gatherer to identify and extract minerals and by combustion to transform them into useful materials. A knowledge of Astronomy is gained preverbally by simply gazing at the sky and over time coming to an understanding of the movements of the stars, as well as the course of the moon and the direction of the rising and setting of the sun. So intuitive is this that a knowledge of the sky is used by migrating birds in relating stars to the North star as a simple example of a species adapting to the predictability that nature has provided. A knowledge of plant biology is obviously essential to herbivores and omnivores as another example of this instinctual primacy. In this regards there is something very different about the social sciences in evolutionary terms. Although they include other species, many notable examples of studies in the social sciences relate to humans. Indeed whole domains of the social sciences are devoted to the characteristics of people and their interactions with each other on both small and large scales. Given the recency of human origins, in evolutionary terms these sciences relate to recent developments in the evolutionary timeline. Indeed over the course of this period there have been further changes which may have influenced the nature of these small and large scale interactions.
Another difference between the subject matter of these sciences is that the social sciences are a product of the complexity of the mind and the human mind in particular. In terms of adaptation to the environment the human mind has demonstrated sophisticated properties. While the human mind may have evolved to make predictions about the immediate world making the above sciences informally indispensable the social sciences raise the question of how the mind can study itself which taken to extreme lengths can be implied to be a logical paradox depending on the instrument of measurement. Another aspect of the human mind is the ability to adapt and this property superficially at least distinguishes it from the stellar bodies continuing on their well-defined courses. The person gazing at the setting sun can choose to walk away or towards it or engage in many different behaviours thereby distinguishing the course of the sun from their more sophisticated behavioural repertoire. Nevertheless the same arguments about prediction can be applied to the mind in theoretical terms at least. In this regards the social sciences also present an existential challenge in that this same expansive behavioural repertoire is incorporated into aspects of shared identity and a careful study and elucidation of these same behavioural repertoires can be interpreted as a minimisation of these important aspects of shared identity. Taking this further such a study can be interpreted in terms of underlying agendas when the same adaptive properties of the mind may respond with the most well-developed of those same adaptive properties. Thus the social sciences possess many unique properties and face many unique challenges. Kuhn brings in the concept of paradigm changes in science while exploring these phenomenon. His work in some senses incorporates and regards the social sciences in coming to a better understanding of science.
This second part is a response to Chapter 1 of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’. In Chapter 1, Kuhn introduces #4 important concepts
#1 Historical revisionism of scientific revolutions obfuscates the antecedent phenomenon
#2 Scientific revolutions occur gradually
#3 Scientific revolutions require dialogue between proponents of conflicting models or views
#4 Normal science is distinct from the science of revolutions and contributes to the generation of the necessary tensions
In eclectic disciplines which draw from disparate sciences the above concepts have the potential to influence the evaluation of those same disciplines. Turning firstly to historical revisionism, an eclectic discipline is at risk of disengaging from the historical events occurring within the sciences which are drawn upon. The expertise necessary to evaluate those same events is contained within the relevant scientific communities and with it the ability to draw lessons from historical events. Even within a circumscribed scientific community, sufficient variation within the community is enough to influence the potential lessons that can be drawn particularly where the predictive utility of the leading model is difficult to evaluate. In such cases there is a risk of circularity in conclusions drawn which further impacts on future developments. One obvious solution is to develop expertise either jointly or resources with expertise in both fields.
With regards to the rate at which scientifc revolutions proceed a primary question is where such revolutions occur? In an eclectic discipline the question must be asked of whether such a revolution is occurring in other fields or within the eclectic discipline. Gradual events occur through subtle conversations within communities which can be missed if there is no direct involvement within that community. Imminent changes can be overlooked without knowledge of this discussion and this can be interpreted as stagnation within the discipline. With eclectic disciplines other avenues are open to the contribution towards scientific revolutions. These would include facilitation of the conversation and the contribution of an overview of perspectives for the purposes of comparison. In this regards such eclectic disciplines offer a natural forum for identifying obviously occurring debates between proponents of differing models and the identification of resolutions.
Normal science is firmly within the domain of the scientfic community and according to Kuhn is a defining characteristic of that same community. An eclectic discipline would fall outside of this domain unless as above, resources are allocated effectively and cooperatively. Normal science is that area which has the most potential to differentiate the eclectic discipline from circumscribed sciences and to create the distinct identities of and tensions between both.
This third part is a response to Chapter 2 of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’. Kuhn named this chapter ‘The Route to Normal Science’ and here he expands on his concept of ‘normal science’ which he carefully contrasts with revolutionary science.
Central to the discussion are the characteristics of the research community. In moving from normal science to revolutionary science and back to normal science again several things happen within the research community. Firstly the research community has a shared and specialised language and central problems to solve. In moving to revolutionary science there is a splintering of the research community as the new paradigm arises. The research community is increasingly attracted to the new paradigm. Eventually the new paradigm succeeds and the process of normal science begins within this new paradigm. The research community initially uses a generalised language before developing a more specialised one which is relatively inaccessible to those outside of the community.
This transition describes a process within the research community itself. Whenever communities contain are linked not just through research but through the practical application of that research then strictly speaking these additional common properties of the community are not included within Kuhn’s arguments and he makes explicit reference to such cases. In the case of eclectic communities which are identified more by practical research applications than research activities, the transition from normal science to revolutionary science to normal science again occurs within the related and distinct research communities. The eclectic community may influence the transition depending on their relationship with the research communities. Such a relationship may involve direct dialogue, indirect communication or the use of shared resources.
The eclectic community may also through involvement with many research communities facilitate revolutionary science. However if the eclectic community does not have the necessary infrastructure then any facilitation of a revolutionary paradigm shift may necessary be followed by the research community taking forward the normal science. If such is the case, it implies that revolutionary science involves more than one community and is differentiated from the single community driven process of normal science.
This third part is a response to Chapter 3 of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’. In this chapter, Kuhn focuses on the nature of normal science.
In moving from a specific science to an eclectic science whose community interacts with many other scientific communities, Kuhn’s conclusions imply that the eclectic scientific community will have a limited role in the normal science of these other scientific communities. However they can play a more influential role in the revolutionary period marking paradigm changes although the conditions under which this occurs are not specified. However in building up a more accurate picture of the complexities of nature, the eclectic scientist must work with a multimodal model with each component sitting within a different community. Whilst a single model may enable a single community to work within relatively controlled conditions, a better approximation to nature through multimodal models necessitates a transition from controlled conditions to increasing boundaries of uncertainty.
This transition necessitates an understanding of the scientific community as well as the need to understand other scientific communities and to be able to build a valid bridge between the central paradigms. This is the crux of the problem. Can an essence of the paradigm within a community be abstracted and integrated with the essence of another paradigm or are the paradigms inherent
in the scientific communities.
The question raises three possible answers. Firstly that the paradigms of different communities are incommensurable which Kuhn suggested was true of paradigms within a community at a time of revolutionary science. The second possibility is that the paradigms are reconcilable but they require an integration of the abstracted essences of these paradigms. The third possibility is also that they are reconcilable but are embedded within the communities and any reconciliation will result from communication between communities and perhaps even the development of a specialised interface language.
This fifth part is a response to Chapter 4 of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’. My understanding of this Chapter in Kuhn’s work is that there is a central paradigm which consists of a central model or set of statements. The model or statements exist within a larger more informal set of rules or assumptions. Thus any successful revolution is not simply a matter of improving upon the central model but also needs to address the surrounding infrastructure which is not just theoretical but also permeates the scientific community. The paradigm also determines which puzzles are considered solvable by the scientific community. This means that scientists may consider questions in light of the paradigm which they are working in and will discount potentially important questions on the basis that they are not thought to be solvable within the paradigm.
There are a number of factors which will lead to scientists considering puzzles solvable. These include the human and financial resources needed, the technical limitations of scientific equipment that is readily available as well as the prevailing values within the scientific community. For all of these factors there are variations within the scientists or departments which enable variation in the puzzles that are selected. We might expect that for each of the factors, the main qualities can be graphed and would form a normalised distribution. For instance, the funding in a department for solving a particular puzzle might show such a distribution. If these factors are normally distributed then the puzzles that can be solved might be those that require resources that fall within the peak of the distribution. Framing this in concrete terms, it may be that the community is more likely to select a puzzle that requires an average of two scientists working over a 2 year period with specific readily available equipment and a budget within a defined range. This would increase the likelihood of reproducibility. When any of these factors lie outside of the 95% confidence interval for these factors it would reduce the likelihood of reliability and perhaps even acceptance of the results within the community.
This sixth part is a response to Chapter 5 of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’. Chapter 5 was reviewed in another post (see Appendix). A theme that Kuhn explored in this chapter was the dissociation of individual research from the surrounding paradigm. One possible way of thinking about this is that there is a common understanding in the community that the paradigm is useful and that there is no further need to think about it. The scientist is left to focus on the problems within that paradigm. One interpretation therefore is that the scientific community have implemented an efficient and abstract way of working together. The scientists within a scientific community operate within different geographical locations and cultures but are connected through a globalised scientific culture which characterises their community. The boundaries of this culture have been explicitly stated in the form of the paradigm. The community has solved the problem of how people can work together efficiently. The rules have been worked out in an abstract form. The acceptance of papers in a peer reviewed journal by the peer group of the scientist can be seen as a tacit acknowledgement that their work fits into the paradigm.
The methodology that forms part of the ‘normal science’ is a well characterised set of behaviours that the scientist must engage in to replicate the conditions that have been established elsewhere. The outside observer may well draw parallels with ritualistic behaviour. However as a result of this ‘ritualised’ behaviour, the scientist is able to produce and document results which can be compared with the results of other studies. In this manner an evolution of knowledge occurs whereas elsewhere ritualistic behaviour although integral to group identity can be associated with invariance in order to maintain the group identity. Furthermore scientists can apply their knowledge in the form of technology. When the science relates accurately to the real world, the technology can enable the users of that technology to better adapt to their environment. Changes in technology can lead to a refinement of the methodological ‘rituals’ in a goal directed manner. This process is accelerated by competition and science is defined by change. Whether this is progress has been discussed elsewhere by Kuhn but the argument is altogether different when we talk about technology which however is outside the scope of the present discussion.
If we frame the discussion in this way it leads inexorably to another question. If one of the characteristics of science is to increase the collaborative efficiency of the global community of scientists, can that efficiency be increased further? If there is a process of doing science that is common to all branches of science then can that process be refined? If this is common to all of the branches of science then a better understanding of this would facilitate the creation of a common language for all scientists which can be refined by a concerted approach by all scientists. This may seem a rather vague comment but can be clarified by means of concrete examples. Aggregating data and knowledge from previous studies enables scientists to better formulate new studies by asking key questions or by generating new hypotheses on the basis of this aggregation. The scientists operating within branches of science as disparate as materials sciences and psychopharmacology use electronic databases of stored scientific papers in order to aggregate these papers. Indeed many software programs have been developed to facilitate this. Improving this process and investigating the most efficient workflows for scientists across all branches of science would enable scientists to improve their efficiency and do so by pooling their resources. In a sense this is a metascience with the potential to accelerate progress within fields. This metascience would include a branch of the social sciences in order to better understand the factors that influence scientific output (there is work on this already including that on citation indices).
Thomas Kuhn’s ‘The Structure of Scientific Revolutions’ was a landmark publication which helped reassess and refine the understanding of the core principles of scientific endeavour. The essence of Kuhn’s work was that scientific activity occurs in two broad categories – normal science and revolutionary science. At the time of revolutionary science, the core principles of the established scientific paradigm within a community are challenged by a competing paradigm. The resulting Hegelian Dialectical involves a replacement of the old paradigm by the new. The process of normal science occurs within a paradigm and describes the most common form of scientific activity where lines of inquiry reflect a tacit acceptance of the framework of assumptions of the guiding paradigm. In Chapter 6 (see Appendix) Kuhn writes about the anomaly in relation to emerging scientific discoveries. The essence of this chapter as I have interpreted it is that the revolutionary and normal scientific activities are inextricably linked. Kuhn suggests that anomalies arise during the course of normal science. A finding occurs which cannot be explained within the framework of the guiding paradigm. Further activity better characterises this anomaly and further lines of inquiry arise. Explanations for this anomaly give rise to a new paradigm – the revolutionary paradigm.
How can such an understanding be applied to a science which is eclectic, pragmatic and empirical in approach? One interpretation is that such a science cannot readily have the normal scientific activities unless these occur within the scientific community operating within the central paradigm. If this same science is eclectic in approach then it is disenfranchised from the above relationship between normal and revolutionary science as the normal science occurs within other scientific communities. The necessary anomalies result from the normal scientific activities of those communities and the anomalies are more readily recognised within those same communities. Additionally those same scientific communities may also be better equipped to investigate these anomalies and generate the foundations of the subsequent paradigms. However this period of revolutionary science is one of de novo generation. The iconic cultural events have yet to occur and eclectic scientific communities are well placed to participate in this movement although not to carry this through unless becoming part of this community. There are solutions which have been discussed in a previous post.
Interestingly individual branches of science may with time diversify to such an extent that rather than being homogenous they may instead come to form a heterogenous group of scientific communities. In this case any common identity necessitates the adoption of an eclectic understanding in contrast with superspecialisation if an identity is to be maintained. Indeed this tension between identity and specialisation may itself generate a misplaced expenditure of resources. This issue of superspecialisation however is distinct from that of Kuhn’s argument about anomalies but interacts as it must at the level of the culture of a scientific community. In his book, Kuhn gives the example of disciplines which are sufficiently refined with time as to become separate branches of science and indeed to generate their own sub-branches. This however was not central to Kuhn’s arguments. The textbook which Kuhn refers to elsewhere must also become an examplar of the eclectic approach to a branch of science being as it is aimed at the student. A distillation of the science for the initiate is necessarily bereft of the cultural nuances which make a scientific community as Kuhn’s work implies that one aspect of science is almost organic – ‘living’ within the scientific community with which it is synonymous. Indeed the distillation is only an approximation of the scientific language which is spoken by the community.
However one last point is that the anomaly is a key concept here as Kuhn is characterising the scientific community and not other communities.
This eighth part is a response to Chapter 7. My review of Chapter 7 can be seen in the Appendix which should clarify some of the subsequent discussion. I have interpreted the essence of this chapter as the need for crises in science. These crises occur when scientists are repeatedly faced with anomalies which cannot be explained by the central paradigm.
At the time of writing, the discover of a Higgs boson-like particle at CERN’s Large Hadron Collider has dominated the news. In a previous post I have argued that this may in fact represent part of normal science since the experimental findings have confirmed the standard model. In other words the findings fit with the guiding paradigm. However there remains the possibility that this could herald a period of revolutionary science. When the LHC is in full operation, it may generate findings which do not fit with the Standard Model and which would represent anomalies. Kuhn predicts that repeated anomalies lead to crises. These crises necessitate an alternative model or theory to explain the occurrence of these repeated anomalies. Thus if the Standard Model generates many findings which are dissonant with the Standard Model these anomalies would lead to an alternative model with a debate between proponents of the two models and perhaps a transition between these models.
Whilst the above is supposition, the revolution in science isn’t an overnight phenomenon but one which takes place over lengthy periods. The culture changes, there are debates and in the twilight years of the passing paradigm the loyal proponents fight a rearguard action. Finally the transition is complete. From this perspective we can see that exciting scientific events widely disseminated in the media and discussed at significant length might not necessarily be the revolutionary science that Kuhn talks about. Instead the dust must settle and we must look not just at the science but at the players – the scientists themselves. We must wait to see the anomalies, the generation of a competing theory, the ensuing theoretical debate between the camps. Then it becomes clear that we are seeing a revolution. Even then though we must wait to see if the new paradigm succeeds.
Kuhn suggests that historical revisionism occurs at a frenetic pace and this is nowhere better exemplified than in the textbook which has a specialised goal of educating the student of science. Historical nuances and the struggle of the moment are transformed into the clear march of progress. The old redundant theory is simply brushed aside as the bold and better new all encompassing theory is pushed to the foreground. Kuhn notes instead that science is not necessarily progressive but that the proponents of science give the illusion of progress. I am inclined to disagree with him on this point although I will discuss this at length in another post. Perhaps the proponents of science are not too dissimilar to the proponents of any other discipline. People naturally form a group identity and perhaps it is the characteristics of this group identity which drive the historic revisionism that may be seen in the textbook. Maybe this approach is even the right one for the goals of education.
With regards to Kuhn’s work I am particularly interested in how it might apply to Psychiatry. There have been several movements that have fallen under the rubric of antipsychiatry or critical psychiatry. Could it be that the antipsychiatrists or critical psychiatrists have found the anomalies which are needed for revolutionary science in Psychiatry? Here is a brief consideration of a few
1. Dr Niall McLaren in his work ‘Humanising Madness’ (see review here) suggests that there is no coherent biopsychosocial model. Whilst this is a very interesting point for debate does it relate to the anomaly that has been discussed above? I would argue that it doesn’t. McLaren’s point is about something more fundamental – the very existence of the model itself. The model is the core of the paradigm and if it is argued that there is no coherent model then there can be no anomaly. However there are models but these occur within single domains (rather than spanning the biopsychosocial domain) and it is here that we can better talk of anomalies.
2. The effectiveness of medication. From time-to-time there are published meta-analyses which purport to show that medications don’t work. There are often ripostes which criticise the methodology of these studies or other meta-analyses which show that they do work. In terms of revolutionary science there is something very distinctive about these debates because there are other areas of consideration. Kuhn does in his work briefly suggest that in terms of science there is something distinctive about Medicine. Finding that a drug might not work is not just interesting from a scientific perspective but also has clinical implications that resonate far beyond the laboratory and must be treated with sensitivity.
From the perspective of revolutionary science does an anomalous finding about the efficacy of a medication herald the beginning of a revolutionary science? There are unlikely to be repeated crises because medications are rigorously tested and it would be unusual for a whole series of trials after some time to start showing the medication doesn’t work. When this does happen for instance in the case of antibiotic resistance there is another explanation altogether. However when meta-analyses are published they can highlight possible difficulties with efficacy although a single publication isn’t the repeated crises that Kuhn talks about.
In the hypothetical example of a medication which goes from being efficacious for a condition to ineffective through empirical trials the end-results don’t necessarily tell us too much about the paradigm. If a drug works or doesn’t work there could be any number of underlying reasons which range from how the medication is metabolised through to the types of receptors that it is acting on or the regulation of those same receptors or interactions with other aspects of treatment. The anomaly in this case is not specific enough to tell us about the underlying model. Perhaps the anomaly can only arise when we investigate specific components of the model – up or down-regulation of receptors types for example. Perhaps our models of treatment have to be multifaceted and the anomalies will occur in a very small component of this model.
3. Social Constructivism. There is the argument that diagnoses are social constructs that are distinct from illness conditions but in many cases overlap. If this were correct would it be an anomaly? Again this is unlikely because elsewhere I have argued that diagnoses categories represent the application of a body of scientific knowledge. There is an involved process which leads to the construction of the diagnostic category. If an illness condition were to go from existence to non-existence I would argue that this would not be an anomaly. The transition does not necessarily lead us into a better understanding of an underlying scientific model. There may for instance be difficulties with one of the stages in the process leading to the construction of the diagnostic entity.
Maybe the dissonant finding here tells us more about process than about science. Perhaps in order to be able to find anomalies we need to ensure that there is a consistency between the epidemiological findings for an illness and the various models of pathogenesis. When such a consistency is ensured then it may be possible to start to identify anomalous findings.
The multiple layers of consideration in the biological, psychological and social domains make the task a complex one but not insurmountable. First of all there must be a revolution in the way these problems are conceptualised.
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.
When Thomas Kuhn published his landmark work on the philosophy of science ‘The Structure of Scientific Revolutions’ he perhaps didn’t realise the impact that this work would have. This work introduced the world to the term ‘paradigm change’ and shifted the focus on scientific revolutions away from the core scientific phenomenon to the characteristics of the scientific community. In Chapter 9, Kuhn looks at the differences between scientific and political revolutions. The key difference between these two types of revolutions is the central role of the anomaly in precipitating a scientific revolution. Let us consider Neuroscience as an example of an eclectic science. Has Neuroscience been undergoing a political rather than a scientific revolution?
In a political rather than a scientific revolution we would expect changes in the social organisation of Neuroscience and at the same time an absence of a central anomaly which drives debate. Is this what we see in practice? Many scientific disciplines have been amalgamating under the umbrella of ‘Neuro’. Indeed bloggers such as the Neurocritic and the Neuroskeptic have been very successful in addressing difficulties (and strengths) in Neuroscience studies particularly where simple ‘neuro’ assumptions are used. Here I refer to a ‘neuro’ assumption as one which fits with a political movement rather than the scientific data.
For instance the tenet of a political Neuroscience movement would be that ‘we can predict how people will behave by using the body of Neuroscience knowledge’. This is a statement of belief. The generation of a hypothesis and testing this against experimental data is an altogether different proposition however. The large number of variables make predictions extremely difficult in all but the simplest circumstances. Instead, the interesting Neuroscience research is more limited in predictive utility but leads to a shifting perspective. The amalgamation of scientific disciplines under the umbrella of Neuroscience is to be welcomed however as it unites scientists in different research communities in pursuit of common interests often with clinical applications which ultimately will relieve suffering.
We see powerful Neuroscience institutes developing around the world and undertaking important research. Neuroscience Journals add to the burgeoning knowledge base and Neuroscience conference and social media networks bring Neuroscientists closer together. Neuroscientists feature increasingly in popular culture through popular books, documentaries and in Newspapers. The success of the Neuroscience movement is incontrovertible.
However the political Neuroscience movement with the mantra of ‘Neuroknowledge’ and ‘Neuropredictions’ is limited as any political scientific movement is by the absence of an accompanying beliefs and values system. Beliefs and values are distinct from scientific knowledge as they are choices rather than truths. Nevertheless they are essential features in any community. Until the problem of combining scientific and humanistic approaches is solved then the Political Neuroscience movement will remain limited in its scope despite its present success. The Positive Psychology movement is one model which offers insights into this process.
The remaining issue is what is the central anomaly in Neuroscience. This is the crux of the issue. We have a powerful Neuroscience movement which is well funded and has many scientific branches affiliated. This though is the exact cause of the problem – what is the central paradigm and where is the central anomaly. There are many paradigms but they occur in only one affiliated field. Indeed many fields would not consider themselves affiliated to Neuroscience but working quite distinctly. Is the Central Paradigm a behavioural model or a cellular model or a neurotransmitter model or a neuroanatomical model or a neurocomputational model.
All of these approaches are currently found under the Neuroscience umbrella and scientists from many disciplines are competing with each other in the Neuroscience arena. However the terms of the debate need to be set and the arena more tightly defined.
In the 10th Chapter of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’, Kuhn asks the reader to consider a central role for the mind in the scientific process. Kuhn’s key argument in this section of the book is that paradigm shifts correlate with perspective differences in looking at the prevalent paradigm in relation to anomalies. The anomaly is an invite for the scientist to shift perspective. What I find interesting here is that Kuhn has opened up the discussion of the mind and the fundamentals of science. Science is a process by which scientific knowledge is arrived at as well as the body of scientific knowledge itself. Another assumption about science is that it is a way of arriving at an approximation of the truth about the universe we live in.
Scientific findings or data can be both organised and disorganised. However the findings can be organised according to a taxonomic framework which is another important characteristic of science. Another framework is the model. The models can range in sophistication from a collection of a few simple statements to an elaborate mathematical model simulated on a computer. Disorganised findings or data includes esoteric findings in niche areas where insufficient resources including time have not allowed for the systematic organisation of data. Areas of scientific investigation that produce very large datasets are an example of data needing to be organised into knowledge.
From all of this we can deduce that the human mind is capable of approximating the truth about the universe through a scientific process. This approximation has a number of caveats. Scientific knowledge is a function of the human mind. This knowledge is predicated on underlying evidence tested against reason, other lines of evidence and expertise. The knowledge is also predicated on reproducibility. The scientist expects the ability to be in control of the knowledge in that sense that either they can test the model directly or can be satisfied that the underlying chain of assumptions for a model have been systematically tested. This is what Kuhn refers to as normal science.
These properties of science are also properties of the human mind. They constitute a set of beliefs and values about how things should be done and also about establishing a hierarchy of beliefs. These beliefs are described as hypotheses, theorems, facts, speculation and models depending on the underlying evidence as well as the views of the community.
The critic may argue that the human mind is irrelevant in this whole process. Newton stated in his second law of motion that Force = Mass x Acceleration. Knowing that this is the case, it does not matter whether we think it to be true or not. The universe carries on without us. A meteorite will continue to accelerate when it is in the gravitational field of Earth whether we believe it will or not.
A Video About Force
The response to the critic is not to get caught up in arguments about the validity of Newtonian Mechanics in view of General Relativity or Quantum Mechanics but to focus on Newton’s Second Law of Motion itself. Newton did not sit down and write his second law of motion for an impassive mechanistic universe. The proverbial apple dropping from the tree does not care if Newton has formulated the Law that anticipates it accelerating towards the ground. It just drops. Newton wrote his Second Law of Motion for the human mind. Scientific knowledge, scientific truth is a product of the human mind for the human mind. The scientist can say that they have discovered a neutral truth about the universe but they must do so within the parameters afforded to them by the human mind.
The scientific community that is central to Kuhn’s work is similarly constrained by the human mind. Scientific knowledge and scientific revolutions are determined by the actions of a mind or minds. Whenever we talk about science or scientific revolutions we see the footprint of the universe and the ‘mindprint’ of the human mind which tries to understand that universe. Newton’s second law of motion is written. Writing implies a shared understanding of what symbols mean. Those symbols are representations of language. Language is a shared mechanism that enables minds to communicate with each other. Newton’s very act of writing the Second Law of Motion was a statement that it was meant to be seen by the eyes, perceived by the brain and interpreted by the mind. The apple continues to drop.
Kuhn encouraged this vision of science. By shifting our perspective he enables us to share in the perspective shifting that occurs in scientific revolutions. However things have moved on since Kuhn wrote this work. Kuhn encourages us to explore these themes. If we move from one paradigm to another then Kuhn says there is a shifting in perspective. What might this mean about the underlying paradigm? To me it might mean that the paradigm being a function of mind operates within the mind. Dawkins refers to memes as successful ideas that occupy the mind. Memes are the ‘fittest’ ideas adapting to the environment of the mind. A paradigm is an organised collection of ‘science memes’.
Science memes must not just be adapted to the mind but must also be adapted to the Universe. Their task is altogether more complicated as like the genome itself they must be organised into a working whole. In this case, it does not matter if individual science memes (hypotheses and assumptions) are not well adapted. If the other science memes in the collection are well adapted then the model itself can be well adapted both to the mind and the Universe. When discussing collections of memes like this we can think of the body of Psychoanalytic Theory, Psychopharmacological Theories (e.g Serotonin and Mood) and the Standard Model of Physics. Indeed using this latter model perhaps we can see mathematics as a pure language of the mind that when tested against the Universe becomes Physics or related fields.
When the pure language of the mind that is language is tested against the Universe we run into more difficulties. This is because language is better adapted to the mind than mathematics. These memes can be disseminated more quickly and adapt more rapidly giving the testing against the Universe less time to catch up. When a Neuroscience finding emerges about the human mind discussion may occur rapidly with varying outcomes for these discussions. When the CERN accelerator produces a finding which may support the standard model the public discourse is extremely limited because the model is couched in complex mathematical terms.
Finally can anthropology tell us something about Neuroscience? I have written elsewhere about my observations about Lemurs. In this video I see some similarities with scientists as the Lemurs investigate the camera. By virtue of their divergent digits they have a degree of flexibility in their ability to manipulate the environment compared to cats and dogs from which they diverged approximately 25 million years previously. Evolution is about adaptation to the environment. The environment is part of the universe.
It doesn’t seem too unreasonable to suppose that adaptation to the environment means that the organism is better able to anticipate the future. This in turn implies an ‘understanding’ of the environment and therefore the Universe. This ‘understanding’ doesn’t need to be the mindful understanding that we possess but instead is a series of hardwired responses to the environment encoded in chemical processes. These responses mean that the organism is better able to obtain nutrients or evade predators. Increasing complexity may have resulted in us being able to communicate this understanding to each other in a flexible way that we call science.
Returning to the Lemur. The Lemur is able to pick up and push objects easily and in so doing is able to test new hypotheses about the environment that Cats and Dogs are less able to. How heavy is the object? How stable is the object? The Lemur’s body is an instrument for exploring the environment and the Lemur’s brain uses this tool to explore. Therefore the Lemur’s brain adapts to the tool it has at its disposal. Maybe the science of the Lemur brain has well developed concepts of weight and stability which have evolved directly from the divergent digits. Maybe Dolphins have an elegant and intuitive paradigm of fluid dynamics that is a function of adaptation to the environment and is hard and softwired into their brains and minds. Maybe that is what Dolphins communicate with each other.
Understanding the Lemur’s digits and their relation to understanding the environment will give us insights into our evolutionary journey and help us to understand how our science came about.
Chapter 11 of Thomas Kuhn’s ‘The Structure of Scientific Revolutions’ is a deep discussion about historic revisionism in science. Kuhn argues that scientific revolutions are later rewritten in a much simplified narrative. In this narrative, two camps emerge both focused on solving a central problem. When the problem is iteratively solved, the successful problem solver is remembered as the revolutionary scientist whose work laid the foundations of the new paradigm.
Kuhn’s lesson from the chapter however is not this simple narrative. His lesson is that reality is more complex and less convenient than the brief explanation needed for the doctrine of a textbook. Kuhn argues that the usual course of historical revisionism is to caricaturise the main players. His insights were gained from a close study of historical events in science and he backs up his assertion with reference to well recognised examples.
If we turn to Neuroscience, we find a relatively young discipline. The term Neuroscience is young that is. However common interpretations of the history of Neuroscience often draw on historical events that date back several thousand years. Here are some examples of resources for the history of Neuroscience.
Whereas some of Kuhn’s examples of scientific revolutions resulted in new branches of science, Neuroscience is currently proceeding in an interesting and novel direction. This direction is one in which the very identity of Neuroscience is being forged. In a previous post I suggested that Neuroscience was undergoing a limited political revolution. A close examination of the above sources reveals that an intelligent reappraisal of history is taking place. In this reappraisal, events which are of historical scientific significance (e.g Descartes Mind-Body dualism, dissection of the Optic and other sensory nerves by Alcmaion of Crotona in 500 BC, the works of Sigmund Freud, Korbinian Brodmann, Santiago Ramón y Cajal and Gordon Holmes as well as contemporary neuroscientists) are integrated into an inclusive but overwhelming collection without a clear narrative.
There is a lot of work ahead in the field of the history of Neuroscience in order to develop an understanding of a remarkable series of discoveries made by people from many civilisations, continents and eras. Unlike other branches of science which Kuhn refers to, the problem is not one of caricaturisation of paradigm shifts but instead making sense of how we got to where we are. This understanding though is integral to establishing an identity of the field of Neuroscience. Perhaps it has taken thousands of years for scientists to get to the stage where they realise that all of these discoveries fall under one umbrella. This umbrella – Neuroscience – is perhaps one of the most complex and challenging scientific fields that has ever developed.
This field is so complex that even the basic question of what are the foundations of Neuroscience and a clear understanding of its identity remain elusive. Even while this identity is being developed a global transformation of the Neuroscience infrastructure is happening with a fast evolving alliance of different scientific communities and technologists. The applications of Neuroscience in clinical specialities such as Psychiatry are without question and the realised and potential benefits to society are immense.
In Chapter 12 of ‘The Structure of Scientific Revolutions’ Thomas Kuhn looks at the process of scientific revolutions as well as the characteristics of the players. The revolutionary scientists can be outside of the research community looking in and are in a position to challenge the paradigm. In a sense they are better suited at playing the role of critics of the paradigm which Kuhn asserts is necessary for a scientific revolution.
He also suggests to us three ways in which a scientific paradigm is established as the successor
1. The paradigm survives criticism (Popper’s falsifiability)
2. The paradigm is supported by most of the evidence (Probabilistic)
3. The paradigm is supported by all of the evidence (Categorical) which is less realistic
Does Neuroscience, a complex branch of science with an emerging identity fit in the above model. In other words if we take the above ingredients will we arrive at a new paradigm? I would argue that the answer is no. The reason is that Neuroscience has a central philosophical problem which is one of integration.
At present there are many theorems within the domain of Neuroscience contained within various scientific communities allied to Neuroscience. However although revolutions can occur within these communities (consistent with Kuhn’s model) the question of what this means to Neuroscience is still not solved. Suppose for example that a new mechanism for the storage of memory in the brain at the cellular level is identified. Suppose also that this challenges the central paradigm of long term potentiation (LTP). What does this mean for our understanding of the social brain? What does it mean for our understanding of the mind? Will it impact on these things at all?
At present Neuroscience is so complex that not only are there pressing philosophical problems but there are also problems associated with the social infrastructure. Solving these challenges will be both interesting and fruitful as it has the potential to benefit many areas of human endeavour and to impact on health and the treatment of illness.
Neuroscience is a relatively young branch of science which is being recognised as increasingly important. Discoveries in Neuroscience are informing clinical practice in Psychiatry, Neurology, Neurosurgery as well as in the wider mental health movement. Neuroscience research is varied and ranges from basic cellular and genetic research through to psychological and social studies. A central problem in Neuroscience has been to present a coherent and understandable narrative about what Neuroscience is and how it came about.
In his classic work ‘The Structure of Scientific Revolutions’, Thomas Kuhn wrote extensively about scientific communities. Kuhn saw the most popular scientific discoveries as resulting from anomalies in the central paradigms owned by these same communities. For Kuhn the scientific community was inseparable from the scientific theories worked on by that community. In one sense the scientific theory was a manifestation of the culture of the scientific community. There was one caveat however. For Kuhn, scientific communities behaved differently to other types of community. They were characterised by standardisation, central paradigms and ‘progress’ of sorts. Kuhn disagreed that there was actual progress. Instead he described the illusion of progress but essentially thought that the ‘gestalt’ paradigm described by the community may have been just as valid as the preceding paradigm.
Kuhn noticed another feature of the scientific community that distinguished scientists from members of other disciplines. Scientists could distill their knowledge in the form of textbooks, standardise their methodology and train scientists efficiently and effectively to undertake specialised scientific research. For Kuhn, science had a quality that led quite naturally to an efficient organisation of the findings from research studies. Although many of these qualities could equally describe other disciplines, the process of science also led quite naturally to the ‘progress’ of normal science. In other words normal science is an activity which must lead to a refinement of the body of scientific knowledge which in turn can reasonably be called progress. In contrast, a new painting in the style of the early 20th century impressionists does not lead inexorably to a refinement of the body of knowledge about impressionism.
For Kuhn, scientists had a strong sense of identity. They knew where they were coming from. They knew the landmark studies. They knew where their research fitted into the greater scheme of things. For Kuhn, historic revisionism produced a seamless historical narrative which obfuscated the complexity of the historical events appreciated by the historiographical connoisseur. There was a kind of practicality about it all. Scientific research led to refinement of the knowledge and historic revisionism pruned the complexity. This practicality was built into the fabric of science. Science was self-contained.
So what might we say about Neuroscience. Neuroscience is very different from other branches of science and shares some of the challenges of Psychiatry. Basic Neuroscience research spans many research communities. Those same communities can reasonably describe themselves as part of the Neuroscience community. The Neuroscience community however is an umbrella community containing a collection of smaller communities. The challenge for Neuroscience is to integrate those communities. This challenge occurs at all levels from the research infrastructure through to the historical narrative and the central paradigms owned by those communities. Indeed for certain communities, there are communities within communities as research becomes ever more specialised.
If as Kuhn asserts, Neuroscientists must establish a historical narrative what would it look like? Perhaps it would consist of a collection of narratives from within those communities. Here the critical question is whether or not Neuroscience needs an overarching historical narrative or a collection of historical narratives. The separate communities continue their research and generate their historical narratives both inside and outside of the wider neuroscience community. However with increasing interdisciplinary research the findings from separate communities become increasingly important and the communities become more interconnected.
Perhaps this is the lesson for Neuroscience – the historical narrative needed for the formation of a core Neuroscience ‘identity’ will be complex and increasingly so as the body of neuroscience knowledge continues to grow. The neuroscience community must address the issue of identity through historical narrative and meet the significant challenges this poses. If Kuhn is correct, the rewards will be significant in helping Neuroscience to progress at an even greater pace and other related disciplines will benefit from the lessons learnt.
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