Identifying Causes in Psychiatry

Explanations in psychiatry often integrate various factors relevant to psychopathology. Identifying genuine causes among them is theoretically and clinically important, but epistemically challenging. Woodward’s interventionism appears to provide a promising tool to achieve this. However, Woodward’s interventionism is too demanding to be applied to psychiatry. I thus introduce difference-making interventionism (DMI), which detects relevance in general rather than causation, to make interventionist reasoning viable in clinical practice. DMI mirrors the empirical reality of psychiatry even more closely than interventionism, but it needs to be supplied with additional heuristics to disambiguate between causes and other difference-makers. To achieve this, I suggest employing heuristics based on multiple experiments, temporal order and scientific domain

Identifying Causes in Psychiatry

Explanations in psychiatry often integrate various factors relevant to psychopathology. Identifying genuine causes among them is theoretically and clinically important, but epistemically challenging. Woodward’s interventionism appears to provide a promising tool to achieve this. However, Woodward’s interventionism is too demanding to be applied to psychiatry. I thus introduce difference-making interventionism (DMI), which detects relevance in general rather than causation, to make interventionist reasoning viable in clinical practice. DMI mirrors the empirical reality of psychiatry even more closely than interventionism, but it needs to be supplied with additional heuristics to disambiguate between causes and other difference-makers. To achieve this, I suggest employing heuristics based on multiple experiments, temporal order and scientific domain

The Enigmatic Metallothioneins: A Case of Upward-Looking Research

In the mid-1950s, Bert Lester Vallee and his colleague Marvin Margoshes discovered a molecule referred to today as metallothionein (MT). Meanwhile, MTs have been shown to be common in many biological organisms. Despite their prevalence, however, it remains unclear to date what exactly MTs do and how they contribute to the biological function of an organism or organ. We investigate why biochemical research has not yet been able to pinpoint the function(s) of MTs. We shall systematically examine both the discovery of and recent research on Dr. Vallee’s beloved family of MT proteins utilizing tools from philosophy of science. Our analysis highlights that Vallee’s initial work exhibited features prototypical of a developing research tradition: it was upward-looking, exploratory, and utilized mere interactions. Since the 1960s, MT research has increasingly become intervention- and hypothesis-based while it remained largely upward-looking in character. Whilst there is no reason to think that upward-looking research cannot successfully yield structure-function mappings, it has not yet been successful in the case of MTs. Thus, we suggest it might be time to change track and consider other research strategies looking into the evolution of MTs. Recent studies in mollusks render research in this direction worthy of pursuit

Disambiguating “Mechanisms” in Pharmacy: Lessons from Mechanist Philosophy of Science

Talk of mechanisms is ubiquitous in the natural sciences. Interdisciplinary fields such as biochemistry and pharmacy frequently discuss mechanisms with the assistance of diagrams. Such diagrams usually depict entities as structures or boxes and activities or interactions as arrows. While some of these arrows may indicate causal or componential relations, others may represent temporal or operational orders. Importantly, what kind of relation an arrow represents may not only vary with context but also be underdetermined by empirical data. In this manuscript, we investigate how an analysis of pharmacological mechanisms in terms of producing and underlying mechanisms—as discussed in the contemporary philosophy of science—may shed light on these issues. Specifically, we shall argue that while pharmacokinetic mechanisms usually describe causal chains of production, pharmacodynamics tends to focus on mechanisms of action underlying the in vivo effects of a drug. Considering the action of thyroid gland hormones in the human body as a case study, we further demonstrate that pharmacodynamic schemes tend to incorporate entities and interactions on multiple levels. Yet, traditional pharmacodynamic schemes are sketched “flat”, i.e., non-hierarchically. We suggest that transforming flat pharmacodynamic schemes into mechanistic multi-level representations may assist in disentangling the different kinds of mechanisms and relations depicted by arrows in flat schemes. The resulting Baumkuchen model provides a powerful and practical alternative to traditional flat schemes, as it explicates the relevant mechanisms and relations more clearly. On a more general note, our discussion demonstrates how pharmacology and related disciplines may benefit from applying concepts from the new mechanist philosophy to guide the interpretation of scientific diagrams

Don't Fear the Bogeyman: On Why There is No Prediction-Understanding Trade-Off for Deep Learning in Neuroscience

Machine learning models, particularly deep artificial neural networks (ANNs), are becoming increasingly influential in modern neuroscience. These models are often complex and opaque, leading some to worry that, by utilizing ANNs, neuroscientists are trading one black box for another. On this view, despite increased predictive power, ANNs effectively hinder our scientific understanding of the brain. We think these worries are unfounded. While ANNs are difficult to understand, there is no fundamental trade-off between the predictive success of a model and how much understanding it can confer. Thus, utilizing complex computational models in neuroscience will not generally inhibit our ability to understand the (human) brain. Rather, we believe, deep learning is best conceived as offering a novel and unique epistemic perspective for neuroscience. As such, it affords insights into the operation of complex systems that are otherwise unavailable. Integrating these insights with those generated by traditional neuroscience methodologies bears the potential to propel the field forward

Mechanistic Inquiry and Scientific Pursuit: The Case of Visual Processing

Why is it rational for scientists to pursue multiple models of a phenomenon at the same time? The literatures on mechanistic inquiry and scientific pursuit each develop answers to a version of this question which is rarely discussed by the other. The mechanistic literature suggests that scientists pursue different complementary models because each model provides detailed insights into different aspects of the phenomenon under investigation. The pursuit literature suggests that scientists pursue competing models because alternative models promise to solve outstanding empirical and conceptual problems. Looking into research on visual processing as a case study, we suggest an integrated account of why it is rational for scientists to pursue both complementary and competing models of the same mechanism in scientific practice

Explaining AI Through Mechanistic Interpretability

Recent work in explainable artificial intelligence (XAI) attempts to render opaque AI systems understandable through a divide-and-conquer strategy. However, this fails to illuminate how trained AI systems work as a whole. Precisely this kind of functional understanding is needed, though, to satisfy important societal desiderata such as safety. To remedy this situation, we argue, AI researchers should seek mechanistic interpretability, viz. apply coordinated discovery strategies familiar from the life sciences to uncover the functional organisation of complex AI systems. Additionally, theorists should accommodate for the unique costs and benefits of such strategies in their portrayals of XAI research