Biologists often study particular biological systems as models of a phenomenon of interest even if they already know that the phenomenon is produced by diverse mechanisms and hence none of those systems alone can sufficiently represent it. To understand this modeling practice, the present paper provides an account of how multiple model systems can be used to study a phenomenon that is produced by diverse mechanisms. Even if generalizability of results from a single model system is significantly limited, generalizations concerning (...) specific aspects of mechanisms often hold across certain ranges of biological systems, which enables multiple model systems to jointly represent such a phenomenon. Comparing mechanisms that operate in different biological systems as examples of the same phenomenon also facilitates characterization and investigation of individual mechanisms. I also compare my account with two existing accounts of the use of multiple model systems and argue that my account is distinct from and complementary to them. (shrink)

I examine some behavioural and heuristic models of individual decision-making and argue that the diverse psychological mechanisms these models posit are too demanding to be implemented, either consciously or unconsciously, by actual decision makers. Accordingly, and contrary to what their advocates typically claim, behavioural and heuristic models are best understood as ‘as-if’ models. I then sketch a version of scientific antirealism that justifies the practice of as-if modelling in decision theory but goes beyond traditional instrumentalism. Finally, I relate my account (...) of decision models to the recent controversy about mentalism versus behaviourism, reject both positions, and offer an alternative view. (shrink)

In this paper, I outline a general framework for cultural analysis consistent with an “analytic” approach to explanation in social science. The proposed approach provides coherent solutions to thorny problems in cultural theory. These include providing a coherent definition of culture (and the “cultural”), specifying the nature of cultural units (both simple and complex), and outlining the processes making possible episodes of cultural genesis, transformation, and reproduction within bounded units characterized as cultural causal systems.

Explanation is commonly considered one of the central goals of science. Although computer simulations have become an important tool in many scientific areas, various philosophical concerns indicate that their explanatory power requires further scrutiny. We examine a case study in which atomistic simulations have been used to examine the factors responsible for the transport selectivity of certain channel proteins located at cell membranes. By elucidating how precisely atomistic simulations helped scientists draw inferences about the molecular system under investigation, we respond (...) to some concerns regarding their explanatory power. We argue that atomistic simulations can be tools for managing molecular complexity and for systematically assessing how the occurrence of the explanandum is sensitive to a range of factors. (shrink)

Mechanism realists assert the existence of mechanisms as objective structures in the world, but their exact metaphysical commitments are unclear. We introduce Local Hierarchy Realism (LHR) as a substantive and plausible form of mechanism realism. The limits of LHR reveal a deep tension between two aspects of mechanists’ explanatory strategy. Functional decomposition identifies locally relevant entities and activities, while these same entities and activities are also embedded in a nested hierarchy of levels. In principle, a functional decomposition may identify entities (...) engaging in causal interactions that crosscut the hierarchical structure of composition relations, violating the mechanist’s injunction against interlevel causation. We argue that this possibility is realized in the example of ephaptic coupling, a subsidiary process of neural computation that crosscuts the hierarchy derived from synaptic transmission. These considerations undermine the plausibility of LHR as a general view, yet LHR has the advantages that (i) its metaphysical implications are precisely stateable; (ii) the structure it identifies is not reducible to mere aggregate causation; and (iii) it clearly satisfies intuitive and informal definitions of mechanism. We conclude by assessing the prospects for a form of mechanism realism weaker than LHR that nevertheless satisfies all three of these requirements. (shrink)

In light of recent criticisms by Woodward (2017) and Rescorla (2018), we examine the relationship between mechanistic explanation and phenomenological laws. We disambiguate several uses of the phrase “phenomenological law” and show how a mechanistic theory of explanation sorts them into those that are and are not explanatory. We also distinguish the problem of phenomenological laws from arguments about the explanatory power of purely phenomenal models, showing that Woodward and Rescorla conflate these problems. Finally, we argue that the temptation to (...) pit mechanistic and interventionist theories of explanation against one another occludes important and scientifically relevant research questions. (shrink)

This paper aims to clarify the nature of understanding in medicine. The first part describes in more detail what it means to understand something and links a type of understanding (i.e., objectual understanding) to explanations. The second part proceeds to investigate what objectual understanding of a disease (i.e., biomedical understanding) requires by considering the case of scurvy from the history of medicine. The main hypothesis is that grasping a mechanistic explanation of a condition is necessary for a biomedical understanding of (...) that condition. The third part of the paper argues that biomedical understanding is necessary, but not sufficient for understanding in a clinical context (i.e., clinical understanding). The hypothesis is that clinical understanding combines biomedical understanding of a _disease_ or pathological condition with understanding _illness,_ which involves some degree of personal understanding of the patient. It is argued that, in many cases, clinical understanding necessitates adopting a particular second-personal stance and using cognitive resources _in addition_ to those involved in biomedical understanding. (shrink)

Enactivism advances an understanding of cognition rooted in the dynamic interaction between an embodied agent and their environment, whilst new mechanism suggests that cognition is explained by uncovering the organised components underlying cognitive capacities. On the face of it, the mechanistic model’s emphasis on localisable and decomposable mechanisms, often neural in nature, runs contrary to the enactivist ethos. Despite appearances, this paper argues that mechanistic explanations of cognition, being neither narrow nor reductive, and compatible with plausible iterations of ideas like (...) emergence and downward causation, are congruent with enactivism. Attention to enactivist ideas, moreover, may serve as a heuristic for mechanistic investigations of cognition. Nevertheless, I show how enactivism and approaches that prioritise mechanistic modelling may diverge in starting assumptions about the nature of cognitive phenomena, such as where the constitutive boundaries of cognition lie. (shrink)

Richard Levins’s (Am Sci 54(4):421–431, 1966) paper sets a landmark for the significance of scientific model-making in biology. Colombo and Palacios (Biol Philos 36(5):1–26. 10.1007/s10539-021-09818-x, 2021) have recently built their critique of the explanatory power of the Free Energy Principle on Levins’s insight into the relationship between generality, realism, and precision. This paper addresses the issue of the plausibility of biological explanations that are grounded in the Free Energy Principle (FEP) and deals with the question of the realist fortitude of (...) FEP’s theoretical framework. It indicates that what is required for establishing the plausibility of the explanation of a target system given a model of that system is the dosage or the harmony between the generality and accuracy of explanatory models. This would also provide a basis for seeing how scientific realism could be a viable option with respect to FEP. (shrink)

Examples of extended cognition typically involve the use of technologically low-grade bio-external resources (e.g., the use of pen and paper to solve long multiplication problems). The present paper describes a putative case of extended cognizing based around a technologically advanced mixed reality device, namely, the Microsoft HoloLens. The case is evaluated from the standpoint of a mechanistic perspective. In particular, it is suggested that a combination of organismic (e.g., the human individual) and extra-organismic (e.g., the HoloLens) resources form part of (...) a common mechanism that realizes a bona fide cognitive routine. In addition to demonstrating how the theoretical resources of neo-mechanical philosophy might be used to evaluate extended cognitive systems, the present paper illustrates one of the ways in which mixed reality devices, virtual objects (i.e., holograms), and online (Internet-accessible) computational routines might be incorporated into human cognitive processes. This, it is suggested, speaks to the recent interest in mixed/virtual reality technologies across a number of disciplines. It also introduces us to issues that cross-cut disparate fields of philosophical research, such as the philosophy of science and the philosophy of technology. (shrink)

Experiments in cognitive neuroscience build a setup whose set of controlled stimuli and rules elicits a cognitive process in a participant. This setup requires researchers to decide the value of quite a few parameters along several dimensions. We call ‘’contextual factors’’ the parameters often assumed not to change the cognitive process elicited and are free to vary across the experiment’s repetitions. Against this assumption, empirical evidence shows that many of these contextual factors can significantly influence cognitive performance. Nevertheless, it is (...) not entirely clear what it means for a cognitive phenomenon to be context-sensitive and how to identify context-sensitivity experimentally. We claim that a phenomenon can be context-sensitive either because it is only triggered within a specific context or because different contexts change its manifestation conditions. Assessing which of these forms of context-sensitivity is present in a given phenomenon requires a criterion for individuating it across contextual variations. We argue that some inter-level experiments that, within the mechanistic approach to explanation, are required to identify relations of constitutive relevance between a phenomenon and a mechanism, are also necessary for individuating the phenomenon across its contextual variations. We articulate a criterion according to which behavioral variations across contexts indicate different phenomena if and only if the mechanistic activities, components and/or organizational properties recruited in each context are different. We support this approach by showing how it is applied in paradigmatic studies addressing cognitive performance differences resulting from contextual variations of task features, such as stimulus type and response modality. Finally, we address the challenge that a form of context-sensitivity possessed by the so-called ‘multifunctional mechanisms’ is incompatible with our proposal because it entails that the same mechanism can be recruited in different contexts to produce different phenomena. We examine key cases of multifunctionality and argue that they are consistent with our proposal because a single mechanism can have different components, activities and/or organizational properties in different contexts. Thus, these modifications may not affect the identity of a mechanism, and they could explain how it produced different phenomena in those contexts. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...) particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

Autoinhibition is a design principle realized in many molecular mechanisms in biology. After explicating the notion of a design principle and showing that autoinhibition is such a principle, we focus on how researchers discovered instances of autoinhibition, using research establishing the autoinhibition of the molecular motors kinesin and dynein as our case study. Research on kinesin and dynein began in the fashion described in accounts of mechanistic explanation but, once the mechanisms had been discovered, researchers discovered that they exhibited a (...) second phenomenon, autoinhibition. The discovery of autoinhibition not only reverses the pattern in terms of which philosophers have understood mechanism discovery but runs counter to the one phenomenon-one mechanism principle assumed to relate mechanisms and the phenomena they explain. The ubiquity of autoinhibition as a design principle, therefore, necessitates a philosophical understanding of mechanisms that recognizes how they can participate in more than one phenomenon. Since mechanisms with this design are released from autoinhibition only when they are acted on by control mechanisms, we advance a revised account of mechanisms that accommodates attribution of multiple phenomena to the same mechanism and distinguishes them from other processes that control them. (shrink)

The purpose of this study is to examine how trends in the use of images in modern life science journals have changed since the spread of computer-based visual and imaging technology. To this end, a new classification system was constructed to analyze how the graphics of a scientific journal have changed over the years. The focus was on one international peer-reviewed journal in life sciences, Cell, which was founded in 1974, whereby 1725 figures and 160 tables from the research articles (...) in Cell were sampled. The unit of classification was defined as a graphic and the figures and tables were divided into 5952 graphics. These graphics were further classified into hierarchical categories, and the data in each category were aggregated every five years. The following categories were observed: data graphics, explanation graphics, and hybrid graphics. Data graphics increased by more than sixfold between 1974 and 2014, and some types of data graphics including mechanical reproduction images and bar charts displayed notable changes. The representation of explanatory graphics changed from hand-painted illustrations to diagrams of Bezier-curves. It is suggested that in addition to the development of experimental technologies such as fluorescent microscopy and big data analysis, continuously evolving application software for image creation and researchers’ motivation to convince reviewers and editors have influenced these changes. (shrink)

Are causal explanations (e.g., “she switched careers because of the COVID pandemic”) treated differently from the corresponding claims that one factor caused another (e.g., “the COVID pandemic caused her to switch careers”)? We examined whether explanatory and causal claims diverge in their responsiveness to two different types of information: covariation strength and mechanism information. We report five experiments with 1,730 participants total, showing that compared to judgments of causal strength, explanatory judgments tend to bemoresensitive to mechanism andlesssensitive to covariation – (...) even though explanatory judgments respond to both types of information. We also report exploratory comparisons to judgments of understanding, and discuss implications of our findings for theories of explanation, understanding, and causal attribution. These findings shed light on the potentially unique role of explanation in cognition. (shrink)

Some recent discussions of mechanistic explanation have focused on control operations. But control is often associated with teleological or normative-sounding concepts like goals and set-points, prompting the question: Does an explanation that refers to parts or mechanisms “controlling” each other thereby fail to be mechanistic? In this paper I introduce and explain a distinction between what I call open-ended and closed-ended control operations. I then argue that explanations that enlist control operations to do explanatory work can count as mechanistic only (...) if such control operations are closed-ended, not open-ended. (shrink)

This book provides a comprehensive overview of my recent theoretical work that aims to explain some of the more puzzling properties of life and mind, in particular agency, goal-directedness and consciousness. It contains published papers as well as new material. Table of contents: Preface - PART I: GROUNDWORK - 1. Introduction - 2. The basic mechanism - 3. Inclusive and extensive fitness - 4. Components of F and X - 5. The consequences: a preview - PART II: LIFE - 6. (...) What qualifies as life? - 7. Biological meaning - 8. Biological functions - PART III: MIND - 9. Minimal agency, goal-directedness and value - 10. Intentionality and meaning - 11. Language - 12. Consciousness - 13. The human self - PART IV: PHILOSOPHY - 14. Strong emergence - 15. The real, the true and the good - 16. Philosophical problems of consciousness - Epilogue - Appendix A: Summaries of computational simulations - Appendix B: Examples of minimal intentionality - References. -/- . (shrink)

Research devoted to characterizing phenomena is underappreciated in philosophical accounts of scientific inquiry. This paper develops a diachronic analysis of research over 100 years that led to the recognition of two related electrophysiological phenomena, the membrane potential and the action potential. A diachronic perspective allows for reconciliation of two threads in philosophical discussions of phenomena—Hacking’s treatment of phenomena as manifest in laboratory settings and Bogen and Woodward’s construal of phenomena as regularities in the world. The diachronic analysis also reveals the (...) epistemic tasks that contribute to establishing phenomena, including the development of appropriate investigative techniques and concepts for characterizing them. (shrink)

The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (...) and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis. I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain. (shrink)

Plausibly, agents act freely iff their actions are responses to reasons. But what sort of relationship between reason and action is required for the action to count as a response? The overwhelmingly dominant answer to this question is modalist. It holds that responses are actions that share a modally robust or secure relationship with the relevant reasons. This thesis offers a new alternative answer. It argues that responses are actions that can be explained by reasons in the right way. This (...) explanationist answer comes apart from the modalist answer. For it holds that actions are responses to reasons if they are explained by those reasons even if they don’t share a modally robust relationship. Explanationism thus offers a novel way of vindicating the intuition that alternative possibilities don’t matter to responding to reasons and (consequently) free agency. The key dialectical position the thesis develops is that both modalism and explanationism constitute attempts to capture he core type of relationship encoded by the notion of a response. Responses to reasons, at core, involve a non-accidental relationship between reason and action. We can either understand non-accidentality as a modal phenomenon – as a modally robust tracking between two facts. Or we can understand non-accidentality as an explanatory phenomenon – as a special explanatory relationship between two facts. According to my rival explanationist proposal, two facts share a non-accidental relationship iff we can give a unified explanation of why both obtain. Unified explanations are explanations of why [p&q] that cannot be decomposed into two (or more) separate independent explanations of p and of q. Consequently, according to explanationism about responding to reasons, actions are responses to reasons iff those reasons offer a rational explanation of the action that cannot be decomposed into separate independent components. (shrink)

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative principle of inference. (...) Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence. (shrink)

Among the notions of levels invoked in accounts of biological phenomena, I focus on two: levels of production mechanisms and levels of control mechanisms. I argue that these two notions of level exhibit different characteristics: production mechanisms are organized hierarchically while control mechanisms are often organized heterarchically. I illustrate the differences in these modes of organization by examining production and control mechanisms involved in cell division in Escherichia coli and in circulation of blood in mammals. I conclude by exploring how (...) these two notions of levels can be integrated by adopting a perspective in which organisms are autonomous systems maintaining themselves far from equilibrium. (shrink)

Organization figures centrally in the understanding of biological systems advanced by both new mechanists and proponents of the autonomy framework. The new mechanists focus on how components of mechanisms are organized to produce a phenomenon and emphasize productive continuity between these components. The autonomy framework focuses on how the components of a biological system are organized in such a way that they contribute to the maintenance of the organisms that produce them. In this paper we analyze and compare these two (...) accounts of organization and argue that understanding biological organisms as cohesively integrated systems benefits from insights from both. To bring together the two accounts, we focus on the notions of control and regulation as bridge concepts. We start from a characterization of biological mechanisms in terms of constraints and focus on a specific type of mechanism, control mechanisms, that operate on other mechanisms on the basis of measurements of variables in the system and its environment. Control mechanisms are characterized by their own set of constraints that enable them to sense conditions, convey signals, and effect changes on constraints in the controlled mechanism. They thereby allow living organisms to adapt to internal and external variations and to coordinate their parts in such a manner as to maintain viability. Because living organisms contain a vast number of control mechanisms, a central challenge is to understand how they are themselves organized. With the support of examples from both unicellular and multicellular systems we argue that control mechanisms are organized heterarchically, and we discuss how this type of control architecture can, without invoking top-down and centralized forms of organizations, succeed in coordinating internal activities of organisms. (shrink)

Proponents of the predictive processing (PP) framework often claim that one of the framework’s significant virtues is its unificatory power. What is supposedly unified are predictive processes in the mind, and these are explained in virtue of a common prediction error-minimisation (PEM) schema. In this paper, I argue against the claim that PP currently converges towards a unified explanation of cognitive processes. Although the notion of PEM systematically relates a set of posits such as ‘efficiency’ and ‘hierarchical coding’ into a (...) unified conceptual schema, neither the frameworks’ algorithmic specifications nor its hypotheses about their implementations in the brain are clearly unified. I propose a novel way to understand the fruitfulness of the research program in light of a set of research heuristics that are partly shared with those common to Bayesian reverse engineering. An interesting consequence of this proposal is that pluralism is at least as important as unification to promote the positive development of the predictive mind. (shrink)

Recent findings in different areas of psychology and cognitive science have brought the unconscious mind back to center stage. However, the unconscious mind worry remains: What renders unconscious phenomena mental? I suggest a new strategy for answering this question, which rests on the idea that categorizing unconscious phenomena as “mental” should be scientifically useful relative to the explanatory research goals. I argue that this is the case if by categorizing an unconscious phenomenon as “mental” one picks out explanatorily relevant similarities (...) to a corresponding paradigmatically mental conscious phenomenon. Explanatory relevance is spelled out in terms of mechanistic norms. (shrink)

Living organisms act as integrated wholes to maintain themselves. Individual actions can each be explained by characterizing the mechanisms that perform the activity. But these alone do not explain how various activities are coordinated and performed versatilely. We argue that this depends on a specific type of mechanism, a control mechanism. We develop an account of control by examining several extensively studied control mechanisms operative in the bacterium E. coli. On our analysis, what distinguishes a control mechanism from other mechanisms (...) is that it relies on measuring one or more variables, which results in setting constraints in the control mechanism that determine its action on flexible constraints in other mechanisms. In the most basic arrangement, the measurement process directly determines the action of the control mechanism, but in more complex arrangements signals mediate between measurements and effectors. This opens the possibility of multiple responses to the same measurement and responses based on multiple measurements. It also allows crosstalk, resulting in networks of control mechanisms. Such networks integrate the behaviors of the organism but also present a challenge in tailoring responses to particular measurements. We discuss how integrated activity can still result in differential, versatile, responses. (shrink)

The new mechanists and the autonomy approach both aim to account for how biological phenomena are explained. One identifies appeals to how components of a mechanism are organized so that their activities produce a phenomenon. The other directs attention towards the whole organism and focuses on how it achieves self-maintenance. This paper discusses challenges each confronts and how each could benefit from collaboration with the other: the new mechanistic framework can gain by taking into account what happens outside individual mechanisms, (...) while the autonomy approach can ground itself in biological research into how the actual components constituting an autonomous system interact and contribute in different ways to realize and maintain the system. To press the case that these two traditions should be constructively integrated we describe how three recent developments in the autonomy tradition together provide a bridge between the two traditions: (1) a framework of work and constraints, (2) a conception of function grounded in the organization of an autonomous system, and (3) a focus on control. (shrink)

In this talk I present the main results from Anta (2021), namely, that the theoretical division between Boltzmannian and Gibbsian statistical mechanics should be understood as a separation in the epistemic capabilities of this physical discipline. In particular, while from the Boltzmannian framework one can generate powerful explanations of thermal processes by appealing to their microdynamics, from the Gibbsian framework one can predict observable values in a computationally effective way. Finally, I argue that this statistical mechanical schism contradicts the Hempelian (...) (1958) thesis that the predictive power of a scientific theory is directly proportional to its explanatory potential, and vice versa. (shrink)

Functionalism about kinds is still the dominant style of thought in the special sciences, like economics, psychology, and biology. Generally construed, functionalism is the view that states or processes can be individuated based on what role they play rather than what they are constituted of or realized by. Recently, Weiskopf has posited a reformulation of functionalism on the model-based approach to explanation. We refer to this reformulation as ‘new functionalism’. In this paper, we seek to defend new functionalism and to (...) recast it in light of the concrete explanatory aims of the special sciences. In particular, we argue that the assessment of the explanatory legitimacy of a functional kind needs to take into account the explanatory purpose of the model in which the functional kind is employed. We aim at demonstrating this by appealing to model-based explanations from the social and behavioral sciences. Specifically, we focus on preferences and signals as functional kinds. Our argument is intended to have the double impact of deflecting criticisms against new functionalism from the perspective of mechanistic decomposition while also expanding the scope of new functionalism to encompass the social and behavioral sciences. (shrink)

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about (...) how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding. (shrink)

Unless one embraces activities as foundational, understanding activities in mechanisms requires an account of the means by which entities in biological mechanisms engage in their activities—an account that does not merely explain activities in terms of more basic entities and activities. Recent biological research on molecular motors exemplifies such an account, one that explains activities in terms of free energy and constraints. After describing the characteristic “stepping” activities of these molecules and mapping the stages of those steps onto the stages (...) of the motors’ hydrolytic cycles, researchers pieced together from images of the molecules in different hydrolyzation states accounts of how the chemical energy in ATP is transformed in the constrained environments of the motors into the characteristic activities of the motors. We argue that New Mechanism’s standard set of analytic categories—entities, activities, and organization—should be expanded to include constraints and energetics. Not only is such an expansion required descriptively to capture research on molecular motors but, more importantly from a philosophical point of view, it enables a non-regressive account of activities in mechanisms. In other words, this expansion enables a philosophical account of mechanistic explanation that avoids a regress of entities and activities “all the way down.” Rather, mechanistic explanation bottoms out in constraints and energetics. (shrink)

This paper discusses the epistemic status of biology from the standpoint of the systemic approach to living systems based on the notion of biological autonomy. This approach aims to provide an understanding of the distinctive character of biological systems and this paper analyses its theoretical and epistemological dimensions. The paper argues that, considered from this perspective, biological systems are examples of emergent phenomena, that the biological domain exhibits special features with respect to other domains, and that biology as a discipline (...) employs some core concepts, such as teleology, function, regulation among others, that are irreducible to those employed in physics and chemistry. It addresses the claim made by Jacques Monod that biology as a science is marginal. It argues that biology is general insofar as it constitutes a paradigmatic example of complexity science, both in terms of how it defines the theoretical object of study and of the epistemology and heuristics employed. As such, biology may provide lessons that can be applied more widely to develop an epistemology of complex systems. (shrink)

The causal efficacy of a material system is usually thought to be produced by the law-like actions and interactions of its constituents. Here, a specific system is constructed and explained that produces a cause that cannot be understood in this way, but instead has novel and autonomous efficacy. The construction establishes a proof-of-feasibility of strong emergence. The system works by utilizing randomness in a targeted and cyclical way, and by relying on sustained evolution by natural selection. It is not vulnerable (...) to standard arguments against strong emergence, in particular ones that assume that the physical realm is causally closed. Moreover, it does not suffer from epiphenomenalism or causal overdetermination. The system uses only standard material components and processes, and is fully consistent with naturalism. It is discussed whether the emergent cause can still be viewed as ‘material’ in the way that term is commonly understood. (shrink)

I want to combine two hitherto largely independent research projects, scientific understanding and mechanistic explanations. Understanding is not only achieved by answering why-questions, that is, by providing scientific explanations, but also by answering what-questions, that is, by providing what I call scientific descriptions. Based on this distinction, I develop three forms of understanding: understanding-what, understanding-why, and understanding-how. I argue that understanding-how is a particularly deep form of understanding, because it is based on mechanistic explanations, which answer why something happens in (...) virtue of what it is made of. I apply the three forms of understanding to two case studies: first, to the historical development of thermodynamics and, second, to the differences between the Clausius and the Boltzmann entropy in explaining thermodynamic processes. (shrink)

Embodied, embedded, enactive, and extended theorists do not typically focus on the ontological frameworks in which they develop their theories. One exception is 4E theories that embrace New Mechanism. In this paper, we endorse the New Mechanist’s general turn to ontology, but argue that their ontology is not the best on the market for 4E theories. Instead, we advocate for a different ontology: causal powers realism. Causal powers realism posits that psychological manifestations are the product of mental powers, and that (...) mental powers are empirically-discoverable features of individuals that account for the causal work those individuals do. We contend that causal powers realism provides a unifying framework for the central commitments of 4E theories, as well as additional resources for theorizing in a 4E framework. And while New Mechanism offers some of these resources as well, we argue that causal powers realism is ultimately the better of the two. (shrink)

Theory of illness causation is an important issue in all biomedical sciences, and solid etiological explanations are needed in order to develop therapeutic approaches in medicine and preventive interventions in public health. Until now, the literature about the theoretical underpinnings of illness causation research has been scarce and fragmented, and lacking a convenient summary. This interdisciplinary book provides a convenient and accessible distillation of the current status of research into this developing field, and adds a personal flavor to the discussion (...) by proposing the etiological stance as a comprehensive approach to identify modifiable causes of illness. (shrink)

Following an analysis of the state of investigations and clinical outcomes in the Alzheimer's research field, I argue that the widely-accepted 'amyloid cascade' mechanistic explanation of Alzheimer's disease appears to be fundamentally incomplete. In this context, I propose that a framework termed 'principled mechanism' (PM) can help with remedying this problem. First, using a series of five 'tests', PM systematically compares different components of a given mechanistic explanation against a paradigmatic set of criteria, and hints at various ways of making (...) the mechanistic explanation more 'complete'. These steps will be demonstrated using the amyloid explanation, and its missing or problematic mechanistic elements will be highlighted. Second, PM makes an appeal for the discovery and application of 'biological principles' (BPs), which approximate ceteris paribus laws and are operative at the level of a biological cell. As such, although thermodynamic, evolutionary, ecological and other laws or principles from chemistry and the broader life sciences could inform them, BPs should be considered ontologically unique. BPs could augment different facets of the mechanistic explanation but also allow further independent nomological explanation of the phenomenon. Whilst this overall strategy can be complementary to certain 'New Mechanist' approaches, an important distinction of the PM framework is its equal attention to the explanatory utility of biological principles. Lastly, I detail two hypothetical BPs, and show how they could each inform and improve the potentially incomplete mechanistic aspects of the amyloid explanation and also how they could provide independent explanations of the cellular features associated with Alzheimer's disease. (shrink)

Recent trends in the debate about natural kinds tend towards increasingly permissive and practice-oriented views. I argue that while these accounts—which I characterize using Boyd’s ([1991]) term ‘enthusiasm’—offer several helpful insights, they often lack the normative force that they want to have; that is, they cannot provide an account of what makes something a good or bad, better or worse, kind for scientific pursuits. I argue that such accounts can regain a minimal sense of normativity in two ways. The first, (...) which has already been widely embraced by theorists, is to index the appropriateness of a scientific kind to the domain within which it is situated. While this is a good solution, it is not clear how it can be applied in interdisciplinary and cross-domain research pursuits, which are increasingly common. The second way of regaining normativity—which is especially, but not exclusively, effective for such cross-domain pursuits—is to attend to the stage at which scientific research is occurring. (shrink)

Gebharter has proposed using one of the best known Bayesian network causal discovery algorithms, PC, to identify the constitutive dependencies underwriting mechanistic explanations. His proposal assumes that mechanistic constitution behaves like deterministic direct causation, such that PC is directly applicable to mixed variable sets featuring both causal and constitutive dependencies. Gebharter claims that such mixed sets, under certain restrictions, comply with PC’s background assumptions. The aim of this article is to show that Gebharter’s proposal incurs severe problems, ultimately rooted in (...) the widespread non-compliance of mechanistic systems with PC’s assumptions. This casts severe doubts on the attempt to implicitly define constitution as a form of deterministic direct causation complying with PC’s assumptions. (shrink)

New Mechanist philosophical models of "phenomenon reconstitution" understand the process to be driven by explanatory considerations. Here I discuss an episode of phenomenon reconstitution that occurred entirely within an experimental program dedicated to characterizing the phenomenon of kinesin motility. Rather than being driven by explanatory considerations, as standard mechanist views maintain, I argue that the phenomenon of kinesin motility was reconstituted to enhance researchers’ primary experimental tool—the single molecule motility assay.

Grounding and explanation are said to be intimately connected. Some even maintain that grounding just is a form of explanation. But grounding and explanation also seem importantly different—on the face of it, the former is ‘worldy’ or ‘objective’ while the latter isn’t. In this paper, we develop and respond to an argument to the effect that there is no way to fruitfully address this tension that retains orthodox views about grounding and explanation but doesn’t undermine a central piece of methodology, (...) namely that explanation is a guide to ground. (shrink)

The homeostatic property cluster theory is widely influential for its ability to account for many natural-kind terms in the life sciences. However, the notion of homeostatic mechanism has never been fully explicated. In 2009, Carl Craver interpreted the notion in the sense articulated in discussions on mechanistic explanation and pointed out that the HPC account equipped with such notion invites interest-relativity. In this paper, we analyze two recent refinements on HPC: one that avoids any reference to the causes of the (...) clustering of properties and one that replaces homeostatic mechanisms with causal networks represented by causal graphs. We argue that the former is too slender to account for some inductive inference in science and the latter, thicker account invites interest-relativity, as the original HPC does. This suggests that human interest will be an un-eliminative part of a satisfactory account of natural kindness. We conclude by discussing the implication of interest-relativity to the naturalness, reality, or objectivity of kinds and indicating an overlooked aspect of natural kinds that requires further studies. (shrink)

Jonathan Y. Tsou examines and defends positions on central issues in philosophy of psychiatry. The positions defended assume a naturalistic and realist perspective and are framed against skeptical perspectives on biological psychiatry. Issues addressed include the reality of mental disorders; mechanistic and disease explanations of abnormal behavior; definitions of mental disorder; natural and artificial kinds in psychiatry; biological essentialism and the projectability of psychiatric categories; looping effects and the stability of mental disorders; psychiatric classification; and the validity of the DSM's (...) diagnostic categories. The main argument defended by Tsou is that genuine mental disorders are biological kinds with harmful effects. This argument opposes the dogma that mental disorders are necessarily diseases that result from biological dysfunction. Tsou contends that the broader ideal of biological kinds offers a more promising and empirically ascertainable naturalistic standard for assessing the reality of mental disorders and the validity of psychiatric categories. (shrink)

Mechanisms, in the prominent biological sense of the term, are historical entities. That is, whether or not something is a mechanism for something depends on its history. Put differently, while your spontaneously-generated molecule-for-molecule double has a heart, and its heart pumps blood around its body, its heart does not have a mechanism for pumping, since it does not have the right history. My argument for this claim is that mechanisms have proper functions; proper functions are historical entities; so, mechanisms are (...) historical entities, too. This thesis runs against the mainstream new mechanist way of thinking about mechanisms, where mechanisms are generally thought of in an ahistorical way. After arguing for this thesis, I draw out some consequences for philosophy of science and metaphysics. (shrink)

We advance an account that grounds cognition, specifically decision-making, in an activity all organisms as autonomous systems must perform to keep themselves viable—controlling their production mechanisms. Production mechanisms, as we characterize them, perform activities such as procuring resources from their environment, putting these resources to use to construct and repair the organism's body and moving through the environment. Given the variable nature of the environment and the continual degradation of the organism, these production mechanisms must be regulated by control mechanisms (...) that select when a production is required and how it should be carried out. To operate on production mechanisms, control mechanisms need to procure information through measurement processes and evaluate possible actions. They are making decisions. In all organisms, these decisions are made by multiple different control mechanisms that are organized not hierarchically but heterarchically. In many cases, they employ internal models of features of the environment with which the organism must deal. Cognition, in the form of decision-making, is thus fundamental to living systems which must control their production mechanisms. (shrink)

It is generally acknowledged by proponents of ‘new mechanism’ that mechanistic explanation involves adopting a perspective, but there is less agreement on how we should understand this perspective-taking or what its implications are for practising science. This paper examines the perspectival nature of mechanistic explanation through the lens of the ‘mechanistic stance’, which falls somewhere between Dennett’s more familiar physical and design stance. We argue this approach implies three distinct and significant ways in which mechanistic explanation can be interpreted as (...) perspectival: ‘phenomenon perspectivism’, ‘pattern perspectivism’ and ‘hierarchy perspectivism’. We evaluate the strength of the perspective-dependency implied by each of these, and along the way, discuss their significance for wider debates within the new mechanism literature, such as the nature of function attribution and an ontic vs epistemic understanding of explanation. (shrink)

In this dissertation, I address the question of how to construct scientific theories of emotions that are both conceptually sound and empirically fruitful. To do this, I offer an analysis of the main challenges scientific theories of emotions face, and I propose a meta-theoretical framework to construct scientific concepts of emotions as explications of folk emotion concepts. Part I discusses the main challenges theories of emotions in psychology and neuroscience encounter. The first states that a proper scientific theory of emotions (...) must explain all and only the phenomena under the vernacular term ‘emotion’ with a common set of conceptual resources and under an overarching generic concept of emotion. The second demands that each emotion category corresponds to well-coordinated sets of neural, physiological, and behavioral patterns of responses. I argue that none of the best contemporary theories of emotions in psychology and neuroscience overcomes these challenges. As a result, a new theory of emotions is required. In Part II, I develop the meta-theoretical framework to construct a theory of emotions that overcomes the challenges above. First, I propose a pluralistic account of scientific kinds based on different patterns of projection that various disciplines may take to justify inductive inferences. These are essentialist, historical, and social patterns. Each of these patterns provides a framework to construct different types of scientific concepts. I argue that among the frameworks for scientific kinds available, the one that is best suited to explicate emotion concepts is a functional framework. Consequently, I conclude by recommending scientists pursue functionalist theories of emotions over essentialist, historical, or social theories. (shrink)