Mechanistic Explanation

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)

We provide an account of mechanistic representation and explanation that has several advantages over previous proposals. In our view, explaining mechanistically is not simply giving an explanation of a mechanism. Rather, an explanation is mechanistic because of particular relations that hold between a mechanical representation, or model, and the target of explanation. Under this interpretation, mechanistic explanation is possible even when the explanatory target is not a mechanism. We argue that taking this view is not only coherent and plausible, it (...) gives a more sophisticated view of the relationship between mechanical models and their targets. This allows us to address some ambiguities within the mechanist framework, and delivers a more intuitive way to interpret scientists' use of the term "mechanism". (shrink)

The idea that causation involves the production of changes due to the exertion of influence of something on something else—the core idea of causal realism—used to be the default view. Today this idea is at the heart of (i) transmission/causal process accounts, (ii) mechanistic accounts, and (iii) powers-based accounts. However, as I have previously argued (Ingthorsson 2021) the above-mentioned approaches are based—to varying degree—on the very problematic assumption that causal influence is essentially unidirectional; that it passes from whatever is the (...) cause to the effect. As first pointed out be Mario Bunge (1959), the idea that causal influence is unidirectional is incompatible with what is today considered a scientific fact, notably that all interactions are perfectly reciprocal; whenever any object exerts an influence on any other object, the latter simultaneously exerts the same kind of influence, of the same magnitude, on the former. Drawing on Bunge’s result, I have developed a fourth approach to efficient causation that accepts the reciprocity of interactions (Ingthorsson 2002 & 2021). According to this view a cause is not the action of something on something else that receives the influence, but the reciprocal action between powerful particulars which results in a modification of both sides, producing a new state of affairs. In this paper, I present the core ideas of the four different approaches to causal efficacy, what I think is problematic with the first three and finally attempt to compare how well the four approaches fare in explaining two often discussed types of causal interactions: (i) collisions between billiard balls, and (ii) how water dissolves salt. I argue that only reciprocal actions between powerful particulars can plausibly claim to fully account for these phenomena, in a manner compatible with the theories and findings of the natural sciences. (shrink)

What kinds of norms constrain mechanistic discovery and explanation? In the mechanistic literature, the norms for good explanations are directly derived from answers to the metaphysical question of what explanations are. Prominent mechanistic accounts thus emphasize either ontic or epistemic norms. Still, mechanistic philosophers on both sides agree that there is no sharp distinction between the processes of discovery and explanation. Thus, it seems reasonable to expect that ontic and epistemic accounts of explanation will be accompanied by ontic and epistemic (...) accounts of discovery, respectively. As we will show here, however, recent discovery accounts implicitly rely on both ontic and epistemic norms to characterize the discovery process. In this paper, we develop an account that makes explicit that, and how, ontic and epistemic norms work together throughout the discovery process. By describing mechanism discovery as a process of pattern recognition we demonstrate that scientists have to develop epistemic activities to distinguish a pattern from its background. Furthermore, they have to determine which epistemic activities successfully describe how the pattern is implemented by identifying the pattern’s components. Our approach reveals that ontic and epistemic norms are equally important in mechanism discovery. (shrink)

Recent breakthroughs in artificial intelligence have primarily resulted from training deep neural networks (DNNs) with vast numbers of adjustable parameters on enormous datasets. Due to their complex internal structure, DNNs are frequently characterized as inscrutable ``black boxes,'' making it challenging to interpret the mechanisms underlying their impressive performance. This opacity creates difficulties for explanation, safety assurance, trustworthiness, and comparisons to human cognition, leading to divergent perspectives on these systems. This chapter examines recent developments in interpretability methods for DNNs, with a (...) focus on interventionist approaches inspired by causal explanation in philosophy of science. We argue that these methods offer a promising avenue for understanding how DNNs process information compared to merely behavioral benchmarking and correlational probing. We review key interventionist methods and illustrate their application through practical case studies. These methods allow researchers to identify and manipulate specific computational components within DNNs, providing insights into their causal structure and internal representations. We situate these approaches within the broader framework of causal abstraction, which aims to align low-level neural computations with high-level interpretable models. While acknowledging current limitations, we contend that interventionist methods offer a path towards more rigorous and theoretically grounded interpretability research, potentially informing both AI development and computational cognitive neuroscience. (shrink)

Philosophers of psychology debate, among other things, which psychological models, if any, are (or provide) mechanistic explanations. This should seem a little strange given that there is rough consensus on the following two claims: 1) a mechanism is an organized collection of entities and activities that produces, underlies, or maintains a phenomenon, and 2) a mechanistic explanation describes, represents, or provides information about the mechanism producing, underlying, or maintaining the phenomenon to be explained (i.e. the explanandum phenomenon) (Bechtel and Abrahamsen (...) 2005; Craver 2007). If there is a rough consensus on what mechanisms are and that mechanistic explanations describe, represent, or provide information about them, then how is there no consensus on which psychological models are (or provide) mechanistic explanations? Surely the psychological models that are mechanistic explanations are the models that describe, represent, or provide information about mechanisms. That is true, of course; the trouble arises when determining what exactly that involves. Philosophical disagreement over which psychological models are mechanistic explanations is often disagreement about what it means to describe, represent, or provide information about a mechanism, among other things (Hochstein 2016; Levy 2013). In addition, one's position in this debate depends on a host of other seemingly arcane metaphysical issues, such as the nature of mechanisms, computational and functional properties (Piccinini 2016), and realization (Piccinini and Maley 2014), as well as the relation between models, methodologies, and explanations (Craver 2014; Levy 2013; Zednik 2015). Although I inevitably advocate a position, my primary aim in this chapter is to spell out all these relationships and canvas the positions that have been taken (or could be taken) with respect to mechanistic explanation in psychology, using dynamical systems models and cognitive models (or functional analyses) as examples. (shrink)

I distinguish three ways in which early modern rationalists seek to apply the principle of sufficient reason to empirical science, and critically assess some of their attempts to do so. I focus especially on how these thinkers assume substantive theories of explanation and intelligibility--which are indebted to the mechanist and experimentalist traditions--in many of their deployments of this rationalist principle. A recurring problem is that these philosophers deploy their standards of intelligibility inconsistently: some of their own favored explanations do not (...) always live up to their standards. I argue that the problem is especially acute for Leibniz and Euler, given their enthusiasm for final-causal explanations. (shrink)

This paper introduces a mechanistic framework for understanding animal emotions, which is designed for biologists studying animal behavior and welfare. Researchers often examine emotions—short-term valenced experiences—through behavioral, somatic, and cognitive indicators. However, proposed indicators are often ambivalent (emerge in contexts with opposing emotional valence) or undetermined (arise in both affective and non-affective processes). To ground hypothesis formulation regarding animal emotions on a better foundation, the paper advocates for building on what we know regarding the mechanisms of human emotions—the behavioral rules (...) that transform sensory input into motor output during emotional episodes. In particular, it integrates key assumptions from three dominant psychological theories of emotion—innate, appraisal, and network theories—into a single framework and argues that this can serve as a common ground to transfer insights from human to animal emotion research. Additionally, the paper tackles the question of how emotions relate to closely linked processes such as decision-making, distinguishing between parallel architecture models—where emotions and decision-making processes interact but remain distinct—and unified models—where affective states are conceived as integral to goal-oriented processes. Finally, we discuss how our mechanistic proposal can help us address four key questions in animal emotion research: Do animals experience emotions? If so, which animals experience emotions? Which emotions do they experience? And how do these emotions compare to human emotions? The paper concludes by emphasizing the need for further empirical research on the mechanisms of animal emotions and their distinction from other processes. (shrink)

Craver's diagram, comprising symbols such as X (entity), S (mechanism), Φ (activity), and Ψ (phenomenon), is widely used to represent biological mechanisms in the New Mechanism. However, this paper demonstrates that Craver’s framework lacks the formal capacity to adequately capture the organizational structures and functional dynamics essential for mechanistic explanations, particularly the temporal interplay among entities and activities or the relational nature of enzymatic state transitions. To address these limitations, this paper proposes a supplementary framework based on category theory, enabling (...) the abstraction of mechanisms as temporally organized structures of state transitions. By integrating key features such as order, frequency, and duration, categorical diagrams provide a cohesive representation of type mechanisms, supporting the deficiencies of token-centric approaches. Using protein synthesis as a case study, the paper illustrates how categorical abstractions enhance the formal representation of biological mechanisms while emphasizing the philosophical importance of organizational dimensions. (shrink)

This paper explores moral judgments as conscious phenomena, highlighting the necessity of an interdisciplinary approach to achieve a comprehensive scientific understanding. Integrating insights from neuroscience, moral psychology, and philosophy of science, it adopts a mechanistic framework to analyze moral judgments through three dimensions: informational diversity, functional dynamics, and intentionality. While the mechanistic approach provides a robust foundation for decomposing moral judgments into their constitutive elements, it faces limitations in establishing causal relationships across neural levels and integrating complex cognitive processes. To (...) address these challenges, it evaluates neuroscientific theories of consciousness, identifying Global Workspace Theory (GWT) as the most suitable framework for unifying distributed neural processes into coherent conscious experiences. By synthesizing mechanistic philosophy with GWT, this paper offers a novel explanatory model for understanding moral judgments as multidimensional, integrative, and conscious phenomena. (shrink)

This paper aims to evaluate the relevance of Kant’s much discussed essentialism and mechanism for present-day philosophy of psychiatry. Kendler et al (2011) have argued that essentialism is inadequate for conceptualizing psychiatric disorders. In this paper, I develop this argument in detail by highlighting a variety of essentialism that differs from the one rejected by Kendler et al. I show that Kant’s essentialism is not directly affected by the argument of Kendler et al (2011), and that Kendler et al’s (2011) (...) argument also does not affect other essentialist positions in psychiatry. Hence, the rejection of essentialism in psychiatry needs more arguments than the one supplied by Kendler et al. Nevertheless, the study of current psychiatry also provides reasons to reject Kant’s essentialism and his transcendental project. I argue that Kant’s theory of mechanical explanation is more relevant for analyzing present-day philosophy of psychiatry, insofar as (a) modern psychiatric research into the causes of psychiatric disorders fits the mechanist paradigm, (b) Kant’s theory of mechanical explanation is importantly similar to modern theories of mechanical explanation applicable to psychiatry, such as those of Bechtel and associates, and (c) Kant’s stance that mechanism constitutes a regulative ideal points to useful arguments for the pursuit of mechanical explanations in psychiatry. (shrink)

After Wesley Salmon’s causal-mechanical stance on explanation in the 1980s, the ontic-epistemic debate of scientific explanations appeared to be resolved in the philosophy of science. However, since the twenty-first century, this debate has been rekindled among philosophers who focus on mechanistic explanations. Nevertheless, its issues have evolved, necessitating scrutiny of the new trends in this debate and a comparison with the original controversy between Carl Hempel and Salmon. The primary objective of this paper is to elucidate three categorical dimensions in (...) the ontic-epistemic debates, spanning from the original to the recent controversies. Subsequently, it will explore why the conception of explanation is linked to representations, what conditions are necessary for linguistic expressions to be explanatory, and what roles norms play in explanation. Consequently, contrary to the common stereotype, it will be argued that mechanistic explanations are more likely to be epistemic rather than ontic. (shrink)

Cognitive ontology has become a popular topic in philosophy, cognitive psychology, and cognitive neuroscience. At its center is the question of which cognitive capacities should be included in the ontology of cognitive psychology and cognitive neuroscience. One common strategy for answering this question is to look at brain structures and determine the cognitive capacities for which they are responsible. Some authors interpret this strategy as a search for neural mechanisms, as understood by the so-called new mechanistic approach. In this article, (...) I will show that this new mechanistic answer is confronted with what I call the triviality problem. A discussion of this problem will show that one cannot derive a meaningful cognitive ontology from neural mechanisms alone. Nonetheless, neural mechanisms play a crucial role in the discovery of a cognitive ontology because they are epistemic proxies for best systematizations. (shrink)

In recent philosophical and (neuro)psychological discussions of phenomena such as motivated forgetting, memory inhibition, self-deception, and implicit bias, various authors have suggested that repression might be a useful notion to make sense of these phenomena, or that these phenomena indeed provide evidence for repression. However, surprisingly, these claims usually do not rely on any explicit model of repression. Consequently, it remains unclear whether invoking repression in these discussions is justified. In this paper, I propose a psychological “how-possibly” model that can (...) serve as a basis for scientific research that I call the hybrid model of repression. This model combines the advantages while avoiding the problems of two types of models of repression—the higher-order model and the separation model—that have been discussed and defended by different authors and that both have their origin in Freud. This “how-possibly” model may then set the stage for further empirical research, which can form the basis for a “how-actually” model in the longer run. (shrink)

When does a model explain? When does it promote understanding? A dominant approach to scientific explanation is the interventionist view. According to this view, when X explains Y, intervening on X can produce, prevent or alter Y in some predictable way. In this paper, I argue for two claims. First, I reject a position that many interventionist theorists endorse. This position is that to explain some phenomenon by providing a model is also to understand that phenomenon. While endorsing the interventionist (...) view, I argue that explaining and understanding are distinct scientific achievements. Second, I defend a novel theory of scientific understanding. According to this view, when some model M promotes understanding, M makes available a distinctive mental state. This state is of the same psychological kind as when we grasp events in a narrative as bearing on some ultimate conclusion. To conclude, I show that, given this view, mechanistic explanations often provide a powerful source of understanding that many causal-historical models lack. This paper will be of interest to both philosophers of science and epistemologists engaged in the topics of explanation and understanding. (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)

The topic of mechanistic explanation in neuroscience has been a subject of recent discussion. There is a lot of interest in understanding what these explanations involve. Furthermore, there is disagreement about whether neurological mechanisms themselves should be viewed as reductionist in nature. In this paper I will explain how these two issues are related. I will, first, describe how mechanisms support a form of antireductionism. This is because the mechanisms that exist should be seen as involving part-whole relations, where the (...) behavior of a whole is more than the sum of its parts. After this, I will consider mechanistic explanations and how they can be understood. While some people think the explanations concern existing entities in the world, I will argue that we can understand the explanations by viewing them in terms of arguments. Despite the fact that it is possible to understand mechanistic explanations in this manner, the antireductionist point remains. (shrink)

Philosophers have studied mechanisms in many fields in biology. The focus has often been on molecular mechanisms in disciplines such as neuroscience, genetics and molecular biology, with some work on population-level mechanisms in ecology and evolution. We present a novel philosophical case study of individual-level mechanisms, mechanisms in ecology and evolution that concern the interactions between an individual and its environment. The mechanisms we analyze are called Niche Choice, Niche Conformance and Niche Construction (NC3) mechanisms. Based on a detailed analysis (...) of biologists’ research practices, we develop metaphysical claims about the components and organization of NC3 mechanisms, the phenomena they bring about and how these phenomena relate to individual differences, a major explanatory target in the field. We provide reasons for why processes of niche choice, conformance and construction are mechanisms and how they differ from molecular mechanisms underlying individual differences. Finally, we demonstrate that a general representation of NC3 mechanisms is highly abstract, with more specific types of NC3 mechanisms in particular study systems exhibiting more complex components organized in more complex ways. Our case study highlights some distinctive features of individual-level mechanisms in ecology and evolution, such as complex and heterogeneous organization and multiple phenomena. (shrink)

We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. (...) We conclude from this that topological explanations are autonomous from their mechanistic counterparts. (shrink)

One assumption of the new mechanistic approach is that there are two kinds of mechanistic explanations: etiological and constitutive ones. While the former explain phenomena in terms of their preceding causes, the latter are supposed to refer to mechanisms that constitute phenomena. Based on arguments by Kaiser and Krickel (Br J Philos Sci 68(3):745–779, 2017) and Krickel (The mechanical world, vol. 13, Springer International Publishing, 2018), I will show that this view is too narrow. Indeed, three different types of explanation (...) are usually subsumed under the label “constitutive explanation”. However, one of those types of explanation is not a version of constitutive explanation. Rather it is a variant of etiological explanation. As a result, I will show that there are four types of mechanistic explanation, two variants of etiological explanation—which I will call output mechanistic explanations and input-output mechanistic explanations—and two variants of constitutive explanation—which I will call filler mechanistic explanations and dimensioned mechanistic explanations. Keeping these apart is crucial as they come with different ontological implications. An evaluation of the mechanistic approach regarding its stance on reduction, levels, and interlevel causation crucially depends on which notion of mechanistic explanation one has in 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)

The relationship between topological explanation and mechanistic explanation is unclear. Most philosophers agree that at least some topological explanations are mechanistic explanations. The crucial question is how to make sense of this claim. Zednik (Philos Psychol 32(1):23–51, 2019) argues that topological explanations are mechanistic if they (i) describe mechanism sketches that (ii) pick out organizational properties of mechanisms. While we agree with Zednik’s conclusion, we critically discuss Zednik’s account and show that it fails as a general account of how and (...) when topological explanations are mechanistic. First, if topological explanations were just mechanism sketches, this implies that they could be enriched by replacing topological terms with mechanistic detail. This, however, conflicts how topological explanations are used in scientific practice. Second, Zednik’s account fails to show how topological properties can be organizational properties of mechanisms that have a place in mechanistic explanation. The core issue is that Zednik’s account ignores that topological properties often are _global_ properties while mechanistic explanantia refer to _local_ properties. We demonstrate how these problems can be solved by a recent account of mechanistic completeness (Craver and Kaplan in Br J Philos Sci 71(1):287–319, 2020; Kohár and Krickel in Calzavarini and Viola (eds) Neural mechanisms—new challenges in the philosophy of neuroscience, Springer, New York, 2021) and use a multilayer network model of Alzheimer’s Disease to illustrate this. (shrink)

Financial modelling is an essential tool for studying the possibility of financial transactions. This paper argues that financial models are conventional tools widely used in formulating and establishing possibility claims about a prospective investment transaction, from a set of governing possibility assumptions. What is distinctive about financial models is that they articulate how a transaction possibly could occur in a non-actual investment scenario given a limited base of possibility conditions assumed in the model. For this reason, it is argued that (...) the epistemic contribution of financial models is that of enabling the model users to envision exactly how a prospective investment could be achieved in various ways through a detailed understanding of the available transaction mechanisms. Thus, financial models provide information about the possibility of an investment scenario by showing how a specific transaction mechanism could result from a small set of initial possibility conditions assumed in the model. (shrink)

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...) competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

This thesis investigates the nature of psychiatric disorders, and to what extent they can form a basis for classification, explanation, and treatment interventions. These questions are important in the light of the “crisis of validity” in psychiatry, according to which current diagnostic categories do not pick out real disorders. I address the questions by defending an account of psychiatric disorders that can better accommodate social aspects and non-epistemic values than the symptom-based model of the Diagnostic and Statistical Manual of Mental (...) Disorders (DSM) and the brain disease model of the biomedical approach, including the Research Domain Criteria (RDoC). I concentrate on three ways that psychiatric disorders can differ from prototypical natural kinds, such as biological species and chemical elements. First, the concept of psychiatric disorder or mental disorder is partly value-laden and cannot be defined based only on scientific facts. Second, the objects of psychiatric classifications are interactive human kinds because people with disorders respond to their classifications through looping effects. Third, sociocultural factors can shape disorders in complex ways, which is indicated by their cross-cultural variation. I argue that these challenges can be overcome with a pluralistic explanatory account that grants an explicit role to value-sensitive considerations and social scientific research. Based on this, I argue that particular disorders can in principle support inductive inferences. ------------------------------------------------------------------------------------------ My key argument is that although whether a condition is considered pathological is partly a value-laden question, this does not rule out a realist account of particular psychiatric disorders as homeostatic property cluster kinds (HPC). I assert that causal mechanisms responsible for psychiatric kinds can be understood non-metaphysically based on the contrastive-counterfactual and interventionist theories of explanation. An advantage of this account is that it can address the concern that psychiatric explanations require one to relate heterogeneous causal factors on different levels, such as genetic, neurological, psychological, and social. My novel argument is that mechanistic explanations of scientific kinds have applicability domains over which they account for specific aspects of kinds and warrant inductive inferences. That is, identifying the applicability domain of an explanation spells out the conditions under which the explanation is expected to be reliable and when it can break down. This helps to understand how different discipline approaches, including social and cultural ones, can be complementary and make an explanation of a psychiatric kind more reliable. On the other hand, the account also shows how the complex nature of psychiatric kinds can license re-classifications for different epistemic and non-epistemic purposes. Finally, in the light of the social implications of psychiatric classifications, I suggest a value-sensitive or ameliorative approach to engineering the concept of psychiatric disorder. In conclusion, my account shows that research on the social and cultural factors that shape psychiatric disorders, and weighing the non-epistemic values that influence classificatory practices, can and should inform classificatory choices as well as policy and treatment interventions. (shrink)

Explanatory Optimism about the Hard Problem of Consciousness argues that despite the worries of explanatory pessimists, consciousness can be fully explained in “easy” scientific terms. The widespread intuition that consciousness poses a hard problem is plausibly based on how consciousness appears to us in first-person access. The book offers a debunking argument to undercut the justificatory link between the first-person appearances and our hard problem intuitions. -/- The key step in the debunking argument involves the development and defense of an (...) empirical model of first-person access: Automated Compression Theory (ACT). ACT holds that first-person access to consciousness is accomplished by automated accessing of compressed sensory information. Because of the distorting nature of this compressed access, it seems to subjects that consciousness possesses “exceptional” properties—properties leading to the hard problem—even though no such properties are present. If there are no exceptional properties to explain, then an explanation in easy terms can fully account for conscious experience. The book presents a range of empirical evidence for ACT and concludes that the burden of proof is now on the pessimists to show why we shouldn’t be optimistic about explaining consciousness. (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)

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 idea at the core of the New Mechanical account of explanation can be summarized in the claim that explaining means showing ‘how things work’. This simple motto hints at three basic features of Mechanistic Explanation (ME): ME is an explanation-how, that implies the description of the processes underlying the phenomenon to be explained and of the entities that engage in such processes. These three elements trace a fundamental contrast with the view inherited from Hume and later from strict logical (...) empiricism (see Creath 2017), focused on epistemic and formal features of science and according to which issues concerning the kind of entities and processes that lie within a theory’s domain are extraneous to science and belong instead to ontology or metaphysics. Philosophers belonging to the new mechanical philosophy believe that the received view of scientific explanation (Hempel 2001), pivoting on the notion of law of nature, overshadows this insight. Since its origin in the 17th century, mechanical philosophy aimed to explain natural phenomena by reducing them to mechanisms. Traditional attempts to define the concept of mechanism involved the identification of a limited set of fundamental elements as, for instance, contact action, action at a distance, inertial motion (see e.g. Hesse 2005), and, more recently, transmission of a mark, or of a conserved quantity (see Frisch, this volume). The new mechanical philosophy rejects this austere characterization of mechanisms and mechanistic explanation and aim at providing a novel, philosophically rigorous explication of the concept of mechanism and of its role in scientific explanation and practice. ME has been adopted with profit in philosophy of special sciences (for instance in biomedical sciences, e.g. in the explanation of chemical transmission at synapses ((Machamer, Darden and Craver 2000), MDC henceforth); but also in social sciences, e.g. the three kinds of social mechanisms in Coleman’s analysis of Max Weber’s account of the role of the Protestant ethic in the growth of capitalism (Hedström and Swedberg 1998)), where exceptionless regularities are rarely ever found. In physics, it is generally possible to formulate explanations in law-based form, with the result that the plurality of explanatory forms might be overlooked. This should not come as a surprise, given that physics was the main inspiration for logical empiricists, and, in particular, Newtonian physics was a template for Hempel’s formulation of the covering law model. However, this situation is unfortunate, since, we will argue, knowing how things work is often part of the explanation of physical phenomena. In this chapter, we provide an introduction to the basic features of ME, with specific focus on its application to physics (section 1). The main part of the chapter is devoted to the defence of two theses: on the one hand, some domains of physics are not compatible with mechanistic reasoning and explanation (section 2); on the other hand, a comprehensive account of explanation in physics can’t dispense with ME (section 3). (shrink)

One of Stuart Glennan's most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms (Glennan, 1996). In this paper I employ the causal Bayes net framework to reconstruct his analysis. This allows for specifying general assumptions which have to be satis ed to get Glennan's approach working. I show that once these assumptions are in place, they imply (against the background of the causal Bayes net machinery) that higher-level causation indeed (...) reduces to interactions between component parts of mechanisms. I also briefly discuss the plausibility of these assumptions and some consequences for the mechanism debate. (shrink)

In recent years what has come to be called the 'New Mechanism' has emerged as a framework for thinking about the philosophical assumptions underlying many areas of science, especially in sciences such as biology, neuroscience, and psychology. This book offers a fresh look at the role of mechanisms, by situating novel analyses of central philosophical issues related to mechanisms within a rich historical perspective of the concept of mechanism as well as detailed case studies of biological mechanisms. It develops a (...) new position, Methodological Mechanism, according to which mechanisms are to be viewed as causal pathways that are theoretically described and are underpinned by networks of difference-making relations. In contrast to metaphysically inflated accounts, this study characterises mechanism as a concept-in-use in science that is deflationary and metaphysically neutral, but still methodologically useful and central to scientific practice. (shrink)

This chapter provides a systematic overview of topological explanations in the philosophy of science literature. It does so by presenting an account of topological explanation that I (Kostić and Khalifa 2021; Kostić 2020a; 2020b; 2018) have developed in other publications and then comparing this account to other accounts of topological explanation. Finally, this appraisal is opinionated because it highlights some problems in alternative accounts of topological explanations, and also it outlines responses to some of the main criticisms raised by the (...) so-called new mechanists. (shrink)

This article addresses the contributions of the literature on the new mechanistic philosophy of science for the scientific practice of model building in ecology. This is reflected in a one-to-one interdisciplinary collaboration between an ecologist and a philosopher of science during science-in-the-making. We argue that the identification, reconstruction and understanding of mechanisms is context-sensitive, and for this case study mechanistic modeling did not present a normative role but a heuristic one. We expect our study to provides useful epistemic tools for (...) the improvement of empirically-riven work in the debates about mechanistic explanation of ecological phenomena. (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)

There have been a number of attempts to apply mechanism-related concepts to the notion of extended cognition. Such accounts appeal to the idea that extended cognitive routines are realized by mechanisms that transcend some salient border or boundary. The present paper describes some of the challenges confronting the effort to develop a mechanistic account of extended cognition. In particular, it describes five problems that must be resolved if we are to make sense of the idea that extended cognition can be (...) understood via an appeal to extended mechanisms. These problems are intended to guide the philosophical effort to develop a mechanistic account of extended cognition. (shrink)

Proponents of the extended mind have suggested that phenomenal transparency may be important to the way we evaluate putative cases of cognitive extension. In particular, it has been suggested that in order for a bio-external resource to count as part of the machinery of the mind, it must qualify as a form of transparent equipment or transparent technology. The present paper challenges this claim. It also challenges the idea that phenomenological properties can be used to settle disputes regarding the constitutional (...) status of bio-external resources in episodes of extended cognizing. Rather than regard phenomenal transparency as a criterion for cognitive extension, we suggest that transparency is a feature of situations that support the ascription of certain cognitive/mental dispositional properties to both ourselves and others. By directing attention to the forces and factors that motivate disposition ascriptions, we arrive at a clearer picture of the role of transparency in arguments for extended cognition and the extended mind. As it turns out, transparency is neither necessary nor sufficient for cognitive extension, but this does not mean that it is entirely irrelevant to our understanding of the circumstances in which episodes of extended cognizing are apt to arise. (shrink)

Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...) I argue that the objection does not stand up to scrutiny. This is because it is riddled with a number of conceptual inconsistencies. By attempting to resolve the tensions, I also draw several general lessons regarding the difficulties of applying abstraction and idealization in scientific practice. Finally, I argue that more care is needed only when speaking of abstraction and idealization in a context in which these concepts play an important role in an argument, such as that on mechanistic explanation. (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)

The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a postulate, an unfalsifiable principle, a natural law, and an imperative. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also (...) unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences. (shrink)

An adequate understanding of the ubiquitous practice of mechanistic explanation requires an account of what Craver termed “constitutive relevance.” Entities or activities are constitutively relevant to a phenomenon when they are parts of the mechanism responsible for that phenomenon. Craver’s mutual manipulability account extended Woodward’s account of manipulationist counterfactuals to analyze how interlevel experiments establish constitutive relevance. Critics of MM argue that applying Woodward’s account to this philosophical problem conflates causation and constitution, thus rendering the account incoherent. These criticisms, we (...) argue, arise from failing to distinguish the semantic, epistemic, and metaphysical aspects of the problem of constitutive relevance. In distinguishing these aspects of the problem and responding to these critics accordingly, we amend MM into a refined epistemic criterion, the “matched interlevel experiments” account. Further, we explain how this epistemological thesis is grounded in the plausible metaphysical thesis that constitutive relevance is causal betweenness. (shrink)

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge the limitations of certain (...) features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts. (shrink)

I propose a dynamic causal approach to characterizing the notion of a mechanism. Levy and Bechtel, among others, have pointed out several critical limitations of the new mechanical philosophy, and pointed in a new direction to extend this philosophy. Nevertheless, they have not fully fleshed out what that extended philosophy would look like. Based on a closer look at neuroscientific practice, I propose that a mechanism is a dynamic causal system that involves various components interacting, typically nonlinearly, with one another (...) to produce a phenomenon of interest. (shrink)

Is the mathematical function being computed by a given physical system determined by the system’s dynamics? This question is at the heart of the indeterminacy of computation phenomenon (Fresco et al. [unpublished]). A paradigmatic example is a conventional electrical AND-gate that is often said to compute conjunction, but it can just as well be used to compute disjunction. Despite the pervasiveness of this phenomenon in physical computational systems, it has been discussed in the philosophical literature only indirectly, mostly with reference (...) to the debate over realism about physical computation and computationalism. A welcome exception is Dewhurst’s ([2018]) recent analysis of computational individuation under the mechanistic framework. He rejects the idea of appealing to semantic properties for determining the computational identity of a physical system. But Dewhurst seems to be too quick to pay the price of giving up the notion of computational equivalence. We aim to show that the mechanist need not pay this price. The mechanistic framework can, in principle, preserve the idea of computational equivalence even between two different enough kinds of physical systems, say, electrical and hydraulic ones. (shrink)

In this paper I argue that historiography employs causal narrative explanations just as other historical sciences such as evolutionary biology or paleontology do. There is a logic of explanation common to all these sciences that centers on causal explanation of unique and unrepeatable events. The explanandum of historiography can further be understood as mechanism in the sense developed by Stuart Glennan and others in recent years. However, causal explanation is not the only way historiography relates to the past. Arthur Danto (...) has given us the theoretical tools to differentiate between causal narratives and conceptual colligations, with both playing a pivotal role in historiography even though Danto himself has not expressed that thought clearly. (shrink)

All biological objects in living systems are composed of their components. They are also involved in biological mechanisms that produce regular phenomena. Mechanistic philosophy is a recent framework for understanding biological sciences. In this dissertation, I will suggest a categorical formalism of mechanism supported by algebraic constraints by virtue of a mathematical language, category theory. First, I formalize the essential groundwork for mechanistic explanations, component parts, and activities by assuming that mechanistic explanations do not allow for infinite decomposition of components, (...) and that they essentially require explanatory baselines. Second, I formalize temporal organizations by introducing Petri nets and monoidal categories. Then, I argue that phenomenal models of mechanisms as explananda of mechanistic explanations are semantic interpretations of models of dynamical systems. By doing so, I show a mathematical alliance of dynamical systems with temporal organizations of biological mechanisms. Third, I formalize spatial organizations and argue that spatial organizations provide significant clues to make mechanistic explanations. Mechanistic explanations must include relevant components of a mechanism, particularly activities. Activities are responsible for producing explanandum phenomena. I will emphasize that spatially organized features of entities are prerequisite resources to determine relevant activities to produce explanandum phenomena. Further, we will notice that hierarchical relations are established between entities and their own sub-parts, not between a mechanism and components. Fourth, I will emphasize that constitutive relations are not associated with causation at all. Consequently, I conclude that categorical formalism is a promising approach to represent biological mechanisms and expect that the mechanistic philosophy will be positioned as an integrative framework of diverse scientific fields such as physics, chemistry, and biology. (shrink)