An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...) explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993. (shrink)

This book offers a philosophy for error-prone humans trying to understand messy systems in the real world.

The world is complex, but acknowledging its complexity requires an appreciation for the many roles context plays in shaping natural phenomena. In _Unsimple Truths, _Sandra Mitchell argues that the long-standing scientific and philosophical deference to reductive explanations founded on simple universal laws, linear causal models, and predict-and-act strategies fails to accommodate the kinds of knowledge that many contemporary sciences are providing about the world. She advocates, instead, for a new understanding that represents the rich, variegated, interdependent fabric of many levels (...) and kinds of explanation that are integrated with one another to ground effective prediction and action. Mitchell draws from diverse fields including psychiatry, social insect biology, and studies of climate change to defend “integrative pluralism”—a theory of scientific practices that makes sense of how many natural and social sciences represent the multi-level, multi-component, dynamic structures they study. She explains how we must, in light of the now-acknowledged complexity and contingency of biological and social systems, revise how we conceptualize the world, how we investigate the world, and how we act in the world. Ultimately _Unsimple Truths _argues that the very idea of what should count as legitimate science itself should change. (shrink)

We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...) explored. (shrink)

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...) required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

This paper analyzes the generation and function of hitherto ignored or misrepresented interfield theories , theories which bridge two fields of science. Interfield theories are likely to be generated when two fields share an interest in explaining different aspects of the same phenomenon and when background knowledge already exists relating the two fields. The interfield theory functions to provide a solution to a characteristic type of theoretical problem: how are the relations between fields to be explained? In solving this problem (...) the interfield theory may provide answers to questions which arise in one field but cannot be answered within it alone, may focus attention on domain items not previously considered important, and may predict new domain items for one or both fields. Implications of this analysis for the problems of reduction and the unity and progress of science are mentioned. (shrink)

Four current accounts of theory reduction are presented, first informally and then formally: (1) an account of direct theory reduction that is based on the contributions of Nagel, Woodger, and Quine, (2) an indirect reduction paradigm due to Kemeny and Oppenheim, (3) an "isomorphic model" schema traceable to Suppes, and (4) a theory of reduction that is based on the work of Popper, Feyerabend, and Kuhn. Reference is made, in an attempt to choose between these schemas, to the explanation of (...) physical optics by Maxwell's electromagnetic theory, and to the revisions of genetics necessitated by partial biochemical reductions of genetics. A more general reduction schema is proposed which: (1) yields as special cases the four reduction paradigms considered above, (2) seems to be in better accord with both the canons of logic and actual scientific practice, and (3) clarifies the problems of meaning variance and ontological reduction. (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

The paper works towards an account of explanatory integration in biology, using as a case study explanations of the evolutionary origin of novelties-a problem requiring the integration of several biological fields and approaches. In contrast to the idea that fields studying lower level phenomena are always more fundamental in explanations, I argue that the particular combination of disciplines and theoretical approaches needed to address a complex biological problem and which among them is explanatorily more fundamental varies with the problem pursued. (...) Solving a complex problem need not require theoretical unification or the stable synthesis of different biological fields, as items of knowledge from traditional disciplines can be related solely for the purposes of a specific problem. Apart from the development of genuine interfield theories, successful integration can be effected by smaller epistemic units (concepts, methods, explanations) being linked. Unification or integration is not an aim in itself, but needed for the aim of solving a particular scientific problem, where the problem's nature determines the kind of intellectual integration required. (shrink)

Back cover: This book develops a philosophical account that reveals the major characteristics that make an explanation in the life sciences reductive and distinguish them from non-reductive explanations. Understanding what reductive explanations are enables one to assess the conditions under which reductive explanations are adequate and thus enhances debates about explanatory reductionism. The account of reductive explanation presented in this book has three major characteristics. First, it emerges from a critical reconstruction of the explanatory practice of the life sciences itself. (...) Second, the account is monistic since it specifies one set of criteria that apply to explanations in the life sciences in general. Finally, the account is ontic in that it traces the reductivity of an explanation back to certain relations that exist between objects in the world (such as part-whole relations and level relations), rather than to the logical relations between sentences. Beginning with a disclosure of the meta-philosophical assumptions that underlie the author’s analysis of reductive explanation, the book leads into the debate about reduction(ism) in the philosophy of biology and continues with a discussion on the two perspectives on explanatory reduction that have been proposed in the philosophy of biology so far. The author scrutinizes how the issue of reduction becomes entangled with explanation and analyzes two concepts, the concept of a biological part and the concept of a level of organization. The results of these five chapters constitute the ground on which the author bases her final chapter, developing her ontic account of reductive explanation. (shrink)

Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems in (...) the literature and by those in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of the order produced by complex systems is that of the Statistical Complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work. (shrink)

Reductionism encompasses a set of ontological, epistemological, and methodological claims about the relation of different scientific domains. The basic question of reduction is whether the properties, concepts, explanations, or methods from one scientific domain (typically at higher levels of organization) can be deduced from or explained by the properties, concepts, explanations, or methods from another domain of science (typically one about lower levels of organization). Reduction is germane to a variety of issues in philosophy of science, including the structure of (...) scientific theories, the relations between different scientific disciplines, the nature of explanation, the diversity of methodology, and the very idea of theoretical progress, as well as to numerous topics in metaphysics and philosophy of mind, such as emergence, mereology, and supervenience. (shrink)

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical (...) equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena, where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism’s ability to respond to perturbations. I offer general conclusions for philosophical accounts of explanation. (shrink)

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation (...) of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors. (shrink)

It is a common complaint that antireductionist arguments are primarily negative. Here I describe an alternative nonreductionist epistemology based on considerations taken from multidisciplinary research in biology. The core of this framework consists in seeing investigation as coordinated around sets of problems (problem agendas) that have associated criteria of explanatory adequacy. These ideas are developed in a case study, the explanation of evolutionary innovations and novelties, which demonstrates the applicability and fruitfulness of this nonreductionist epistemological perspective. This account also bears (...) on questions of conceptual change and theory structure in philosophy of science. †To contact the author, please write to: Department of Philosophy, University of Minnesota, 831 Heller Hall, 271 19th Ave. S., Minneapolis, MN 55455; e‐mail: [email protected]. (shrink)

Several articles have recently appeared arguing that there really are no viable alternatives to mechanistic explanation in the biological sciences (Kaplan and Bechtel; Kaplan and Craver). We argue that mechanistic explanation is defined by localization and decomposition. We argue further that systems neuroscience contains explanations that violate both localization and decomposition. We conclude that the mechanistic model of explanation needs to either stretch to now include explanations wherein localization or decomposition fail or acknowledge that there are counterexamples to mechanistic explanation (...) in the biological sciences. (shrink)

A classification of models of reduction into three categories — theory reductionism, explanatory reductionism, and constitutive reductionism — is presented. It is shown that this classification helps clarify the relations between various explications of reduction that have been offered in the past, especially if a distinction is maintained between the various epistemological and ontological issues that arise. A relatively new model of explanatory reduction, one that emphasizes that reduction is the explanation of a whole in terms of its parts is (...) also presented in detail. Finally, the classification is used to clarify the debate over reductionism in molecular biology. It is argued there that while no model from the category of theory reduction might be applicable in that case, models of explanatory reduction might yet capture the structure of the relevant explanations. (shrink)

In this paper, I argue that the newly developed network approach in neuroscience and biology provides a basis for formulating a unique type of realization, which I call topological realization. Some of its features and its relation to one of the dominant paradigms of realization and explanation in sciences, i.e. the mechanistic one, are already being discussed in the literature. But the detailed features of topological realization, its explanatory power and its relation to another prominent view of realization, namely the (...) semantic one, have not yet been discussed. I argue that topological realization is distinct from mechanistic and semantic ones because the realization base in this framework is not based on local realisers, regardless of the scale but on global realizers. In mechanistic approach, the realization base is always at the local level, in both ontic and epistemic accounts. The explanatory power of realization relation in mechanistic approach comes directly from the realization relation-either by showing how a model is mapped onto a mechanism, or by describing some ontic relations that are explanatory in themselves. Similarly, the semantic approach requires that concepts at different scales logically satisfy microphysical descriptions, which are at the local level. In topological framework the realization base can be found at different scales, but whatever the scale the realization base is global, within that scale, and not local. Furthermore, topological realization enables us to answer the “why” questions, which according to Polger 2010 make it explanatory. The explanatoriness of topological realization stems from understanding mathematical consequences of different topologies, not from the mere fact that a system realizes them. (shrink)

Methodological reductionists practice ‘wannabe reductionism’. They claim that one should pursue reductionism, but never propose how. I integrate two strains in prior work to do so. Three kinds of activities are pursued as “reductionist”. “Successional reduction” and inter-level mechanistic explanation are legitimate and powerful strategies. Eliminativism is generally ill-conceived. Specific problem-solving heuristics for constructing inter-level mechanistic explanations show why and when they can provide powerful and fruitful tools and insights, but sometimes lead to erroneous results. I show how traditional metaphysical (...) approaches fail to engage how science is done. The methods used do so, and support a pragmatic and non-eliminativist realism. (shrink)

A belief common among philosophers and biologists alike is that Mendelian genetics has been or is in the process of being reduced to molecular genetics, in the sense of formal theory reduction current in the literature. The purpose of this paper is to show that there are numerous empirical and conceptual difficulties which stand in the way of establishing a systematic inferential relation between Mendelian and molecular genetics. These difficulties, however, have little to do with the traditional objections which have (...) been raised to reduction. (shrink)

The somatic mutation theory (SMT) of cancer has been and remains the prevalent theory attempting to explain how neoplasms arise and progress. This theory proposes that cancer is a clonal, cell‐based disease, and implicitly assumes that quiescence is the default state of cells in multicellular organisms. The SMT has not been rigorously tested, and several lines of evidence raise questions that are not addressed by this theory. Herein, we propose experimental strategies that may validate the SMT. We also call attention (...) to an alternative theory of carcinogenesis, the tissue organization field theory (TOFT), which posits that cancer is a tissue‐based disease and that proliferation is the default state of all cells. Based on epistemological and experimental evidence, we argue that the TOFT compellingly explains carcinogenesis, while placing it within an evolutionarily relevant context.magnified imagemagnified imageAna Soto and Carlos Sonnenschein are at Tufts University School of Medicine, Boston, MA, USA. (shrink)

Philosophers have proposed various kinds of relations between Mendelian genetics and molecular biology: reduction, replacement, explanatory extension. This paper argues that the two fields are best characterized as investigating different, serially integrated, hereditary mechanisms. The mechanisms operate at different times and contain different working entities. The working entities of the mechanisms of Mendelian heredity are chromosomes, whose movements serve to segregate alleles and independently assort genes in different linkage groups. The working entities of numerous mechanisms of molecular biology are larger (...) and smaller segments of DNA plus related molecules. Discovery of molecular DNA mechanisms filled black boxes that were noted, but unilluminated, by Mendelian genetics. (shrink)

Cancer is not one, but many diseases, and each is a product of a variety of causes acting at distinct temporal and spatial scales, or ‘‘levels’’ in the biological hierarchy. In part because of this diversity of cancer types and causes, there has been a diversity of models, hypotheses, and explanations of carcinogenesis. However, there is one model of carcinogenesis that seems to have survived the diversification of cancer types: the multi-stage model of carcinogenesis. This paper examines the history of (...) the multistage theory, and uses the theory as a case study in the limits and goals of unification as a theoretical virtue, comparing and contrasting it with ‘‘integrative’’ research. (shrink)

In the contemporary life sciences more and more researchers emphasize the “limits of reductionism” (e.g. Ahn et al. 2006a, 709; Mazzocchi 2008, 10) or they call for a move “beyond reductionism” (Gallagher/Appenzeller 1999, 79). However, it is far from clear what exactly they argue for and what the envisioned limits of reductionism are. In this paper I claim that the current discussions about reductionism in the life sciences, which focus on methodological and explanatory issues, leave the concepts of a reductive (...) method and a reductive explanation too unspecified. In order to fill this gap and to clarify what the limits of reductionism are I identify three reductive methods that are crucial in the current practice of the life sciences: decomposition, focusing on internal factors, and studying parts in isolation. Furthermore, I argue that reductive explanations in the life sciences exhibit three characteristics: first, they refer only to factors at a lower level than the phenomenon at issue, second, they focus on internal factors and thus ignore or simplify the environment of a system, and, third, they cite only the parts of a system in isolation. (shrink)

Due to the variation, contingency and complexity of living systems, biology is often taken to be a science without fundamental theories, laws or general principles. I revisit this question in light of the quest for design principles in systems biology and show that different views can be reconciled if we distinguish between different types of generality. The philosophical literature has primarily focused on generality of specific models or explanations, or on the heuristic role of abstraction. This paper takes a different (...) approach in emphasizing a theory-constituting role of general principles. Design principles signify general dependency-relations between structures and functions, given a set of formally defined constraints. I contend that design principles increase our understanding of living systems by relating specific models to general types. The categorization of types is based on a delineation of the scope of biological possibilities, which serves to identify and define the generic features of classes of systems. To characterize the basis for general principles through generic abstraction and reasoning about possibility spaces, I coin the term constraint-based generality. I show that constraint-based generality is distinct from other types of generality in biology, and argue that general principles play a unifying role that does not entail theory reduction. (shrink)

Multilevel research strategies characterize contemporary molecular inquiry into biological systems. We outline conceptual, methodological, and explanatory dimensions of these multilevel strategies in microbial ecology, systems biology, protein research, and developmental biology. This review of emerging lines of inquiry in these fields suggests that multilevel research in molecular life sciences has significant implications for philosophical understandings of explanation, modeling, and representation.

Watson and Crick’s discovery of the structure of DNA led to developments that transformed many biological sciences. But what were the relevant developments and how did they transform biology? Much of the philosophical discussion concerning this question can be organized around two opposing views: theoretical reductionism and layer-cake antireductionism. Theoretical reductionist and their anti-reductionist foes hold two assumptions in common. First, both hold that biological knowledge is structured like a layer cake, with some biological sciences, such as molecular biology cast (...) at lower levels of organization, and others, such as classical genetics, cast at higher levels. Second, both assume that scientific knowledge is structured by theory and that the productivity of scientific research depends on whether the underlying theory identifies the fundamentals upon which the phenomena to be explained and investigated depend. In the first part of this paper, I challenge these assumptions. In the second part, I show how recasting the basic theory of classical genetics made it possible to retool the methodologies of genetics. It was the investigative power of these retooled methodologies, and not the explanatory power of a gene-based theory, that transformed biology. (shrink)

The building of a global model of carcinogenesis is one of modern biology’s greatest challenges. The traditional somatic mutation theory is now supplemented by a new approach, called the Tissue Organization Field Theory. According to TOFT, the original source of cancer is loss of tissue organization rather than genetic mutations. In this paper, we study the argumentative strategy used by the advocates of TOFT to impose their view. In particular, we criticize their claim of incompatibility used to justify the necessity (...) to definitively reject SMT. First, we note that since it is difficult to build a non-ambiguous experimental demonstration of the superiority of TOFT, its partisans add epistemological and metaphysical arguments to the debate. This argumentative strategy allows them to defend the necessity of a paradigm shift, with TOFT superseding SMT. To do so, they introduce a notion of incompatibility, which they actually use as the Kuhnian notion of incommensurability. To justify this so-called incompatibility between the two theories of cancer, they move the debate to a metaphysical ground by assimilating the controversy to a fundamental opposition between reductionism and organicism. We show here that this argumentative strategy is specious, because it does not demonstrate clearly that TOFT is an organicist theory. Since it shares with SMT its vocabulary, its ontology and its methodology, it appears that a claim of incompatibility based on this metaphysical plan is not fully justified in the present state of the debate. We conclude that it is more cogent to argue that the two theories are compatible, both biologically and metaphysically. We propose to consider that TOFT and SMT describe two distinct and compatible causal pathways to carcinogenesis. This view is coherent with the existence of integrative approaches, and suggests that they have a higher epistemic value than the two theories taken separately. (shrink)

The building of a global model of carcinogenesis is one of modern biology’s greatest challenges. The traditional somatic mutation theory is now supplemented by a new approach, called the Tissue Organization Field Theory. According to TOFT, the original source of cancer is loss of tissue organization rather than genetic mutations. In this paper, we study the argumentative strategy used by the advocates of TOFT to impose their view. In particular, we criticize their claim of incompatibility used to justify the necessity (...) to definitively reject SMT. First, we note that since it is difficult to build a non-ambiguous experimental demonstration of the superiority of TOFT, its partisans add epistemological and metaphysical arguments to the debate. This argumentative strategy allows them to defend the necessity of a paradigm shift, with TOFT superseding SMT. To do so, they introduce a notion of incompatibility, which they actually use as the Kuhnian notion of incommensurability. To justify this so-called incompatibility between the two theories of cancer, they move the debate to a metaphysical ground by assimilating the controversy to a fundamental opposition between reductionism and organicism. We show here that this argumentative strategy is specious, because it does not demonstrate clearly that TOFT is an organicist theory. Since it shares with SMT its vocabulary, its ontology and its methodology, it appears that a claim of incompatibility based on this metaphysical plan is not fully justified in the present state of the debate. We conclude that it is more cogent to argue that the two theories are compatible, both biologically and metaphysically. We propose to consider that TOFT and SMT describe two distinct and compatible causal pathways to carcinogenesis. This view is coherent with the existence of integrative approaches, and suggests that they have a higher epistemic value than the two theories taken separately. (shrink)

Several philosophers have expressed concerns with some recent uses of the term ‘cognition’. Underlying a number of these concerns are claims that cognition is only located in the brain and that no compelling case has been made to use ‘cognition’ in any way other than as a cause of behavior that is representational in nature. These concerns center on two primary misapprehensions: First, that some adherents of dynamical cognitive science think DCS implies the thesis of extended cognition and the rejection (...) of representation, and second, that cognition is mistakenly equated with behavior. We make three points in response to these claims: First, there is no thoroughly entrenched conception of cognition as distinct from behavior that is being illegitimately disregarded. Second, we present Shapiro’s exposition of dynamical systems theory as revealing a misunderstanding of the way that dynamical models are used in explanations of cognition and related phenomena. Accordingly, a proper conception of DCS’s methods facilitates an appreciation of extended cognition as a legitimate phenomenon of scientific investigation. Finally, we demonstrate that practicing cognitive scientists and psychologists are far more pluralistic in the phenomena they apply ‘cognition’ to than is suggested by some. At the heart of our disagreement with these concerned folks is that although we think it likely that some cognitive phenomena are representational, non-extended, and only in-between the ears, we also think there are good conceptual and empirical reasons to believe that many cognitive phenomena are non-representational, extended, and not confined to the brain. (shrink)

This chapter discusses several kinds of reduction that are often found in the biomedical sciences, in contrast to reduction in fields such as physics. This includes reduction as a methodological assumption for how to investigate phenomena like complex diseases, and reduction as a conceptual tool for relating distinct models of the same phenomenon. The case of Parkinson’s disease illustrates a wide variety of ways in which reductionism is an important tool in medicine.

Constitutivemechanisticexplanationsexplainapropertyofawholewith the properties of its parts and their organization. Carl Craver’s mutual manipulability criterion for constitutive relevance only captures the explanatory relevance of causal properties of parts and leaves the organization side of mechanistic explanation unaccounted for. We use the contrastive counterfactual theory of explanation and an account of the dimensions of organization to build a typology of organizational dependence. We analyse organizational explanations in terms of such dependencies and emphasize the importance of modular organizational motifs. We apply this framework (...) to two cases from social science and systems biology, both fields in which organization plays a crucial explanatory role: agent-based simulations of residential segregation and the recent work on network motifs in transcription regulation networks. (shrink)

Cancer research has been at the forefront of biomedical activity in recent decades, and advances in molecular biology have provided a growing amount of information on the mechanisms involved in the etiopathogenesis of tumors. Nevertheless, despite these advances, the complexity of cancer is more evident, especially as different levels of phenomena are considered to explain the heterogeneity of the neoplastic process. A synthetic analysis of advances in cancer research illustrates these changes. In attempting to overcome the limits of epistemological reductionism, (...) there is a move from a view of a genetic basis for cancer to a more comprehensive perspective aiming to integrate a large amount of information and to understand the neoplastic phenomenon at higher levels of biological complexity through a new interpretative framework. (shrink)

Mechanistic frameworks of investigation and explanation dominate the cognitive, neural, and psychological sciences. In this dissertation, I argue that mechanistic frameworks cannot, in principle, explain some kinds of cognition. In its place, I argue that complexity science has methods and theories more appropriate for investigating and explaining some cognitive phenomena. -/- I begin with an examination of the term 'cognition.' I defend the idea that "cognition" has been a moving target of investigation in the relevant sciences. As such it is (...) not historically true that there has been a thoroughly entrenched and agreed upon conception of "cognition." Next, I take up mechanistic frameworks. Although 'mechanism' is an umbrella term for a set of loosely related characteristics, there are common features: linearity, localization, and component dominance. I then describe complexity science, with emphasis on its utilization of dynamical systems modeling. Next, I discuss two phenomena that typically fall under the purview of complexity science: nonlinearity and interaction dominance. -/- A complexity science framework guided by the theory of self-organized criticality and utilizing the methods of dynamical systems modeling can surmount a number of challenges that face mechanistic frameworks when investigating some kinds of cognition. The first challenge is epistemic and concerns the inadequacy of mechanistic frameworks to facilitate the comprehensibility of massive amounts of data across various scales and areas of inquiry. I argue that complexity science is more appropriate for making big data comprehensible when investigating cognition, particularly across disciplines. I demonstrate this via an approach called nested dynamical modeling (NDM). NDM can facilitate comprehensibility of large amounts of data obtained from various scales of investigation by eliminating irrelevant degrees of freedom of that system as relates to the target of investigation. -/- The second shortcoming concerns ontological blind spots within mechanistic frameworks. Cognitive phenomena like extended cognition often fail to meet most, if not all, of the criteria assumed by many mechanistic approaches, especially component dominance. I argue that research guided by the notion of interaction dominance allows for extended cognition to be a real, empirically supported phenomenon within complex systems frameworks. In this chapter I discuss some of my experimental work on extended cognitive systems. -/- The search for mechanisms can be a reasonable starting position when attempting to explain natural phenomena in the life sciences. However, too strict of an adherence to theoretical and methodological commitments such as linearity, localization, and component dominance can result in intractable epistemic challenges and ontological blind spots. Complexity science has theories and methods to overcome such challenges in the investigation of cognition. (shrink)