Organisms' environments are thought to play a fundamental role in determining their fitness and hence in natural selection. Existing intuitive conceptions of environment are sufficient for biological practice. I argue, however, that attempts to produce a general characterization of fitness and natural selection are incomplete without the help of general conceptions of what conditions are included in the environment. Thus there is a "problem of the reference environment"—more particularly, problems of specifying principles which pick out those environmental conditions which determine (...) fitness. I distinguish various reference environment problems and propose solutions to some of them. While there has been a limited amount of work on problems concerning what I call "subenvironments", there appears to be no earlier work on problems of what I call the "whole environment". The first solution I propose for a whole environment problem specifies the overall environment for natural selection on a set of biological types present in a population over a specified period of time. The second specifies an environment relevant to extinction of types in a population; this kind of environment is especially relevant to certain kinds of long-term evolution. (shrink)

Discussions over whether these natural kinds exist, what is the nature of their existence, and whether natural kinds are themselves natural kinds aim to not only characterize the kinds of things that exist in the world, but also what can knowledge of these categories provide. Although philosophically critical, much of the past discussions of natural kinds have often answered these questions in a way that is unresponsive to, or has actively avoided, discussions of the empirical use of natural kinds and (...) what I dub “activities of natural kinding” and “natural kinding practices”. The natural kinds of a particular discipline are those entities, events, mechanisms, processes, relationships, and concepts that delimit investigation within it—but we might reasonably ask: How are these natural kinds discovered?, How are they made?, Are they revisable?, and Where do they come from? A turn to natural kinding practices reveals a new set of questions open for investigation: How do natural kinds explain through practice?, What are natural kinding practices and classifications and why should we care?, What is the nature of natural kinds viewed as a set of activities?, and How do practice approaches to natural kinds shape and reconfigure scientific disciplines? Natural kinds have traditionally been discussed in terms of how they classify the contents of the world. The metaphysical project has been one which identifies essences, laws, sameness relations, fundamental properties, and clusters of family resemblances and how these map out the ontological space of the world. But actually how this is done has been less important in the discussion than the resultant categories that are produced. I aim to rectify these omissions and suggest a new metaphysical project investigating kinds in practice. (shrink)

According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...) explanation. In C. G. Hempel (Ed.), Aspects of scientific explanation (pp. 331–496). New York: Free Press; Kitcher (1989); Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25). My concern is with the minimal suggestion that an adequate philosophical theory of scientific explanation can limit its attention to the format or structure with which theories are represented. The representational subsumption view is a plausible hypothesis about the psychology of understanding. It is also a plausible claim about how scientists present their knowledge to the world. However, one cannot address the central questions for a philosophical theory of scientific explanation without turning one’s attention from the structure of representations to the basic commitments about the worldly structures that plausibly count as explanatory. A philosophical theory of scientific explanation should achieve two goals. The first is explanatory demarcation. It should show how explanation relates with other scientific achievements, such as control, description, measurement, prediction, and taxonomy. The second is explanatory normativity. It should say when putative explanations succeed and fail. One cannot achieve these goals without undertaking commitments about the kinds of ontic structures that plausibly count as explanatory. Representations convey explanatory information about a phenomenon when and only when they describe the ontic explanations for those phenomena. (shrink)

This paper offers a critique of an account of explanatory integration that claims that explanations of cognitive capacities by functional analyses and mechanistic explanations can be seamlessly integrated. It is shown that achieving such explanatory integration requires that the terms designating cognitive capacities in the two forms of explanation are stable but that experimental practice in the mind-brain sciences currently is not directed at achieving such stability. A positive proposal for changing experimental practice so as to promote such stability is (...) put forward and its implications for explanatory integration are briefly considered. (shrink)

This paper addresses philosophical issues concerning whether mental disorders are natural kinds and how the DSM should classify mental disorders. I argue that some mental disorders (e.g., schizophrenia, depression) are natural kinds in the sense that they are natural classes constituted by a set of stable biological mechanisms. I subsequently argue that a theoretical and causal approach to classification would provide a superior method for classifying natural kinds than the purely descriptive approach adopted by the DSM since DSM-III. My argument (...) suggests that the DSM should classify natural kinds in order to provide predictively useful (i.e., projectable) diagnostic categories and that a causal approach to classification would provide a more promising method for formulating valid diagnostic categories. (shrink)

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...) related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding. (shrink)

My dissertation explores the ways in which Rudolf Carnap sought to make philosophy scientific by further developing recent interpretive efforts to explain Carnap’s mature philosophical work as a form of engineering. It does this by looking in detail at his philosophical practice in his most sustained mature project, his work on pure and applied inductive logic. I, first, specify the sort of engineering Carnap is engaged in as involving an engineering design problem and then draw out the complications of design (...) problems from current work in history of engineering and technology studies. I then model Carnap’s practice based on those lessons and uncover ways in which Carnap’s technical work in inductive logic takes some of these lessons on board. This shows ways in which Carnap’s philosophical project subtly changes right through his late work on induction, providing an important corrective to interpretations that ignore the work on inductive logic. Specifically, I show that paying attention to the historical details of Carnap’s attempt to apply his work in inductive logic to decision theory and theoretical statistics in the 1950s and 1960s helps us understand how Carnap develops and rearticulates the philosophical point of the practical/theoretical distinction in his late work, offering thus a new interpretation of Carnap’s technical work within the broader context of philosophy of science and analytical philosophy in general. (shrink)

There is a long-standing debate in the philosophy of mind and philosophy of science regarding how best to interpret the relationship between neuroscience and psychology. It has traditionally been argued that either the two domains will evolve and change over time until they converge on a single unified account of human behaviour, or else that they will continue to work in isolation given that they identify properties and states that exist autonomously from one another (due to the multiple-realizability of psychological (...) states). In this paper, I argue that progress in psychology and neuroscience is contingent on the fact that both of these positions are false. Contra the convergence position, I argue that the theories of psychology and the theories of neuroscience are scientifically valuable as representational tools precisely because they cannot be integrated into a single account. However, contra the autonomy position, I propose that the theories of psychology and neuroscience are deeply dependent on one another for further refinement and improvement. In this respect, there is an irreconcilable codependence between psychology and neuroscience that is necessary for both domains to improve and progress. The two domains are forever linked while simultaneously being unable to integrate. (shrink)

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate (...) models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists’ discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. (shrink)

Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...) of intractability is solved by recourse to ‘as if’ explanations critically depends, inter alia, on the semantics of the ‘as if’ connective. We examine the five most sensible explications in the literature, and conclude that none of them circumvents the problem. As a result, rational ‘as if’ explanations must obey the minimal computational constraint of tractability. (shrink)

“Proof of concept” is a phrase frequently used in descriptions of research sought in program announcements, in experimental studies, and in the marketing of new technologies. It is often coupled with either a short definition or none at all, its meaning assumed to be fully understood. This is problematic. As a phrase with potential implications for research and technology, its assumed meaning requires some analysis to avoid it becoming a descriptive category that refers to all things scientifically exciting. I provide (...) a short analysis of proof of concept research and offer an example of it within synthetic biology. I suggest that not only are there activities that circumscribe new epistemological categories but there are also associated normative ethical categories or principles linked to the research. I examine these and provide an outline for an alternative ethical account to describe these activities that I refer to as “extended agency ethics”. This view is used to explain how the type of research described as proof of concept also provides an attendant proof of principle that is the result of decision-making that extends across practitioners, their tools, techniques, and the problem solving activities of other research groups. (shrink)

Glennan appeals to interventions to solve the ontological and explanatory regresses that threaten his mechanistic account of causality . I argue that Glennan’s manoeuvre fails. The appeal to interventions is not able to address the ontological regress, and it blocks the explanatory regress only at the cost of making the account inapplicable to non-modular mechanisms. I offer a solution to the explanatory regress that makes use of dynamic Bayesian networks. My argument is illustrated by a case study from systems biology, (...) namely, the mechanism for the irreversibility of apoptosis. I conclude by pointing out the implications of my argument for Glennan’s mechanistic account of causality and, more generally, for accounts of mechanistic explanation based on interventions. 1 Introduction2 Glennan’s Account of Causality3 Objections to Glennan’s Account4 Glennan’s Replies5 Ontological Symmetry?6 Explanatory Symmetry?7 A Solution to the Explanatory Regress8 The Prospects of Glennan’s Account. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I (...) argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

In a recent opinion piece, Denis Duboule has claimed that the increasing shift towards systems biology is driving evolutionary and developmental biology apart, and that a true reunification of these two disciplines within the framework of evolutionary developmental biology may easily take another 100 years. He identifies methodological, epistemological, and social differences as causes for this supposed separation. Our article provides a contrasting view. We argue that Duboule’s prediction is based on a one-sided understanding of systems biology as a science (...) that is only interested in functional, not evolutionary, aspects of biological processes. Instead, we propose a research program for an evolutionary systems biology, which is based on local exploration of the configuration space in evolving developmental systems. We call this approach—which is based on reverse engineering, simulation, and mathematical analysis—the natural history of configuration space. We discuss a number of illustrative examples that demonstrate the past success of local exploration, as opposed to global mapping, in different biological contexts. We argue that this pragmatic mode of inquiry can be extended and applied to the mathematical analysis of the developmental repertoire and evolutionary potential of evolving developmental mechanisms and that evolutionary systems biology so conceived provides a pragmatic epistemological framework for the EvoDevo synthesis. (shrink)

In this article I address the issue of the ontological conditions of possibility for a naturalistic notion of emergence, trying to determine its fundamental differences from the atomist, vitalist, preformationist and potentialist alternatives. I will argue that a naturalistic notion of ontological emergence can only succeed if we explicitly refuse the atomistic fundamental ontological postulate that asserts that every entity is endowed with a set of absolutely intrinsic properties, being qualitatively immutable through its extrinsic relations. Furthermore, it will be shown (...) that, ironically enough, this metaphysical assumption is implicitly shared by all the above mentioned alternatives to Emergentism. The current article concludes that the notion of organization by itself is not enough, and that ontological emergence can only be justified by assuming a relational ontological perspective that, in opposition both to atomism and holism, defends that the existence-conditions, the identity and the causal behavior of any emergent systemic property can only be conceived, and explained, as constructed by and through specific networks of qualitatively transformative relational processes that occur between the system’s components and between the system and its environment. Additionally, I try to explain how one can make sense of the idea that an emergent phenomenon is both dependent on, and autonomous from, its emergence base. (shrink)

Scientists use models to understand the natural world, and it is important not to conflate model and nature. As an illustration, we distinguish three different kinds of populations in studies of ecology and evolution: theoretical, laboratory, and natural populations, exemplified by the work of R.A. Fisher, Thomas Park, and David Lack, respectively. Biologists are rightly concerned with all three types of populations. We examine the interplay between these different kinds of populations, and their pertinent models, in three examples: the notion (...) of “effective” population size, the work of Thomas Park on /Tribolium/ populations, and model-based clustering algorithms such as /Structure/. Finally, we discuss ways to move safely between three distinct population types while avoiding confusing models and reality. (shrink)

Upshot: In our response we focus on five questions that point to important common themes in the commentaries: why start in wicked problems, what kind of system is a scientific perspective, what is the nature of second-order research processes, what does this mean for understanding interdisciplinary work, and how may polyocular research help make real-world decisions.

Context: The problems that are most in need of interdisciplinary collaboration are “wicked problems,” such as food crises, climate change mitigation, and sustainable development, with many relevant aspects, disagreement on what the problem is, and contradicting solutions. Such complex problems both require and challenge interdisciplinarity. Problem: The conventional methods of interdisciplinary research fall short in the case of wicked problems because they remain first-order science. Our aim is to present workable methods and research designs for doing second-order science in domains (...) where there are many different scientific knowledges on any complex problem. Method: We synthesize and elaborate a framework for second-order science in interdisciplinary research based on a number of earlier publications, experiences from large interdisciplinary research projects, and a perspectivist theory of science. Results: The second-order polyocular framework for interdisciplinary research is characterized by five principles. Second-order science of interdisciplinary research must: 1. draw on the observations of first-order perspectives, 2. address a shared dynamical object, 3. establish a shared problem, 4. rely on first-order perspectives to see themselves as perspectives, and 5. be based on other rules than first-order research. Implications: The perspectivist insights of second-order science provide a new way of understanding interdisciplinary research that leads to new polyocular methods and research designs. It also points to more reflexive ways of dealing with scientific expertise in democratic processes. The main challenge is that this is a paradigmatic shift, which demands that the involved disciplines, at least to some degree, subscribe to a perspectivist view. Constructivist content: Our perspectivist approach to science is based on the second-order cybernetics and systems theories of von Foerster, Maruyama, Maturana & Varela, and Luhmann, coupled with embodied theories of cognition and semiotics as a general theory of meaning from von Uexküll and Peirce. (shrink)

We argue that the mathematization of science should be understood as a normative activity of advocating for a particular methodology with its own criteria for evaluating good research. As a case study, we examine the mathematization of taxonomic classification in systematic biology. We show how mathematization is a normative activity by contrasting its distinctive features in numerical taxonomy in the 1960s with an earlier reform advocated by Ernst Mayr starting in the 1940s. Both Mayr and the numerical taxonomists sought to (...) formalize the work of classification, but Mayr introduced a qualitative formalism based on human judgment for determining the taxonomic rank of populations, while the numerical taxonomists introduced a quantitative formalism based on automated procedures for computing classifications. The key contrast between Mayr and the numerical taxonomists is how they conceptualized the temporal structure of the workflow of classification, specifically where they allowed meta-level discourse about difficulties in producing the classification. (shrink)

Many philosophers are skeptical about the scientific value of the concept of biological information. However, several have recently proposed a more positive view of ascribing information as an exercise in scientific modeling. I argue for an alternative role: guiding empirical data collection for the sake of theorizing about the evolution of semantics. I clarify and expand on Bergstrom and Rosvall’s suggestion of taking a “diagnostic” approach that defines biological information operationally as a procedure for collecting empirical cases. The more recent (...) modeling-based accounts still perpetuate a theory-centric view of scientific concepts, which motivated philosophers’ misplaced skepticism in the first place. (shrink)

A survey of contemporary philosophical and scientific literatures reveals that different authors employ the term ‘heuristic’ in ways that deviate from, and are sometimes inconsistent with, one another. Given its widespread use in philosophy and cognitive science generally, it is striking that there appears to be little concern for a clear account of what phenomena heuristics pick out or refer to. In response, I consider several accounts of ‘heuristic’, and I draw a number of distinctions between different sorts of heuristics (...) in order to make sense of various research programmes. I then develop a working characterization of ‘heuristic’ which is shown to be coherent and robust enough to serve as a kind within philosophy and cognitive science broadly. My intent is not to pursue a unified account of ‘heuristic’, but to highlight some features of certain kinds of heuristics that are important for theorizing about cognition in order to proceed with a science of the mind/brain. 1 Why We Need an Appropriate Characterization of ‘Heuristic’2 Inadequate Definitions2.1 A positive attempt2.2 Heuristics versus guaranteed correct outcomes3 A Taxonomy of Heuristics3.1 Stimulus-driven versus heuristic3.2 Perceptual versus cognitive heuristics3.3 Computational versus cognitive heuristics3.4 Methodological versus inferential heuristics3.5 Summary of distinctions4 Characterizing Cognitive Heuristics4.1 Heuristics as rules of thumb4.2 Beyond rules of thumb: exploiting information5 Concluding Remarks. (shrink)

Despite the burgeoning interest in new and more complex accounts of the organism-environment dyad by biologists and philosophers, little attention has been paid in the resulting discussions to the history of these ideas and to their deployment in disciplines outside biology—especially in the social sciences. Even in biology and philosophy, there is a lack of detailed conceptual models of the organism-environment relationship. This volume is designed to fill these lacunae by providing the first multidisciplinary discussion of the topic of organism-environment (...) interaction. It brings together scholars from history, philosophy, psychology, anthropology, medicine, and biology to discuss the common focus of their work: entangled life, or the complex interaction of organisms and environments. (shrink)

This article argues that Durkheim’s founding insight – uniquely social phenomena – presents us with both a foundation for the discipline of sociology and the risk that the discipline will become isolated. This, we argue, has happened. Our contention is that the emergent social phenomena need to be understood in relation to, but not reduced to, their biological and psychological substrates. Similarly, there are a number of other characteristics, notably of self-organization, which are distinguishing properties of social phenomena but also (...) of quite different phenomena. The comparison is instructive. We therefore argue for an ecological approach to sociological theory, which has important relationships to the general theories and philosophy of ecology and biology. We explore a number of terminological and conceptual parallels that may inform our understanding of the relation of social theory to these and other disciplines. (shrink)

In this article we use a hybrid methodology to better understand the skilful performance of sports teams as an exemplar of distributed cognition. We highlight key differences between a team of individual experts and an expert team, and outline the kinds of shared characteristics likely to be found in an expert team. We focus on the way that shared knowledge contributes to expert team performance. In particular, we suggest that certain kinds of shared knowledge and shared skill, potentially developed through (...) a team’s history of playing and training together, facilitate successful coordination. These kinds of shared knowledge and skill may be less developed in a team of experts without a shared history. Exploring the expert performance of sports teams informs our understanding of distributed cognition and collaboration more generally and creates avenues for further philosophical and empirical investigation. (shrink)

Exact sciences are described as sciences whose theories are formalized. These are contrasted to inexact sciences, whose theories are not formalized. Formalization is described as a broader category than mathematization, involving any form/content distinction allowing forms, e.g., as represented in theoretical models, to be studied independently of the empirical content of a subject-matter domain. Exactness is a practice depending on the use of theories to control subject-matter domains and to align theoretical with empirical models and not merely a state of (...) a science. Inexact biological sciences tolerate a degree of “mismatch” between theoretical and empirical models and concepts. Three illustrations from biological sciences are discussed in which formalization is achieved by various means: Mendelism, Weismannism, and Darwinism. Frege’s idea of a “conceptual notation” is used to further characterize the notion of a form/content distinction. (shrink)

The many-faced relationship between chemistry and physics is one of the most discussed topics in the philosophy of chemistry. In his recent book Reducing Chemistry to Physics. Limits, Models, Consequences, Hinne Hettema conceives this relationship as a reduction link, and devotes his work to defend this position on the basis of a “naturalized” concept of reduction. In the present paper I critically review three kinds of issues stemming from Hettema’s argumentation: philosophical, scientific and methodological.

In this chapter I investigate the kinds of changes that psychiatric kinds undergo when they become explanatory targets of areas of sciences that are not “mature” and are in the early stages of discovering mechanisms. The two areas of science that are the targets of my analysis are cognitive neuroscience and cognitive neurobiology.

In 2007, ten world-renowned neuroscientists proposed “A Decade of the Mind Initiative.” The contention was that, despite the successes of the Decade of the Brain, “a fundamental understanding of how the brain gives rise to the mind [was] still lacking” (2007, 1321). The primary aims of the decade of the mind were “to build on the progress of the recent Decade of the Brain (1990-99)” by focusing on “four broad but intertwined areas” of research, including: healing and protecting, understanding, enriching, (...) and modeling the mind. These four aims were to be the result of “transdisciplinary and multiagency” research spanning “across disparate fields, such as cognitive science, medicine, neuroscience, psychology, mathematics, engineering, and computer science.” The proposal for a decade of the mind prompted many questions (See Spitzer 2008). In this chapter, I address three of them: (1) How do proponents of this new decade conceive of the mind? (2) Why should a decade be devoted to understanding it? (3) What should this decade look like? (shrink)

What does it look like when a group of scientists set out to re-envision an entire field of biology in symbolic and formal terms? I analyze the founding and articulation of Numerical Taxonomy between 1950 and 1970, the period when it set out a radical new approach to classification and founded a tradition of mathematics in systematic biology. I argue that introducing mathematics in a comprehensive way also requires re-organizing the daily work of scientists in the field. Numerical taxonomists sought (...) to establish a mathematical method for classification that was universal to every type of organism, and I argue this intrinsically implicated them in a qualitative re-organization of the work of all systematists. I also discuss how Numerical Taxonomy’s re-organization of practice became entrenched across systematic biology even as opposing schools produced their own competing mathematical methods. In this way, the structure of the work process became more fundamental than the methodological theories that motivated it. (shrink)

Traditional accounts of the role of learning in evolution have concentrated upon its capacity as a source of fitness to individuals. In this paper I use a case study from invasive species biology—the role of conditioned taste aversion in mitigating the impact of cane toads on the native species of Northern Australia—to highlight a role for learning beyond this—as a source of evolvability to populations. This has two benefits. First, it highlights an otherwise under-appreciated role for learning in evolution that (...) does not rely on social learning as an inheritance channel nor “special” evolutionary processes such as genetic accommodation (both of which many are skeptical about). Second, and more significantly, it makes clear important and interesting parallels between learning and exploratory behaviour in development. These parallels motivate the applicability of results from existing research into learning and learning evolution to our understanding of the evolution of evolvability more generally. (shrink)

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and (...) asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence—i.e. mechanisms—although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation. (shrink)

The introduction to the volume and the overview of the idea of naturalizing the mind.

Two controversies exist regarding the appropriate characterization of hierarchical and adaptive evolution in natural populations. In biology, there is the Wright-Fisher controversy over the relative roles of random genetic drift, natural selection, population structure, and interdemic selection in adaptive evolution begun by Sewall Wright and Ronald Aylmer Fisher. There is also the Units of Selection debate, spanning both the biological and the philosophical literature and including the impassioned group-selection debate. Why do these two discourses exist separately, and interact relatively little? (...) We postulate that the reason for this schism can be found in the differing focus of each controversy, a deep difference itself determined by distinct general styles of scientific research guiding each discourse. That is, the Wright-Fisher debate focuses on adaptive process, and tends to be instructed by the mathematical modeling style, while the focus of the Units of Selection controversy is adaptive product, and is typically guided by the function style. The differences between the two discourses can be usefully tracked by examining their interpretations of two contested strategies for theorizing hierarchical selection: horizontal and vertical averaging. (shrink)

One way that scientifically recognized properties are multiply realized is by “compensatory differences” among realizing properties. If a property G is jointly realized by two properties F1 and F2, then G can be multiply realized by having changes in the property F1 offset changes in the property F2. In some cases, there are scientific laws that articulate how distinct combinations of physical quantities can determine one and the same value of some other physical quantity. One moral to draw is that (...) in such cases we have the multiple realization of a single determinate, “fine grained” property instance that is exactly similar to another instance. As simple as this moral is, it has ramifications for a number of recent discussions of multiple realization in science. Taken collectively, these ramifications indicate that multiple realization by compensatory adjustments merits greater attention in the philosophy of science literature than it has hitherto received. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...) shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

Wegner, Giuliano, and Hertel (1985) defined the notion of a transactive memory system (TMS) as a group level memory system that “involves the operation of the memory systems of the individuals and the processes of communication that occur within the group (p. 191). Those processes are the collaborative procedures (“transactions”) by which groups encode, store, and retrieve information that is distributed among their members. Over the past 25+ years, the conception of a TMS has progressively garnered an increased interest among (...) social and organizational psychologists, communication scholars, and management theorists (Ren & Argote 2011). But there remains considerable disagreement about how exactly Wegner’s appeal to group memory should be understood. My goal in this paper is contribute to this debate, by articulating more clearly the value of conceptualizing groups as TMSs. This value, I argue, consists in providing us with a blueprint for how to explain group memory in terms of collective information-processing mechanisms. Collective information-processing mechanisms are dependent on, and interact with, the brain-bound information-processing of individuals, but cannot be reduced to the latter. In my analysis, I lean on extant accounts of mechanistic explanation in the philosophy of science (Bechtel & Richardson 1993; Machamer, Darden, & Craver 2000; Wimsatt 2007) that have been used to analyze the explanatory practices of psychology and cognitive neuroscience (Bechtel 2008, 2009). Based on my reconstruction of Wegner’s conceptualization of a TMS, I argue that the reality of emergent group cognition is compatible with its mechanistic explanation. More generally, my analysis shows that group cognition cannot be reduced to individual cognition, while avoiding the false dilemma between “wholism” and “nothing but-ism” which has hampered traditional construals of the “group mind” thesis (Allport 1968). (shrink)

Many scientific models are non-representational in that they refer to merely possible processes, background conditions and results. The paper shows how such non-representational models can be appraised, beyond the weak role that they might play as heuristic tools. Using conceptual distinctions from the discussion of how-possibly explanations, six types of models are distinguished by their modal qualities of their background conditions, model processes and model results. For each of these types, an actual model example – drawn from economics, biology, psychology (...) or sociology – is discussed. For each case, contexts and purposes are identified in which the use of such a model offers a genuine opportunity to learn – i.e. justifies changing one’s confidence in a hypothesis about the world. These cases then offer novel justifications for modelling practices that fall between the cracks of standard representational accounts of models. (shrink)

Standard microbial evolutionary ontology is organized according to a nested hierarchy of entities at various levels of biological organization. It typically detects and defines these entities in relation to the most stable aspects of evolutionary processes, by identifying lineages evolving by a process of vertical inheritance from an ancestral entity. However, recent advances in microbiology indicate that such an ontology has important limitations. The various dynamics detected within microbiological systems reveal that a focus on the most stable entities (or features (...) of entities) over time inevitably underestimates the extent and nature of microbial diversity. These dynamics are not the outcome of the process of vertical descent alone. Other processes, often involving causal interactions between entities from distinct levels of biological organisation, or operating at different time scales, are responsible not only for the destabilisation of pre-existing entities, but also for the emergence and stabilisation of novel entities in the microbial world. In this article we consider microbial entities as more or less stabilised functional wholes, and sketch a network-based ontology that can represent a diverse set of processes including, for example, as well as phylogenetic relations, interactions that stabilise or destabilise the interacting entities, spatial relations, ecological connections, and genetic exchanges. We use this pluralistic framework for evaluating (i) the existing ontological assumptions in evolution (e.g. whether currently recognized entities are adequate for understanding the causes of change and stabilisation in the microbial world), and (ii) for identifying hidden ontological kinds, essentially invisible from within a more limited perspective. We propose to recognize additional classes of entities that provide new insights into the structure of the microbial world, namely “processually equivalent” entities, “processually versatile” entities, and “stabilized” entities. (shrink)

This paper addresses the relationship between psychological capacities, as they are understood within cognitive psychology, and neurobiological activities. First, Lycan’s (1987) account of this relationship is examined and certain problems with his account are explained. According to Lycan, psychological capacities occupy a higher level than neurobiological activities in a hierarchy of levels of nature, and psychological entities can be decomposed into neurobiological entities. After discussing some problems with Lycan’s account, a similar, more recent account built around levels of mechanisms is (...) examined (Craver 2007). In the second half of this paper, an alternative is laid out. This new account uses levels of organization and levels of explanation to create a two-dimensional model. Psychological capacities occupy a high level of explanation relative to the cellular and molecular levels of organization. As a result, according to this model, psychological capacities are a particular way of describing the activities that occur at the cellular and molecular levels of organization. (shrink)

More and more researchers are examining grammar acquisition from theoretical perspectives that treat it as an emergent phenomenon. In this essay, I argue that a robustly developmental perspective provides a potential explanation for some of the well-known crosslinguistic features of early child language: the process of acquisition is shaped in part by the developmental constraints embodied in von Baer’s law of development. An established model of development, the Developmental Lock, captures and elucidates the probabilistic generalizations at the heart of von (...) Baer’s law. When this model is applied to the acquisition of grammar, it predicts that grammatical achievements that are more generatively entrenched will emerge earlier in development and will be more developmentally resilient than those that are less generatively entrenched. I show that the first prediction is supported by a wealth of psycholinguistic evidence involving typically developing children and that the second prediction is supported by numerous studies involving both children who receive deficient linguistic input and children who experience various language impairments. The success of this model demonstrates the analytic potential of a developmental approach to the study of language acquisition. (shrink)

Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent exchange between two groups (...) of mathematical modelers in plant biology. Scientific debate can arise from different modeling philosophies. (shrink)

The cis-regulatory hypothesis is one of the most important claims of evolutionary developmental biology. In this paper I examine the theoretical argument for cis-regulatory evolution and its role within evolutionary theorizing. I show that, although the argument has some weaknesses, it acts as a useful example for the importance of current scientific debates for science education.

This paper compares the two known logical forms of hierarchy, both of which have been used in models of natural phenomena, including the biological. I contrast their general properties, internal formal relations, modes of growth (emergence) in applications to the natural world, criteria for applying them, the complexities that they embody, their dynamical relations in applied models, and their informational relations and semiotic aspects.

A classic analytic approach to biological phenomena seeks to refine definitions until classes are sufficiently homogenous to support prediction and explanation, but this approach founders on cases where a single process produces objects with similar forms but heterogeneous behaviors. I introduce object spaces as a tool to tackle this challenging diversity of biological objects in terms of causal processes with well-defined formal properties. Object spaces have three primary components: (1) a combinatorial biological process such as protein synthesis that generates objects (...) with parts that are modular, independent, and organized according to an invariant syntax; (2) a notion of “distance” that relates the objects according to rules of change over time as found in nature or useful for algorithms; (3) mapping functions defined on the space that map its objects to other spaces or apply an evaluative criterion to measure an important quality, such as parsimony or biochemical function. Once defined, an object space can be used to represent and simulate the dynamics of phenomena on multiple scales; it can also be used as a tool for predicting higher-order properties of the objects, including stitching together series of causal processes. Object spaces are the basis for a strategy of theorizing, discovery, and analysis in biology: as heuristic idealizations of biology, they help us transform inchoate, intractable problems into articulated, well-structured ones. Developing an object space is a research strategy with a long, successful history under many other names, and it offers a unifying but not overreaching approach to biological theory. (shrink)

The concept of hierarchical organization is commonplace in science. Subatomic particles compose atoms, which compose molecules; cells compose tissues, which compose organs, which compose organisms; etc. Hierarchical organization is particularly prominent in ecology, a field of research explicitly arranged around levels of ecological organization. The concept of levels of organization is also central to a variety of debates in philosophy of science. Yet many difficulties plague the concept of discrete hierarchical levels. In this paper, we show how these difficulties undermine (...) various implications ascribed to hierarchical organization, and we suggest the concept of scale as a promising alternative to levels. Investigating causal processes at different scales offers a way to retain a notion of quasi-levels that avoids the difficulties inherent in the classic concept of hierarchical levels of organization. Throughout, our focus is on ecology, but the results generalize to other invocations of hierarchy in science and philosophy of science. (shrink)

There is a growing consensus among philosophers of science that scientific endeavors of understanding the human mind or the brain exhibit explanatory pluralism. Relatedly, several philosophers have in recent years defended an interventionist approach to causation that leads to a kind of causal pluralism. In this paper, I explore the consequences of these recent developments in philosophy of science for some of the central debates in philosophy of mind. First, I argue that if we adopt explanatory pluralism and the interventionist (...) approach to causation, our understanding of physicalism has to change, and this leads to what I call pluralistic physicalism. Secondly, I show that this pluralistic physicalism is not endangered by the causal exclusion argument. (shrink)

Evo-Devo exhibits a plurality of scientific “cultures” of practice and theory. When are the cultures acting—individually or collectively—in ways that actually move research forward, empirically, theoretically, and ethically? When do they become imperialistic, in the sense of excluding and subordinating other cultures? This chapter identifies six cultures – three /styles/ (mathematical modeling, mechanism, and history) and three /paradigms/ (adaptationism, structuralism, and cladism). The key assumptions standing behind, under, or within each of these cultures are explored. Characterizing the internal structure of (...) the cultures is necessary for understanding how they collaborate or compete, and how they are fragmented or integrated, in the rich interdisciplinary /trading zone/ (Galison 1997) of Evo-Devo. Evo-Devo is an important example of how science can progress through a radical plurality of perspectives and cultures. (shrink)