The Explanatory Model of Scientific Understanding is a deflationary thesis recently advocated by Kareem Khalifa. EMU is committed to two key ideas: all understanding-relevant knowledge is propositional in nature; and the abilities we use to generate understanding are merely our usual logical reasoning skills. In this paper I provide an argument against both ideas, suggesting that scientific understanding requires a significant amount of non-propositional knowledge not captured by logical relations. I use the (...) Inferential Model of Scientific Understanding to reveal how we can better represent what constitutes understanding a scientific event. In particular, this model accounts for not only logical and probabilistic inferences, but also those conceptual associations and categorizations we must make to comprehend an explanation. (shrink)

In this article I argue that two current accounts of scientific understanding are incorrect and I propose an alternative theory. My new account draws on recent research in cognitive psychology which reveals the importance of making causal and logical inferences on the basis of incoming information. To understand a phenomenon we need to make particular kinds of inferences concerning the explanations we are given. Specifically, we come to understand a phenomenon scientifically by developing mental models that incorporate the (...) correct causal and logical properties responsible for the causes or logical properties of the phenomenon being explained. (shrink)

In this article, I use a mental models computational account of representation to illustrate some details of my previously presented inferential model of scientific understanding. The hope is to shed some light on possible mechanisms behind the notion of scientific understanding. I argue that if mental models are a plausible approach to modelling cognition, then understanding can best be seen as the coupling of specific rules. I present our beliefs as ?ordinary? conditional rules, and (...) the coupling process as one where the consequent of one ordinary rule (OR) matches and activates the antecedent of the rule to which it is coupled in virtue of the activation of an intermediate ?inference? rule. I argue that on this approach knowledge of an explanation is the activation of ORs in a cognitive hierarchy, while understanding is achieved when those activated ORs are also coupled via correct inference rules. I do not directly address issues regarding the plausibility of mental models themselves. This article should therefore be seen as an exercise in refining the inferential model within an already presupposed computational setting, not one of arguing for the psychological adequacy of computational approaches. (shrink)

A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw (...) ampliative inferences about the world from models. I present and critically assess two leading accounts of model assessment, arguing that neither is sufficient to provide a complete understanding of model evaluation. I introduce a more powerful framework incorporating elements of the two views, which can help answer these three questions: What is the target of evaluation in model assessment? How does that evaluation proceed? What licenses us in making inferences about the real world, based on the evaluation of our models as successful? The framework identifies two distinct targets of model evaluation: representational similarity between the model and target system, and the adequacy of the model as a tool to answer questions. Both assessments must be relativized to a purpose, of which there are three general kinds: descriptive, predictive, and explanatory. These purposes differ in the way they inform the similarity relation, which is relevant for the similarity assessment, and the output they produce, which is relevant for the adequacy assessment. Any model can be assessed relative to any purpose, however a model encodes certain decisions made during the model’s construction, which impact its ability to be applied to a new purpose or new domain. My framework shows that extending a model, and drawing inferences from it, depends on its representational similarity. I apply this framework to several examples taken from astrophysics showing in detail how it can help illuminate the structure of the models, as well as make the justification for inferences made from them clear. The final chapter is a detailed analysis of a contemporary debate surrounding the use of models in astrophysics, between proponents of MOND and the standard ΛCDM model. (shrink)

We introduce a framework that can be used to model both mathematics and human reasoning about mathematics. This framework involves stochastic mathematical systems (SMSs), which are stochastic processes that generate pairs of questions and associated answers (with no explicit referents). We use the SMS framework to define normative conditions for mathematical reasoning, by defining a “calibration” relation between a pair of SMSs. The first SMS is the human reasoner, and the second is an “oracle” SMS that can be interpreted (...) as deciding whether the question–answer pairs of the reasoner SMS are valid. To ground thinking, we understand the answers to questions given by this oracle to be the answers that would be given by an SMS representing the entire mathematical community in the infinite long run of the process of asking and answering questions. We then introduce a slight extension of SMSs to allow us to model both the physical universe and human reasoning about the physical universe. We then define a slightly different calibration relation appropriate for the case of scientific reasoning. In this case the first SMS represents a human scientist predicting the outcome of future experiments, while the second SMS represents the physical universe in which the scientist is embedded, with the question–answer pairs of that SMS being specifications of the experiments that will occur and the outcome of those experiments, respectively. Next we derive conditions justifying two important patterns of inference in both mathematical and scientific reasoning: (i) the practice of increasing one’s degree of belief in a claim as one observes increasingly many lines of evidence for that claim, and (ii) abduction, the practice of inferring a claim’s probability of being correct from its explanatory power with respect to some other claim that is already taken to hold for independent reasons. (shrink)

In this paper, we develop and refine the idea that understanding is a species of explanatory knowledge. Specifically, we defend the idea that S understands why p if and only if S knows that p, and, for some q, S’s true belief that q correctly explains p is produced/maintained by reliable explanatory evaluation. We then show how this model explains the reception of James Bjorken’s explanation of scaling by the broader physics community in the late 1960s (...) and early 1970s. The historical episode is interesting because Bjorken’s explanation initially did not provide understanding to other physicists, but was subsequently deemed intelligible when Feynman provided a physical interpretation that led to experimental tests that vindicated Bjorken’s model. Finally, we argue that other philosophical models of scientific understanding are best construed as limiting cases of our more general model. (shrink)

A systematic attempt to understand cognitive characteristics of translation by bringing its logical, pragmatic, and hermeneutic features together and examining a number of scientific, logical (philosophical and formal),and philosophical applications. The notion of translation investigated here is called explanatory since it is not a translation in the standard, meaning-saving sense but aims to provide an explanation for the meaning change in exact terms.

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 provides an inferentialist account of model-based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still (...) provide factive explanations. Finally, the paper shows how the contrastive counterfactual theory of explanation can provide tools for assessing the explanatory power of models. (shrink)

In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Kitcher’s unificationist account supports IBE; Railton’s deductive–nomological–probabilistic model, Salmon’s statistical-relevance model, and (...) van Fraassen’s erotetic account are incompatible with IBE; and Salmon’s causal–mechanical model is merely consistent with IBE. In short, many influential models of scientific explanation do not support IBE. I end by outlining three possible conclusions to draw: (1) either philosophers of science or defenders of IBE have seriously misconstrued the concept of explanation, (2) philosophers of science and defenders of IBE do not use the term ‘explanation’ univocally, and (3) the ampliative conception of IBE, which is compatible with any model of scientific explanation, deserves a closer look. (shrink)

The theory of explanatory unification was first proposed by Friedman (1974) and developed by Kitcher (1981, 1989). The primary motivation for this theory, it seems to me, is the argument that this account of explanation is the only account that correctly describes the genesis of scientific understanding. Despite the apparent plausibility of Friedman's argument to this effect, however, I argue here that the unificationist thesis of understanding is false. The theory of explanatory unification as articulated (...) by Friedman and Kitcher thus emerges as fundamentally misconceived. (shrink)

Scientific understanding as a subject of inquiry has become widely discussed in philosophy of science and is often addressed through case studies from history of science. Even though these historical reconstructions engage with details of scientific practice, they usually provide only limited information about the gradual formation of understanding in ongoing processes of model and theory construction. Based on a qualitative ethnographic study of an ecological research project, this article shifts attention from understanding in (...) the context of historical case studies to evidence of current case studies. By taking de Regt’s contextual theory of scientific understanding into the field, it confirms core tenets of the contextual theory suggesting a normative character with respect to scientific activities. However, the case study also shows the limitations of de Regt’s latest version of this theory as an attempt to explain the development of understanding in current practice. This article provides a model representing the emergence of scientific understanding that exposes main features of scientific understanding such as its gradual formation, its relation to skills and imagination, and its capacity for knowledge selectivity. The ethnographic evidence presented here supports the claim that something unique can be learned by looking into ongoing research practices that can’t be gained by studying historical case studies. (shrink)

Findlay and Lumsden have proposed a model of creative potential which accounts for divergent thinking but not for convergent thinking. This limitation impedes the applicability of the model to scientific creativity, where competence and thus convergent thinking play a fundamental role since the early stages of creation. This limitation is a natural consequence of the fact that Findlay and Lumsden's model is purely intrapsychic. This paper proposes a model of scientists' creative potential which accounts for (...) both divergent and convergent processes. First, drawing from Csikszentmihalyi's and Kuhn's theories, scientists' creative potential is defined as a matching between the cognitive structure and the epistemic and social structure of the domain. Second, based on this sociopsychological definition, an extension of Findlay and Lumsden's model is proposed. The model states that the highest creative potential is achieved by those scientists who deeply understand the core of the domain and still have cognitive power to access, and experiment on many ideas that are not presently included in the domain. The new model is shown to have greater explanatory power than Findlay and Lumsden's model and to include the latter as a limiting case. (shrink)

This book argues for the explanatory autonomy of the biological sciences. It does so by showing that scientific explanations in the biological sciences cannot be reduced to explanations in the fundamental sciences such as physics and chemistry and by demonstrating that biological explanations are advanced by models rather than laws of nature. To maintain the explanatory autonomy of the biological sciences, the author argues against explanatory reductionism and shows that explanation in the biological sciences can be (...) achieved without reduction. Then, he demonstrates that the biological sciences do not have laws of nature. Instead of laws, he suggests that biological models usually do the explanatory work. To understand how a biological model can explain phenomena in the world, the author proposes an inferential account of model explanation. The basic idea of this account is that, for a model to be explanatory, it must answer two kinds of questions: counterfactual-dependence questions that concern the model itself and hypothetical questions that concern the relationship between the model and its target system. The reason a biological model can answer these two kinds of questions is due to the fact that a model is a structure, and the holistic relationship between the model and its target warrants the hypothetical inference from the model to its target and thus helps to answer the second kind of question. The Explanatory Autonomy of the Biological Sciences will be of interest to researchers and advanced students working in philosophy of science, philosophy of biology and metaphysics. (shrink)

From the nineteen sixties onwards a branch of philosophy of science has come to development, called history-oriented philosophy of science. This development constitutes a reaction on the then prevailing logical empiricist conception of scientific knowledge. The latter was increasingly seen as suffering from insurmountable internal problems, like e. g. the problems with the particular "observational-theoretical distinction" on which it drew. In addition the logical empiricists' general approach was increasingly criticized for two external shortcomings. Firstly, the examples of scientific (...) knowledge that the logical empiricists were focusing on were con sidered as too simplistic to be informative on the nature of real life science. Secondly, it was felt that the attention of these philosophers of science was restricted to the static aspects of scientific knowledge, while neglecting its developmental aspects. History-oriented philosophy of science has taken up the challenge implicit in the latter two criticisms, i. e. to develop accounts of science that would be more adequate for understanding the development 1 of real life science. One of the more successful products of this branch of philosophy of science is Lakatos's theory of scientific development, sometimes called the "methodology of scientific research programmes". This theory conceives science as consisting of so called research program mes developing in time, and competing with each other over the issue which one generates the best explan~tions of the phenomena that they address. (shrink)

This paper represents a response to the criticisms made by Eric Barnes in “Explanatory Unification and the Problem of Asymmetry” and “Explanatory Unification and Scientific Understanding” against the thesis of Explanatory Unification. This paper responds to Barnes‟ two main criticisms, that of derivational skepticism and causal asymmetry, and successfully refutes his objections. This paper also defends the plausibility of the unificationist account of scientific explanation because of its ability to render coherent the notion of (...) scientific understanding, focusing in particular on the work by Michael Friedman and Philip Kitcher. (shrink)

The basic theory of scientific understanding presented in Sections 1–2 exploits three main ideas.First, that to understand a phenomenonP (for a given agent) is to be able to fitP into the cognitive background corpusC (of the agent).Second, that to fitP intoC is to connectP with parts ofC (via arguments in a very broad sense) such that the unification ofC increases.Third, that the cognitive changes involved in unification can be treated as sequences of shifts of phenomena inC. How the (...) theory fits typical examples of understanding and how it excludes spurious unifications is explained in detail. Section 3 gives a formal description of the structure of cognitive corpuses which contain descriptive as well as inferential components. The theory of unification is then refined in the light of so called puzzling phenomena, to enable important distinctions, such as that between consonant and dissonant understanding. In Section 4, the refined theory is applied to several examples, among them a case study of the development of the atomic model. The final part contains a classification of kinds of understanding and a discussion of the relation between understanding and explanation. (shrink)

Should models of consciousness be detailed _mechanistic_ models of particular types of systems, or should they be _minimal_ models that abstract away from the underlying mechanistic details and provide generalisations? Detailed mechanistic models may afford a complete and precise account of consciousness in human beings and other, physiologically similar mammals. But they do not provide a good model of consciousness in other animals, such as non-vertebrates, let alone artificial systems. Minimal models can be applicable to a wide range of (...) different conscious systems. But do they provide genuine explanations that are autonomous from explanations by detailed mechanistic models? This paper provides a taxonomy of minimal models and measures of consciousness, clarifies their relation to detailed mechanistic models, and highlights benefits of different minimalist approaches to consciousness. (shrink)

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

In lieu of an abstract, here is a brief excerpt of the content:Four Philosophical Models of the Relation Between Theory and PracticeEstelle R. JorgensenSince music education straddles theory and practice, my purpose is to sketch the strengths and weaknesses of four philosophical models of the relationship between theory and practice. I demonstrate that none of them suffices when taken alone; each has something to offer and its own detractions. And I conclude with four suggested ways in which the analysis can (...) be helpful to music teachers and those interested in their work.Clarifying what is meant by theory and practice raises a nest of conceptual problems. Concerning theory, it may be tempting to equate the terms "theory" and "philosophy." Among their shared properties, these terms deal with conceptual and abstract entities that, from the Enlightenment at least, have been held to be distinct from the phenomenal world. They also clarify ideas and distinguish one concept from another. Among their different purposes, the object of philosophy is the asking of questions in a search for wisdom or truth whereas theory is formulated for explanatory purposes, especially within the frame of scientific propositions that can be refuted through empirical observations. The tests of philosophy are, therefore, those of symbolic logic evidenced in principles such as [End Page 21] internal consistency, correspondence with evidence (deductive, inductive, or analogical), and coherence within a unity or whole. Although logical criteria may also be important in formulating and testing theory,1 it is tested and refuted primarily through means of qualitative descriptions and quantitative assessments of observable phenomena.Regarding practice, one is also confronted with an ambiguous construct that may be interpreted descriptively (what is done in the phenomenal world or taken to be the general state of affairs) or normatively (what is desirable or ideal). There is also the distinction between the practice of a seasoned performer and practice in the sense of striving towards the mastery of a competent and expert practice. In this regard, Vernon Howard distinguishes between "practice" (or the skills exemplified by proficient or expert performers) and "practise (or acquiring the level of mastery of proficient or expert performance)."2 For him, the ambiguous and paradoxical nature of artistic practice highlights the complexity of degrees of attaining mastery of skills or exemplary practice and the diverse array of skills ranging from habit to critical thinking involved in the practice of any art. Although each practice is distinguished by its own belief systems, rituals, expectations, and commonsense, boundaries between differing practices are sometimes "soft" or fuzzy, as one seeps into another.3 Consequently, it is often difficult to distinguish one from another at the margins where one practice becomes another.DichotomyThe notion of theory and practice as independent or a dichotomous relationship between the phenomenal world on the one hand and the mind and spirit on the other has its roots in the ancient world. For Plato, the world of "appearances," referring to the phenomenal world understood by imagination (eikasia) and belief (pistis), should be distinguished from the higher, abstract "intelligible world" grasped through thinking (dianoia), knowledge (episteme), and intelligence (noesis).4 René Descartes' later bifurcation of the inner and outer worlds and inversion of Platonic values and understandings resulted in the ascendancy of careful and deliberate observation of the phenomenal world over philosophical reflection.5 His ideas helped to set the stage for the Enlightenment and the Industrial, Scientific, and Information Revolutions. Empiricism, science, and technology gradually displaced philosophy as primary ways of knowing the phenomenal world and understanding the nature of mind, human cognition, feeling, and being. Making hard-boundaried distinctions between within and without one's self, the phenomenal world and the world of spiritual things, and theory and practice were important insights. These distinctions provided for limits on the sorts of ways in which truth could be known. Reasoned deliberation [End Page 22] and imaginative thought have particular usefulness in the world of theory while sensory perception and careful observation are especially useful in the phenomenal world of practice. So while the scholar might speculate about phenomena, observational tools are also useful in coming to know and understand them. Reason and observation each have particular strengths, and it ought not be assumed that philosophy or scientific observation... (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Four Philosophical Models of the Relation Between Theory and PracticeEstelle R. JorgensenSince music education straddles theory and practice, my purpose is to sketch the strengths and weaknesses of four philosophical models of the relationship between theory and practice. I demonstrate that none of them suffices when taken alone; each has something to offer and its own detractions. And I conclude with four suggested ways in which the analysis can (...) be helpful to music teachers and those interested in their work.Clarifying what is meant by theory and practice raises a nest of conceptual problems. Concerning theory, it may be tempting to equate the terms "theory" and "philosophy." Among their shared properties, these terms deal with conceptual and abstract entities that, from the Enlightenment at least, have been held to be distinct from the phenomenal world. They also clarify ideas and distinguish one concept from another. Among their different purposes, the object of philosophy is the asking of questions in a search for wisdom or truth whereas theory is formulated for explanatory purposes, especially within the frame of scientific propositions that can be refuted through empirical observations. The tests of philosophy are, therefore, those of symbolic logic evidenced in principles such as [End Page 21] internal consistency, correspondence with evidence (deductive, inductive, or analogical), and coherence within a unity or whole. Although logical criteria may also be important in formulating and testing theory,1 it is tested and refuted primarily through means of qualitative descriptions and quantitative assessments of observable phenomena.Regarding practice, one is also confronted with an ambiguous construct that may be interpreted descriptively (what is done in the phenomenal world or taken to be the general state of affairs) or normatively (what is desirable or ideal). There is also the distinction between the practice of a seasoned performer and practice in the sense of striving towards the mastery of a competent and expert practice. In this regard, Vernon Howard distinguishes between "practice" (or the skills exemplified by proficient or expert performers) and "practise (or acquiring the level of mastery of proficient or expert performance)."2 For him, the ambiguous and paradoxical nature of artistic practice highlights the complexity of degrees of attaining mastery of skills or exemplary practice and the diverse array of skills ranging from habit to critical thinking involved in the practice of any art. Although each practice is distinguished by its own belief systems, rituals, expectations, and commonsense, boundaries between differing practices are sometimes "soft" or fuzzy, as one seeps into another.3 Consequently, it is often difficult to distinguish one from another at the margins where one practice becomes another.DichotomyThe notion of theory and practice as independent or a dichotomous relationship between the phenomenal world on the one hand and the mind and spirit on the other has its roots in the ancient world. For Plato, the world of "appearances," referring to the phenomenal world understood by imagination (eikasia) and belief (pistis), should be distinguished from the higher, abstract "intelligible world" grasped through thinking (dianoia), knowledge (episteme), and intelligence (noesis).4 René Descartes' later bifurcation of the inner and outer worlds and inversion of Platonic values and understandings resulted in the ascendancy of careful and deliberate observation of the phenomenal world over philosophical reflection.5 His ideas helped to set the stage for the Enlightenment and the Industrial, Scientific, and Information Revolutions. Empiricism, science, and technology gradually displaced philosophy as primary ways of knowing the phenomenal world and understanding the nature of mind, human cognition, feeling, and being. Making hard-boundaried distinctions between within and without one's self, the phenomenal world and the world of spiritual things, and theory and practice were important insights. These distinctions provided for limits on the sorts of ways in which truth could be known. Reasoned deliberation [End Page 22] and imaginative thought have particular usefulness in the world of theory while sensory perception and careful observation are especially useful in the phenomenal world of practice. So while the scholar might speculate about phenomena, observational tools are also useful in coming to know and understand them. Reason and observation each have particular strengths, and it ought not be assumed that philosophy or scientific observation... (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)

What does it mean to understand the world religiously? How is such understanding to be distinguished from scientific understanding? What does it have to do with religious practice, transfiguring love, and spiritual well-being? New Models of Religious Understanding investigates these questions to set a new and exciting agenda for philosophy of religion. Featuring contributions from leading scholars in the field, the volume cuts across the supposed divide between analytic and continental approaches to the subject and engages (...) the interest of a broad range of philosophical and theological readers. (shrink)

Levins and Lewontin have contributed significantly to our philosophical understanding of the structures, processes, and purposes of biological mathematical theorizing and modeling. Here I explore their separate and joint pleas to avoid making abstract and ideal scientific models ontologically independent by confusing or conflating our scientific models and the world. I differentiate two views of theorizing and modeling, orthodox and dialectical, in order to examine Levins and Lewontin’s, among others, advocacy of the latter view. I compare the (...) positions of these two views with respect to four points regarding ontological assumptions: (1) the origin of ontological assumptions, (2) the relation of such assumptions to the formal models of the same theory, (3) their use in integrating and negotiating different formal models of distinct theories, and (4) their employment in explanatory activity. Dialectical is here used in both its Hegelian–Marxist sense of opposition and tension between alternative positions and in its Platonic sense of dialogue between advocates of distinct theories. I investigate three case studies, from Levins and Lewontin as well as from a recent paper of mine, that show the relevance and power of the dialectical understanding of theorizing and modeling. (shrink)

The paper investigates the question as to which features of hypotheses make them explanatory. Given the intuitive appeal of causal explanations, one might suspect that explanatoriness is deeply connected with causation. I argue in detail that this is wrong by showing that none of the dominant analyses of causation are suited for general accounts of explanatoriness. In the second part, I provide the outlines of an account of explanatoriness that connects it with scientific understanding, which in turn (...) is argued to be analyzable as the cognitive realization of scientific models. (shrink)

One of the main goals of scientific research is to provide a description of the empirical data which is as accurate and comprehensive as possible, while relying on as few and simple assumptions as possible. In this paper, I propose a definition of the notion of few and simple assumptions that is not affected by known problems. This leads to the introduction of a simple model of scientific progress that is based only on empirical accuracy and conciseness. (...) An essential point in this task is the understanding of the role played by measurability in the formulation of a scientific theory. This is the key to prevent artificially concise formulations. The model is confronted here with many possible objections and with challenging cases of real progress. Although I cannot exclude that the model might have some limitations, it includes all the cases of genuine progress examined here, and no spurious one. In this model, I stress the role of the state of the art, which is the collection of all the theories that are the only legitimate source of scientific predictions. Progress is a global upgrade of the state of the art. (shrink)

I argue that the key to understand many fundamental issues in philosophy of science lies in understanding the subtle relation between the non-empirical cognitive values used in science and the constraints imposed by measurability. In fact, although we are not able to fix the interpretation of a scientific theory through its formulation, I show that measurability puts constraints that can at least exclude some implausible interpretations. This turns out to be enough to define at least one cognitive value (...) that is able to penalize, without damages, precisely those bad features that deceive purely empirical assessments. This leads to the formulation of a simple model of scientific progress, that is based only on empirical accuracy and conciseness. The model is confronted here with many possible objections and with challenging cases of real progress. Although I cannot exclude that the model might be incomplete, it includes all the cases of genuine progress examined here, and no spurious one. In this model, I stress the role of the state of the art, which is the collection of all the theories that are the only legitimate source of scientific predictions. Progress is a global upgrade of the state of the art. (shrink)

University Abstract Philosophers have sought to improve upon the logical empiricists’ model of scientific reduction. While opportunities for integration between the cognitive and the neural sciences have increased, most philosophers, appealing to the multiple realizability of mental states and the irreducibility of consciousness, object to psychoneural reduction. New Wave reductionists offer a continuum of comparative goodness of intertheoretic mapping for assessing reductions. Their insistence on a unified view of intertheoretic relations obscures epistemically significant crossscientific relations and engenders dismissive (...) conclusions about psychology. Richer, more sensitive accounts of explanatory pluralism and mechanistic explanation in science advocate multi-level approaches in cross-scientific settings and criticize the distance of the standard philosophical objections from working scientists’ practices and discoveries. The Heuristic Identity Theory, a new, scientifically informed version of the psycho-physical identity theory, incorporates these insights, showing how multiple realizability is an argument for (not against) identities in science and why, therefore, consciousness is not irreducible. (shrink)

Unified, all-purpose, philosophical models of reduction in science lack resources for capturing varieties of cross-scientific relations that have proven critical to understanding some scientific achievements. Not only do those models obscure the distinction between successional and cross-scientific relations, their preoccupations with the structures of both theories and things provide no means for accommodating the contributions to various sciences of theories and research about long-term diachronic processes involving large-scale, distributed systems. Darwin's theory of evolution by natural selection (...) is the parade case. Explanatory pluralism accommodates a wider range of connections between theories and inquiries in science than all-purpose models of reduction do. Consequently, it provides analytical tools for understanding the roles of the theoretical proposals about the evolution of the human mind/brain that have proliferated over the last two decades. Those proposals have testable implications pertaining to both structure and processing in the modern human mind/brain. An example of such research illustrates how those proposals and investigative tools and experiments cut across both explanatory levels and modes of analysis within the cognitive sciences and how those studies can yield evidence that bears on the assessment of competing theories and models. (shrink)

In this work, I attempt to develop what I call a co-evolutionary model of scientific change, which I expect to afford a more balanced view on both the continuous and discontinuous aspects of scientific change. Supposing that scientific goals, methods and theories constitute the main components of scientific inquiry, I focus on the relationships among these components and their changing patterns. First of all, I identify explanatory power and empirical adequacy as primary goals of (...) science and explore the possibility of evaluating scientific goals. Then I try to bring out the major features of how the main components of science are related to each other. One major feature is that they mutually constrain each other, and as such each main component operates as a selective force on the other components. Another major feature is that the main components of science induce changes reciprocally, but with certain intervals. Other important features are the modes and tempos of changes in the main components of scientific inquiry. All these features together, I conclude, suggest that scientific change is evolutionary, as well as co-evolutionary. Finally I argue that this co-evolutionary model of scientific change does not yield to what I call the problems of circularity and scientific progress. (shrink)

“Explanation” refers to a wide range of activities, with a family resemblance between them. Most satisfactory explanations in a discipline for a domain fail to satisfy some general desiderata, while fulfilling others. This can happen in various ways. Why? An idealizing response would be to say that in real science explanations fall short along some dimensions, so that for any explanatory failure there is a conceivable improvement that addresses its shortcomings. The improvement may be more accurate causally or possess (...) more unifying power, or deliver deeper understanding. We formulate a drastically less idealizing response. We argue that there are typically trade-offs in explanation, so that in strengthening one explanatory virtue one will usually weaken another. Scientific explanations, if this is correct, are constrained by such trade-offs. Particular trade-offs are appropriate for particular explanatory vehicles. There are the overarching equations of theoretical physics, which produce unification at the expense of causal detail; there are theoretical models of phenomena that occupy a middle ground between generality and the detailed workings of particular cases and get closer to explaining the workings of specific systems at the expense of unification. Sometimes experiments aim at general causal patterns at the expense of particular detail; and sometimes they are designed to give us information of particular detail at the expense of generality. There are further trade-offs associated with other vehicles of explanation. We provide examples from physics and biology. (shrink)

In the following essay I present a narrative of the development of astronomical models from Eudoxus to Kepler, as a case-study that vindicates an insightful and influential recent account of the concept of scientific understanding. Since this episode in the history of science and the concept of understanding are subjects to which Professor Roberto Torretti has dedicated two wonderful books—De Eudoxo a Newton: modelos matemáticos en la filosofía natural (2007), and Creative Understanding: philosophical reflections on physics (...) (1990), respectively—this essay is my contribution to celebrate his outstanding work and career in this volume. I dedicate this piece to Roberto, dear friend and mentor, in gratitude for all his inspirational work and personal support, which has greatly helped me, and many others, to better understand that human wonder we call scientific knowledge. (shrink)

In this paper we examine the epistemic value of highly idealized agent-based models of social aspects of scientific inquiry. On the one hand, we argue that taking the results of such simulations as informative of actual scientific inquiry is unwarranted, at least for the class of models proposed in recent literature. Moreover, we argue that a weaker approach, which takes these models as providing only “how-possibly” explanations, does not help to improve their epistemic value. On the other hand, (...) we suggest that if ABMs of science underwent two types of robustness analysis, they could indeed have a clear epistemic function, namely by providing evidence for philosophical and historical hypotheses. In this sense, ABMs can obtain evidential and explanatory properties and thus be a useful tool for integrated history and philosophy of science. We illustrate our point with an example of a model—building on the work by Kevin Zollman—which we apply to a concrete historical case study. (shrink)

In this paper, I outline a heuristic for thinking about the relation between explanation and understanding that can be used to capture various levels of “intimacy”, between them. I argue that the level of complexity in the structure of explanation is inversely proportional to the level of intimacy between explanation and understanding, i.e. the more complexity the less intimacy. I further argue that the level of complexity in the structure of explanation also affects the explanatory depth in (...) a similar way to intimacy between explanation and understanding, i.e. the less complexity the greater explanatory depth and vice versa. (shrink)

In recent years there has been increasing interest in scientific understanding as an epistemic success term that is distinct from scientific knowledge (see, for example, De Regt, Leonelli and Eigner 2009). Although this literature is diverse, three dominant strands can be found that have rather deeper roots in the philosophy of science: understanding as unification (Friedman 1974; Kitcher 1981); understanding through mechanistic thinking as in certain types of causal modelling (Salmon 1998; Woodward 2003); and a (...) kind of contextualist pluralist approach to understanding (De Regt and Dieks 2005; De Regt 2009; 2014), which is in some ways similar to the account offered by Nelson Goodman (1968, 1978) and Catherine Elgin (1997, 2004). Proponents of these views often see them as complimentary or at least not contradictory. However, they have not yet to been brought neatly together in a single account. This is what I propose to do. At the heart of my approach is the thought that we should treat the characteristic content of understanding as pictorial, in contrast to the characteristic content of knowledge, which is propositional. By virtue of the distinctive ways in which they present their content, epistemically efficacious pictures exemplify, unify, show mechanical (and other) causal relations, allow for multiple readings and facilitate the contextualisation of their content, while still having determinate content. In other words, features of pictorial content facilitate cognitive and evaluative procedures that are characteristic of understanding and have already been identified as such in the literature. That understanding should be treated as something like “getting the picture” is supported, albeit weakly, by multiple remarks, more or less well-developed, linking understanding to picturing that can also be found in the literature on scientific understanding, as we shall see. (shrink)

This book offers a comprehensive and accessible introduction to the epistemology of science. It not only introduces readers to the general epistemological discussion of the nature of knowledge, but also provides key insights into the particular nuances of scientific knowledge. No prior knowledge of philosophy or science is assumed by The Nature of Scientific Knowledge. Nevertheless, the reader is taken on a journey through several core concepts of epistemology and philosophy of science that not only explores the characteristics (...) of the scientific knowledge of individuals but also the way that the development of scientific knowledge is a particularly social endeavor. The topics covered in this book are of keen interest to students of epistemology and philosophy of science as well as science educators interested in the nature of scientific knowledge. In fact, as a result of its clear and engaging approach to understanding scientific knowledge The Nature of Scientific Knowledge is a book that anyone interested in scientific knowledge, knowledge in general, and any of a myriad of related concepts would be well advised to study closely. (shrink)

In The Explanatory Dispensability of Idealizations, Sam Baron suggests a possible strategy enabling the indispensability argument to break the symmetry between mathematical claims and idealization assumptions in scientific models. Baron’s distinction between mathematical and non-mathematical idealization, I claim, is in need of a more compelling criterion, because in scientific models idealization assumptions are expressed through mathematical claims. In this paper I argue that this mutual dependence of idealization and mathematics cannot be read in terms of symmetry and (...) that Baron’s non-causal notion of mathematical difference-making is not effective in justifying any symmetry-breaking between mathematics and idealization. The function of making a difference that Baron attributes to mathematics cannot be referred to physical facts, but to the features of quantities, such as step lengths or time intervals taken into account in the models. It appears, indeed, that it does not follow from Baron’s argument that idealizations do not help to carry the explanatory load at least for two reasons: mathematics is not independent of idealizations in modelling and idealizations help mathematics to carry the explanatory load of a model in different degrees. (shrink)

In The Explanatory Dispensability of Idealizations, Sam Baron suggests a possible strategy enabling the indispensability argument to break the symmetry between mathematical claims and idealization assumptions in scientific models. Baron’s distinction between mathematical and non-mathematical idealization, I claim, is in need of a more compelling criterion, because in scientific models idealization assumptions are expressed through mathematical claims. In this paper I argue that this mutual dependence of idealization and mathematics cannot be read in terms of symmetry and (...) that Baron’s non-causal notion of mathematical difference-making is not effective in justifying any symmetry-breaking between mathematics and idealization. The function of making a difference that Baron attributes to mathematics cannot be referred to physical facts, but to the features of quantities, such as step lengths or time intervals taken into account in the models. It appears, indeed, that it does not follow from Baron’s argument that idealizations do not help to carry the explanatory load at least for two reasons: mathematics is not independent of idealizations in modelling and idealizations help mathematics to carry the explanatory load of a model in different degrees. (shrink)

Ecological-enactive approaches to cognition aim to explain cognition in terms of the dynamic coupling between agent and environment. Accordingly, cognition of one’s immediate environment (which is sometimes labeled “basic” cognition) depends on enaction and the picking up of affordances. However, ecological-enactive views supposedly fail to account for what is sometimes called “higher” cognition, i.e., cognition about potentially absent targets, which therefore can only be explained by postulating representational content. This challenge levelled against ecological-enactive approaches highlights a putative explanatory gap (...) between basic and higher cognition. In this paper, we examine scientific cognition—a paradigmatic case of higher cognition—and argue that it shares fundamental features with basic cognition, for enaction and affordance selection are central to the scientific enterprise. Our argument focuses on modeling, and on how models promote scientific understanding. We base our argument on a non-representational account of scientific understanding and on the material engagement theory, for models are hereby conceived as material objects designed for scientific engagements. Having done so, we conclude that the explanatory gap is significantly less threatening to the ecological-enactive approach than it might appear. (shrink)

Disease transmission is a fruitful domain in which to examine how scientific and folk theories interrelate, given laypeople’s access to multiple sources of information to explain events of personal significance. The current paper reports an in-depth survey of U.S. adults’ causal reasoning about two viral illnesses: a novel, deadly disease that has massively disrupted everyone’s lives, and a familiar, innocuous disease that has essentially no serious consequences. Participants received a series of closed-ended and open-ended questions probing their reasoning about (...) disease transmission, with a focus on causal mechanisms underlying disease contraction, transmission, treatment, and prevention; non-visible biological processes; and ontological frameworks regarding what kinds of entities viruses are. We also assessed participants’ attitudes, such as their trust in scientific experts and willingness to be vaccinated. Results indicated complexity in people’s reasoning, consistent with the co-existence of multiple explanatory frameworks. An understanding of viral transmission and viral replication existed alongside folk theories, placeholder beliefs, and lack of differentiation between viral and non-viral disease. For example, roughly 40% of participants who explained illness in terms of the transmission of viruses also endorsed a non-viral folk theory, such as exposure to cold weather or special foods as curative. Additionally, participants made use of competing modes of construal when explaining how viruses operate, such as framing the immune system response as cells trying to fight off the virus. Indeed, participants who displayed greater knowledge about viral transmission were significantly more likely to anthropomorphize bodily processes. Although comparisons of COVID-19 and the common cold revealed relatively few differences, the latter, more familiar disease elicited consistently lower levels of accuracy and greater reliance on folk theories. Moreover, for COVID-19 in particular, accuracy positively correlated with attitudes, self-protective behaviors, and willingness to be vaccinated. For both diseases, self-assessed knowledge about the disease negatively predicted accuracy. The results are discussed in relation to challenges for formal models of explanatory reasoning. (shrink)

One of the ironies of our time is the sparsity of useful analytic tools for understanding change and development within technology itself. For all the diatribes about the disastrous effects of technology on modern life, for all the equally uncritical paeans to technology as the panacea for human ills, the vociferous pro- and anti-technology movements have failed to illuminate the nature of technology. On a more scholarly level, in the midst of claims by Marxists and non-Marxists alike about the (...) technological underpinnings of the major social and economic changes of the last couple of centuries, and despite advice given to government and industry about managing science and technology by a small army of consultants and policy analysts, technology itself remains locked inside an impenetrable black box, a deus ex machina to be invoked when all other explanations of puzzling social and economic pheoomena fail. The discipline that has probably done most to penetrate that black box in recent years by studying the 1 internal development of technology is history. Historians of technology and certain economic historians have carried out careful and detailed studies on the genesis and impact of technological innovations, and the structu-re of the social systems associated with those innovations. Within the past few decades tentative consensus about the periodization and the major traditions within the history of technology has begun to emerge, at least as far as Britain and America in the eighteenth and nineteenth century are concerned. (shrink)

Explanatory pluralism has been defended by several philosophers of history and social science, recently, for example, by Tor Egil Førland in this journal. In this article, we provide a better argument for explanatory pluralism, based on the pragmatist idea of epistemic interests. Second, we show that there are three quite different senses in which one can be an explanatory pluralist: one can be a pluralist about questions, a pluralist about answers to questions, and a pluralist about both. (...) We defend the last position. Finally, our third aim is to argue that pluralism should not be equated with “anything goes”: we will argue for non-relativistic explanatory pluralism. This pluralism will be illustrated by examples from history and social science in which different forms of explanation (for example, structural, functional, and intentional explanations) are discussed, and the fruitfulness of our framework for understanding explanatory pluralism is shown. (shrink)

Simple Reduction and Beyond Traditional and New Wave models of reduction in science have not lacked for ambition. Philosophers have presented single models to account for the full range of interesting intertheoretic relations, for scientific progress, and for the unity of science (Nagel, 1961; Oppenheim and Putnam, 1958). Early critics attacked the logical empiricists' proposals about the character of intertheoretic connections (Feyerabend, 1962; Kuhn, 1970). New Wave reductionists have similarly argued that various intertheoretic relations fall at different points on (...) a continuum of goodness-of-intertheoretic-mapping. Still, whatever their differences with the logical empiricists, New Wave reductionists have retained traditional aspirations for a single, comprehensive model of reduction that will make sense of the wide range of intertheoretic relations, of progressive scientific change, and of how the various sciences hang together (Hooker, 1981; Churchland and Churchland, 1990; Bickle, 1998). Both logical empiricists and their New Wave successors proffer such unified, multi-purpose models. Regardless of the field, multi-purpose tools typically sacrifice precision for versatility. Recent analyses of mechanistic explanation have helped to reveal that these models of scientific reduction are no exceptions to this rule, and the cost of sacrificing precision is one that the mechanists are unwilling to pay. Traditional and New Wave reductionists manifest allegiances (1) to the (virtually exclusive) analysis of theories and intertheoretic relations and (2) to conceptions of explanatory levels in science rooted in considerations pertaining to the size of and the mereological relations between the sciences' objects of study. (Discussions of both explanatory levels and mereological relations follow in subsequent sections.) By contrast, advocates of mechanistic analysis offer detailed accounts of particular systems' functioning that survey their components, their operations, and the larger systems to which they contribute (Bechtel and Richardson, 1993; Glennan, 1996; Machamer, Darden and Craver, 2000; Craver, 2001; Bechtel, 2006).. (shrink)

Research on natural and artificial brains is proceeding at a rapid pace. However, the understanding of the essence of consciousness has changed slightly over the millennia, and only the last decade has brought some progress to the area. Scientific ideas emerged that the soul could be a product of the material body and that calculating machines could imitate brain processes. However, the authors of this book reject the previously common dualism—the view that the material and spiritual-psychic processes are (...) separate and require a completely different substance as their foundation. -/- Reductive Model of the Conscious Mind is a forward-thinking book wherein the authors identify processes that are the essence of conscious thinking and place them in the imagined, simplified structure of cells able to memorize and transmit information in the form of impulses, which they call neurons. The purpose of the study is to explain the essence of consciousness to the degree of development of natural sciences, because only the latter can find a way to embed the concept of the conscious mind in material brains. The book is divided into three parts. Part 1 works to convince readers that the emergence of consciousness does not require detailed knowledge of the structure and morphology of the brain, with the exception of some specific properties of the neural network structure that the authors attempt to point out. Part 2 proves that the biological structure of many natural brains fulfills the necessary conditions for consciousness and intelligent thinking. Similarly, Part 3 shows the ways in which artificial creatures imitating natural brains can meet these conditions, which gives great hopes for building artificially intelligent beings endowed with consciousness. Covering topics that include cognitive architecture, the embodied mind, and machine learning, this book is ideal for cognitive scientists, philosophers of mind, neuroscientists, psychologists, researchers, academicians, and advanced-level students. The book can also help to focus the research of linguists, neurologists, and biophysicists on the biophysical basis of postulated information processing into knowledge structures. (shrink)

Although predictive power and explanatory insight are both desiderata of scientific models, these features are often in tension with each other and cannot be simultaneously maximized. In such situations, scientists may adopt what I term a ‘division of cognitive labor’ among models, using different models for the purposes of explanation and prediction, respectively, even for the exact same phenomenon being investigated. Adopting this strategy raises a number of issues, however, which have received inadequate philosophical attention. More specifically, while (...) one implication may be that it is inappropriate to judge explanatory models by the same standards of quantitative accuracy as predictive models, there still needs to be some way of either confirming or rejecting these model explanations. Here I argue that robustness analyses have a central role to play in testing highly idealized explanatory models. I illustrate these points with two examples of explanatory models from the field of geomorphology. (shrink)

This paper analyzes recent work in psychology on the nature of the representation of complex forms of knowledge with the goal of understanding how theories are represented. The analysis suggests that, as a psychological form of representation, theories are mental structures that include theoretical entities (usually nonobservable), relationships among the theoretical entities, and relationships of the theoretical entities to the phenomena of some domain. A theory explains the phenomena in its domain by providing a conceptual framework for the phenomena (...) that leads to a feeling of understanding in the reader/hearer. The explanatory conceptual framework goes beyond the original phenomena, integrates diverse aspects of the world, and shows how the original phenomena follow from the framework. This analysis is used to argue that mental models are the subclass of theories that use causal/mechanical explanatory frameworks. In addition, an argument is made for a new psychologism in the philosophy of science, in which the mental representation of scientific theories must be taken into account. (shrink)