The realizers and vehicles of mental representation

The neural vehicles of mental representation play an explanatory role in cognitive psychology that their realizers do not. Cognitive psychology individuates neural structures as representational vehicles in terms of the specific causal properties to which cognitive mechanisms are sensitive. Explanations that appeal to properties of vehicles can capture generalisations which are not available at the level of their neural realizers. In this paper, I argue that the individuation of realizers as vehicles restricts the sorts of explanations in which they can participate. I illustrate this with reference to Rupert’s (2011) claim that representational vehicles can play an explanatory role in psychology in virtue of their quantity or proportion. I propose that such quantity-based explanatory claims can apply only to realizers and not to vehicles, in virtue of the particular causal role that vehicles play in psychological explanations.     

Where explanation ends: Understanding as the place the spade turns in the social sciences

Explanations implicitly end with something that makes sense, and begin with something that does not make sense. A statistical relationship, for example, a numerical fact, does not make sense; an explanation of this relationship adds something, such as causal information, which does make sense, and provides an endpoint for the sense-making process. Does social science differ from natural science in this respect? One difference is that in the natural sciences, models are what need “understanding.” In the social sciences, matters are more complex. There are models, such as causal models, which need to be understood, but also depend on background knowledge that goes beyond the model and the correlations that make it up, which produces a regress. The background knowledge is knowledge of in-filling mechanisms, which are normally made up of elements that involve the direct understanding of the acting and believing subjects themselves. These models, and social science explanations generally, are satisfactory only when they end the regress in this kind of understanding or use direct understanding evidence to decide between alternative mechanism explanations.     

The naturalism of the sciences

The sciences are characterized by what is sometimes called a “methodological naturalism,” which disregards talk of divine agency. In response to those who argue that this reflects a dogmatic materialism, a number of philosophers have offered a pragmatic defense. The naturalism of the sciences, they argue, is provisional and defeasible: it is justified by the fact that unsuccessful theistic explanations have been superseded by successful natural ones. But this defense is inconsistent with the history of the sciences. The sciences have always exhibited what we call a domain naturalism. They have never invoked divine agency, but have always focused on the causal structure of the natural world. It is not the case, therefore, that the sciences once employed theistic explanations and then abandoned them. The naturalism of the sciences is as old as science itself.     

Using genetically-defined rodent strains for the identification of hippocampal traits relevant for two-way avoidance behavior: a non-invasive approach

Summary Genetically-defined rodent strains permit the identification of hippocampal traits which are of functional relevance for the performance of two-way avoidance behavior. This is exemplified here by analyzing the relationship between infrapyramidal mossy fibers (a tiny projection terminating upon the basal dendrites of hippocampal pyramidal neurons) and two-way avoidance learning in about 800 animals. The necessary steps include 1) identification of structural traits sensitive to selective breeding for extremes in two-way avoidance, 2) testing the robustness of the associations found by studying individual and genetical correlations between hippocampal traits and behavior, 3) establishing causal relationships by Mendelian crossing of strains with extreme structural traits and studying the behavioral consequences of such structural randomization, 4) confirming causal relationships by manipulating the structural variable in inbred (isogenic) strains, thereby eliminating the possibility of genetic linkage, and 5) ruling out the possibility of spurious associations by studying the correlations between the hippocampal trait and other behaviors known to depend on hippocampal functioning.In comparison with the classical lesion approach for identifying relationships between brain and behavior, the present procedure appears to be superior in two aspects: it is non-invasive, and it focuses automatically on those brain traits which are used by natural selection to shape behaviorally-defined animal populations, i.e., it reveals the natural regulators of behavior.     

Using genetically-defined rodent strains for the identification of hippocampal traits relevant for two-way avoidance behavior: a non-invasive approach

Genetically-defined rodent strains permit the identification of hippocampal traits which are of functional relevance for the performance of two-way avoidance behavior. This is exemplified here by analyzing the relationship between infrapyramidal mossy fibers (a tiny projection terminating upon the basal dendrites of hippocampal pyramidal neurons) and two-way avoidance learning in about 800 animals. The necessary steps include 1) identification of structural traits sensitive to selective breeding for extremes in two-way avoidance, 2) testing the robustness of the associations found by studying individual and genetical correlations between hippocampal traits and behavior, 3) establishing causal relationships by Mendelian crossing of strains with extreme structural traits and studying the behavioral consequences of such structural 'randomization', 4) confirming causal relationships by manipulating the structural variable in inbred (isogenic) strains, thereby eliminating the possibility of genetic linkage, and 5) ruling out the possibility of spurious associations by studying the correlations between the hippocampal trait and other behaviors known to depend on hippocampal functioning. In comparison with the classical lesion approach for identifying relationships between brain and behavior, the present procedure appears to be superior in two aspects: it is non-invasive, and it focuses automatically on those brain traits which are used by natural selection to shape behaviorally-defined animal populations, i.e., it reveals the natural regulators of behavior.     

Collaborative explanation and biological mechanisms

This paper motivates and outlines a new account of scientific explanation, which I term ‘collaborative explanation.’ My approach is pluralist: I do not claim that all scientific explanations are collaborative, but only that some important scientific explanations are—notably those of complex organic processes like development. Collaborative explanation is closely related to what philosophers of biology term ‘mechanistic explanation’ (e.g., Machamer et al., Craver, 2007). I begin with minimal conditions for mechanisms: complexity, causality, and multilevel structure. Different accounts of mechanistic explanation interpret and prioritize these conditions in different ways. This framework reveals two distinct varieties of mechanistic explanation: causal and constitutive. The two have heretofore been conflated, with philosophical discussion focusing on the former. This paper addresses the imbalance, using a case study of modeling practices in Systems Biology to reveals key features of constitutive mechanistic explanation. I then propose an analysis of this variety of mechanistic explanation, in terms of collaborative concepts, and sketch the outlines of a general theory of collaborative explanation. I conclude with some reflections on the connection between this variety of explanation and social aspects of scientific practice.     

Non-locality in intrinsic topologically ordered systems

Intrinsic topologically ordered (ITO) condensed matter systems are claimed to exhibit two types of non-locality. The first is associated with topological properties and the second is associated with a particular type of quantum entanglement. These characteristics are supposed to allow ITO systems to encode information in the form of quantum entangled states in a topologically non-local way that protects it against local errors. This essay first clarifies the sense in which these two notions of non-locality are distinct, and then considers the extent to which they are exhibited by ITO systems. I will argue that while the claim that ITO systems exhibit topological non-locality is unproblematic, the claim that they also exhibit quantum entanglement non-locality is less clear, and this is due in part to ambiguities associated with the notion of quantum entanglement. Moreover, any argument that claims some form of "long-range" entanglement is necessary to explain topological properties is incomplete if it fails to provide a convincing reason why mechanistic explanations should be favored over structural explanations of topological phenomena.     

Essentially narrative explanations

This essay argues that narrative explanations prove uniquely suited to answering certain explanatory questions, and offers reasons why recognizing a type of statement that requires narrative explanations crucially informs on their assessment. My explication of narrative explanation begins by identifying two interrelated sources of philosophical unhappiness with them. The first I term the problem of logical formlessness and the second the problem of evaluative intractability. With regard to the first, narratives simply do not appear to instantiate any logical form recognized as inference licensing. But absent a means of identifying inferential links, what justifies connecting explanans and explanandum? Evaluative intractability, the second problem, thus seems a direct consequence. This essay shows exactly why these complaints prove unfounded by explicating narrative explanations in the process of answering three interrelated questions. First, what determines that an explanation has in some critical or essential respect a narrative form? Second, how does a narrative in such cases come to constitute a plausible explanation? Third, how do the first two considerations yield a basis for evaluating an explanation offered as a narrative? Answers to each of these questions include illustrations of actual narrative explanations and also function to underline attendant dimensions of evaluation.     

Prediction,diagnosis, and causal thinking in forecasting

While forecasting involves forward/predictive thinking, it depends crucially on prior diagnosis for suggesting a model of the phenomenon, for defining‘relevant’variables, and for evaluating forecast accuracy via the model. The nature of diagnostic thinking is examined with respect to these activities. We first consider the difficulties of evaluating forecast accuracy without a causal model of what generates outcomes. We then discuss the development of models by considering how attention is directed to variables via analogy and metaphor as well as by what is unusual or abnormal. The causal relevance of variables is then assessed by reference to probabilistic signs called‘cues to causality’. These are: temporal order, constant conjunction, contiguity in time and space, number of alternative explanations, similarity, predictive validity, and robustness. The probabilistic nature of the cues is emphasized by discussing the concept of spurious correlation and how causation does not necessarily imply correlation. Implications for improving forecasting are considered with respect to the above issues.     

Will the real scientists please stand up? dead ends and live issues in the explanation of scientific knowledge

Reflection on the method of science has become increasingly thinner since Kant. If there's any upshot of that part of modern philosophy, it's that the scientists didn't have a secret. There isn't something there that's either effable or ineffable. To understand how they do what they do is pretty much like understanding how any other bunch of skilled craftsmen do what they do. Kuhn's reduction of philosophy of science to sociology of science doesn't point to an ineffable secret of success; it leaves us without the notion of the secret of success.Relativism is the view that every belief on a certain topic, or perhaps, about any topic, is as good as every other. No one holds this view. Except for the occasional co-operative freshman, one cannot find anybody who says that two incompatible opinions on an important topic are equally good. The philosophers who get called ‘relativists’ are those who say that the grounds for choosing between such opinions are less algorithmic than had been thought.Richard Rorty1,2     

Testing hypotheses in macroevolution

Experimental manipulation of microevolution (changes in frequency of heritable traits in populations) has shed much light on evolutionary processes. But many evolutionary processes occur on scales that are not amenable to experimental manipulation. Indeed, one of the reasons that macroevolution (changes in biodiversity over time, space and lineages) has sometimes been a controversial topic is that processes underlying the generation of biological diversity generally operate at scales that are not open to direct observation or manipulation. Macroevolutionary hypotheses can be tested by using them to generate predictions then asking whether observations from the biological world match those predictions. Each study that identifies significant correlations between evolutionary events, processes or outcomes can generate new predictions that can be further tested with different datasets, allowing a cumulative process that may narrow down on plausible explanations, or lead to rejection of other explanations as inconsistent or unsupported. A similar approach can be taken even for unique events, for example by comparing patterns in different regions, lineages, or time periods. I will illustrate the promise and pitfalls of these approaches using a range of examples, and discuss the problems of inferring causality from significant evolutionary associations.     

Ten reasons to embrace scientism

A strong version of scientism, such as that of Alex Rosenberg, says, roughly, that natural science reliably delivers rational belief or knowledge, whereas common sense sources of belief, such as moral intuition, memory, and introspection, do not. In this paper I discuss ten reasons that adherents of scientism have or might put forward in defence of scientism. The aim is to show which considerations could plausibly count in favour of scientism and what this implies for the way scientism ought to be formulated. I argue that only three out of these ten reasons potentially hold water and that the evidential weight is, therefore, on their shoulders. These three reasons for embracing scientism are, respectively, particular empirical arguments to the effect that there are good debunking explanations for certain common sense beliefs, that there are incoherences and biases in the doxastic outputs of certain common sense sources of belief, and that beliefs that issue from certain common sense doxastic sources are illusory. From what I argue, it follows that only a version of scientism that is significantly weaker than many versions of scientism that we find in the literature is potentially tenable. I conclude the paper by stating what such a significantly weaker version of scientism could amount to.     

Explanation,understanding, and unrealistic models

How can false models be explanatory? And how can they help us to understand the way the world works? Sometimes scientists have little hope of building models that approximate the world they observe. Even in such cases, I argue, the models they build can have explanatory import. The basic idea is that scientists provide causal explanations of why the regularity entailed by an abstract and idealized model fails to obtain. They do so by relaxing some of its unrealistic assumptions. This method of ‘explanation by relaxation’ captures the explanatory import of some important models in economics. I contrast this method with the accounts that Daniel Hausman and Nancy Cartwright have provided of explanation in economics. Their accounts are unsatisfactory because they require that the economic model regularities obtain, which is rarely the case. I go on to argue that counterfactual regularities play a central role in achieving ‘understanding by relaxation.’ This has a surprising implication for the relation between explanation and understanding: Achieving scientific understanding does not require the ability to explain observed regularities.     

Hobbes on natural philosophy as “True Physics” and mixed mathematics

In this paper, I offer an alternative account of the relationship of Hobbesian geometry to natural philosophy by arguing that mixed mathematics provided Hobbes with a model for thinking about it. In mixed mathematics, one may borrow causal principles from one science and use them in another science without there being a deductive relationship between those two sciences. Natural philosophy for Hobbes is mixed because an explanation may combine observations from experience (the ‘that’) with causal principles from geometry (the ‘why’). My argument shows that Hobbesian natural philosophy relies upon suppositions that bodies plausibly behave according to these borrowed causal principles from geometry, acknowledging that bodies in the world may not actually behave this way. First, I consider Hobbes's relation to Aristotelian mixed mathematics and to Isaac Barrow's broadening of mixed mathematics in Mathematical Lectures (1683). I show that for Hobbes maker's knowledge from geometry provides the ‘why’ in mixed-mathematical explanations. Next, I examine two explanations from De corpore Part IV: (1) the explanation of sense in De corpore 25.1-2; and (2) the explanation of the swelling of parts of the body when they become warm in De corpore 27.3. In both explanations, I show Hobbes borrowing and citing geometrical principles and mixing these principles with appeals to experience.     

Process tracing in political science: What's the story?

Methodologists in political science have advocated for causal process tracing as a way of providing evidence for causal mechanisms. Recent analyses of the method have sought to provide more rigorous accounts of how it provides such evidence. These accounts have focused on the role of process tracing for causal inference and specifically on the way it can be used with case studies for testing hypotheses. While the analyses do provide an account of such testing, they pay little attention to the narrative elements of case studies. I argue that the role of narrative in case studies is not merely incidental. Narrative does cognitive work by both facilitating the consideration of alternative hypotheses and clarifying the relationship between evidence and explanation. I consider the use of process tracing in a particular case (the Fashoda Incident) in order to illustrate the role of narrative. I argue that process tracing contributes to knowledge production in ways that the current focus on inference tends to obscure.     

How could models possibly provide how-possibly explanations?

One puzzle concerning highly idealized models is whether they explain. Some suggest they provide so-called ‘how-possibly explanations’. However, this raises an important question about the nature of how-possibly explanations, namely what distinguishes them from ‘normal’, or how-actually, explanations? I provide an account of how-possibly explanations that clarifies their nature in the context of solving the puzzle of model-based explanation. I argue that the modal notions of actuality and possibility provide the relevant dividing lines between how-possibly and how-actually explanations. Whereas how-possibly explanations establish claims of possible explanations, how-actually explanations establish claims of actual ones. Models, in turn, simply provide evidence for these claims.     

Naturalized epistemology, or what the Strong Programme can’t explain

In this paper I argue that the Strong Programme’s aim to provide robust explanations of belief acquisition is limited by its commitment to the symmetry principle. For Bloor and Barnes, the symmetry principle is intended to drive home the fact that epistemic norms are socially constituted. My argument here is that even if our epistemic standards are fully naturalized—even relativized—they nevertheless can play a pivotal role in why individuals adopt the beliefs that they do. Indeed, sometimes the fact that a belief is locally endorsed as rational is the only reason why an individual holds it. In this way, norms of rationality have a powerful and unique role in belief formation. But if this is true then the symmetry principle’s emphasis on ‘sameness of type’ is misguided. It has the undesirable effect of not just naturalizing our cognitive commitments, but trivializing them. Indeed, if the notion of ‘similarity’ is to have any content, then we are not going to classify as ‘the same’ beliefs that are formed in accordance with deeply entrenched epistemic norms as ones formed without reflection on these norms, or ones formed in spite of these norms. My suggestion here is that we give up the symmetry principle in favor of a more sophisticated principle, one that allows for a taxonomy of causes rich enough to allow us to delineate the unique impact epistemic norms have on those individuals who subscribe to them.     

Models and the dynamics of theory-building in physics. Part I—Modeling strategies

A deeper understanding of models is sought in considering what models do, rather what they are. This distinction emphasizes how two different modeling strategies, as they pursue different purposes, do invest in different options, in particular in regard to rigor and immediate empirical relevance. The analysis focuses therefore on the services expected from models by the scientists who construct them: models are sought for how they contribute to exploring and testing the context in which they operate. In a forthcoming Part II these general considerations will be anchored in the presentation of specific case studies.     

Understanding,explanation, and unification

In this article I argue that there are two different types of understanding: the understanding we get from explanations, and the understanding we get from unification. This claim is defended by first showing that explanation and unification are not as closely related as has sometimes been thought. A critical appraisal of recent proposals for understanding without explanation leads us to discuss the example of a purely classificatory biology: it turns out that such a science can give us understanding of the world through unification of the phenomena, even though it does not give us any explanations. The two types of understanding identified in this paper, while strictly separate, do have in common that both consist in seeing how the individual phenomena of the universe hang together. Explanations give us connections between the phenomena through the asymmetric, ‘vertical’ relation of determination; unifications give us connections through the symmetric, ‘horizontal’ relation of kinship. We then arrive at a general definition of understanding as knowledge of connections between the phenomena, and indicate that there might be more than two types of understanding.