Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and (...) I emphasise the importance of what I call the “format” of a representation for the inferences agents can draw from it. Then, I show that the very same model can be presented under different formats, which do not enable scientists to perform the same inferences. Assuming that the main function of a representation is to allow one to draw predictions and explanations of the phenomena by reasoning with it, I conclude that imaginary models in abstracto are not used as representations: scientists always reason with formatted representations. Therefore, the problem of scientific representation does not lie in the relationship of imaginary entities with real systems. One should rather focus on the variety of the formats that are used in scientific practice. (shrink)

Linkage (or genetic) maps are graphs, which are intended to represent the linear ordering of genes on the chromosomes. They are constructed on the basis of statistical data concerning the transmission of genes. The invention of this technique in 1913 was driven by Morgan's group's adoption of a set of hypotheses concerning the physical mechanism of heredity. These hypotheses were themselves grounded in Morgan's defense of the chromosome theory of heredity, according to which chromosomes are the physical basis of genes. (...) In this paper, I analyze the 1919 debate between William Castle and Morgan's group, about the construction of genetic maps. The official issue of the debate concerns the arrangement of genes on chromosomes. However, the disputants tend to carry out the discussions about how one should model the data in order to draw predictions concerning the transmission of genes; the debate does not bear on the data themselves, nor does it focus on the hypotheses explaining these data. The main criteria that are appealed to by the protagonists are simplicity and predictive efficacy. However, I show that both parties' assessments of the simplicity and predictive efficacy of different ways of modeling the data themselves depend on background theoretical positions. I aim at clarifying how preference for a given model and theoretical commitments articulate. (shrink)

Gergely and Csibra's theory, known as "natural pedagogy theory", is meant to explain how infants fast-learn generic knowledge from adults. In this paper, my goal is to assess the explanatory import of this theory in a particular case, namely the phenomena known as "A-not-B errors". I first propose a clarification of natural pedagogy theory's fundamental hypotheses. Then, I describe Topál et al.'s (Science, 321, 1831-1834, 2008) experiments, which consist in applying natural pedagogy theory's framework to the A-not-B errors. Finally, I (...) show that natural pedagogy theory, in its actual stage of development, does not suffice to choose between various interpretations of Topál et al.'s experimental results. (shrink)

Models are generally used by scientists to obtain predictions and to provide explanations about phenomena. Their predictive and explanatory power is generally thought of as depending on their representative power. It is still not clear, though, in virtue of which features models allow scientists to draw inferences about the system they stand for. In this paper, I focus on a special kind of models, namely imaginary models (I-models) such as the simple pendulum. The main question I address is: how do (...) scientists use I-models in representing target systems? First, I propose a clarification of the very notion of representation, by emphasizing the importance of what I call the format of a representation to the inferences cognitive agents can draw from it. Then, I analyze the various representational relationships that are in play in the use of I-models. I finally conclude that there is no special semantics to be applied to I-models, and that the study of the representational power of models in general should instead focus on the variety of the formats that are used in scientific practice. (shrink)

In this paper, I wish to challenge theory-biased approaches to scientific knowledge, by arguing for a study of theorizing, as a cognitive activity, rather than of theories, as abstract structures independent from the agents’ understanding of them. Such a study implies taking into account scientists’ reasoning processes, and their representational practices. Here, I analyze the representational practices of geneticists in the 1910s, as a means of shedding light on the content of classical genetics. Most philosophical accounts of classical genetics fail (...) to distinguish between the purely genetic, or Mendelian level, and the cytological one. I distinguish between them by characterizing them in terms of their respective associated representational practices. I then present how the two levels were articulated within Morgan’s theory of crossing-over, and I describe the representational technique of linkage mapping, which embodies the “merging” of the Mendelian and cytological levels. I propose an analysis of the mapping scheme, as a means of enlightening the conceptual articulation of Mendelian and cytological hypotheses within classical genetics. Finally, I present the respective views of three opponents to Morgan in the 1910s, who had a different understanding of the articulation of cytology and Mendelism, and entertained different views concerning the role and proper interpretation of maps. I propose to consider these diverging perspectives as instantiating what I call different “versions” of classical genetics. (shrink)

Computer simulations are widely used in current scientific practice, as a tool to obtain information about various phenomena. Scientists accordingly rely on the outputs of computer simulations to make statements about the empirical world. In that sense, simulations seem to enable scientists to acquire empirical knowledge. The aim of this paper is to assess whether computer simulations actually allow for the production of empirical knowledge, and how. It provides an epistemological analysis of present-day empirical science, to which the traditional epistemological (...) categories cannot apply in any simple way. Our strategy consists in acknowledging the complexity of scientific practice, and trying to assess its rationality. Hence, while we are careful not to abstract away from the details of scientific practice, our approach is not strictly descriptive: our goal is to state in what conditions empirical science can rely on computer simulations. In order to do so, we need to adopt a renewed epistemological framework, whose categories would enable us to give a finer-grained, and better-fitted analysis of the rationality of scientific practice. (shrink)

During the last few decades, models have become the centre of attention in both cognitive science and philosophy of science. In cognitive science, the claim that humans reason with mental models, rather than mentally manipulate linguistic symbols, is the majority view. Similarly, philosophers of science almost unanimously acknowledge that models have to be taken as a central unit of analysis. Moreover, some philosophers of science and cognitive scientists have suggested that the cognitive hypothesis of mental models is a promising way (...) of accounting for the use of models in science. However, once the importance of models in cognition as well as in science has been acknowledged, much more needs to be said about how models enable agents to make predictions, and to understand the world. In this paper, our goal (as a cognitive scientist, working on causal reasoning, and a philosopher of science, working on models and representations) is twofold. We would like to further develop the notion of mental models, and to explore the parallels between mental models as a concept in cognitive science, and models in science. While acknowledging that the parallel move towards models in cognitive science and in philosophy of science is in the right direction, we think that: i. the notion of mental models needs to be clarified in order to serve as a useful tool, ii. the relation between the hypothesis of mental models and the use of models in science is still to be clarified. First, we will briefly recall a few points about the mental model hypothesis, on the one hand, and the model-centred view of science, on the other hand. Then, we will present our parallel criticisms, and put forward our own proposals. (shrink)

In this paper, I analyze the significance of Ernest Nagel's introduction of the notion of model in his reconstruction of scientific theories. Nagel's account is generally considered as a version of the "received view" of theories, whose main advocate is Carnap. However, I will show that Nagel's considerations on models imply a renunciation to the logical empiricists' project of the formalization of scientific theories. I will argue that Nagel implicitly acknowledges that, in order to study the content of theories, one (...) cannot abstract away from the agents' understanding of theories, and from the reasoning processes they perform when using them. (shrink)

The book edited by Roman Frigg and Matthew C. Hunter is a great example of interdisciplinary collaborative work, bringing together contributions by scholars of science and of art, around the topic of representation. The collection consists of eleven essays, seven of which were presented in early form at a conference organized by the two editors at the London School of Economics and the Courtauld Institute of Art in June 2006; the other four have been added subsequently. The result is a (...) high-standard, remarkably edited book. (shrink)

Résumé — La question de l’éducation, telle qu’elle se pose dès les premiers écrits de Simone Weil, où elle est pensée comme un apprentissage de la nécessité par l’action, puis dans ses réflexions politiques sur l’éducation syndicale d’une part et celle d’un peuple tout entier de l’autre, pour enfin se retrouver dans la pensée mystique des dernières années, offre un angle intéressant pour aborder l’œuvre de la philosophe. Jamais thématisée comme telle par Simone Weil, la notion d’éducation est pourtant transversale (...) et permet de comprendre à la fois l’unité profonde et les apparentes contradictions de sa pensée. Dans cet article, nous dégageons un certain nombre de caractéristiques importantes de la notion d’éducation qui se retrouvent aux plans individuel, politique et finalement mystique. On verra peu à peu émerger l’importance de la notion de symbole qui nous permettra de proposer, à partir de la " philosophie de l’éducation " de Simone Weil, un " mode d’emploi " pour la lecture de son œuvre même.— The issue of education provides us with an interesting perspective to approach Simone Weil’s works : though she never takes education as such as an object of study, it is a ubiquitous notion which enables us to understand both the deep unity and the apparent contradictions of her thought. In her first works, education is thought of as concrete learning of the necessity by one’s own actions on the world. In her political reflections, Simone Weil is concerned with both the education of workers by the syndicate and the education of a whole country. Finally, the issue of education is central, as I will show, to her late mystical thought. In this paper, I emphasize some important features of " the notion of " education as it appears in the individual, political and mystical works of Simone Weil. The notion of symbol will turn out to be of central importance, and I will finally show that it provides us with some " reading instructions " of the very writings of Simone Weil from the perspective of her philosophy of education. (shrink)

In this paper, I propose a study of the invention and development of the technique of genetic mapping in the 1920's. I show that what is usually taken as one and the same theory (Classical Genetics) is in fact the result of the articulation of various levels of explanations corresponding to two different disciplines, with different methods and representational practices -- namely Mendelian theory and cytology. The merging of these two disciplinary frameworks is embodied in the very rules underlying the (...) construction and interpretation of genetic maps. Moreover, the debates between the geneticists around how to display data within these maps reveal that they have different understanding of the articulation of these disciplines (and different theoretical commitments). (shrink)