Online research in philosophy

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)

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)

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)

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 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)

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)

It is quite widely acknowledged, in the field of cognitive science, that the format in which a set of data is displayed (lists, graphs, arrays, etc.) matters to the agents' performances in achieving various cognitive tasks, such as problem-solving or decision-making. This paper intends to show that formats also matter in the case of theoretical representations, namely general representations expressing hypotheses, and not only in the case of data displays. Indeed, scientists have limited cognitive abilities, and representations in different formats (...) have different inferential affordances for them. Moreover, this paper shows that, once agents and their limited cognitive abilities get into the picture, one has to take into account both the way content is formatted and the cognitive abilities and epistemic peculiarities of agents. This paves the way to a dynamic and pragmatic picture of theorizing, as a cognitive activity consisting in creating new inferential pathways between representations. (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)