Even though it has been argued that scientific cognition is distributed, there is no consensus on the exact nature of distributed cognition. This paper aims to characterize distributed cognition as appropriate for philosophical studies of science. I first classify competing characterizations into three types: the property approach, the task approach, and the system approach. It turns out that the property approach and the task approach are subject to criticism. I then argue that the most preferable way to understand distributed cognition (...) in science is provided by the system approach that takes a distributed-cognitive system as the unit of analysis. I clarify this position by considering possible objections and replies. (shrink)

In this article, I will view realist and non-realist accounts of scientific models within the larger context of the cultural significance of scientific knowledge. I begin by looking at the historical context and origins of the problem of scientific realism, and claim that it is originally of cultural and not only philosophical, significance. The cultural significance of debates on the epistemological status of scientific models is then related to the question of ‘intelligibility’ and how science, through models, can give us (...) knowledge of the world by presenting us with an ‘intelligible account/picture of the world’, thus fulfilling its cultural-epistemic role. Realists typically assert that science can perform this role, while non-realists deny this. The various strategies adopted by realists and non-realists in making good their respective claims, is then traced to their cultural motivations. Finally I discuss the cultural implications of adopting realist or non-realist views of models through a discussion of the views of Rorty, Gellner, Van Fraassen and Clifford Hooker on the cultural significance of scientific knowledge.Keywords: Models; Intelligibility; Scientific realism; Culture. (shrink)

This thesis studies the role of conceptual content in inference and reasoning. The first two chapters offer a theoretical and historical overview of the relation between inference and meaning in philosophy and psychology. In particular, a critical analysis of the formality thesis, i.e., the idea that rational inference is a rule-based and topic-neutral mechanism, is advanced. The origins of this idea in logic and its influence in philosophy and cognitive psychology are discussed. Chapter 3 consists of an analysis of the (...) relationship between inference and representation. It is argued that inference has to be studied from a pluralistic per- spective due to its dependence on different formats of representing information. The following four chapters apply conceptual spaces, a formal theory of concepts within cognitive semantics, to three concept-based inference-types. First, an explication of Sellars notion of material inference is advanced. Later, the model is extended to account for nonmonotonic inference by studying the role expectations in reasoning. Finally, a conceptual space-model of category-based induction is presented. This model predicts most of the empirical properties of this psychological phenomenon and subsumes some of the previous theories in psychology. It is stated that the explanatory fruitfulness of this new approach is evidence for the failure of the formality thesis and calls for a unified model of rational inference that puts semantics at center stage. The last chapter of the thesis discusses how inference and concepts interact in scientific reasoning, which makes constant use of hybrid symbolic structures for representing conceptual information. Stephen Toulmin’s notions of method of representation and inferential technique are developed and applied in a case study about the emergence of the notion of instantaneous speed during the passage from geometrical physics to analytical mechanics. It is claimed that this analysis provides support to the pluralistic perspective for theorizing about reasoning. (shrink)

Philosophers have recently paid more attention to the physical aspects of scientific models. The attention is motivated by the prospect that a model’s physical features strongly affect its use and that this suggests re-thinking modelling in terms of extended or distributed cognition. This paper investigates two ways in which physical features of scientific models affect their use and it asks whether modelling is an instance of extended cognition. I approach these topics with a historical case study, in which scientists kept (...) records not only of their findings, but also of some the mental operations that generated the findings. The case study shows how scientists can employ a physical model as an external information store, which allows alternating between mental manipulations, recording the outcome externally, and then feeding the outcome back into subsequent mental manipulations. The case study also demonstrates that a models’ physical nature allows replacing explicit reasoning with visuospatial manipulations. I argue, furthermore, that physical modelling does not need to exemplify a strong kind of extended cognition, the sort for which external features are mereological parts of cognition. It can exemplify a weaker kind, instead. (shrink)

Drawing on a range of literature, I introduce two new concepts for understanding and exploring distributed cognition: resilience engineering and degeneracy. By re-examining Ed Hutchins’ (1995) ethnographic study of the navigation team I show how a focus on the developmental acquisition of cognitive practices can draw out several crucial insights that have been overlooked. Firstly, that the way in which agents learn and acquire cognitive practices enables a form of resilience engineering: the process by which the system is able to (...) overcome and adapt to errors and the vagaries of nature. Secondly, that the best way to engineer a resilient system is through promoting degeneracy – how differing structures produce the same function – at the level of cognitive practices. These two features show that focusing on cognitive practices and developmental trajectories is important for both greater explanatory leverage; and is also useful in regard to the practicalities of designing real-world cognitive systems. (shrink)

We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: to identify the benefits of an ethnographic approach in philosophy of science and to structure an ethnographic approach for philosophical investigation best adapted to provide information relevant to philosophical interests and epistemic values. To this end, we advocate for a purpose-guided form of cognitive ethnography that mediates between the explanatory and normative interests of philosophy of science, while maintaining openness and (...) independence when framing such an investigation to achieve robust unbiased results. (shrink)