Abstract:The (dynamic) frame model, originating in artificial intelligence and cognitive psychology, has recently been applied to change-phenomena traditionally studied within history and philosophy of science. Its application purpose is to account for episodes of conceptual dynamics in the empirical sciences (allegedly) suggestive of incommensurability as evidenced by "ruptures" in the symbolic forms of historically successive empirical theories with similar classes of applications. This article reviews the frame mode… Show more
“…In particular, because dimensions are correlated, some regions in conceptual spaces are more inhabited than others. Zenker (2014) suggests including knowledge about such correlations in terms of population patterns: 'To mimic this in conceptual spaces, [...] one may speak of sub-regions of a conceptual space being empty (or comparatively unpopulated)' (Zenker 2014, p. 82f). For example, certain combinations of colour and flavour are very common in berries: red and dark colours indicate sweetness.…”
Section: Natural Kinds Realism and Correlationsmentioning
Conceptual spaces are a frequently applied framework for representing concepts. One of its central aims is to find criteria for what makes a concept natural. A prominent demand is that natural concepts cover convex regions in conceptual spaces. The first aim of this paper is to analyse the convexity thesis and the arguments that have been advanced in its favour or against it. Based on this, I argue that most supporting arguments focus on single-domain concepts (e.g., colours, smells, shapes). Unfortunately, these concepts are not the primary examples of natural concepts. Building on this observation, the second aim of the paper is to develop criteria for natural multi-domain concepts. The representation of such concepts has two main aspects: features that are associated with the concept and the probabilistic correlation pattern which the concept captures. Conceptual spaces, together with probabilistic considerations, provide a helpful framework to approach these aspects. With respect to feature representation, the existence of characteristic features (i.e., that apples have a specific taste) is essential. Moreover, natural concepts capture peaks of a probabilistic distribution over complex spaces. They carve up nature at its joints, that is, at areas with no or low probabilistic density. This last aspect is shown to be closely related to the convexity demand.
“…In particular, because dimensions are correlated, some regions in conceptual spaces are more inhabited than others. Zenker (2014) suggests including knowledge about such correlations in terms of population patterns: 'To mimic this in conceptual spaces, [...] one may speak of sub-regions of a conceptual space being empty (or comparatively unpopulated)' (Zenker 2014, p. 82f). For example, certain combinations of colour and flavour are very common in berries: red and dark colours indicate sweetness.…”
Section: Natural Kinds Realism and Correlationsmentioning
Conceptual spaces are a frequently applied framework for representing concepts. One of its central aims is to find criteria for what makes a concept natural. A prominent demand is that natural concepts cover convex regions in conceptual spaces. The first aim of this paper is to analyse the convexity thesis and the arguments that have been advanced in its favour or against it. Based on this, I argue that most supporting arguments focus on single-domain concepts (e.g., colours, smells, shapes). Unfortunately, these concepts are not the primary examples of natural concepts. Building on this observation, the second aim of the paper is to develop criteria for natural multi-domain concepts. The representation of such concepts has two main aspects: features that are associated with the concept and the probabilistic correlation pattern which the concept captures. Conceptual spaces, together with probabilistic considerations, provide a helpful framework to approach these aspects. With respect to feature representation, the existence of characteristic features (i.e., that apples have a specific taste) is essential. Moreover, natural concepts capture peaks of a probabilistic distribution over complex spaces. They carve up nature at its joints, that is, at areas with no or low probabilistic density. This last aspect is shown to be closely related to the convexity demand.
“…Conceptual spaces thus amount to more than a combination of extant ideas from frame theory and prototype theory, since the geometry of the domains yields predictions that are not possible in either. (For a comparison of frames with conceptual spaces, see Zenker (2014)). In his contribution to this volume, Zwarts demonstrates how existing feature analyses for particular domains can be used to construct conceptual spaces in which notions like convexity can be systematically studied.…”
Section: Properties and Conceptsmentioning
confidence: 99%
“…These magnitudes can be modeled as collections of dimensions with their inter-relations, that is, as conceptual spaces Zenker 2011, 2013;Zenker and Gärdenfors 2014;Zenker 2014). Put schematically, an empirical theory, T, depends on a conceptual framework, F, that is modeled as a conceptual space, S.…”
Section: Conceptual Framework As Spatial Entitiesmentioning
“…Footnote 13 continued sets of statements (Carnap) or as sets of models (Sneed, Stegmüller) is of great interest for the comparison of different methods of theory-representation as, for example, it is done by Zenker (2014) and Zenker and Gärdenfors (2014), who compare the representation of conceptual change in empirical science by frames, conceptual spaces, and sets of models.…”
According to a seminal paper by Barsalou (Frames, fields, and contrasts, 1992), frames are attribute-value-matrices for representing exemplars or concepts. Frames have been used as a tool for reconstructing scientific concepts as well as conceptual change within scientific revolutions (Andersen and Nersessian, in Philos Andersen et al., in The cognitive structure of scientific revolutions, 2006; Votsis and Schurz, in Stud Hist Philos Sci 43:105-114, 2012, in Concept types and frames. Application in language, cognition, and science, 2014). In the frame-based representations of scientific concepts developed so far the semantic content of concepts is (partially) determined by a set of attribute-specific values. This way of representing semantic content works best for prototype concepts and defined concepts of a conceptual taxonomy satisfying the no-overlap principle. In addition to the semantic content of prototype concepts and defined concepts, frames can also contain empirical knowledge that is represented as constraints between the values of the frame. Beside prototype concepts and defined concepts, theoretical concepts that are multiply operationalized play an important role in science. However, so far no frame-based representation of theoretical concepts has been proposed. In this paper, it will be shown that theoretical concepts can be represented by frames and that frame-based representations of prototype concepts and defined concepts have another structure than frame-based representations of theoretical concepts. In order to explicate this difference, we will develop a frame-based method for representing all three kinds of concepts by means of mathematical graph-theory. One important consequence will be that the constraints of a frame representing a B Stephan Kornmesser
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