Frank Zenker

Theory change as dimensional change: conceptual spaces applied to the dynamics of empirical theories

This paper offers a novel way of reconstructing conceptual change in empirical theories. Changes occur in terms of the structure of the dimensions—that is to say, the conceptual spaces—underlying the conceptual framework within which a given theory is formulated. Five types of changes are identified: (1) addition or deletion of special laws, (2) change in scale or metric, (3) change in the importance of dimensions, (4) change in the separability of dimensions, and (5) addition or deletion of dimensions. Given this classification, the conceptual development of empirical theories becomes more gradual and rationalizable. Only the most extreme type—replacement of dimensions—comes close to a revolution. The five types are exemplified and applied in a case study on the development within physics from the original Newtonian mechanics to special relativity theory.

Using Conceptual Spaces to Model the Dynamics of Empirical Theories

In Conceptual Spaces (Gardenfors 2000), dimensions and their relations provide a topological representation of a concept’s constituents and their mode of combination. When concepts are modeled as n-dimensional geometrical structures, conceptual change denotes the dynamic development of these structures. Following this basic assumption, we apply conceptual spaces to the dynamics of empirical theories. We show that the terms of the structuralist view of empirical theories can be largely recovered. Based on the logically possible change operations which a concept’s dimensions can undergo (singularly or in combination), we identify four types of (increasingly radical) change to an empirical theory. The incommensurability issue as well as the importance of measurement procedures for the identification of a radical theory change are briefly discussed.

From Features via Frames to Spaces : Modeling Scientific Conceptual Change Without Incommensurability or Aprioricity

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 model and traces its development from the feature list model. Drawing on extant literature, examples of frame-reconstructed taxonomic change are presented. This occurs for purposes of comparison with an alternative tool, conceptual spaces. The main claim is that conceptual spaces save the merits of the frame model and provide a powerful model for conceptual change in scientific knowledge, since distinctions arising in measurement theory are native to the model. It is suggested how incommensurability as incomparability of theoretical frameworks might be avoided (thus coming on par with a key-result of applying frames). Moreover, as non(inter-)translatability of world-views, it need not to be treated as a genuine problem of conceptual representation. The status of laws vis a vis their dimensional bases as well as diachronic similarity measures are (inconclusively) discussed.

Bayesian Argumentation: The practical side of probability

Relevant to, and drawing from, a range of disciplines, the chapters in this collection show the diversity, and applicability, of research in Bayesian argumentation. Together, they form a challenge to philosophers versed in both the use and criticism of Bayesian models who have largely overlooked their potential in argumentation. Selected from contributions to a multidisciplinary workshop on the topic held in Sweden in 2010, the authors count linguists and social psychologists among their number, in addition to philosophers. They analyze material that includes real-life court cases, experimental research results, and the insights gained from computer models. The volume provides, for the first time, a formal measure of subjective argument strength and argument force, robust enough to allow advocates of opposing sides of an argument to agree on the relative strengths of their supporting reasoning. With papers from leading figures such as Michael Oaksford and Ulrike Hahn, the book comprises recent research conducted at the frontiers of Bayesian argumentation and provides a multitude of examples in which these formal tools can be applied to informal argument. It signals new and impending developments in philosophy, which has seen Bayesian models deployed in formal epistemology and philosophy of science, but has yet to explore the full potential of Bayesian models as a framework in argumentation. In doing so, this revealing anthology looks destined to become a standard teaching text in years to come.

Reconstructing Recent Work on Macrosocial Stress as a Research Program

ABSTRACT We reconstruct recent work on macrosocial stress (Chou, Parmar, & Galinsky, 2016) as if it were an instance of a research strategy that tests point-alternative hypotheses within a full-fledged research program. Because this strategy is free of various deficits that beset dominant strategies (e.g., meta-analysis, Bayes factor analysis), our article demonstrates one way in which the confidence crisis may be overcome.

Applications of Conceptual Spaces: the Case for Geometric Knowledge Representation

This volume provides an overview of applications of conceptual spaces theory, beginning with an introduction to the modeling tool that unifies the chapters. The first section explores issues of linguistic semantics, including speakers negotiation of meaning. Further sections address computational and ontological aspects of constructing conceptual spaces, while the final section looks at philosophical applications. Domains include artificial intelligence and robotics, epistemology and philosophy of science, lexical semantics and pragmatics, agent-based simulation, perspectivism, framing, contrast, sensory modalities, and music, among others. This collection provides evidence of the wide application range of this theory of knowledge representation. The papers in this volume derive from international experts across different fields including philosophy, cognitive science, linguistics, robotics, computer science and geography. Each contributor has successfully applied conceptual spaces theory as a modeling tool in their respective areas of expertise. Graduates as well as researchers in the areas of epistemology, linguistics, geometric knowledge representation, and the mathematical modeling of cognitive processes should find this book of particular interest. (Less)

Extending a Multilab Preregistered Replication of the Ego-Depletion Effect to a Research Program

ABSTRACT We reanalyze the recent multilab preregistered study on ego-depletion by Hagger and Chatzisarantis (2016) as if their data were obtained under the research program-strategy (Witte & Zenker, 2016a, 2016b). This strengthens Hagger and Chatzisarantis’s (2016) main conclusion, because our reanalysis more directly corroborates the absence of a medium-sized, or a small-sized, ego-depletion effect (d = .50 under α = β = .05; d = .20 under α = β = .01). We explain how a smaller ego-depletion effect of d = .04 can be tested under similar conditions, having determined this value by maximum likelihood estimation, and compare the research program-strategy to a standard meta-analytic integration.

Beyond Schools : Reply to Marsman, Ly, and Wagenmakers

ABSTRACT We respond to comments by Marsman, Ly, and Wagenmakers (2016) on Witte and Zenker (2016b).

Communication, rationality and conceptual changes in scientific theories

This article outlines how conceptual spaces theory applies to modeling changes of scientific frameworks when these are treated as spatial structures rather than as linguistic entities. The theory is briefly introduced and five types of changes are presented. It is then contrasted with Michael Friedman’s neo-Kantian account that seeks to render Kuhn’s “paradigm shift” as a communicatively rational historical event of conceptual development in the sciences. Like Friedman, we refer to the transition from Newtonian to relativistic mechanics as an example of “deep conceptual change.” But we take the communicative rationality of radical conceptual change to be available prior to the philosophical meta-paradigms that Friedman deems indispensable for this purpose.

From discovery to justification: Outline of an ideal research program in empirical psychology

The gold standard for an empirical science is the replicability of its research results. But the estimated average replicability rate of key-effects that top-tier psychology journals report falls between 36 and 39% (objective vs. subjective rate; Open Science Collaboration, 2015). So the standard mode of applying null-hypothesis significance testing (NHST) fails to adequately separate stable from random effects. Therefore, NHST does not fully convince as a statistical inference strategy. We argue that the replicability crisis is “home-made” because more sophisticated strategies can deliver results the successful replication of which is sufficiently probable. Thus, we can overcome the replicability crisis by integrating empirical results into genuine research programs. Instead of continuing to narrowly evaluate only the stability of data against random fluctuations (discovery context), such programs evaluate rival hypotheses against stable data (justification context).