Scientific Visualisation

Epistemic Weight and Surpluses

by Marianne Richter (Author)
©2014 Thesis 264 Pages


Much of the recent confidence in the future of science and technology stems from advances in scientific visualisation. But is it right to assume that visual – and especially pictorial – measures carry special epistemic weight in the context of scientific reasoning? Do pictorial approaches have any surpluses, compared to other semiotic types? This book delves into these issues from the point of view of the philosophy of science. New examples from the field of scientific visualisation are introduced in order to account for the epistemic weight and surpluses of syntactically dense – pictorial – symbol systems.

Table Of Contents

  • Cover
  • Title
  • Copyright
  • About the author
  • About the book
  • This eBook can be cited
  • Table of Contents
  • Abbreviations and Acronyms
  • Table of Figures
  • List of Tables
  • Preface
  • I Introduction
  • II Picture-like Means in Science
  • 2.1 ‘Figure’ – ‘Image’ – ‘Picture’
  • 2.1.1 Extended definition
  • 2.1.2 Specifications
  • History-based specification
  • Case-based specification
  • Exclusive concept-based specification
  • Inclusive concept-based specification
  • 2.1.3 Lessons learned
  • 2.2 An epistemic account of picture-like means
  • 2.2.1 Epistemic problems
  • Defining ‘epistemic’
  • Analysing ‘function’ and functions
  • Desiderata: epistemic weight and epistemic surpluses
  • 2.2.2 Figures and picture-likeness
  • What are scientific figures?
  • What makes scientific figures ‘picture-like’?
  • 2.3 Picture-like means in science as a philosophical problem
  • 2.3.1 The failure of the purely linguistic view of representation
  • 2.3.2 The role of picture-like means in recent accounts of science
  • 2.3.3 Techno-science and the emergence of scientific visualisation
  • III Examples
  • 3.1 Example 1: modelling mesenchymal stem cell differentiation
  • 3.1.1 Research objectives
  • 3.1.2 Motivators
  • 3.1.3 Initial assumptions (a)
  • 3.1.4 Initial assumptions (b)
  • 3.1.5 Model
  • 3.1.6 Stability properties
  • 3.1.7 Bifurcation properties
  • 3.1.8 Single cell switching
  • 3.1.9 Cell population effects
  • 3.1.10 Discussion
  • 3.2 Example 2: modelling signal transduction in mammal cells
  • 3.2.1 Research objectives and motivators
  • 3.2.2 Related work
  • 3.2.3 Techniques combined
  • 3.2.4 Schematic illustration of the cell model
  • 3.2.5 Rendering scheme for proteins and trajectories
  • 3.2.6 Rendering scheme for reactions
  • 3.2.7 Tractability measures: cuts and transections
  • 3.2.8 Tractability measures: depth cues
  • 3.2.9 Alternative rendering: microscopy-like images
  • 3.2.10 Application I: transduction types for MAPK
  • 3.2.11 Application II: delivery of drug molecules
  • 3.2.12 Technical aspects
  • 3.2.13 Results
  • 3.2.14 Conclusion and future work
  • 3.3 Abstractions
  • 3.3.1 Functional involvement of figures
  • 3.3.2 Practical implications of figure-likeness
  • 3.3.3 Methodological relevance of figures
  • 3.3.4 Compositional and representational aspects of figures
  • 3.3.5 Observations on the prevalence of figures
  • IV Epistemic Weight and Epistemic Surpluses
  • 4.1 Recent accounts of picture-like means in science
  • 4.1.1 Operational accounts: analysing modes of implementation
  • 4.1.2 Ontological accounts: analysing traits of occurrence or identification
  • 4.1.3 Functional accounts: analysing practical embeddings
  • 4.1.4 Interim summary
  • 4.2 An extended argument for the epistemic weight of picture-like means
  • 4.2.1 The role of picture-like means
  • Picture-like means as arguments
  • Picture-like means in mere support of arguments
  • Picture-like means in arguments
  • 4.2.2 Picture-likeness revisited
  • Learning from misconceptions
  • From referential systems towards symbol systems
  • What is a symbol system?
  • What makes a symbol system picture-like?..
  • 4.2.3 A case for the epistemic weight of picture-like means
  • 4.2.4 Perini’s account: problems and gaps
  • 4.2.5 A case for the epistemic weight of syntactically dense systems
  • Continuous vs. strict dichotomies
  • The concept of ad hoc syntaxes
  • Explanatory potential
  • Ad hoc syntaxes in formal systems
  • Differences to other approaches
  • 4.3 A new account of the epistemic surpluses of picture-like means
  • 4.3.1 Issues with defining epistemic surpluses
  • 4.3.2 Deficiency arguments
  • Some ambiguities about ambiguity issues
  • On the (im-)possibility of formalising pictorial systems
  • 4.3.3 Efficiency arguments
  • The new master argument: spatial efficiency
  • Internalised codes and the haunting intuition about similarity
  • Refining the notion of interaction.
  • V Outlook
  • Summary (English)
  • Summary (German)
  • Literature

← 8 | 9 → Abbreviations and Acronyms


Chondrogenic cell (Chondrocyte)

DN model

Deductive-Nomological model


Depth of field


Mitogen-Activated Protein Kinase


Mitogen-Activated-phosphorylated Protein Kinase




Mesenchymal Stem Cell


Osteogenic cell (Osteoblast)


Progenitor cell (Progenitor)


Philosophical Investigations


Wittgenstein (2001) 11953


Tractatus logico-philosophicus


Wittgenstein (1974) 11922


Transcriptional Regulator


Vertex Buffers


Chondrogenic TR


Osteogenic TR


Progenitor maintenance factor


Chondrogenic stimulus


Pro-differentiation stimulus


Osteogenic stimulus




three-dimensional← 9 | 10 →

← 10 | 11 → Table of Figures

Fig. 1: Microscopy-like images (cf. Falk et al. 2009, p. 173)d

Fig. 2: Confocal laser scanning micrograph (cf. Falk et al. 2009, p. 169)

Fig. 3: Motivator [I]: Revision of the purely linguistic view of representation

Fig. 4: Pictogram by O. Neurath (1933ff.) showing Vienna’s net expenditure (cf. Neurath 1991, p. 322)

Fig. 5: Motivator [II]: Focal shifts in the philosophy of science

Fig. 6: Solar system modeled by Johannes Kepler, 1596 (cf. Kepler 1938)

Fig. 7: Motivator [III]: Status shift concerning pictorial procedures in science

Fig. 8: Biological account of cell types and transitions (cf. Schittler et al. 2010, p. 2)

Fig. 9: Components incorporated into the mathematical model (cf. Schittler et al. 2010, p. 3)

Fig. 10: Exemplary parameter set (cf. Schittler et al. 2010, p. 3)

Fig. 11: Model (cf. Schittler et al. 2010, p. 3)

Fig. 12: Stable solutions (cf. Schittler et al. 2010, p. 4)

Fig. 13: Unpublished version (D. Schittler)

Fig. 14: Points of transition (cf. Schittler et al. 2010, p. 4)

Fig. 15: Scenarios for different stimuli inputs (cf. Schittler et al. 2010, p. 5)

Fig. 16: Exemplary solution of the stochastic simulation (cf. Schittler et al. 2010, p. 7)

Fig. 17: Stochastic simulation: model (cf. Schittler et al. 2010, p. 7)

Fig. 18: Simulation for additional time parameter (cf. Schittler et al. 2010, p. 7)

Fig. 19: Introductory sampling of screenshots (cf. Falk et al. 2009, p. 169)

Fig. 20: Unpublished source file (M. Falk)

Fig. 21: Variations of graphical rendition (cf. Falk et al. 2009, p. 171)

← 11 | 12 → Fig. 22: Modes of signal transport (cf. Falk et al. 2009, p. 172)

Fig. 23: Coding scheme for reactions (cf. Falk et al. 2009, p. 171)

Fig. 24: Signal transduction mechanism (cf. Falk et al. 2009, p. 172)

Fig. 25: Differences in protein concentration and crowding (cf. Falk et al. 2009, p. 173)

Fig. 26: Drug delivery to cancer cells in a tumour (cf. Falk et al. 2009, p. 173)

Fig. 27: Size of tumour and drug molecules (cf. Falk et al. 2009, p. 175)

Fig. 28: Number of components per type (cf. Falk et al. 2009, p. 174)

Fig. 29: Rendering performance (cf. Falk et al. 2009, p. 174)

Fig. 30: Levels of adjustment

Fig. 31: Disappointed chimpanzee (cf. Darwin 1872, p. 141)

Fig. 32: Visualization pipeline (cf. Ertl 2009, 2nd lecture)

Fig. 33: C. H. Waddington: Intra-cellular decision-making (cf. Waddington 1957, p. 29)

Fig. 34: Taxonomy of the conditions of language- and picture-likeness

Fig. 35: Signifiers and signification of ad hoc syntaxes

Fig. 36: Extended scope of systematic integration

Fig. 37: Extended scope of systematic integration

Fig. 38: B. Bertin’s graphical variables (cf. Bertin 1974, p. 49ff.)


© 2009 IEEE. Fig. 1, Fig, 2, Fig. 19, Fig. 21, Fig. 22, Fig. 23, Fig. 24, Fig. 25, Fig. 26, Fig. 27, Fig. 28, Fig. 29 are reprinted from M. Falk, M. Klann, M. Reuss & Th. Ertl (2009). Visualization of Signal Transduction Processes in the Crowded Environment of the Cell. IEEE Pacific Visualization Symposium (PacificVis 2009), p. 169–176. Copyright 2009, IEEE.

© 2010 AIP Publishing LLC. Fig. 8, Fig. 9, Fig. 10, Fig. 11, Fig. 12, Fig. 14, Fig. 15, Fig. 16, Fig. 17, Fig. 18 are reprinted with permission from D. Schittler, J. Hasenauer, F. Allgöwer & S. Waldherr (2010). Cell differentiation modeled via a coupled two-switch regulatory network. CHAOS 20, 045121. Copyright 2010, AIP Publishing LLC.

© 1957 Allen & Unwin. Taylor & Francis Royalty Department. Fig. 33 is reprinted with permission from C. H. Waddington (1957). The Strategy of the Genes: A Discussion of some Aspects of Theoretical Biology. London: Allen & Unwin, p. 29.

← 12 | 13 → ©Daniella Schittler. Fig. 13 is published with personal permission.

© Martin Falk. Fig. 20 is published with personal permission.

© Thomas Ertl. Fig. 32 is published with personal permission.

References to original sources appear with all reprinted material.← 13 | 14 →

← 14 | 15 → List of Tables

Tab. 1← 15 | 16 →

← 16 | 17 → Preface

The philosophical interest in scientific visualisation had already reached a peak when I started working on this thesis in 2009. Nevertheless, the various contributors to the thriving debates have kept on nurturing diverging intuitions on the nature, exploitability and methodological status of visual – and especially pictorial – means. It seems that while the prevalence of pictorial means was never called into question their status as aids to the scientist – and thus as subjects of interest to the philosophy of science – is repeatedly in need of reconsideration. Meanwhile, the range of attributions comprises everything from redundant or distractive to enabling or instructive, which only reinforces the friction between “those who think that the image is an extremely rudimentary system […] and those who think that signification cannot exhaust the image’s ineffable richness”1 (Barthes 1977, p. 32). The implementation of study programs, such as Visual Computing which grew out of computer science, or Visual Studies which involves several disciplines within the humanities, added further fuel to a fire which finally spread to the philosophy of science as well, where it presents a particluar challenge to those who try to analyse the notion of science (and notions related to it) in terms of its integral and contingent components. Following this track, the question arises as to whether the long-neglected pictorial means can be integral to core measures of scientific practice and thus carry epistemic weight in a non-trivial sense.

In my thesis I have also made attempts to use this strategy of conceptualisation. That is, I assume that there are measures, such as representation and argumentation, which are integral to the scientific practices under consideration, and which can be modelled in such a way that we can truly say whether or not a pictorial symbol system has the capacity to become integral to them. Needless to say, I am not about to resolve once and for all the tensions between different ideas and intuitions that such an analysis brings about with regards to the role and performance of pictorial means in science, even more so as these tensions arise in various empirical ← 17 | 18 → contexts, such as research-, conference- and publishing-practices, which is why one can hardly uphold the claim that they are principally or broadly induced by a certain, perhaps incorrect running argument or inadequate framework. Instead of tackling an over-generalised idea of the so-called ‘state of art’ and its deficiencies, I therefore decided to spell out the traits of exemplary scientific practices in order to obtain provocations and correctives for the discussion of – again exemplary – epistemological and aesthetic accounts. The choice of reference frameworks is substantiated by two literature surveys – one with a methodological and one with a conceptual focus. Provided the reader (philosopher, scientists, both or other) identifies with the chosen frameworks and revisions that I would like to propose to the recent discussion, s/he will gain a conceptual toolset that helps at least to take a local stance on the controversy.

My work on that toolset would not have been possible without the vital support that I received at the University of Stuttgart and beyond. At this point, I would like to thank all persons and institutions that helped me to complete this project. First of all, I would like to thank my examiners Jun.-Prof. Dr. Ulrike Pompe-Alama, Prof. Dr. Thomas Ertl and Prof. Roman Frigg, Ph. D., for their encouragement and guidance throughout this project. I also owe gratitude to Prof. Dr. Christoph Hubig who followed the project with vigorous interest and who was the examinder who passed it for the Milestone Examination. For their valuable advice and time devoted to discussing my ideas, I am also grateful to Prof. Dr. Catrin Misselhorn, Prof. Dr. Dr. Rafaela Hillerbrand, Angela Matthies, M. A., Dr. Juan Duran, Dr. Eckhart Arnold and Dr. Tillmann Pross. Furthermore, I would like to thank Michael Poznic, M. A. and Dipl. Musician Tanya Newman for proofreading the first drafts. Special thanks go to my colleagues from the natural and engineering sciences, especially Dipl.-Inf. Daniella Schittler, Dr. Martin Falk, Dipl.-Phys. Michael Raschke and Dipl.-Phys. David Molnar, who allowed me to participate in their work and who incited me to do so by their curiosity about a ‘philosophical view’. A great deal of interest has also been shared by the Richters, especially Sabine, Edda and Rudolf, and my friends, whom I cannot thank enough for following me through the highs and lows that accompanied this research adventure. Last but not least, I would like to thank the Cluster of Excellence Simulation Technology (EXC SIMTECH) and the Deutsche Forschungsgemeinschaft (DFG) for making it all possible.

Stuttgart, October 2013

Marianne Richter


ISBN (Hardcover)
Publication date
2014 (January)
symbolic systems Symbolsysteme Wissenschaftliche Visualisierung Bildmittel Picture-based reasoning
Frankfurt am Main, Berlin, Bern, Bruxelles, New York, Oxford, Wien, 2014. 264 pp., 28 coloured fig., 10 b/w fig., 1 table

Biographical notes

Marianne Richter (Author)

Marianne Richter studied philosophy and literature in Stuttgart, Erlangen (Germany) and London. She received her doctoral degree in philosophy at the University of Stuttgart. Her interests are devoted to issues at the various interfaces between science, philosophy of science, ethics and aesthetics.


Title: Scientific Visualisation