To explain why, in scientific problem solving, a diagram can be “worth ten thousand words,” Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be “informationally” equivalent but differ “computationally,” just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the “imagery debate” of the 1970s on whether there are image-like (...) mental representations. Simon (1972, 1978) hoped to solve this debate by thoroughly reducing the differences between forms of mental representations (e.g., between images and sentences) to differences in computational efficiency; to carry out this reduction, he borrowed from computer science the concepts of data type and of data structure. I argue that, in the end, his account amounted to nothing more than characterizing representations by the fast operations on them. This analysis then allows me to assess what Simon’s approach actually achieves when transported from psychology to the study of scientific representations, as in Larkin and Simon (1987): it allows comparing, not representations in and of themselves, but rather the computational roles they play in particular problem-solving processes—that is, representations together with a particular way of using them. (shrink)

Pictorial representations such as diagrams and figures are widely used in scientific literature for explanatory and descriptive purposes. The intuitive nature of pictorial representations coupled with texts foster a better understanding of the objects of study. Biological mechanisms and processes can be clearly illustrated and grasped in pictures. I argue that pictorial representations describe biological phenomena by telling stories. I elaborate on the role of narrative structures of pictures in the frontier research using a case study in immunology. I articulate (...) that pictures with an inherent narrative structure are crucial in biological sciences. (shrink)

Analog representation is often cast in terms of an engineering distinction between smooth and discrete systems. The engineering notion cuts across interesting representational categories, however, so it is poorly suited to thinking about kinds of representation. This paper suggests that analog representations support a pattern of interaction, specifically open-ended searches for content across levels of abstraction. They support the pattern by sharing a structure with what they represent. Continuous systems that satisfy the engineering notion are exemplars of this kind because (...) they are uninterpretable unless they are structure-preserving. Analog representations, so understood, include pictures, images, diagrams, and most graphs. This conception of analogicity also fits well with a line of thought about what makes perceptual states distinctive: they satisfy a “parts principle”. (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)

This paper examines Karl Alfred von Zittel’s practice in order to uncover the roots of so-called idiographic paleontology. The great American paleontologist Stephen Jay Gould (1941–2002) defined the discipline of idiographic paleontology as illustration and description of the morphological features of extinct species. However, this approach does not investigate macroevolutionary patterns and processes. On the contrary, the paleobiological revolution of the 1970s implemented an epistemic methodology that illustrates macrovelutionary patterns and laws by combining idiographic data with a nomothetic form of (...) explanation. This article elucidates the features of the idiographic data as well as the acquired knowledge coupled with this approach. First of all, Heinrich G. Bronn’s (1800–1862) statistical method is analyzed. Zittel’s practice arose as a reaction against the approximate conclusions reached by Bronn’s quantitative approach. Second, the details of Zittel’s methodology are described in order to bring out its peculiarities. The microscope played a pivotal role in creating and forming Zittel’s morphological data. This analysis sheds new light on the reasons behind the so-called ideographic paleontology, thus revising Gould’s historical reconstruction, as well as on the notion of paleontological data. However, even though Zittel aimed at reaching precise and stable conclusions, his data cannot be used for elucidating evolutionary mechanisms: they are scientific in a purely descriptive sense, but completely useless for biological investigations. Finally, this paper examines how Zittel’s methodology affects the contemporary paleobiological enterprise and thereby reflects upon the notion of natural history. (shrink)

The iconic revolution changing the routine of everyday communication is gradually leading to the creation of a linguistic structure that combines visual and verbal tools in both formal and semantic aspects. Computer and mobile applications today enable high-tech imaging that ensures the spread of iconic communication in mundane interactions and the possibility of a creative combination of verbal and iconic codes for language users who navigate in a world of images in an increasingly confident manner. The iconic revolution that accompanies (...) this expansion of new communication technologies thus leads to serious changes in language use, thereby enhancing the transmission of verbal and iconic language to become a key element of mundane communication. This article argues that this turn in communication technology guided the attention of Kristóf Nyíri, an eminent figure in contemporary Hungarian philosophy, to the problem of the relationship between icon, language, and tradition. The aim of the present essay is twofold: to shed light on the relevance of Nyíri’s analysis and thoughts on the relationship between tradition and language in the history of communication, and to identify, using Nyíri’s model of pictorial meaning as a starting point, the relevant aspects of the conception of technological determinism in answering questions from the philosophy of language and mind. (shrink)

Biological sciences have strived to adopt the conceptual framework of physics and have become increasingly quantitatively oriented, aiming to refute the assertion that biology appears unquantifiable, unpredictable, and messy. But despite all effort, biology is characterized by a paucity of quantitative statements with universal applications. Nonetheless, many biological disciplines—most notably molecular biology—have experienced an ascendancy over the last 50 years. The underlying core concepts and ideas permeate and inform many neighboring disciplines. This surprising success is probably not so much attributable (...) to mathematical and statistical approaches in molecular biology, but rather to the preponderance of qualitative approaches, especially visualization. Visualizations can be afforded by quantitative research, but usually they rely on both, quantitative and qualitative research. I claim the following three features to be responsible for the unceasing zeal for using visualizations: visual representations facilitate reasoning, images can be cognitively processed in a “fast” manner, and abstractions of visual representations prompt conceptual advances. In summary, visualizations have largely contributed to the success of molecular biology by conveying its concepts to other disciplines, and at the same time, eclipsing mere quantitative approaches. However, visualizations also bear the risk of misinterpretation when traversing neighboring disciplines and even more so when pervading nonscientific domains. (shrink)

In line with Ken Manders’s seminal account of Euclid’s diagrammatic method in the “The Euclidean Diagram,” two proof systems with a diagrammatic syntax have been advanced as formalizations of the method FG and Eu. In a paper examining Eu, Nathaniel Miller, the creator of FG, has identified a variety of technical problems with the formal details of Eu. This response shows how the problems are remedied.

Without doubt, there is a widespread usage of visualisations in science. However, what exactly the _epistemic status_ of these visual representations in science may be remains an open question. In the following, I will argue that at least some scientific visualisations are indispensible for our cognitive processes. My thesis will be that, with regard to the activity of _learning_, visual representations are of relevance in the sense of contributing to the aim of _scientific_ _understanding_. Taking into account that understanding can (...) be regarded as an epistemic desideratum in its own right, I will argue that, at least in some instances, no understanding can be achieved without the aid of visualisations. Consequently, they are of crucial importance in this process. Moreover, to support this thesis we will make use of some findings in educational psychology. (shrink)

Essay Review of The Tragic Sense of Life: Ernst Haeckel and the Struggle over Evolutionary Thought by Robert J. Richards [2009].

If we set aside personal edification, what reasons remain for a philosopher of science to study the intellectual biography of a famous (or infamous) scientist? This question raises familiar and perhaps tired arguments about the relationship between history of science and philosophy of science, but it is also practical: why take the time to digest almost 600 pages devoted to the controversial German zoologist Ernst Haeckel? A preliminary answer is the author. The historical investigations of Robert Richards have been of (...) ongoing interest to philosophers, whether it be evolutionary explanations of mind and behavior in the nineteenth century (Dennett 1989) or his contentious claims—reinvigorated in the present volume—about Darwin’s commitment to embryonic recapitulation (Lennox 1994). Richards has a knack for unearthing details germane to conceptual reflection, in no small part because of his own philosophical predilections (e.g., a selection model of scientific theory development). Here I entertain three more reasons to follow the injunction tolle lege: the prescient synthesis exemplified in Haeckel’s evolutionary theorizing, the impact of model organism choice, and the critical role of pictures in scientific reasoning accented by Haeckel’s artistic proclivities. (shrink)

In this article, I argue that pictorial styles are individuated in terms of different degrees of determinacy. For example, Morandi’s still-life etchings and Monet’s cathedral paintings embody different styles in that in the former, shape properties are differentiated in a fine-grained manner, and in the latter, coarse grained. I develop this view by critically examining John Kulvicki’s analysis of how we interpret pictures. According to Kulvicki, we rarely interpret pictures as differing in terms of features that belong to the vehicle (...) of representation—that is, the design-content relation. Rather, he thinks that we individuate pictorial styles in terms of the content that each picture represents. Contra Kulvicki, I argue that we interpret the vehicular features of pictures in light of our knowledge of picture production, and that the diversity of pictorial styles involves both content and vehicle. (shrink)

Mathematical models are often expected to provide not only predictions about the phenomenon that they represent, but also explanations. These explanations are answers to why-questions and particularly answers to why the predicted phenomenon should occur. For instance, models can be used to calculate when the next total solar eclipse will happen, and then to explain why it will take place on July 2, 2019. In this regard we can obtain explanations from a model if we can solve the model equations (...) which govern the phenomenon under study. But some equations have no explicit solution or are too complicated to solve. In these cases it is difficult for a... (shrink)

In practice, scientists must convey data in a “representational style”. Various authors seek to explain the epistemic role of scientific visual representation in terms of formal conventions. Goodman also tends to dismiss the epistemic relevance of human cognition. My position is that visual conventions are nonarbitrary, in that they play to scientists’ cognitive abilities and limitations. My account draws on Perini's formal analysis, scientific case studies, and empirical literature on global pattern detection in neurotypicals, autistics, and dyslexics.

In this paper, I characterize visual epistemic representations as concrete two- or three-dimensional tools for conveying information about aspects of their target systems or phenomena of interest. I outline two features of successful visual epistemic representation: that the vehicle of representation contain sufficiently accurate information about the phenomenon of interest for the user’s purpose, and that it convey this information to the user in a manner that makes it readily available to her. I argue that actual epistemic representation may involve (...) tradeoffs between these and is successful to the extent that they are present. (shrink)

Recent philosophical analyses of the epistemic dimension of images in the sciences show a certain trend in acknowledging potential roles of these images beyond their merely decorative or pedagogical functions. We argue, however, that this new debate has yet paid little attention to a special type of pictures, we call ‘visual metaphor’, and its versatile heuristic potential in organizing data, supporting communication, and guiding research, modeling, and theory formation. Based on a case study of Conrad Hal Waddington’s epigenetic landscape images (...) in biology, we develop a descriptive framework applicable to heuristic roles of various visual metaphors in the sciences. (shrink)

Recent philosophical analyses of the epistemic dimension of images in the sciences show a certain trend in acknowledging potential roles of these images beyond their merely decorative or pedagogical functions. We argue, however, that this new debate has yet paid little attention to a special type of pictures, we call ‘visual metaphor’, and its versatile heuristic potential in organizing data, supporting communication, and guiding research, modeling, and theory formation. Based on a case study of Conrad Hal Waddington’s epigenetic landscape images (...) in biology, we develop a descriptive framework applicable to heuristic roles of various visual metaphors in the sciences. (shrink)

In this paper I aim to defend a twofold thesis. On one hand, I will sup-port, against Perini [7], the indispensability of diagrams when structurally complex biomolecules are concerned, since it is not possible to satisfactorily use linguistic-sentential representations at that domain. On the other hand, even when diagrams are dispensable I will defend than they will generally be more effective than other representations in encoding biomolecular knowledge, relying on Kulvicki-Shimojima’s diagrammatic effectiveness thesis. Finally, I will ground many epistemic virtues (...) of biomolecular diagrams (understandability, explanatory power, prediction and hypothesis evaluation) on their cognitive-computational indispensability and their semantic-epistemic effectiveness. (shrink)