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Scientific Visualisation

Epistemic Weight and Surpluses

Marianne Richter

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.
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4.2.3 A case for the epistemic weight of picture-like means


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There might be surprises amongst these classifications. For example, what seems to be picture-like in respect of the dichotomy between serial and spatial orderings – namely the diagram (Fig. 24) – turns out rather language-like, when taking into account the condition of syntactic discreteness. Perhaps equally surprising, yet tending towards the opposite direction, the graph (Fig. 13) is classified as ‘rather picture-like’. This is because the blue and green curves are densely composed; yet, still, the system builds on a distinct semantics, as each atomic part represents exactly one value (cf. Perini 2005, p. 271; The microscopy-like image (cf. Fig. 25), in turn, is classified as a genuinely picture-like means. In this connection, it should be important to add that the classification works without, and only without, information about the computer-based or -generated environment of the figures. In fact, I firmly insist that the technical origin should not make a difference in the classification (which has been argued, e.g., by Fischer 1997). Whether or not something is construed as being syntactically dense depends solely on the agenda pursued (resp. on the coding scheme employed). Certainly, it might be that a pixel is considered as a syntactic class, but this is not true for every case. The cell model (cf. Fig. 25), for instance, works best when it is read as being densely composed, since this fits the biological descriptions of the target system on this scale of description. Surely, this does not mean...

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