Abstract
Given that many imaging technologies in biology and medicine are non-optical and generate data that is essentially numerical, it is a striking feature of these technologies that the data generated using them are most frequently displayed in the form of semi-naturalistic, photograph-like images. In this paper, I claim that three factors underlie this: (1) historical preferences, (2) the rhetorical power of images, and (3) the cognitive accessibility of data presented in the form of images. The third of these can be argued to provide an epistemic advantage to images, but I will further argue that this is often misleading and that images can in many cases be less informative than the corresponding mathematical data.
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Notes
Julie Fiez (personal communication) reports that it is her experience that preferences for photograph-like images vs. other forms of data display vary considerably among researchers in her field.
However, Daston and Galison (1992) point out that what counts as objectivity has changed over time and is reflected in the practices of scientific image-making. It is not always clear which objects, if reliable images are produced of them, serve as reliable information about some phenomenon or feature of the world. For instance, in examining the number of immune cells of a particular type that are present in different layers of the skin in normal as opposed to scar tissue, should I always photograph and count random fields of view or ought I to instead require that fields be randomly selected but meet some additional criteria—perhaps that they not contain any tears or that the tear not cover more than a certain percentage of the area of the field. Some such criteria undoubtedly enhance the reliability of the data, but it is not obvious just what sort of constraints are there on the criteria that count as legitimate.
Photoshop and other types of digital manipulation have obviously changed this to some extent, but we still tend to take photographic images presented as evidence by credible sources to be reliable.
It may be unreliable for certain purposes, but even then we can usually identify the purposes for which it is unreliable (e.g. for discriminating between green and blue eyes).
See (Crick 1994).
For instance, they would be unlikely to identify a photograph where all the cells in the tissue were uniformly stained as indicating a problem with the staining technique.
Hereafter I will use the term “photograph” to refer to actual photographs as well as other images such as X-rays that are produced by processes that bear a physical similarity to optical photography.
An additional difficulty in identifying photographs as reliable or not is digital manipulation of images after their initial production. This is a very real concern today given the ease with which images can be altered and has been addressed in several recent pieces in Science and Nature (Ottino 2003; Pearson 2005; Greene 2005). Part of the difficulty is in establishing what degree or type of manipulation is legitimate—i.e. does not compromise the truthfulness or reliability of the data and may, in fact, aid the viewer in making relevant discriminations - and what constitutes fraud or misrepresentation of what was originally perfectly reliable data. These sorts of conditions on selection and manipulation of data, however, are not specific to images but apply to all sorts of data production methods.
More accurately, this constraint is enforced not only be the instrument (which has defined start and end points, as described for the case of PET), but by the experimental set-up including elements upstream of the instrument..
“Correct” here refers to the discrimination(s) needed to answer the question of interest in any given case. It will sometimes be the case that display formats that make some features of the data more easily discriminable by the user also obscure or make impossible to discriminate other features. For instance, if what is needed to answer a particular question is the ability to discriminate relatively small differences within a specific, limited range of intensity values, a different pseudocolor may be assigned to small intensity intervals within this range and larger intervals outside of it. This will, in effect, visually eliminate some differences that occur outside of the intensity range of primary interest. This is, of course, purely a matter of the representation-user relationship, the differences are not eliminated from the numerical data (the object-representation relationship is unchanged by this sort of manipulation).
This will not be true of all other types of instruments. An instrument that uses X-ray or photographic film as the detector, for instance, does not first represent the data in numerical form. The image format in such cases is not optional in the sense that it is with something like PET. The data can be converted to numerical format (e.g. by scanning or otherwise digitizing the image) and then represented in other formats, but in this case it is not strictly accurate to claim that the same data is displayed as an image or in other forms..
Wimsatt (1991) similarly identifies visual representations as the simplest and most inferentially productive means of analyzing multidimensional data and processing information about motion.
This term (or “revolution”) is used in almost every paper that makes reference to the period that is usually taken to begin with the discovery and cloning of green fluorescent protein (GFP).
For a history of medical imaging, see Kevles (1996).
This is no longer true, however, since X-rays data is often collected digitally rather than by using photographic film..
Along similar lines, in discussing the representational style of electron micrographs, Rasmussen (1997) claims that it was strongly influenced by the way that previous types of cytological images were presented.
The idea that we like to create pictures that resemble the world around us can be traced back as far as Aristotle’s claim that humans have a natural tendency toward mimetic activity (Poetics xxx).
Though awareness of the extent to which photographs are digitally manipulated has undoubtedly reduced the degree to which photographs are seen as reliable and objective representations of the world.
Julie Fiez (Associate Professor, Departments of Psychology and Neuroscience, University of Pittsburgh), personal communication..
The same information could be extracted from the numerical data but it would almost certainly require using a computer and some sort of pattern recognition tool to identify the required features. Except perhaps in very simple cases, we could not easily identify these features from numerical PET data on our own.
Trying to minimize the range of values represented by each color by using as many colors as the human visual system can discriminate would only reduce the ease with which we could make any discriminations.
Depending on the experimental question and design, it is often possible to apply MVPA methods to data which has undergone only standard pre-processing (slice-scan-time correction, motion correction, and trend removal) with no intervening univariate analysis (Mur et al. 2009).
The interested reader is directed to Mur et al. (2009) for a good review of these techniques.
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