I argue that maps do not feature predication, as analyzed by Frege and Tarski. I take as my foil (Casati and Varzi, Parts and places, 1999), which attributes predication to maps. I argue that the details of Casati and Varzi’s own semantics militate against this attribution. Casati and Varzi emphasize what I call the Absence Intuition: if a marker representing some property (such as mountainous terrain) appears on a map, then absence of that marker from a map coordinate signifies absence (...) of the corresponding property from the corresponding location. Predication elicits nothing like the Absence Intuition. “F(a)” does not, in general, signify that objects other than a lack property F. On the basis of this asymmetry, I argue that attaching a marker to map coordinates is a different mode of semantic composition than attaching a predicate to a singular term. (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)

Problems concerning scientists’ uses of representations have received quite a bit of attention recently. The focus has been on how such representations get their contents and on just what those contents are. Less attention has been paid to what makes certain kinds of scientific representations different from one another and thus well suited to this or that epistemic end. This article considers the latter question with particular focus on the distinction between images and graphs on the one hand and descriptions (...) and related representations on the other. *Received January 2008; revised September 2009. †To contact the author, please write to: Department of Philosophy, Dartmouth College, Hanover, NH 03755; e‐mail: [email protected]. (shrink)

In this paper, I sketch a new model for the format of the content of understanding states, Compressible Graph Maximalism (CGM). In this model, the format of the content of understanding is graphical, and compressible. It thus combines ideas from approaches that stress the link between understanding and holistic structure (like as reported by Grimm (in: Ammon SGCBS (ed) Explaining Understanding: New Essays in Epistemollogy and the Philosophy of Science, Routledge, New York, 2016)), and approaches that emphasize the connection between (...) understanding and compression (like Wilkenfeld (Synthese 190(6):997–1016, 2018)). I argue that the combination of these ideas has several attractive features, and I defend the idea against some challenges. (shrink)

Although scientific images have begun to receive significant attention from philosophers, one type of image has thus far been ignored: moving images. As techniques such as live cell imaging and videomicroscopy are becoming increasingly important in many areas of biology, however, this oversight needs to be corrected. Biologists often claim that there are relevant differences between video and static images. Most interesting is the idea that video images allow us to see causal relationships. By identifying the conditions that would be (...) required for this to be true and showing that they are not satisfied at the micro level, I will show that videomicroscopy does not provide us with special access to causal information. †To contact the author, please write to: Department of Philosophy, University of Calgary, 2500 University Dr. NW, Calgary, Alberta T2N 1N4; e-mail: [email protected]. (shrink)

Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by these considerations, survey (...) a range of neurobiological findings and behavioral data consistent with it, and conclude by mentioning some of the more challenging directions for future research. (shrink)