Online research in philosophy

Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as (...) a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world. (shrink)

Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as (...) a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world. (shrink)

b>. Computational models of colour vision assume that the biological function of colour vision is to detect surface reflectance. Some philosophers invoke these models as a basis for 'externalism' about perceptual content (content is distal) and 'objectivism' about colour (colour is surface reflectance). In an earlier article (Thompson et al. 1992), I criticized the 'computational objectivist' position on the basis of comparative colour vision: There are fundmental differences among the colour vision of animals and these differences do not converge on (...) the detection of any single type of environmental property. David R. Hilbert (1992) has recently defended computational objectivism against my 'comparative argument;' his arguments are based on the externalist approach to perceptual content originally developed by Mohan Matthen (1988) and on the computationally inspired theory of the evolutionary basis for trichromacy developed by Roger N. Shepard (1990). The present article provides a reply to Hilbert with extensive criticism of both Matthen's and Shepard's theories. I argue that the biological function of colour vision is not to detect surface reflectance, but to provide a set of perceptual categories that can apply to objects in a stable way in a variety of conditions. Comparative research indicates that both the perceptual categories and the distal stimuli will differ according to the animal and its visual ecology; therefore externalism and objectivism must be rejected. (shrink)

Where are we to look for the unique hues? Out in the world? In the eye? In more central processing? 1. There are difficulties looking for the structure of the unique hues in simple combinations of cone-response functions like ( L − M ) and ( S − ( L + M )): such functions may fit pretty well the early physiological processing, but they don’t correspond to the structure of unique hues. It may seem more promising to look to, (...) e.g., Hurvich & Jameson’s ‘chromatic response functions’; but these report on psychophysical behaviour, not on underlying physiology. So ‘opponent processing’ isn’t any particular help on the unique hues—and even physiology in general seems not to have come up with any good correlate or explanation. 2. Wright ( Review of Philosophy and Psychology 2: 1–17, 2011 ) looks in a different place: to (a) magnitude of total visual response that a stimulus light provokes, the maxima and minima of which, he thinks, give us the boundaries of the main hue categories (Wright connects these with Thornton’s ‘prime’ and ‘antiprime’ colours in an illuminant: Journal of the Optical Society of America 61 ( 1971 ): 1155–1163); and (b) the ratio of chromatic to achromatic response, the maxima and minima of which, he suggests, give us the focal points of the unique hues. The suggestions are extremely interesting; but the desired correspondences have some counterexamples; and where they hold, one could wonder how much they depend upon the particular choice of functions to measure (a) and (b); and one could hope for more of an explanatory linkage between the sets of items in question. 3. Could the unique hues come from, so to speak, the external world? White and black can easily be defined as particular kinds of reflectance. What of the standard four unique hues? Variation in kinds of sunlight and skylight coincides well with variation along a line from unique yellow to unique blue (cf. Shepard 1992 , Mollon 2006 ). If we wanted something to calibrate our standards for unique yellow and blue as the lens of the eye changes with age, and despite interpersonal cone differences, this would be a good basis—and there are several ways this can be extended to surface colours. But is there any essential connection between these things: is there any rationale why the light of the sun and the sky should be counted as unique hued ? An answer may be: because in our environment, these illuminants are as close to white (or the natural illuminant colour) as you can get—to see things tinged with sunlight or skylight should be to see them minimally tinged with any alien colour. Whereas other hues in an illuminant would be treated as tinging with a more alien colour the thing seen. (shrink)

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to (...) a single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

We argue that confusability between items should be distinguished from generalization between items. Shepard's data concern confusability, but the theories proposed by Shepard and by Tenenbaum & Griffiths concern generalization, indicating a gap between theory and data. We consider the empirical and theoretical work involved in bridging this gap. [Shepard; Tenenbaum & Griffiths].