Fechner's conception of psychophysics included both “outer psychophysics” the relation between stimulus intensity and the response reflecting sensation strength, and “inner psychophysics” the relation between neurelectric responses and sensation strength. In his own time outer psychophysics focussed on the form of the psychophysical law, with Fechner espousing a logarithmic law, Delboeuf a variant of the logarithmic law incorporating a resting level of neural activity, and Plateau a power law. One of the issues on which the dispute was focussed concerned the (...) appearance of contrasts if the overall illumination was increased or decreased; another issue was the question of whether a sensation of a “just noticeable difference” established for one value of a sensory dimension appeared the same for a value elsewhere on the dimension. The development of “inner psychophysics” led through the works of Delboeuf, Solomons, Jastrow, and Thurstone to modern signal detection theory. A third line of research, devoted to the question of what was meant by the “measurement” of sensation strength, stemmed from the criticism of Fechner's work by von Kries (1882) and others. Although a valid body of science could be built up without the intervening variable called “sensation strength,” such a science might be a cumbersome representation of reality. When an optical contrast is set up, and its overall illumination is increased or decreased, subjective contrasts involving medium levels of lightness vary little as illumination varies (as a power law based on sensation ratios or a logarithmic law based on sensation differences predict), but subjective contrasts involving extreme levels of lightness might be subject to the effects of other variables. (shrink)

In this paper I discuss the relationship between model, theories, and laws in the practice of experimental scale modeling. The methodology of experimental scale modeling, also known as physical similarity, differs markedly from that of other kinds of models in ways that are important to issues in philosophy of science. Scale models are not discussed in much depth in mainstream philosophy of science. In this paper, I examine how scale models are used in making inferences. The main question I address (...) in this talk is ``How are fundamental laws involved in the construction of, and inferences drawn from, experimental scale models?'' We shall see that there is a refreshing alternative to the mainstream view that models can serve only as intermediaries between theory and experiment. Using the methodology of scale models, one can use observations on one piece of the world to make inferences about another piece of the world, without involving an intermediate abstract model about which one reasons. The philosophical significance of that point to philosophy of science is that the method of physical similarity, which provides the basis for inferences based upon scale models, is a qualitatively different way in which fundamental laws can be used in analogical reasoning that is truly informative. Finally, as this method provides a formal basis for case-based reasoning, it may be helpful in formalizing methods used in some of the so-called ``special sciences''. (shrink)

Is there anything more to temperature than the ordering of things from colder to hotter? Are there also facts, for example, about how much hotter (twice as hot, three times as hot...) one thing is than another? There certainly are---but the only strong justification for this claim comes from statistical mechanics. What we knew about temperature before the advent of statistical mechanics (what we knew about it from thermodynamics) provided only weak reasons to believe it.

This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement—direct, derived, and complex—are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes. Atmospheric data assimilation is then considered as a case study. This practice, which involves combining information (...) from conventional observations and simulation-based forecasts, is characterized as a complex measuring practice that is still under development. The case study reveals challenges that are likely to resurface in other measuring practices that embed computer simulation. It is also noted that some practices that embed simulation are difficult to classify; they suggest a fuzzy boundary between measurement and non-measurement. 1 Introduction2 A Contemporary View of Measurement3 Three Types of Measurement4 Can Computer Simulations Measure Real-World Target Systems?5 Case Study: Atmospheric Data Assimilation5.1 Why data assimilation?5.2 A complex measuring practice under development5.3 Epistemic iteration6 The Boundaries of Measurement7 Epistemology, Not Terminology. (shrink)

In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by examining whether, and under (...) what conditions, the features of empiricality and novelty could be displayed by computer simulation data. I show that, to the extent that certain familiar measurement results have these features, so can some computer simulation results. (shrink)

This work develops an epistemology of measurement, that is, an account of the conditions under which measurement and standardization methods produce knowledge as well as the nature, scope, and limits of this knowledge. I focus on three questions: (i) how is it possible to tell whether an instrument measures the quantity it is intended to? (ii) what do claims to measurement accuracy amount to, and how might such claims be justified? (iii) when is disagreement among instruments a sign of error, (...) and when does it imply that instruments measure different quantities? Based on a series of case studies conducted in collaboration with the US National Institute of Standards and Technology (NIST), I argue for a model-based approach to the epistemology of physical measurement. To measure a physical quantity, I argue, is to estimate the value of a parameter in an idealized model of a physical process. Such estimation involves inference from the final state (‘indication’) of a process to the value range of a parameter (‘outcome’) in light of theoretical and statistical assumptions. Contrary to contemporary philosophical views, measurement outcomes cannot be obtained by mapping the structure of indications. Instead, measurement outcomes as well as claims to accuracy, error and quantity individuation can only be adjudicated relative to a choice of idealized modelling assumptions. (shrink)