This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized. Consider the following sentence: “Einstein would not have been able to come up with his e � mc 2 had he not possessed such an extraordinary intelligence. ” What does this sentence express? It relates observable behavior (Einstein’s writing e � mc 2)toan unobservable attribute (his extraordinary intelligence), and it does so by assigning to the unobservable attribute a causal role in

Contexts are maximal collections of co-measurable observables “bundled together ” to form a “quasi-classical mini-universe. ” Different notions of contexts are discussed for classical, quantum and generalized urn–automaton systems. PACS numbers: 02.10.-v,02.50.Cw,02.10.Ud

Inasmuch as physical theories are formalizable, set theory provides a framework for theoretical physics. Four speculations about the relevance of set theoretical modeling for physics are presented: the role of transcendental set theory (i) hr chaos theory, (ii) for paradoxical decompositions of solid three-dimensional objects, (iii) in the theory of effective computability (Church-Turhrg thesis) related to the possible "solution of supertasks," and (iv) for weak solutions. Several approaches to set theory and their advantages and disadvatages for" physical applications are discussed: Cantorian "naive" (i.e., nonaxiomatic) set theory, contructivism, and operationalism, hr the arrthor's ophrion, an attitude of "suspended attention" (a term borrowed from psychoanalysis) seems most promising for progress. Physical and set theoretical entities must be operationalized wherever possible. At the same thne, physicists shouM be open to "bizarre" or "mindboggling" new formalisms, which treed not be operationalizable or testable at the thne of their " creation, but which may successfully lead to novel fields of phenomenology and technology.

This review article attempts to highlight from my personal perspective some of the major developments in the representational theory of measurement during the past 50 years. Emphasis is placed on the ongoing interplay between the development of abstract theory and the attempts to apply it to empirically testable phenomena. The article has four major sections. The first concerns classical representational measurement, which was the successful attempt to formulate the major measurement methods of classical physics: extensive and additive conjoint structures, their distributive interlock in dimensional analysis, and intensive (averaging) structures. The second illustrates a nontrivial behavioral example using both extensive and conjoint measurement plus functional equations to arrive at rank- and sign-dependent utility (also called cumulative prospect) representations for decision making under risk. The third section, contemporary representational measurement, somewhat overlaps the classical one but includes new findings and approaches: representations of nonadditive concatenation and conjoint structures; a general theory of scale types; results for general, finitely unique, homogeneous structures; structures that are homogeneous between singular points; generalized distributive triples; and a generalization of dimensional analysis to include any ratio scalable attribute; and the concept of meaningfulness. The final section concerns applications of the latter ideas to psychophysical scaling and merging functions.] 1996 Academic Press, Inc. 1.

This paper analyzes the theoretical, pragmatic, and substantive factors that have hampered the integration between psychology and psychometrics. Theoretical factors include the operationalist mode of thinking which is common throughout psychology, the dominance of classical test theory, and the use of “construct validity ” as a catch-all category for a range of challenging psychometric problems. Pragmatic factors include the lack of interest in mathematically precise thinking in psychology, inadequate representation of psychometric modeling in major statistics programs, and insufficient mathematical training in the psychological curriculum. Substantive factors relate to the absence of psychological theories that are sufficiently strong to motivate the structure of psychometric models. Following the identification of these problems, a number of promising recent developments are discussed, and suggestions are made to further the integration of psychology and psychometrics.

One of the main goals of computer graphics research is to develop techniques for creating images that look real -- i.e., indistinguishable from photographs. Most existing work on this problem has focused on image synthesis methods, such as the simulation of the physics of light transport and the reprojection of photographic samples. However, the existing research has been conducted without a clear understanding of how it is that people determine whether an image looks real or not real. There has never been an objectively tested, operational definition of realism for images, in terms of the visual factors that comprise them. If the perceptual cues behind the assessment of realism were understood, then rendering algorithms could be developed to directly target these cues. This work

Abstract: This article critically examines the views that psychology first came into existence as a discipline ca. 1879, that philosophy and psychology were estranged in the ensuing decades, that psychology finally became scientific through the influence of logical empiricism, and that it should now disappear in favor of cognitive science and neuroscience. It argues that psychology had a natural philosophical phase (from antiquity) that waxed in the seventeenth and eighteenth centuries, that this psychology transformed into experimental psychology ca. 1900, that philosophers and psychologists collaboratively discussed the subject matter and methods of psychology in the first two decades of the twentieth century, that the neobehaviorists were not substantively influenced by the Vienna Circle, that the study of perception and cognition in psychology did not disappear in the behaviorist period and so did not reemerge as a result of artificial intelligence, linguistics, and the computer analogy, that although some psychologists adopted the language-of-thought approach of traditional cognitive science, many did not, and that psychology will not go away because it contributes independently of cognitive science and neuroscience.

Bayesian decision theory (BDT) is a mathematical framework that allows the experimenter to model ideal performance in a wide variety of visuomotor tasks. The experimenter can use BDT to compute benchmarks for ideal performance in such tasks and compare human performance to ideal. Recently, researchers have asked whether BDT can also be treated as a process model of visuomotor processing. It is unclear what sorts of experiments are appropriate to testing such claims and whether such claims are even meaningful. Any such claim presupposes that observers ’ performance is close to ideal, and typical experimental tests involve comparison of human performance to ideal. We argue that this experimental criterion, while necessary, is weak. We illustrate how to achieve near-optimal performance in combining perceptual cues with a process model bearing little resemblance to BDT. We then propose experimental criteria termed transfer criteria that constitute more powerful tests of BDT as a model of perception and action. We describe how recent work in motor control can be viewed as tests of transfer properties of BDT. The transfer properties discussed here comprise the beginning of an operationalization (Bridgman, 1927) of what it means to claim that perception is or is not Bayesian inference (Knill & Richards, 1996). They are particularly relevant to research concerning natural scenes since they assess the ability of the organism to rapidly adapt to novel tasks in familiar environments or carry out familiar tasks in novel environments without learning.

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Model construction is one of the key scientific activities. In distinction to the majority of the previous machine discovery systems, model formation applies in theory-rich context. We start from the distinction between models and theories, which is critical to the presented approach. We also distinguish between modeling and two scientific activities, which are different but which support modeling: construction of operational definitions and experiments. Then we present the basic steps of the mechanism for scientific model construction, outlining data structures and an algorithm which, using a number of feedback loops, incrementally develops a model of a natural phenomenon. A walk through example is used to present the algorithm: motion of a cylinder that rolls downwards on an inclined plane. Modeling: a walk-through example Scientific modeling is a complex activity. In this paper we analyze the main steps on a walk-through example. It is based on a famous experiment o...