Item response models for the measurement of thresholds

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on July 28, 2009.Thesis (Ph. D.) University of Missouri-Columbia 2008.At least since Fechner (1860) described examples of human sensory thresholds, the concept of a threshold has been foundational in psychology. Thresholds exist when a sensation can be so weak that it does not lead to detection. Recently, however, thresholds have been abandoned in psychology as a result of the advent of the Theory of Signal Detection (Green & Swets, 1966). I argue that this abandonment was premature and that the concept of a threshold is useful in psychological theory. Thresholds may be defined as the maximum stimulus intensity for which performance is equal to a chance baseline. The measurement of thresholds, however, remains a difficult problem. I present statistical models designed to allow the efficient measurement of thresholds. The models, which have much in common with Item Response Theory models, are hierarchical and are analyzed by Bayesian methods. The models perform well both in simulation and in application to data. Finally, I apply the general model to data from two subliminal priming experiments to test the phenomenon of subliminal priming.Includes bibliographical reference

Teaching Bayes' Theorem: strength of evidence as predictive accuracy

Although teaching Bayes’ theorem is popular, the standard approach—targeting posterior distributions of parameters—may be improved. We advocate teaching Bayes’ theorem in a ratio form where the posterior beliefs relative to the prior beliefs equals the conditional probability of data relative to the marginal probability of data. This form leads to an interpretation that the strength of evidence is relative predictive accuracy. With this approach, students are encouraged to view Bayes’ theorem as an updating mechanism, to obtain a deeper appreciation of the role of the prior and of marginal data, and to view estimation and model comparison from a unified perspective

The Color-Sharing Bonus:Roles of Perceptual Organization and Attentive Processes in Visual Working Memory

Color repetitions in a visual scene boost memory for its elements, a phenomenon known as the color-sharing effect. This may occur because improved perceptual organization reduces information load or because the repetitions capture attention. The implications of these explanations differ drastically for both the theoretical meaning of this effect and its potential value for applications in design of visual materials. If repetitions capture attention to the exclusion of other details, then use of repetition in visual displays should be confined to emphasized details, but if repetitions reduce the load of the display, designers can assume that the nonrepeated information is also more likely to be attended and remembered. We manipulated the availability of general attention during a visual memory task by comparing groups of participants engaged in meaningless speech or attention-demanding continuous arithmetic. We also tracked eye movements as an implicit indicator of selective attention. Estimated memory capacity was always higher when color duplicates were tested, and under full attention conditions this bonus spilled over to the unique colors too. Analyses of gazes showed that with full attention, participants tended to glance earlier at duplicate colors during stimulus presentation but looked more at unique colors during the retention interval. This pattern of results suggests that the color-sharing bonus reflects efficient perceptual organization of the display based on the presence of repetitions, and possibly strategic attention allocation when attention is available.<br/

Bayesian benefits for the pragmatic researcher

The practical advantages of Bayesian inference are demonstrated here through two concrete examples. In the first example, we wish to learn about a criminal’s IQ: a problem of parameter estimation. In the second example, we wish to quantify and track support in favor of the null hypothesis that Adam Sandler movies are profitable regardless of their quality: a problem of hypothesis testing. The Bayesian approach unifies both problems within a coherent predictive framework, in which parameters and models that predict the data successfully receive a boost in plausibility, whereas parameters and models that predict poorly suffer a decline. Our examples demonstrate how Bayesian analyses can be more informative, more elegant, and more flexible than the orthodox methodology that remains dominant within the field of psychology

MEASURING GAINS IN OPERATIONAL EFFICIENCY FROM INFORMATION TECHNOLOGY: A STUDY OF THE POSITRAN DEPLOYMENT AT HARDEE'S INC.

This paper presents a new approach to measuring the input productivity gains from information technology (IT) in complex managerial environments. The approach is illustrated in the context of a study of a pilot deployment at Hardee's Inc. of a new cash register point-of-safe and order-coordination technology called âPositran." The method employs data envelopment analysis (DEA) and nonparametric production frontier hypothesis testing to determine whether the performance of restaurants that have deployed Positran is better, on average, than for those that have not. The design of the study is of special interest because it approximates a controlled experiment. Our results show that Positran helped to reduce input materials costs, since restaurants that deployed the technology were less likely to be inefficient It is further possible to characterize the class of restaurants for which the relationship holds. Operational efficiency measures such as the ones we have developed provide managers with the opportunity to implement deployment strategies for new ITs in order to maximize value.Information Systems Working Papers Serie

MEASURING INPUT PRODUCTIVITY GAINS FROM INFORMATION TECHNOLOGY

This paper proposes a new method to measure the input productivity gains from information technology in complex managerial environments. The method employs a production function which maps output and relating moderating variables in the managerial environment into input resource consumption, with a random inefficiency component which can be affected by IT deployment. Sample hypotheses and a sketch of the F-tests used to identify reductions in input inefficiency are presented, and then illustrated for a new information technology which has recently been deployed in fast food restaurants.Information Systems Working Papers Serie

Testing Order Constraints: Qualitative Differences Between Bayes Factors and Normalized Maximum Likelihood

We compared Bayes factors to normalized maximum likelihood for the simple case of selecting between an order-constrained versus a full binomial model. This comparison revealed two qualitative differences in testing order constraints regarding data dependence and model preference

Is There a Free Lunch in Inference?

The field of psychology, including cognitive science, is vexed by a crisis of confidence. Although the causes and solutions are varied, we focus here on a common logical problem in inference. The default mode of inference is significance testing, which has a free lunch property where researchers need not make detailed assumptions about the alternative to test the null hypothesis. We present the argument that there is no free lunch; that is, valid testing requires that researchers test the null against a well-specified alternative. We show how this requirement follows from the basic tenets of conventional and Bayesian probability. Moreover, we show in both the conventional and Bayesian framework that not specifying the alternative may lead to rejections of the null hypothesis with scant evidence. We review both frequentist and Bayesian approaches to specifying alternatives, and we show how such specifications improve inference. The field of cognitive science will benefit because consideration of reasonable alternatives will undoubtedly sharpen the intellectual underpinnings of research

The role of modality: Auditory and visual distractors in Stroop interference

As a commonly used measure of selective attention, it is important to understand the factors contributing to interference in the Stroop task. The current research examined distracting stimuli in the auditory and visual modalities to determine whether the use of auditory distractors would create additional interference, beyond what is typically observed in the print-based Stroop task. Research by Cowan and Barron supported the additive effects of auditory and visual distractors; however, there is only one empirical demonstration of this finding to date. Using different versions of the Stroop colour-naming task, behavioural analyses of reaction times (RT) were conducted, along with distributional RT analyses. The results indicated that a combination of visual and auditory distraction did not lead to a larger interference effect than visually based distraction alone. These findings suggest that methodological issues may have influenced the prior finding of additive effects of the two modalities, and are discussed in relation to the word production architecture account of Stroop effects