A dominant theme in modeling human perceptual judgments is that sensory neural activity is summed or integrated until a critical bound is reached. Such models predict that, in general, the shape of response time distributions change across conditions, although in practice, this shape change may be subtle. An alternative view is that response time…

Causal considerations must be relevant for those making decisions. Whether to bring an umbrella or leave it at home depends on the causal consequences of these options. However, most current decision theories do not address causal reasoning. Here, the authors propose a causal model theory of choice based on causal Bayes nets. The critical ideas…

Professional psychologists are often confronted with the task of making binary decisions about individuals, such as predictions about future behavior or employee selection. Test users familiar with linear models and Bayes's theorem are likely to assume that the accuracy of decisions is consistently improved by combination of outcomes across valid…

A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994; D. Norris, J. M. McQueen, A. Cutler, & S. Butterfield, 1997) and shares many of its key assumptions: parallel competitive evaluation of multiple lexical hypotheses, phonologically abstract prelexical and lexical representations, a feedforward…

Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect…

Clinical decision-making is challenging mainly because of two factors: (1) patient conditions are often complicated with partial and changing information; (2) people have cognitive biases in their decision-making and information-seeking. Consequentially, misdiagnoses and ineffective use of resources may happen. To better support clinical…

This article reviews the concepts and methods of Bayesian statistical analysis, which can offer innovative and powerful solutions to some challenging analytical problems that characterize developmental research. In this article, we demonstrate the utility of Bayesian analysis, explain its unique adeptness in some circumstances, address some…

In general, candidates selected for employment will probably perform worse than estimated. Bayesian statistical methods may be useful in adjusting the estimates. (Author)

Optimal sequential decision rules are proposed for adapting the appropriate amount of instruction to learning needs. The framework for the approach is derived from Bayesian decision theory and the assumption that three actions (master, partial master, and nonmaster) are open to the decision maker. (SLD)

A Bayesian approach for simultaneous optimization of test-based decisions is presented using the example of a selection decision for a treatment followed by a mastery decision. A distinction is made between weak and strong rules, and conditions for monotonicity of optimal weak and strong rules are presented. (Author/SLD)

Statistical procedures based on Bayesian estimation for obtaining estimates of a propensity (which would include estimates of proportions or relative frequencies) are described for the special case where the observer can only note whether the propensity exceeds or does not exceed a constant between 0 and 1. (JKS)

This paper presents some Bayesian theories of simultaneous optimization of decision rules for test-based decisions. Simultaneous decision making arises when an institution has to make a series of selection, placement, or mastery decisions with respect to subjects from a population. An obvious example is the use of individualized instruction in…

The application of the Minnesota Adaptive Instructional System (MAIS) decision procedure by R. D. Tennyson et al. (1975, 1977) is examined. The MAIS is a computer-based adaptive instructional system. The problems of determining the optimal number of interrogatory examples in the MAIS can be formalized as a problem of Bayesian decision making. Two…

The decision process required for Individually Prescribed Instruction (IPI), an adaptive instructional program developed at the University of Pittsburgh, is described. In IPI, short tests are used to determine the level of proficiency of each student in precisely defined learning objectives. The output of these tests is used to guide instructional…

Bayesian statistical inference is unfamiliar to many educational evaluators. While the classical model is useful in educational research, it is not as useful in evaluation because of the need to identify solutions to practical problems based on a wide spectrum of information. The reason Bayesian analysis is effective for decision making is that it…