Response error and processing biases in confidence judgment

Previous studies showed that random error can explain overconfidence effects typically observed in the literature. One of these studies concluded that, after accounting for random error effects in the data, there is little support for cognitive‐processing biases in confidence elicitation. In this paper, we investigate more closely the random error explanation for overconfidence. We generated data from four models of confidence and then estimated the magnitude of random error in the data. Our results show that, in addition to the true magnitude of random error specified in the simulations, the error estimates are influenced by important cognitive‐processing biases in the confidence elicitation process. We found that random error in the response process can account for the degree of overconfidence found in calibration studies, even when that overconfidence is actually caused by other factors. Thus, the error models say little about whether cognitive biases are present in the confidence elicitation process. Copyright © 2008 John Wiley & Sons, Ltd.     

Data‐driven approaches to information access

This paper summarizes three lines of research that are motivated by the practical problem of helping users find information from external data sources, most notably computers. The application areas include information retrieval, text categorization, and question answering. A common theme in these applications is that practical information access problems can be solved by analyzing the statistical properties of words in large volumes of real world texts. The same statistical properties constrain human performance, thus we believe that solutions to practical information access problems can shed light on human knowledge representation and reasoning.     

BEHAVIORAL MOMENTUM IN THE TREATMENT OF NONCOMPLIANCE

Behavioral momentum refers to the tendency for behavior to persist following a change in environmental conditions. The greater the rate of reinforcement, the greater the behavioral momentum. The intervention for noncompliance consisted of issuing a sequence of commands with which the subject was very likely to comply (i.e., high-probability commands) immediately prior to issuing a low-probability command. In each of five experiments, the high-probability command sequence resulted in a “momentum” of compliant responding that persisted when a low-probability request was issued. Results showed the antecedent high-probability command sequence increased compliance and decreased compliance latency and task duration. “Momentum-like” effects were shown to be distinct from experimenter attention and to depend on the contiguity between the high-probability command sequence and the low-probability command.     

Canonical analysis of two convex polyhedral cones and applications

Canonical analysis of two convex polyhedral cones consists in looking for two vectors (one in each cone) whose square cosine is a maximum. This paper presents new results about the properties of the optimal solution to this problem, and also discusses in detail the convergence of an alternating least squares algorithm. The set of scalings of an ordinal variable is a convex polyhedral cone, which thus plays an important role in optimal scaling methods for the analysis of ordinal data. Monotone analysis of variance, and correspondence analysis subject to an ordinal constraint on one of the factors are both canonical analyses of a convex polyhedral cone and a subspace. Optimal multiple regression of a dependent ordinal variable on a set of independent ordinal variables is a canonical analysis of two convex polyhedral cones as long as the signs of the regression coefficients are given. We discuss these three situations and illustrate them by examples.     

A method of optimal scaling for multivariate ordinal data and its extensions

This paper develops a method of optimal scaling for multivariate ordinal data, in the framework of a generalized principal component analysis. This method yields a multidimensional configuration of items, a unidimensional scale of category weights for each item and, optionally, a multidimensional configuration of subjects. The computation is performed by alternately solving an eigenvalue problem and executing a quasi-Newton projection method. The algorithm is extended for analysis of data with mixed measurement levels or for analysis with a combined weighting of items. Numerical examples and simulations are provided. The algorithm is discussed and compared with some related methods.Earlier results of this research appeared in Saito and Otsu (1983). The authors would like to acknowledge the helpful comments and encouragement of the editor.     

Setting the sales budget: An exploratory study

This article explores how the sales budget is set in a business organization. Findings from a longitudinal participant observation study revealed that the sales budget is considered of great importance to the organization as sales estimates serve as premises for planning of production and supply and new product developments. It was observed that the sales budget was based on a narrow, inner-directed perspective, almost looking away from important environmental driving forces such as competitors and customers. The findings also showed that individuals involved in the sales budgeting process behaved opportunistically, influencing the budgetary process and outcomes. Theoretical and normative implications are highlighted.     

Functional measurement approach to self-estimation in multiattribute evaluation

Multiattribute analysis depends on measurement of values and weights. Unless these measures reflect the decision maker's true values and weights, the multiattribute formula may put a less-preferred alternative in first place. To avoid such disordinality requires stringent measurement conditions: First, the values and weights must be on linear (equal interval) or ratio (known zero) scales. Second, these scales must satisfy a condition of common unit across disparate attribute dimensions. Most methods of range adjustment beg both of these measurement questions. Functional measurement theory can solve both problems and so can be useful in multiattribute analysis. Past work has established the operation of a general cognitive algebra as an empirical reality. The averaging model, in particular, makes possible the definition and estimation of weights and values as distinct psychological parameters. It can also solve the problem of common unit. Cognitive algebra thus provides a grounded theoretical foundation on which to develop self-estimation methodology, in which decision makers provide direct estimates of their values and weights. The logic is straightforward. Functional measurement can analyze global judgments to obtain validated psychological scales. These scales may then be used as validational criteria for the self-estimates. Procedures to eliminate biases in the self-estimates can thus be tested and refined in well-learned multiattribute tasks, such as judgments of meals, in which global judgments are trustworthy. Once developed, such self-estimation procedures may be used with some confidence for general multiattribute analysis. A number of studies from 20-odd years of work on the theory of information integration are summarized to show good, although not unmixed promise for self-estimation.     

Factor invariance of the Millon Clinical Multiaxial Inventory

Several factor analyses of the Millon Clinical Multiaxial Inventory (MCMI) have resulted in very similar solutions. Interpretation of this consistency is hampered by the fact that the 20 scales of the inventory share items. Overlapping items cause the scales to be linearly dependent and may create structure in the interscale correlation matrix which is separate from the subject response patterns. A factor analysis was performed on the matrix of item-overlap coefficients which describes the underlying artifactual structure of the instrument. Data from two new subject samples were factor analyzed and compared to previously published studies. Similarity coefficients among factors across studies were calculated.     

PROMOTING LAW ENFORCEMENT FOR CHILD PROTECTION: A COMMUNITY ANALYSIS

The Colorado Occupant Protection Project (COPP) intervention provided police with brief instruction concerning the importance of citations for drivers' failure to use child safety seats and special coupons to accompany citations. Coupons were exchangeable by drivers for a safety seat and brief training in its use, plus a waiver of the $50 citation fine. Over 4.5 years of archival records were employed, using an ABA design and a comparison community to evaluate the program. Few tickets were issued for nonuse of safety seats during the 3-year baseline in either community. Citations for nonuse of safety seats increased to over 50 per month during the intervention period at the test site, whereas rates remained essentially zero at the comparison site. After the COPP intervention was removed at the intervention site, citation rates for nonuse of safety seats decreased to about 15 per month. Differences between intervention conditions and settings were statistically significant. During the intervention, officers were 44 times more likely to write citations than were controls. Results suggested that a behavioral program can increase police citation writing for child protection purposes.     

COVARIATION WITHIN FUNCTIONAL RESPONSE CLASSES: IMPLICATIONS FOR TREATMENT OF SEVERE PROBLEM BEHAVIOR

Two studies examined the effects of a reductive treatment versus instruction-based treatments on the generalized reduction of problem behaviors. Each study involved a detailed analysis of multiple problem behaviors performed by school-aged youth with severe intellectual disabilities. The analysis examined the contrasting effects of one of two different positive intervention procedures (teaching a positive alternative behavior or providing additional teacher assistance during instruction) versus blocking and/or verbally reprimanding a problem behavior. The focus of each analysis was on the covariation of multiple problem behaviors within functional response classes. Results of the investigation indicated that when only one member of the response class was blocked, a collateral increase was observed in one or more different problem behaviors from the same response class. Alternatively, when 1 participant was taught a functionally equivalent mand response, all problem behaviors in the response class were reduced. Problem behaviors also were reduced for the remaining participant by presenting antecedent teacher assistance. Implications of the research extend to analysis of covariation within response classes and to procedures that result in generalized reduction of problem behaviors within a response class.