Probabilistic choice (models) as a result of balancing multiple goals

We conceptualize probabilistic choice as the result of the simultaneous pursuit of multiple goals in a vector optimization representation, which is reduced to a scalar optimization that implies goal balancing. The majority of prior theoretical and empirical work on such probabilistic choice is based on random utility models, the most basic of which assume that each choice option has a valuation that has a deterministic (systematic) component plus a random component determined by some specified distribution. An alternate approach to probabilistic choice has considered maximization of one quantity (e.g., utility), subject to constraints on one or more other quantities (e.g., cost). The multiple goal perspective integrates the results regarding the well-studied multinomial logit model of probabilistic choice that has been derived from each of the above approaches; extends the results to other models in the generalized extreme value (GEV) class; and relates them to recent axiomatic work on the utility of gambling.     

Orthogonal higher order structure of the Wechsler Intelligence Scale For Children--fourth edition

According to J. B. Carroll's (1993) 3-stratum theory, performance on any subtest reflects a mixture of both 2nd-order and 1st-order factors. To disentangle these influences, variance explained by the general factor should be extracted first. The 1st-order factors are then residualized, leaving them orthogonal to the general factor and each other. When these methods were applied to the WISC-IV standardization sample, the general factor accounted for the greatest amount of common (71.3%) and total (38.3%) variance. The largest contribution by a first-order factor was 6.5% of total variance. It was recommended that interpretation of the WISC-IV not discount the strong general factor.     

ADHD and college students: exploratory and confirmatory factor structures with student and parent data

Exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) were used to investigate the structure of the Student Report Inventory (SRI) and Parent Report Inventory (PRI) of the College Attention-Deficit/Hyperactivity Disorder (ADHD) Response Evaluation. The sample was composed of 1,080 college students and their parents and was stratified by ethnicity, gender, ability level, age, grade, region of residence, and psychoeducational classification status. Results varied according to the information source (self-report vs. parent). EFA uncovered and CFA confirmed 3 distinct and reliable dimensions for student reports: Inattention, Hyperactivity, and Impulsivity. By contrast, EFA and CFA uncovered a reliable 2-dimension structure for the parent-report data. Factor structures replicated across genders (3 factors for the SRI, and 2 factors for the PRI). Results are discussed in terms of the divergence of structures.     

Integrating Cognitive Process and Descriptive Models of Attitudes and Preferences

Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well‐established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi‐attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator‐based models of decision making used in response time research can also provide process‐level instantiations for random utility models.     

A solution to a problem raised in Luce and Marley (2005)

Luce and Marley [2005. Ranked additive utility representations of gambles: Old and new axiomatizations. Journal of Risk and Uncertainty, 30, 21-62] examined various relations between mathematical forms for the utility of joint receipt ⊕ of gambles and for the utility of uncertain gambles. Their assumptions lead to a bisymmetry functional equation which, when the gambles are ranked, is defined on a restricted domain. Maksa [1999. Solution of generalized bisymmetry type equations without surjectivity. Aequationes Mathematicae, 57, 50-74] solved the general case and Kocsis [2007. A bisymmetry equation on restricted domain. Aequationes Mathematicae, 73, 280-284] presents the solution for the ranked case. The latter solution allows us to solve open problem 5 in Luce and Marley (2005) by showing that the assumptions of their Theorem 19 for an order-preserving ranked additive utility (RAU) representation U imply that U is a ranked weighted utility (RWU) representation that is additive over ⊕.     

Temporal stability of WISC-III subtest composite: strengths and weaknesses

The Wechsler Intelligence Scale for Children--Third Edition (D. Wechsler, 1991; WISC-III) is often used to identify subtest-based cognitive strengths and weaknesses that are subsequently used to generate interventions. Given that intelligence is presumed to be an enduring trait, cognitive strengths and weaknesses identified via subtest analysis should also be stable over time. This was evaluated with 579 students who were twice tested with the WISC-III. Based on 66 subtest composites, 6 or 7 interpretable cognitive strengths and weaknesses were found on each WISC-III administration. However, subtest-based strengths and weaknesses replicated across test-retest occasions at chance levels. Because subtest-based cognitive strengths and weaknesses are unreliable, recommendations based on them will also be unreliable.     

Choice and response time processes in the identification and categorization of unidimensional stimuli

Lacouture and Marley (1991, 1995, 2001) have successfully modeled the probabilities of correct responses and the mean correct response times (RTs) in unidimensional absolute identification tasks for various stimulus ranges and stimulus/response set sizes, for individual and group data. These fits include those to a set of phenomena often referred to as end-anchor effects. A revised model, with the independent accumulator decision process replaced by a leaky competing accumulator decision process, fits the probabilities of correct responses and the full distributions of RTs in unidimensional absolute identification. The revised model is also applied successfully to a particular class of unidimensional categorization tasks. We discuss possible extensions for handling sequential effects in unidimensional absolute identification, and other extensions of the given class of categorization tasks that are of potential empirical and theoretical importance as a supplement to the study of multidimensional absolute identification tasks.