Feature importance in feature generation and typicality rating

Many theoretical models of cognition are based on the assumption that cognitive processes and representations are hierarchically structured. The issue of whether these hierarchies are mere constructs or conceptual tools, or whether they have psychological and neurological reality, is addressed. Theories across a range of cognitive domains are reviewed and four kinds of evidence are considered: behavioural, neuropsychological, ontogenetic, and logical. Logical constraints are identified in relation to operating principles of control, access, economy and analogy. The general characteristics of hierarchical structures and their advantages and disadvantages are discussed.     

A Thurstonian Analysis of Preference Change

This paper presents a Thurstonian model for the analysis of preference change. Preferences are expressed in the form of rankings, possibly with ties. A vector-autoregression framework is used to investigate relationships between past and current rankings. It is shown that this approach yields a parsimonious and easily interpretable representation of individual preference differences in time-dependent ranking data. A detailed analysis of the 1992 National Election Study illustrates the proposed approach.     

Online measurement of mental representations of complex spatial decision problems: Comparison of CNET and hard laddering

This paper introduces the online Causal Network Elicitation Technique (CNET), as a technique for measuring components of mental representations of choice tasks and compares it with the more common technique of online ‘hard’ laddering (HL). While CNET works in basically two phases, one in open question format and one as guided linking of attributes and benefits, HL works completely structured with revealed attributes and benefits. Mental representations of two activity travel tasks were collected with both techniques among members of a nationwide Dutch household panel. The results confirm the hypothesis that the revealed format of variables in HL has an effect on the indication of variables as the elicited mental representations are almost twice as big for HL than for CNET. Furthermore, it turned out that CNET is more sensitive in measuring shifts among attributes in the mental representations for situational changes of the activity-travel task.     

Multi‐set factor analysis by means of Parafac2

We consider multi‐set data consisting of observations, k = 1,…, K (e.g., subject scores), on J variables in K different samples. We introduce a factor model for the J × J covariance matrices , k = 1,…, K, where the common part is modelled by Parafac2 and the unique variances , k = 1,…, K, are diagonal. The Parafac2 model implies a common loadings matrix that is rescaled for each k, and a common factor correlation matrix. We estimate the unique variances by minimum rank factor analysis on for each k. The factors can be chosen orthogonal or oblique. We present a novel algorithm to estimate the Parafac2 part and demonstrate its performance in a simulation study. Also, we fit our model to a data set in the literature. Our model is easy to estimate and interpret. The unique variances, the factor correlation matrix and the communalities are guaranteed to be proper, and a percentage of explained common variance can be computed for each k. Also, the Parafac2 part is rotationally unique under mild conditions.     

A two‐part mixed effects model for cigarette purchase task data

The Cigarette Purchase Task is a behavioral economic assessment tool designed to measure the relative reinforcing efficacy of cigarette smoking across different prices. An exponential demand equation has become a standard model for analyzing purchase task data, but its utility is compromised by its inability to accommodate values of zero consumption. We propose a two‐part mixed effects model that keeps the same exponential demand equation for modeling nonzero consumption values, while providing a logistic regression for the binary outcome of zero versus nonzero consumption. Therefore, the proposed model can accommodate zero consumption values and retain the features of the exponential demand equation at the same time. As a byproduct, the logistic regression component of the proposed model provides a new demand index, the “derived breakpoint”, for the price above which a subject is more likely to be abstinent than to be smoking. We apply the proposed model to data collected at baseline from college students (N = 1,217) enrolled in a randomized clinical trial utilizing financial incentives to motivate tobacco cessation. Monte Carlo simulations showed that the proposed model provides better fits than an existing model. We note that the proposed methodology is applicable to other purchase task data, for example, drugs of abuse.     

Visual analysis of data in a multielement design

Ninety Board Certified Behavior Analysts (BCBAs) and 19 editorial board members evaluated hypothetical data presented in a multielement design. We manipulated the variability, trend, and mean shift of the data and asked the participants to determine if the data demonstrated experimental control. The results showed that variability, trend, and mean shift interacted to affect the participants’ ratings of experimental control. The level of agreement between participants was variable, but was generally lower than in previous research.     

A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness,Kurtosis, and Covariance Matrix

We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empirically that popular test statistics in covariance analysis tend to reject true models more often under the IG transform than under the VM transform. This implies that overly optimistic evaluations of estimators and fit statistics in covariance structure analysis may be tempered by including the IG transform for nonnormal data generation. We provide an implementation of the IG transform in the R environment.     

Happiness and life satisfaction in Rwanda

This study investigated predictors of happiness and life satisfaction in Rwanda. Data from the World Values Survey and gathered from 3 030 Rwandese (age ranging 16 to 90 years, mean age = 34.2, SD = 12.7; females = 50.5%) were pooled for the analysis. For the comparison, international World Values Survey data were utilised. A fixed effects multilevel regression model was used to predict happiness and life satisfaction from gender, health, socio-economic, and some subjective measures. Males had greater self-rated happiness and life satisfaction scores than females. State of health and sense of freedom of choice predicted both happiness and life satisfaction. Valuing of friends, weekly religious attendance, and national pride positively predicted happiness, whereas household’s financial satisfaction, full-time employment, high-income group, being a student, and sense of trust predicted life satisfaction. This study suggests that health status, household’s financial satisfaction and emancipative values could maximise subjective well-being in Rwanda.     

Health‐adjusted neuropsychological test norms based on 463 older Swedish car drivers

There is a need for improved normative information in particular for older persons. The present study provides neuropsychological test norms on seven cognitive tests used in a sample representing the general older driving population, when uncontrolled and controlled for physical health. A group of 463 healthy Swedish car drivers, aged 65 to 84 years, participated in a medical and neuropsychological examination. The latter included tests of visual scanning, mental shifting, visual spatial function, memory, reaction time, selective attention, and simultaneous capacity. Hierarchical regression analyses demonstrated that, when uncontrolled for health, old age was associated with significant impairment on all seven tests. Education was associated with a significant advantage for all tests except most reaction time subtests. Women outperformed men on selective attention. Controlling for health did not consistently change the associations with education, but generally weakened those with age, indicating rises in normative scores of up to 0.36 SD (residual). In terms of variance explained, impaired health predicted on average 2.5%, age 2.9%, education 2.1% and gender 0.1%. It was concluded (1) that individual regression‐based predictions of expected values have the advantage of allowing control for the impact of health on normative scores in addition to the adjustment for various demographic and performance‐related variables and (2) that health‐adjusted norms have the potential to classify functional status more accurately, to the extent that these norms diverge from norms uncontrolled for physical health.