Change one can believe in: Adding learning to computational models of self-regulation

Theories of self-regulation describe motivation as a dynamic process of goal choice and goal striving. To facilitate those processes, individuals learn about themselves and their environment, which is an internal dynamic process. However, the precise nature of the relationship between these learning and motivational processes is not well specified. This article integrates formal models of learning, goal choice, and goal striving using a single information processing structure found in self-regulatory models of motivation. Results from two published studies ( and ) validate the model. In both cases, the integrated model accounts for findings that previous theories of self-regulation could not explain. Discussion focuses on additional tests to validate the model and on the value of incorporating formal models from the cognitive, learning, and motivational literatures to account for behavior in complex settings and over time.     

Changes in ADHD Symptom Endorsement: Preschool to School Age

To investigate endorsement patterns among the 18 DSM-IV symptoms of ADHD in a longitudinal sample of children with and without ADHD (n?=?144), as assessed at ages 4-5, 5-6, and 6-7 years. Symptom endorsements and diagnoses were determined at all time-points via K-SADS-PL interview administered to parents and supplemented by teacher questionnaires and clinician observations. Changes in endorsement patterns over time for each of the 18 DSM-IV symptoms were ascertained. Several symptoms, particularly those of inattention, were infrequently endorsed and of apparently limited diagnostic utility at ages 4-5; hyperactive/impulsive symptoms were more frequently endorsed among young children with ADHD than were inattentive symptoms. However, by ages 6-7, inattention items were somewhat superior at discriminating ADHD from Non-ADHD children. Several DSM-IV and now DSM-V symptoms provide limited diagnostic differentiation prior to school-age, particularly those most commonly observed in the context of formal schooling. Consideration should be made in future iterations of the DSM that account for such developmental and contextual differences.     

Development and Validation of a New Measure of Intuition: The Types of Intuition Scale

The Types of Intuition Scale (TIntS) measures three types of intuition identified in a literature review by Pretz and Totz (2007): holistic, inferential, and affective. Holistic intuitions integrate diverse sources of information in a Gestalt‐like, non‐analytical manner; inferential intuitions are based on previously analytical processes that have become automatic; and affective intuitions are based on feelings. Current intuition measures inadequately assess these distinct types. We report four validity studies: Study 1 reports the reliability and factor structure of the TIntS and correlations with extant intuition and personality measures. Study 2 presents a confirmatory factor analysis. Studies 3 and 4 examine the predictive validity of the TIntS with respect to clinical decision making in occupational therapy and musical performance. Scales were internally consistent and stable over time, and factor analyses supported the predicted distinctions among them. Correlations with existing measures of intuition, personality, and behavior showed that the TIntS is unique in its assessment of all three types of intuition in one measure. Copyright © 2014 John Wiley & Sons, Ltd.     

Children's cognitive representation of the mathematical number line

Learning of the mathematical number line has been hypothesized to be dependent on an inherent sense of approximate quantity. Children's number line placements are predicted to conform to the underlying properties of this system; specifically, placements are exaggerated for small numerals and compressed for larger ones. Alternative hypotheses are based on proportional reasoning; specifically, numerals are placed relative to set anchors such as end points on the line. Traditional testing of these alternatives involves fitting group medians to corresponding regression models which assumes homogenous residuals and thus does not capture useful information from between‐ and within‐child variation in placements across the number line. To more fully assess differential predictions, we developed a novel set of hierarchical statistical models that enable the simultaneous estimation of mean levels of and variation in performance, as well as developmental transitions. Using these techniques we fitted the number line placements of 224 children longitudinally assessed from first to fifth grade, inclusive. The compression pattern was evident in mean performance in first grade, but was the best fit for only 20% of first graders when the full range of variation in the data are modeled. Most first graders' placements suggested use of end points, consistent with proportional reasoning. Developmental transition involved incorporation of a mid‐point anchor, consistent with a modified proportional reasoning strategy. The methodology introduced here enables a more nuanced assessment of children's number line representation and learning than any previous approaches and indicates that developmental improvement largely results from midpoint segmentation of the line.     

Lessons learned: challenges in applying current constraints on research on chimpanzees to other animals

The Institute of Medicine (IOM) Committee on the Necessity of the Use of Chimpanzees in Biomedical and Behavioral Research made a series of recommendations that, as of an announcement on June 26, 2013, the National Institutes of Health (NIH) is turning into implemented guidelines. Many advocates, including some researchers and scholars, have suggested that the Committee’s recommendations could be applied successfully to other animal species. This article examines, from my perspective as the IOM Committee’s chair, some of the most important features of the Committee’s work, addresses whether chimpanzees represent a special or unique case for the purpose of research policy, and suggests an approach for evaluating the applicability of the Committee’s recommendations for other animal species used in research. I first present my perspective on the features of the Committee’s work that influenced its approach and conclusions. I then argue that despite the fact that chimpanzees represent a somewhat unique case for restricted research use, their case still offers important lessons for policy regarding the use of other species. Finally, I offer some observations regarding the recommendations and implications of the report from the NIH Working Group charged with crafting guidelines for implementing the IOM Committee’s recommendations.     

Immigrant Skill Utilization: Trends and Policy Issues

Since 1996, the problem of underutilization of immigrant skills in Canada has grown significantly. University-educated immigrants are more numerous, and census analysis shows their access to skilled occupations in the professions and management decline between 1996 and 2006. The decline in access since 2001 coincided with increased program efforts, including foreign credential assessment, bridge training, and others. Policy differences among provinces, or in occupational groups targeted, also have had little impact on aggregate trends. The value (in today’s dollars) of work lost to the Canadian economy grew from about $4.80 billion annually in 1996 to about $11.37 billion in 2006.     

Fitting correlated residual error structures in nonlinear mixed-effects models using SAS PROC NLMIXED

Nonlinear mixed-effects (NLME) models remain popular among practitioners for analyzing continuous repeated measures data taken on each of a number of individuals when interest centers on characterizing individual-specific change. Within this framework, variation and correlation among the repeated measurements may be partitioned into interindividual variation and intraindividual variation components. The covariance structure of the residuals are, in many applications, consigned to be independent with homogeneous variances, $ {\sigma}^2{\mathbf{I}}_{n_i} $ , not because it is believed that intraindividual variation adheres to this structure, but because many software programs that estimate parameters of such models are not well-equipped to handle other, possibly more realistic, patterns. In this article, we describe how the programmatic environment within SAS may be utilized to model residual structures for serial correlation and variance heterogeneity. An empirical example is used to illustrate the capabilities of the module.