Modeling the effects of within-person characteristic and goal-level attributes on personal project pursuit over time

The current study aims to overcome methodological constraints of previous goal pursuit research by exploring how people simultaneously rely upon person-level characteristic adaptations and unique goal-level attributes to pursue their personal projects. Undergraduate participants identified 10 projects they would pursue over an academic quarter and rated project meaningfulness, effort exerted, patience employed, and progress satisfaction at five time points. Multilevel structural equation models revealed the relative influence of person-level and project-level attributes on project appraisals. Person-level adaptations accounted for a large portion of variance in project pursuit appraisals, though significant project-specific trends were found over time as well, including mutual positive cross-lag influences between meaning, patience, and effort, and a negative predictive effect of progress satisfaction on meaning.     

Physical activity and motor skills in children: A differentiated approach

Being physically active plays an essential role in a child's physical development. While there is ample evidence for a positive association between physical activity (PA) and motor skills in children, the question of how PA should be implemented to optimally foster motor skill proficiency is less clear. To address this gap, the current longitudinal study compared four groups of children with different patterns of leisure-time PA engagement—namely children engaging in either structured PA, unstructured PA, a combination of structured and unstructured PA, or no PA at all—with respect to their gross and fine motor skill development. Results of repeated measures mixed modeling procedures revealed that engaging in structured PA—either exclusively or in combination with unstructured PA—is beneficial for children's gross motor development, whereas engaging in unstructured PA lacks such effectiveness. As to fine motor skills, a beneficial tendency of structured PA was observed as well. Hence, PA seems to be beneficial for motor skill development particularly when implemented in a formal setting with guided opportunities for practice. In conclusion, regularly engaging in structured PA constitutes a promising way to promote motor skills and support motor development over the long term.     

Postselection Inference in Structural Equation Modeling

Abstract

Most statistical inference methods were established under the assumption that the fitted model is known in advance. In practice, however, researchers often obtain their final model by some data-driven selection process. The selection process makes the finally fitted model random, and it also influences the sampling distribution of the estimator. Therefore, implementing naive inference methods may result in wrong conclusions—which is probably a prime source of the reproducibility crisis in psychological science. The present study accommodates three valid state-of-the-art postselection inference methods for structural equation modeling (SEM) from the statistical literature: data splitting (DS), postselection inference (PoSI), and the polyhedral (PH) method. A simulation is conducted to compare the three methods with the commonly used naive procedure under selection events made by L₁-penalized SEM. The results show that the naive method often yields incorrect inference, and that the valid methods control the coverage rate in most cases with their own pros and cons. Real world data examples show the practical use of the valid inference methods.     

Extending problem-solving procedures through reflection

A large-sample (n = 75) fMRI study guided the development of a theory of how people extend their problem-solving procedures by reflecting on them. Both children and adults were trained on a new mathematical procedure and then were challenged with novel problems that required them to change and extend their procedure to solve these problems. The fMRI data were analyzed using a combination of hidden Markov models (HMMs) and multi-voxel pattern analysis (MVPA). This HMM–MVPA analysis revealed the existence of 4 stages: Encoding, Planning, Solving, and Responding. Using this analysis as a guide, an ACT-R model was developed that improved the performance of the HMM–MVPA and explained the variation in the durations of the stages across 128 different problems. The model assumes that participants can reflect on declarative representations of the steps of their problem-solving procedures. A Metacognitive module can hold these steps, modify them, create new declarative steps, and rehearse them. The Metacognitive module is associated with activity in the rostrolateral prefrontal cortex (RLPFC). The ACT-R model predicts the activity in the RLPFC and other regions associated with its other cognitive modules (e.g., vision, retrieval). Differences between children and adults seemed related to differences in background knowledge and computational fluency, but not to the differences in their capability to modify procedures.     

Regularization of languages by adults and children: A mathematical framework

The fascinating ability of humans to modify the linguistic input and “create” a language has been widely discussed. In the work of Newport and colleagues, it has been demonstrated that both children and adults have some ability to process inconsistent linguistic input and “improve” it by making it more consistent. In Hudson Kam and Newport (2009), artificial miniature language acquisition from an inconsistent source was studied. It was shown that (i) children are better at language regularization than adults and that (ii) adults can also regularize, depending on the structure of the input. In this paper we create a learning algorithm of the reinforcement-learning type, which exhibits patterns reported in Hudson Kam and Newport (2009) and suggests a way to explain them. It turns out that in order to capture the differences between children’s and adults’ learning patterns, we need to introduce a certain asymmetry in the learning algorithm. Namely, we have to assume that the reaction of the learners differs depending on whether or not the source’s input coincides with the learner’s internal hypothesis. We interpret this result in the context of a different reaction of children and adults to implicit, expectation-based evidence, positive or negative. We propose that a possible mechanism that contributes to the children’s ability to regularize an inconsistent input is related to their heightened sensitivity to positive evidence rather than the (implicit) negative evidence. In our model, regularization comes naturally as a consequence of a stronger reaction of the children to evidence supporting their preferred hypothesis. In adults, their ability to adequately process implicit negative evidence prevents them from regularizing the inconsistent input, resulting in a weaker degree of regularization.     

Empirical Bayes Derivative Estimates

Abstract

A dynamic system is a set of interacting elements characterized by changes occurring over time. The estimation of derivatives is a mainstay for exploring dynamics of constructs, particularly when the dynamics are complicated or unknown. The presence of measurement error in many social science constructs frequently results in poor estimates of derivatives, as even modest proportions of measurement error can compound when estimating derivatives. Given the overlap in the specification of latent differential equation models and latent growth curve models, and the equivalence of latent growth curve models and mixed models under some conditions, derivatives could be estimated from estimates of random effects. This article proposes a new method for estimating derivatives based on calculating the Empirical Bayes estimates of derivatives from a mixed model. Two simulations compare four derivative estimation methods: Generalized Local Linear Approximation, Generalized Orthogonal Derivative Estimates, Functional Data Analysis, and the proposed Empirical Bayes Derivative Estimates. The simulations consider two data collection scenarios: short time series (≤10 observations) from many individuals or occasions, and long individual time series (25–500 observations). A substantive example visualizing the dynamics of intraindividual positive affect time series is also presented.     

Modeling contagion in policy systems

Scholars of the policy process offer compelling explanations for patterns in the aggregate-level attention of policymakers. Yet, we have little systematic understanding of the day-to-day behavior of these individuals. Why does a given policymaker, on a given day, decide to focus on one pressing issue while ignoring many others? I approach this question from a cognitive systems perspective and argue that policymakers are highly interdependent actors who are subject to cognitive limits and have incentives to closely monitor the political environment. These tendencies contribute to the emergence of widespread herd behavior in their individual attention to policy issues, a phenomenon I conceptualize as ‘issue contagion.’ I then utilize the methods of computational social science to build an agent-based simulation model of policymakers’ issue attention over time. I also outline three empirical expectations regarding the density of communication ties between actors, the presence of segmented groups (e.g. political parties and coalitions), and the rate at which actors take cues from one another. Through a series of sensitivity tests, I document the internal validity of the model and show that incremental changes in network density, segmentation, and cue-taking all generate clear and visible trends in the frequency of issue contagion events.     

An individual differences perspective on pragmatic abilities in the preschool years

Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities.

Research Highlights

• Individual differences in pragmatic abilities are important to understanding variation in language development.
• Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures.
• We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability.
• We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.

     

A Computational and Empirical Investigation of Graphemes in Reading

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full‐blown model of multi‐syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading.