Skeletal representations of shape in human vision: Evidence for a pruned medial axis model.

A representation of shape that is low dimensional and stable across minor disruptions is critical for object recognition. Computer vision research suggests that such a representation can be supported by the medial axis-a computational model for extracting a shape's internal skeleton. However, few studies have shown evidence of medial axis processing in humans, and even fewer have examined how the medial axis is extracted in the presence of disruptive contours. Here, we tested whether human skeletal representations of shape reflect the medial axis transform (MAT), a computation sensitive to all available contours, or a pruned medial axis, which ignores contours that may be considered "noise." Across three experiments, participants (N = 2062) were shown complete, perturbed, or illusory two-dimensional shapes on a tablet computer and were asked to tap the shapes anywhere once. When directly compared with another viable model of shape perception (based on principal axes), participants' collective responses were better fit by the medial axis, and a direct test of boundary avoidance suggested that this result was not likely because of a task-specific cognitive strategy (Experiment 1). Moreover, participants' responses reflected a pruned computation in shapes with small or large internal or external perturbations (Experiment 2) and under conditions of illusory contours (Experiment 3). These findings extend previous work by suggesting that humans extract a relatively stable medial axis of shapes. A relatively stable skeletal representation, reflected by a pruned model, may be well equipped to support real-world shape perception and object recognition.

Recommendation System for Adaptive Learning.

An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.

The relationship between screen time, nighttime sleep duration, and behavioural problems in preschool children in China.

The purpose of this study was to evaluate the relationships between screen time (ST), nighttime sleep duration, and behavioural problems in a sample of preschool children in China. A sample of 8900 children aged 3-6 years was enrolled from 35 kindergartens, in four cities, in two provinces, in China to evaluate the relationships between ST, nighttime sleep duration, and behavioural problems. Children's ST and nighttime sleep duration were assessed by questionnaires completed by parents or guardians. Behavioural problems were assessed using the Strengths and Difficulties Questionnaire (SDQ), and the Clancy Autism Behaviour Scale (CABS). Multivariate analysis was used to assess the associations between ST, nighttime sleep duration, and behavioural problems. The total SDQ and CABS scores were higher in children with ST ≥2 h/day and sleep duration <9.15 h/day (a P < 0.001 for all). After adjusting for potential confounders, children with ST ≥2 h/day had a significantly increased risk of having total difficulties, emotional symptoms, conduct problems, hyperactivity, peer problems, and prosocial problems, as well as behavioural symptoms of autism spectrum disorder. Similar results were found in children with sleep duration <9.15 h/day. No significantly increased risk of emotional symptoms was observed for short sleep duration. Preschool children with more ST and short nighttime sleep duration were significantly more likely to have behavioural problems. These results may contribute to a better understanding of prevention and intervention for psychosocial problems in children.

Exploratory Item Classification Via Spectral Graph Clustering.

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire.

Cesarean section may increase the risk of both overweight and obesity in preschool children.

BACKGROUND: The increase rates of cesarean section (CS) occurred at the same period as the dramatic increase of childhood overweight/obesity. In China, cesarean section rates have exponentially increased in the last 20 years and they now exceed World Health Organization (WHO) recommendation. Such high rates demand an understanding to the long-term consequences on child health. We aim to examine the association between CS and risk of overweight and obesity among preschool children. METHOD: We recruited 9103 children from 35 kindergartens in 4 cities located in East China. Children anthropometric measurements were taken in person by trained personnel. The mode of delivery was classified as vaginal or CS, in sub-analyses we divided cesarean delivery into elective or non-elective. The mode of delivery and other parental information were self-reported by parents. Multivariate logistic regression analysis was used to examine the associations. RESULTS: In our cross-sectional study of 8900 preschool children aged 3-6 years, 67.3 % were born via CS, of whom 15.7 % were obese. Cesarean delivery was significantly associated with the risk of overweight [OR 1.24; (95 % CI 1.07-1.44); p = 0.003], and the risk of obesity [OR 1.29; (95 % CI 1.13-1.49); p < 0.001] in preschool children. After adjusted for child characteristics, parental factors and family income, the odd of overweight was 1.35 and of obesity was 1.25 in children delivered by elective CS. CONCLUSION: The associations between CS and overweight/obesity in preschool children are influenced by potential confounders. Both children delivered by elective or non-elective CS are at increased risk of overweight/obesity. Potential consequences of CS on the health of the children should be discussed among both health care professionals and childbearing women.

A Note on Ising Network Analysis with Missing Data.

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method's performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

DIF Analysis with Unknown Groups and Anchor Items.

Ensuring fairness in instruments like survey questionnaires or educational tests is crucial. One way to address this is by a Differential Item Functioning (DIF) analysis, which examines if different subgroups respond differently to a particular item, controlling for their overall latent construct level. DIF analysis is typically conducted to assess measurement invariance at the item level. Traditional DIF analysis methods require knowing the comparison groups (reference and focal groups) and anchor items (a subset of DIF-free items). Such prior knowledge may not always be available, and psychometric methods have been proposed for DIF analysis when one piece of information is unknown. More specifically, when the comparison groups are unknown while anchor items are known, latent DIF analysis methods have been proposed that estimate the unknown groups by latent classes. When anchor items are unknown while comparison groups are known, methods have also been proposed, typically under a sparsity assumption - the number of DIF items is not too large. However, DIF analysis when both pieces of information are unknown has not received much attention. This paper proposes a general statistical framework under this setting. In the proposed framework, we model the unknown groups by latent classes and introduce item-specific DIF parameters to capture the DIF effects. Assuming the number of DIF items is relatively small, an L 1 -regularised estimator is proposed to simultaneously identify the latent classes and the DIF items. A computationally efficient Expectation-Maximisation (EM) algorithm is developed to solve the non-smooth optimisation problem for the regularised estimator. The performance of the proposed method is evaluated by simulation studies and an application to item response data from a real-world educational test.

Pairwise stochastic approximation for confirmatory factor analysis of categorical data.

Pairwise likelihood is a limited-information method widely used to estimate latent variable models, including factor analysis of categorical data. It can often avoid evaluating high-dimensional integrals and, thus, is computationally more efficient than relying on the full likelihood. Despite its computational advantage, the pairwise likelihood approach can still be demanding for large-scale problems that involve many observed variables. We tackle this challenge by employing an approximation of the pairwise likelihood estimator, which is derived from an optimization procedure relying on stochastic gradients. The stochastic gradients are constructed by subsampling the pairwise log-likelihood contributions, for which the subsampling scheme controls the per-iteration computational complexity. The stochastic estimator is shown to be asymptotically equivalent to the pairwise likelihood one. However, finite-sample performance can be improved by compounding the sampling variability of the data with the uncertainty introduced by the subsampling scheme. We demonstrate the performance of the proposed method using simulation studies and two real data applications.

DIF Statistical Inference Without Knowing Anchoring Items.

Establishing the invariance property of an instrument (e.g., a questionnaire or test) is a key step for establishing its measurement validity. Measurement invariance is typically assessed by differential item functioning (DIF) analysis, i.e., detecting DIF items whose response distribution depends not only on the latent trait measured by the instrument but also on the group membership. DIF analysis is confounded by the group difference in the latent trait distributions. Many DIF analyses require knowing several anchor items that are DIF-free in order to draw inferences on whether each of the rest is a DIF item, where the anchor items are used to identify the latent trait distributions. When no prior information on anchor items is available, or some anchor items are misspecified, item purification methods and regularized estimation methods can be used. The former iteratively purifies the anchor set by a stepwise model selection procedure, and the latter selects the DIF-free items by a LASSO-type regularization approach. Unfortunately, unlike the methods based on a correctly specified anchor set, these methods are not guaranteed to provide valid statistical inference (e.g., confidence intervals and p-values). In this paper, we propose a new method for DIF analysis under a multiple indicators and multiple causes (MIMIC) model for DIF. This method adopts a minimal [Formula: see text] norm condition for identifying the latent trait distributions. Without requiring prior knowledge about an anchor set, it can accurately estimate the DIF effects of individual items and further draw valid statistical inferences for quantifying the uncertainty. Specifically, the inference results allow us to control the type-I error for DIF detection, which may not be possible with item purification and regularized estimation methods. We conduct simulation studies to evaluate the performance of the proposed method and compare it with the anchor-set-based likelihood ratio test approach and the LASSO approach. The proposed method is applied to analysing the three personality scales of the Eysenck personality questionnaire-revised (EPQ-R).

Rotation to Sparse Loadings Using [Formula: see text] Losses and Related Inference Problems.

Researchers have widely used exploratory factor analysis (EFA) to learn the latent structure underlying multivariate data. Rotation and regularised estimation are two classes of methods in EFA that they often use to find interpretable loading matrices. In this paper, we propose a new family of oblique rotations based on component-wise [Formula: see text] loss functions [Formula: see text] that is closely related to an [Formula: see text] regularised estimator. We develop model selection and post-selection inference procedures based on the proposed rotation method. When the true loading matrix is sparse, the proposed method tends to outperform traditional rotation and regularised estimation methods in terms of statistical accuracy and computational cost. Since the proposed loss functions are nonsmooth, we develop an iteratively reweighted gradient projection algorithm for solving the optimisation problem. We also develop theoretical results that establish the statistical consistency of the estimation, model selection, and post-selection inference. We evaluate the proposed method and compare it with regularised estimation and traditional rotation methods via simulation studies. We further illustrate it using an application to the Big Five personality assessment.

Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework.

Latent variable models have been playing a central role in psychometrics and related fields. In many modern applications, the inference based on latent variable models involves one or several of the following features: (1) the presence of many latent variables, (2) the observed and latent variables being continuous, discrete, or a combination of both, (3) constraints on parameters, and (4) penalties on parameters to impose model parsimony. The estimation often involves maximizing an objective function based on a marginal likelihood/pseudo-likelihood, possibly with constraints and/or penalties on parameters. Solving this optimization problem is highly non-trivial, due to the complexities brought by the features mentioned above. Although several efficient algorithms have been proposed, there lacks a unified computational framework that takes all these features into account. In this paper, we fill the gap. Specifically, we provide a unified formulation for the optimization problem and then propose a quasi-Newton stochastic proximal algorithm. Theoretical properties of the proposed algorithms are established. The computational efficiency and robustness are shown by simulation studies under various settings for latent variable model estimation.

Reassessing Diabetes and APOE Genotype as Potential Interacting Risk Factors for Alzheimer's Disease.

Objective: To assess whether diabetes alone or in association with Apolipoprotein E (APOE) ε4 genotype increases the risk of Alzheimer's Disease (AD) diagnosis. Methods: A retrospective cohort study of 33,456 participants from the National Alzheimer's Coordinating Center database. Results: Participants with one or two APOE ε4 alleles had 2.71 (CI:2.55-2.88) and 9.37 (CI:8.14-10.78) times higher odds of AD diagnosis, respectively, relative to those with zero ε4 alleles. In contrast, diabetic participants showed 1.07 (CI:0.96-1.18) times higher odds of AD relative to nondiabetics. Diabetes did not exacerbate the odds of AD in APOE ε4 carriers. APOE ε4 carriage was correlated with declines in long-term memory and verbal fluency, which were strongly correlated with conversion to AD. However, diabetes was correlated with working memory decline, which had a relatively weak correlation with AD. Conclusions: Unlike APOE ε4, there was little evidence that diabetes was a risk factor for AD.

Establishment and validation of a prediction model for intraparenchymal hematoma expansion in patients with cerebral contusion: A reliable Nomogram.

BACKGROUND AND OBJECTIVE: Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). Traumatic intraparenchymal hematoma (TICH) expansion severely affects the patient's prognosis. In this study, the baseline data, imaging features, and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a nomogram predictive model assessing the risk factors for TICH expansion. METHODS: Totally 258 patients were included and retrospectively analyzed herein, who met the CC inclusion criteria, from July 2018 to July 2021. TICH expansion was defined as increased hematoma volume ≥ 30% relative to primary volume or an absolute hematoma increase ≥ 5 ml at CT review. RESULTS: Univariate and binary logistic regression analyses were performed to screen out the independent predictors significantly correlated with TICH expansion: Age, subdural hematoma (SDH), contusion site, multihematoma fuzzy sign (MFS), contusion volume, and traumatic coagulation abnormalities (TCA). Based on these, the nomogram model was established. The differences between the contusion volume and glasgow outcome scale (GOS) were analyzed by the nonparametric tests. Larger contusion volume was associated with poor prognosis. CONCLUSION: This study established a Nomogram model to predict TICH expansion in patients with CC. Meanwhile, the study found that the risk of bleeding tended to decrease when the hematoma volume was > 15 ml, but the larger initial hematoma volume would indicate worse prognosis. We advocate the use of predictive models for TICH expansion risk assessment in hospitalized CC patients, which is low-cost and easy-to-apply, especially in acute settings.

Inhibition of TRPA1 Attenuates Oxidative Stress-induced Damage After Traumatic Brain Injury via the ERK/AKT Signaling Pathway.

Neuron apoptosis is a feature of secondary injury after traumatic brain injury (TBI). Evidence implies that excess calcium (Ca2+) ions and reactive oxidative species (ROS) play critical roles in apoptosis. In reaction to increased ROS, the anti-oxidative master transcription factor, Transient receptor potential Ankyrin 1 (TRPA1) allows Ca2+ ions to enter cells. However, the effect of TBI on the expression of TRPA1 and the role of TRPA1 in TBI are unclear. In the present study, TBI in the mouse brain was simulated using the weight-drop model. The process of neuronal oxidative stress was simulated in HT22 neuronal cells by treatment with hydrogen peroxide. We found that TRPA1 was significantly upregulated in neurons at 24 h after TBI. Neuronal apoptosis was increased in the in vivo and in vitro models; however, this increase was reduced by the functional inhibition of TRPA1 in both models. After TBI, TRPA1 was upregulated via nuclear factor, erythroid 2 like 2 (Nrf2) in neurons. TRPA1-mediated neuronal apoptosis after TBI might be achieved in part through the CaMKII/AKT/ERK signaling pathway. To sum up, TBI-triggered TRPA1 upregulation in neurons is mediated by Nrf2 and the functional blockade of TRPA1 attenuates neuronal apoptosis and improves neuronal dysfunction, partially mediated through the activation of the calcium/calmodulin dependent protein kinase II (CaMKII) extracellular regulated kinase (ERK)/protein kinase B (AKT) signaling pathway. Our results suggest that functional blockade of TRPA1 might be a promising therapeutic intervention related to ROS and Nrf2 in TBI.

A Continuous-Time Dynamic Choice Measurement Model for Problem-Solving Process Data.

Problem solving has been recognized as a central skill that today's students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A popular tool for the measurement of problem solving is simulated interactive tasks, which require students to uncover some of the information needed to solve the problem through interactions with a computer-simulated environment. A computer log file records a student's problem-solving process in details, including his/her actions and the time stamps of these actions. It thus provides rich information for the measurement of students' problem-solving competency. On the other hand, extracting useful information from log files is a challenging task, due to its complex data structure. In this paper, we show how log file process data can be viewed as a marked point process, based on which we propose a continuous-time dynamic choice model. The proposed model can serve as a measurement model for scaling students along the latent traits of problem-solving competency and action speed, based on data from one or multiple tasks. A real data example is given based on data from Program for International Student Assessment 2012.

A Latent Gaussian process model for analysing intensive longitudinal data.

Intensive longitudinal studies are becoming progressively more prevalent across many social science areas, and especially in psychology. New technologies such as smart-phones, fitness trackers, and the Internet of Things make it much easier than in the past to collect data for intensive longitudinal studies, providing an opportunity to look deep into the underlying characteristics of individuals under a high temporal resolution. In this paper we introduce a new modelling framework for latent curve analysis that is more suitable for the analysis of intensive longitudinal data than existing latent curve models. Specifically, through the modelling of an individual-specific continuous-time latent process, some unique features of intensive longitudinal data are better captured, including intensive measurements in time and unequally spaced time points of observations. Technically, the continuous-time latent process is modelled by a Gaussian process model. This model can be regarded as a semi-parametric extension of the classical latent curve models and falls under the framework of structural equation modelling. Procedures for parameter estimation and statistical inference are provided under an empirical Bayes framework and evaluated by simulation studies. We illustrate the use of the proposed model though the analysis of an ecological momentary assessment data set.

A Note on Likelihood Ratio Tests for Models with Latent Variables.

The likelihood ratio test (LRT) is widely used for comparing the relative fit of nested latent variable models. Following Wilks' theorem, the LRT is conducted by comparing the LRT statistic with its asymptotic distribution under the restricted model, a [Formula: see text] distribution with degrees of freedom equal to the difference in the number of free parameters between the two nested models under comparison. For models with latent variables such as factor analysis, structural equation models and random effects models, however, it is often found that the [Formula: see text] approximation does not hold. In this note, we show how the regularity conditions of Wilks' theorem may be violated using three examples of models with latent variables. In addition, a more general theory for LRT is given that provides the correct asymptotic theory for these LRTs. This general theory was first established in Chernoff (J R Stat Soc Ser B (Methodol) 45:404-413, 1954) and discussed in both van der Vaart (Asymptotic statistics, Cambridge, Cambridge University Press, 2000) and Drton (Ann Stat 37:979-1012, 2009), but it does not seem to have received enough attention. We illustrate this general theory with the three examples.

A Note on Exploratory Item Factor Analysis by Singular Value Decomposition.

We revisit a singular value decomposition (SVD) algorithm given in Chen et al. (Psychometrika 84:124-146, 2019b) for exploratory item factor analysis (IFA). This algorithm estimates a multidimensional IFA model by SVD and was used to obtain a starting point for joint maximum likelihood estimation in Chen et al. (2019b). Thanks to the analytic and computational properties of SVD, this algorithm guarantees a unique solution and has computational advantage over other exploratory IFA methods. Its computational advantage becomes significant when the numbers of respondents, items, and factors are all large. This algorithm can be viewed as a generalization of principal component analysis to binary data. In this note, we provide the statistical underpinning of the algorithm. In particular, we show its statistical consistency under the same double asymptotic setting as in Chen et al. (2019b). We also demonstrate how this algorithm provides a scree plot for investigating the number of factors and provide its asymptotic theory. Further extensions of the algorithm are discussed. Finally, simulation studies suggest that the algorithm has good finite sample performance.

An improved stochastic EM algorithm for large-scale full-information item factor analysis.

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including an adaptive-rejection-based Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization in the M step, and diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000, Bernoulli, 6, 457), as well as advanced sampling and optimization techniques. The proposed algorithm is computationally efficient and virtually tuning-free, making it scalable to large-scale data with many latent traits (e.g. more than five latent traits) and easy to use for practitioners. Standard errors of parameter estimation are also obtained based on the missing-information identity (Louis, 1982, Journal of the Royal Statistical Society, Series B, 44, 226). The performance of the algorithm is evaluated through simulation studies and an application to the analysis of the IPIP-NEO personality inventory. Extensions of the proposed algorithm to other latent variable models are discussed.

Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis.

Joint maximum likelihood (JML) estimation is one of the earliest approaches to fitting item response theory (IRT) models. This procedure treats both the item and person parameters as unknown but fixed model parameters and estimates them simultaneously by solving an optimization problem. However, the JML estimator is known to be asymptotically inconsistent for many IRT models, when the sample size goes to infinity and the number of items keeps fixed. Consequently, in the psychometrics literature, this estimator is less preferred to the marginal maximum likelihood (MML) estimator. In this paper, we re-investigate the JML estimator for high-dimensional exploratory item factor analysis, from both statistical and computational perspectives. In particular, we establish a notion of statistical consistency for a constrained JML estimator, under an asymptotic setting that both the numbers of items and people grow to infinity and that many responses may be missing. A parallel computing algorithm is proposed for this estimator that can scale to very large datasets. Via simulation studies, we show that when the dimensionality is high, the proposed estimator yields similar or even better results than those from the MML estimator, but can be obtained computationally much more efficiently. An illustrative real data example is provided based on the revised version of Eysenck's Personality Questionnaire (EPQ-R).