Multidimensional item response theory (MIRT) offers psychometric models for various data settings, most popularly for dichotomous and polytomous data. Less attention has been devoted to count responses. A recent growth in interest in count item response models (CIRM)—perhaps sparked by increased occurrence of psychometric count data, e.g., in the form of process data, clinical symptom frequency, number of ideas or errors in cognitive ability assessment—has focused on unidimensional models. Some recent unidimensional CIRMs rely on the Conway–Maxwell–Poisson distribution as the conditional response distribution which allows conditionally over-, under-, and equidispersed responses. In this article, we generalize to the multidimensional case, introducing the Multidimensional Two-Parameter Conway–Maxwell–Poisson Model (M2PCMPM). Using the expectation-maximization (EM) algorithm, we develop marginal maximum likelihood estimation methods, primarily for exploratory M2PCMPMs. The resulting discrimination matrices are rotationally indeterminate. Recently, regularization of the discrimination matrix has been used to obtain a simple structure (i.e., a sparse solution) for dichotomous and polytomous data. For count data, we also (1) rotate or (2) regularize the discrimination matrix. We develop an EM algorithm with lasso ($\ell _1$) regularization for the M2PCMPM and compare (1) and (2) in a simulation study. We illustrate the proposed model with an empirical example using intelligence test data.

Studies have shown that some covertly conscious brain-injured patients, who are behaviorally unresponsive, can reply to simple questions via neuronal responses. Given the possibility of such neuronal responses, Andrew Peterson et al. have argued that there is warrant for some covertly conscious patients being included in low-stakes medical decisions using neuronal responses, which could protect and enhance their autonomy. The justification for giving credence to alleged neuronal responses must be analyzed from various perspectives, including neurology, bioethics, law, and as we suggest, philosophy of mind. In this article, we analyze the warrant for giving credence to neuronal responses from two different views in philosophy of mind. We consider how nonreductive physicalism’s causal exclusion problem elicits doubt about interpreting neural activity as indicating a conscious response. By contrast, such an interpretation is supported by the mind-body powers model of neural correlates of consciousness inspired by hylomorphism.

Treating inertial measurement unit (IMU) measurements as inputs to a motion model and then preintegrating these measurements have almost become a de facto standard in many robotics applications. However, this approach has a few shortcomings. First, it conflates the IMU measurement noise with the underlying process noise. Second, it is unclear how the state will be propagated in the case of IMU measurement dropout. Third, it does not lend itself well to dealing with multiple high-rate sensors such as a lidar and an IMU or multiple asynchronous IMUs. In this paper, we compare treating an IMU as an input to a motion model against treating it as a measurement of the state in a continuous-time state estimation framework. We methodically compare the performance of these two approaches on a 1D simulation and show that they perform identically, assuming that each method’s hyperparameters have been tuned on a training set. We also provide results for our continuous-time lidar-inertial odometry in simulation and on the Newer College Dataset. In simulation, our approach exceeds the performance of an imu-as-input baseline during highly aggressive motion. On the Newer College Dataset, we demonstrate state-of-the art results. These results show that continuous-time techniques and the treatment of the IMU as a measurement of the state are promising areas of further research. Code for our lidar-inertial odometry can be found at: https://github.com/utiasASRL/steam_icp.

Despite the versatility of generalized linear mixed models in handling complex experimental designs, they often suffer from misspecification and convergence problems. This makes inference on the values of coefficients problematic. In addition, the researcher’s choice of random and fixed effects directly affects statistical inference correctness. To address these challenges, we propose a robust extension of the “two-stage summary statistics” approach using sign-flipping transformations of the score statistic in the second stage. Our approach efficiently handles within-variance structure and heteroscedasticity, ensuring accurate regression coefficient testing for 2-level hierarchical data structures. The approach is illustrated by analyzing the reduction of health issues over time for newly adopted children. The model is characterized by a binomial response with unbalanced frequencies and several categorical and continuous predictors. The proposed approach efficiently deals with critical problems related to longitudinal nonlinear models, surpassing common statistical approaches such as generalized estimating equations and generalized linear mixed models.

The focus of job satisfaction literature remains on the subordinate even though supervisors are responsible for evaluating employee performance, determining employee pay, raises, promotions, growth opportunities, etc., all of which impact employees’ subsequent performance that contributes (or not) to organizational success. Using a psychological contracts lens, we develop and test theoretical arguments predicting supervisors’ response to contributions is not uniformly positive depending on the type and amount of contribution involved. Across two studies, we ask supervisors to evaluate subordinates’ delivered contributions relative to promised contributions. Our results challenge the assumption that supervisors always desire larger amounts of work from their subordinates; excess contributions were associated with lower supervisors’ satisfaction with subordinates for some types of contributions. The results imply that subordinates’ contributions of work to supervisors may influence supervisors’ satisfaction with subordinates perhaps affecting their performance reviews and career opportunities.

The aim of this article and the ensuing Special Issue is to assess, après fifteen years, the effects on the EU legal and political system of the overhaul of executive delegated powers inaugurated by the Lisbon Treaty. It identifies core parameters – i.e. (institutional) balance of powers, (democratic) legitimacy, control and accountability, effectiveness of EU policy implementation – considered by the contributions to this Special Issue to map and examine, both constitutionally and normatively, the EU system of delegated powers in law and practice. It also puts forward seven overarching reflections revealing some of the core issues and challenges posed by the current stage of development of the post-Lisbon EU system of delegated powers.

Some effects are considered to be higher level than others. High-level effects provide expressive and succinct abstraction of programming concepts, while low-level effects allow more fine-grained control over program execution and resources. Yet, often it is desirable to write programs using the convenient abstraction offered by high-level effects, and meanwhile still benefit from the optimizations enabled by low-level effects. One solution is to translate high-level effects to low-level ones.

This paper studies how algebraic effects and handlers allow us to simulate high-level effects in terms of low-level effects. In particular, we focus on the interaction between state and nondeterminism known as the local state, as provided by Prolog. We map this high-level semantics in successive steps onto a low-level composite state effect, similar to that managed by Prolog’s Warren Abstract Machine. We first give a translation from the high-level local-state semantics to the low-level global-state semantics, by explicitly restoring state updates on backtracking. Next, we eliminate nondeterminism altogether in favour of a lower-level state containing a choicepoint stack. Then we avoid copying the state by restricting ourselves to incremental, reversible state updates. We show how these updates can be stored on a trail stack with another state effect. We prove the correctness of all our steps using program calculation where the fusion laws of effect handlers play a central role.

A common way of acquiring multiword expressions is through language input, such as during reading and listening. However, this type of learning is slow. Identifying approaches that optimize learning from input, therefore, is an important language-learning endeavor. In the present study, 85 learners of English as a foreign language read short texts with 42 figurative English phrasal verbs, repeated three times. In a counterbalanced design, we manipulated access to definitions (before text, after text, no definition) and typographic enhancement (with bolding, without bolding). The learning was measured by immediate and delayed gap-fill and meaning generation posttests. All posttests showed that learning with definitions was better than without, and that access to definitions after reading was more beneficial than before reading. Typographic enhancement effectively promoted contextual learning of phrasal verbs and increased the learning advantage associated with presenting definitions after reading.

We document significant increases in the suspension of ongoing drug projects following the passage of the Food and Drug Administration Amendments Act of 2007 (FDAAA), which mandates that pharmaceutical companies publicly disclose detailed clinical study results. Our results suggest a causal interpretation through difference-in-differences analyses that exploit variations in pre-FDAAA information environments. We also show evidence that fewer new projects are initiated after the FDAAA. Drug developers’ learning from peer failures is the primary mechanism, further amplified by financial constraints. We also examine the consequences of enhanced information disclosure, including changes in firm investment efficiency, drug quality, and disease morbidity.

Multivariate analysis using graphical models is rapidly gaining ground in psychology. In particular, Markov random field (MRF) graphical models have become popular because their graph structure reflects the conditional associations between psychological variables. Despite the fact that most psychological variables are assessed on an ordinal scale, the analysis of MRFs for ordinal variables has received little attention in the psychometric literature. To fill this gap, we present an MRF for ordinal data that so far has not been considered in network psychometrics. We present statistical methodology to test the structure of the proposed MRF, which requires us to determine the plausibility of the opposing hypotheses of conditional dependence and independence. To this end, we develop a Bayesian approach using the inclusion Bayes factor to quantify the (lack of) evidence for a given edge. We use a Bayesian variable selection approach to model the inclusion and exclusion of edges in the network, and Bayesian model averaging to compare network structures with and without the given edge. We provide an implementation in the new R package bgms, evaluate its performance in simulations, and illustrate it with empirical data.