Numerous developmental findings suggest that infants and toddlers engage predictive processing during language comprehension. However, a significant limitation of this research is that associative (bottom-up) and predictive (top-down) explanations are not readily differentiated. Following adult studies that varied predictiveness relative to semantic-relatedness to differentiate associative vs. predictive processes, the present study used eye-tracking to begin to disentangle the contributions of bottom-up and top-down mechanisms to infants’ real-time language processing. Replicating prior results, infants (14-19 months old) use successive semantically-related words across sentences (e.g., eat, yum, mouth) to predict upcoming nouns (e.g., cookie). However, we also provide evidence that using successive semantically-related words to predict is distinct from the bottom-up activation of the word itself. In a second experiment, we investigate the potential effects of repetition on the findings. This work is the first to reveal that infant language comprehension is affected by both associative and predictive processes.

Consumers typically overstate their intentions to purchase products, compared to actual rates of purchases, a pattern called “hypothetical bias”. In laboratory choice experiments, we measure participants’ visual attention using mousetracking or eye-tracking, while they make hypothetical as well as real purchase decisions. We find that participants spent more time looking both at price and product image prior to making a real “buy” decision than making a real “don’t buy” decision. We demonstrate that including such information about visual attention improves prediction of real buy decisions. This improvement is evident, although small in magnitude, using mousetracking data, but is not evident using eye-tracking data.

This study aimed to create a risk prediction model with artificial intelligence (AI) to identify patients at higher risk of postpartum hemorrhage using perinatal characteristics that may be associated with later postpartum hemorrhage (PPH) in twin pregnancies that underwent cesarean section. The study was planned as a retrospective cohort study at University Hospital. All twin cesarean deliveries were categorized into two groups: those with and without PPH. Using the perinatal characteristics of the cases, four different machine learning classifiers were created: Logistic regression (LR), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). LR, RF, and SVM models were created a second time by including class weights to manage the underlying imbalances in the data. A total of 615 twin pregnancies were included in the study. There were 150 twin pregnancies with PPH and 465 without PPH. Dichorionity, PAS, and placenta previa were significantly higher in the PPH-positive group (p = .045, p = .004, p = .001 respectively). In our model, LR with class weight was the best model with the highest negative predictive value. The AUC in our LR with class weight model was %75.12 with an accuracy of 70.73%, a PPV of 47.92%, and an NPV of 85.33% in our data. Although the application of machine learning to create predictive models using clinical risk factors and our model’s 70% accuracy rate are encouraging, it is not sufficient. Machine learning modeling needs further study and validation before being incorporated into clinical use.

The two main large-scale features of Arctic sea-ice drift are the Beaufort Gyre and the Transpolar Drift Stream. They exhibit strong intraseasonal and interannual variability. Winter 2016/17 showed increased cyclone activity, leading to the collapse of the Beaufort Sea high and the reversal of the Beaufort Gyre. Winter 2020/21 displayed decreased cyclone activity and intense anticyclonic ice transport in the Beaufort Gyre. Here we show that the European Centre for Medium-Range Weather Forecasts’s (ECMWF) extended-range (46 days) retrospective forecasts were able to predict the ice motion during these cases. The initial contrasts in sea level pressure, surface winds and ice drift were well captured, and their temporal evolution—including the reversal of the usual drift direction—well reproduced by the forecasts initialized about a week before the event. Sea-ice thickness in the forecast exhibited initial errors even greater than 1 m that persisted throughout the forecast and negatively affected the ice speed forecast. Despite these shortcomings, the dynamic forecast outperformed the persistence and climatology forecast and represented the observed relation between surface winds and ice drift well. The benefit of dynamic forecasts is especially clear in cases that differ from climatology, like the one we focus on.

Incorporating temporal decline in predictive validity into mental testing theory is outlined. Starting from multivariate regression of criterion on repeated measurements, an analytic extension results in a weighting function for repeated measurements, replacing the beta weights. Besides optimizing, the procedure permits an evaluation of any particular prognosis setting: In cases of exponentially declining predictive validity, prognostic range can be extended if concurrent validity is nonperfect by optimal weighting (“predictive filtering”) of repeated measurements. Considerable gain in prognostic range over the traditional approach can be achieved if predictive validity declines concavely downwards.

In predicting\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde y$$\end{document} scores from p > 1 observed scores\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(\tilde x)$$\end{document} in a sample of size ñ, the optimal strategy (minimum expected loss), under certain assumptions, is shown to be based upon the least squares regression weights\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(\hat \beta )$$\end{document} computed from a previous sample. Letting\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde r(\hat \beta )$$\end{document} represent the correlation between\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde y$$\end{document} and the predicted values\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(\hat \beta '\tilde x)$$\end{document}, and letting\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde r(w)$$\end{document} represent the correlation between\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde y$$\end{document} and a different set of predicted values\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(w'\tilde x)$$\end{document}, where w is any weighting system which is not a function of\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde y$$\end{document}, it is shown that the probability of\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde r(\hat \beta )$$\end{document} being less than\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\tilde r(w)$$\end{document} cannot exceed .50. The relationship of this result to previous research and practical implications are discussed.

Issues of model selection have dominated the theoretical and applied statistical literature for decades. Model selection methods such as ridge regression, the lasso, and the elastic net have replaced ad hoc methods such as stepwise regression as a means of model selection. In the end, however, these methods lead to a single final model that is often taken to be the model considered ahead of time, thus ignoring the uncertainty inherent in the search for a final model. One method that has enjoyed a long history of theoretical developments and substantive applications, and that accounts directly for uncertainty in model selection, is Bayesian model averaging (BMA). BMA addresses the problem of model selection by not selecting a final model, but rather by averaging over a space of possible models that could have generated the data. The purpose of this paper is to provide a detailed and up-to-date review of BMA with a focus on its foundations in Bayesian decision theory and Bayesian predictive modeling. We consider the selection of parameter and model priors as well as methods for evaluating predictions based on BMA. We also consider important assumptions regarding BMA and extensions of model averaging methods to address these assumptions, particularly the method of Bayesian stacking. Simple empirical examples are provided and directions for future research relevant to psychometrics are discussed.

Under a formula score instruction (FSI), test takers omit items. If students are encouraged to answer every item (under a rights-only scoring instruction, ROI), the score distribution will be different. In this paper, we formulate a simple statistical model to predict the score ROI distribution using the FSI data. Estimation error is also provided. In addition, a preliminary investigation of the probability of guessing correctly on omitted items and its sensitivity is presented in the paper. Based on the data used in this paper, the probability of guessing correctly may be close or slightly greater than the chance score.

Prediction is a crucial mechanism of language comprehension. Our research question asked whether learners of Spanish were capable of using word order cues to predict the semantic class of the upcoming verb, and how this ability develops with proficiency. To answer this question, we conducted a self-paced reading study with three L2 Spanish groups at different proficiency levels and one native control group. Among the advanced L2 learners and native speakers, we found that reading times increased after the verb appeared in a word order not strongly associated with its semantic class. Because the only cue to the sentences’ word order was the presence or absence of the object marker a before the first noun, we suggest that these groups use this morphosyntactic cue to anticipate the semantic class of the upcoming verb. However, this pattern of processing behavior was not detected in our less experienced L2 groups.

This study investigated the predictive use of dative verb constraints in Mandarin among home-country-raised native speakers and classroom learners (including both sequential L2 learners and heritage speakers). In a visual world eye-tracking experiment, participants made anticipatory looks to the upcoming argument (recipient versus theme) following categorical restrictions of non-alternating verbs and gradient bias of alternating verbs before the acoustic onset of the disambiguating noun. Crucially, no delay or reduction in the prediction effects was observed among L2 learners and heritage speakers in comparison with home-country-raised native speakers. Mandarin proficiency and dominant language (English versus other) did not modulate prediction effects among classroom learners. These findings provide direct support for the assumption of error-driven learning accounts of the dative alternation, that is, language users actively predict upcoming arguments based on verb information during real-time sentence processing.

We tested whether verb-based prediction in late bilinguals is facilitated when the verb is a cognate versus non-cognate. Spanish–English bilinguals and Chinese–English bilinguals (control) listened to English sentences such as “The girl will adopt the dog” while viewing a scene containing either a dog and unadoptable objects (predictable condition) or a dog and other adoptable animals (unpredictable condition). The verb was either a cognate or non-cognate between Spanish and English and never a cognate between Chinese and English. Both groups of bilinguals were more likely to look at the target (the dog) in the predictable versus unpredictable condition. However, only low-proficient L1 Spanish bilinguals showed greater and earlier prediction when the verb was cognate than when it was non-cognate, suggesting that cognate facilitation effect occurs not only on the cognate word itself but also on prediction based on this cognate word, and that this effect is modulated by L2 proficiency.

The aspirations-ability framework proposed by Carling has begun to place the question of who aspires to migrate at the center of migration research. In this article, building on key determinants assumed to impact individual migration decisions, we investigate their prediction accuracy when observed in the same dataset and in different mixed-migration contexts. In particular, we use a rigorous model selection approach and develop a machine learning algorithm to analyze two original cross-sectional face-to-face surveys conducted in Turkey and Lebanon among Syrian migrants and their respective host populations in early 2021. Studying similar nationalities in two hosting contexts with a distinct history of both immigration and emigration and large shares of assumed-to-be mobile populations, we illustrate that a) (im)mobility aspirations are hard to predict even under ‘ideal’ methodological circumstances, b) commonly referenced “migration drivers” fail to perform well in predicting migration aspirations in our study contexts, while c) aspects relating to social cohesion, political representation and hope play an important role that warrants more emphasis in future research and policymaking. Methodologically, we identify key challenges in quantitative research on predicting migration aspirations and propose a novel modeling approach to address these challenges.