The Relation Between Media Exposure, Risk Behaviour and Anxiety in Adolescents During the COVID-19 Pandemic

This report is focused on a theoretical and empirical analysis of an on-line questionnaire implemented with 246 adolescent participants from Croatia in 2020 during the COVID-19 pandemic. The aim of the study was to analyse the relation between the COVID-19 pan­demic related fear and risk behaviour, anxiety and depression in adolescents, including the importance of sociodemographic traits, family/school/peer relationships and media use as risk-protective factors. The research was implemented during the March and April 2020 lockdown in Croatia with participants aged from 18-35 years old. The questionnaire consisted of 5 major parts: socio-demographic measures; media use and interests; Mean world syndrome; risk behaviour; and 2 standardized scales on anxiety and depression with a high validity of the Cronbach’s alpha coefficients ranging from α = .81 to α = .94. Correlation analysis demonstrated significant positive relations between COVID-19 media exposure, risk behaviour and anxiety or depression. Our regression analysis established positive moderate predictive relations between risk behaviour, anxiety and depression (p < .01). No significant effects for risk behaviour in relation to the participants’ sociodemographic traits were found.

The Role of General and Specific Cognitive Abilities in Predicting Performance of Three Occupations: Evidence from Bifactor Models

Cognitive abilities are related to job performance. However, there is less agreement about the relative contribution of general versus specific cognitive abilities to job performance. Similarly, it is not clear how cognitive abilities operate in the context of complex occupations. This study assessed the role of cognitive abilities on the performance of three aviation-related jobs: flying, navigation, and air battle management (ABM). Correlated-factor and bifactor models were used to draw a conclusion about the predictive relations between cognitive abilities and job performance. Overall, the importance of particular cognitive abilities tends to vary across the three occupations, and each occupation has different sets of essential abilities. Importantly, the interplay of general versus specific abilities is different across occupations, and some specific abilities also show substantial predictive power.

A longitudinal study of early pretense: Metarepresentational or not

The metarepresentational aspect of early pretend play (make-believe activities where children create or participate in creating a new situation different from a real one) has been theoretically debated. In the present longitudinal study of N = 83 children, we tested for predictive relations of shared attention at 12–18 months, implicit false belief (FB) at 18 months, and pretend production at 18 months, as well as comprehension at 24 months. We also tested for long-term predictive relations of pretense production and comprehension with theory of mind (ToM) at the age of 4–5 years. Only pretense production directed toward others (but not self) was specifically related to infancy measures of shared attention. Early pretense, either production or comprehension, was not related to implicit FB or later ToM measures. The findings are discussed in terms of different theoretical accounts of early pretense.

Statistical learning reflects inferences about unique predictive relations

The mind adeptly registers statistical regularities in experience, often incidentally and implicitly. We used a visual statistical learning paradigm to study what kinds of statistics it spontaneously computes in such conditions. We found that participants’ learning of pairwise predictive relations was best explained by an inferentially sophisticated quantity, deltaP, which reflects whether a high conditional probability between an event pair is unique. We showed that uniqueness can be reduced by either a strong competing predictor or an overall high base rate of the outcome. Both can result from normalization: if predictors of the same effect trade off, a predictor must raise the probability of the effect more than the others to be effective. Adding normalization to the Rescorla-Wagner learning model captures these results. We argue that the uniqueness of a relation is an intrinsically important statistical property that governs learning without incentive or deliberation.

The Difficulties of Predicting the Coefficient of Friction in Cold Flat Rolling

The flat rolling process is initiated when the frictional forces draw the strip to be rolled into the roll gap. These forces depend on the coefficient of friction, knowledge of which is essential to understand, describe, and analyze the process. Several predictive formulae for the coefficient have been presented in the technical literature. Contradictions are observed, however, when their predictions are compared to each other. The data obtained while cold rolling aluminum and steel strips are used in the analyses. A model of the rolling process—accounting for strain hardening, frictional events, and varying speeds—is then used to determine the coefficients of friction. The use of statistical analyses is found to yield more reliable results than the use of the predictive relations.

An efficient algorithm to simulate hazard-targeted site-based synthetic ground motions

This study presents an efficient algorithm that can be used to simulate ground motion waveforms using the site-based approach developed by Dabaghi and Der Kiureghian, and Rezaeian and Der Kiureghian that not only correspond to a specified seismic scenario (e.g. magnitude, distance, site conditions) but are also certain to achieve a target ground motion intensity measure within a narrow range. The suggested algorithm alleviates the need to scale simulated ground motions generated using the above-mentioned site-based approach; the resulting hazard-targeted simulated ground motions have consistent amplitude and time- and frequency-domain characteristics, which are required for proper seismic demand analysis of structures. The proposed algorithm takes as input a set of seismic Event Parameters and the target hazard intensity measure [Formula: see text] and generates a corresponding set of Model Parameters (i.e. input to the site-based ground motion simulation model). These Model Parameters are then used to simulate ground motion waveforms that not only represent the set of input Event Parameters ( Mw, Rrup, Vs30) but also maintain the target [Formula: see text]. To generate the set of Model Parameters, predictive relations between the Model Parameters and [Formula: see text] of ground motions are developed. Among the Model Parameters, the ones classified as important by statistical procedures (such as Random Forests, Forward Selection) are used to develop the predictive relations. The developed relations are then validated against a large dataset of recorded ground motions. The final implementation is provided in terms of graphic-user interface (GUI) called “Hazard-Targeted Time-Series Simulator” ( HATSim), which efficiently simulates site-based ground motions with minimum inputs.

Controlling, Confounding, and Construct Clarity: A Response to Criticisms of 'Revisiting the Marshmallow Test'

Longitudinal studies of development often rely on correlational methods to examine linkages between early-life constructs and later-life outcomes. As highlighted by responses to our article, “Revisiting the Marshmallow Test: A Conceptual Replication Investigating Links Between Delay of Gratification and Later Outcomes,” interpretations of these linkages can be difficult. In this commentary, we address criticisms that our approach “over-controlled” for key factors related to a child’s ability to delay gratification, allay concerns over multicollinearity, and discuss how multivariate regression techniques can help clarify the interpretation of observed predictive relations.

Implicit binding between predictive relations

Knowledge of predictive relations is a core aspect of learning. Beyond individual relations, we also represent intuitive theories of the world, which include interrelated sets of relations. We asked whether individual predictive relations learned incidentally in the same context become automatically associatively bound and whether they influence later learning. Participants performed a cover task while watching three sequences of events. Each sequence contained the same set of events, but differed in how the events related to each other. The first two sequences each had two strong predictive relations (R1 &amp; R2, and R3 &amp; R4). The third contained either a consistent pairing of relations (R1 &amp; R2) or an inconsistent pairing (R1 &amp; R3). We found that participants’ learning of the individual relations in the third sequence was affected by pairing consistency, suggesting the mind associates relations to each other as part of the intrinsic way it learns about the world. This was despite participants’ minimal ability to verbally describe most of the relations they had learned. Thus, participants spontaneously developed the expectation that pairs of relations should cohere, and this affected their ability to learn new evidence. Such associative binding of relational information may help us build intuitive theories.

QUANTILOGRAMS UNDER STRONG DEPENDENCE

We develop the limit theory of the quantilogram and cross-quantilogram under long memory. We establish the sub-root-n central limit theorems for quantilograms that depend on nuisance parameters. We propose a moving block bootstrap (MBB) procedure for inference and establish its consistency, thereby enabling a consistent confidence interval construction for the quantilograms. The newly developed reduction principles for the quantilograms serve as the main technical devices used to derive the asymptotics and establish the validity of MBB. We report some simulation evidence that our methods work satisfactorily. We apply our method to quantile predictive relations between financial returns and long-memory predictors.