To advance the study of children's knowledge and understanding of disease, we devised a methodology for assessing key features of intuitive theories laid out by Wellman and Gelman (1998). We then assessed a disease-relevant biological ontology, causal propositions involving unobservables, and coherence in explanations of influenza offered by children aged 8 to 13. Use of disease-relevant terms and mention of propositions in a biological theory of flu causality, although not coherence or connectedness of ideas, increased with age. Measures were moderately correlated with one another and with a traditional Piagetian measure of level of disease understanding, each contributing uniquely to the characterization of children's thinking. In multiple regression analyses, scores were highest for older children, Latino/minority children, and children of more educated parents with other factors controlled. Specific gaps in children's intuitive theories are identified to guide theory-based interventions aimed at helping children understand and protect themselves from infectious diseases.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7437969PMC
http://dx.doi.org/10.1016/j.cogdev.2019.100809DOI Listing

Publication Analysis

Top Keywords

intuitive theories
12
children's intuitive
8
children
5
characterizing children's
4
theories disease
4
disease case
4
case flu
4
flu advance
4
advance study
4
study children's
4

Similar Publications

Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents' self-reported wellbeing.

View Article and Find Full Text PDF

The prevalence of nanoplastics (NPs) and sulfonamide antibiotics (SAs) in the aquatic environment is potentially harmful to the environment, and these pollutants are often present in the environment in the form of composite ones, thereby introducing more complex effects and hazards to the environment. Therefore, it is crucial to investigate the toxic effects of the individual target pollutants and their mixtures. In this study, we used Scenedesmus obliquus as the test organisms, two types of NPs: polystyrene (PS) and amine-modified (NH-PS), four SAs: sulfapyridine (SPY), sulfamethazine (SMR), sulfamethoxypyridazine (SMP), and sulfamethoxazole (SMZ), and their eight binary mixtures were examined.

View Article and Find Full Text PDF

Flow and intuition: a systems neuroscience comparison.

Neurosci Conscious

January 2025

VERSES AI Research Lab, Los Angeles, CA, United States.

This paper explores the relationship between intuition and flow from a neurodynamics perspective. Flow and intuition represent two cognitive phenomena rooted in nonconscious information processing; however, there are clear differences in both their phenomenal characteristics and, more broadly, their contribution to action and cognition. We propose, extrapolating from dual processing theory, that intuition serves as a rapid, nonconscious decision-making process, while flow facilitates this process in action, achieving optimal cognitive control and performance without [conscious] deliberation.

View Article and Find Full Text PDF

A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting.

Sci Rep

December 2024

Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions.

View Article and Find Full Text PDF

Atomic Decompositions of Periodic Electronic-Structure Simulations.

J Phys Chem A

January 2025

DTU Chemistry, Technical University of Denmark, Kemitorvet Bldg. 206, 2800 Kgs., Lyngby 2800, Denmark.

We present a new theory for partitioning simulations of periodic and solid-state systems into physically sound atomic contributions at the level of Kohn-Sham density functional theory. Our theory is based on spatially localized linear combinations of crystalline Gaussian-type orbitals and, as such, capable of exposing local features within periodic electronic structures in a more intuitive and robust manner than alternatives based on the spatial distribution of atomic basis functions alone. Early decomposed cohesive energies of both molecular polymers and different crystalline polymorphs demonstrate how the atomic properties yielded by our theory convincingly align with the expected charge polarization in these systems, also whenever partial charges and Madelung energies may lend themselves somewhat ambiguous to interpretation.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!