Young children often struggle to answer the question "what would have happened?" particularly in cases where the adult-like "correct" answer has the same outcome as the event that actually occurred. Previous work has assumed that children fail because they cannot engage in accurate counterfactual simulations. Children have trouble considering what to change and what to keep fixed when comparing counterfactual alternatives to reality. However, most developmental studies on counterfactual reasoning have relied on binary yes/no responses to counterfactual questions about complex narratives and so have only been able to document when these failures occur but not why and how. Here, we investigate counterfactual reasoning in a domain in which specific counterfactual possibilities are very concrete: simple collision interactions. In Experiment 1, we show that 5- to 10-year-old children (recruited from schools and museums in Connecticut) succeed in making predictions but struggle to answer binary counterfactual questions. In Experiment 2, we use a multiple-choice method to allow children to select a specific counterfactual possibility. We find evidence that 4- to 6-year-old children (recruited online from across the United States) do conduct counterfactual simulations, but the counterfactual possibilities younger children consider differ from adult-like reasoning in systematic ways. Experiment 3 provides further evidence that young children engage in simulation rather than using a simpler visual matching strategy. Together, these experiments show that the developmental changes in counterfactual reasoning are not simply a matter of whether children engage in counterfactual simulation but also how they do so. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/dev0001140 | DOI Listing |
Int J Antimicrob Agents
December 2024
School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China. Electronic address:
Objectives: The research aimed to provide a worldwide evaluation of antimicrobial resistance (AMR), focusing specifically on AMR related to lower respiratory infections (LRI).
Methods: The data were derived from the Global Antimicrobial Resistance Burden 2021 (GARB 2021). Two counterfactuals were utilized to estimate the deaths attributable to AMR and the deaths associated with AMR.
Neural Netw
December 2024
College of Science, Shantou University, Shantou 515063, China. Electronic address:
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a heuristic trade-off between the two.
View Article and Find Full Text PDFSoc Sci Med
November 2024
Health Economics Unit, Institute of Applied Health Research, University of Birmingham, UK. Electronic address:
Complex health system questions often have a case study (such as a country) as the unit of analysis. Process tracing, a method from policy studies, is a flexible approach for causal analysis within case studies, increasingly used in applied health research. The aim of this study was to identify the ways in which process tracing methods have been used in health research, and provide insights for best practice.
View Article and Find Full Text PDFJ Mol Graph Model
December 2024
Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh,11623, Saudi Arabia. Electronic address:
The work being presented now combines severe gradient boosting with Shapley values, a thriving merger within the field of explainable artificial intelligence. We also use a genetic algorithm to analyse the HDAC1 inhibitory activity of a broad pool of 1274 molecules experimentally reported for HDAC1 inhibition. We conduct this analysis to ascertain the HDAC1 inhibitory activity of these molecules.
View Article and Find Full Text PDFLancet Reg Health Eur
December 2024
School of Health and Wellbeing, University of Glasgow, UK.
Background: Socioeconomic inequality in infant mortality in the UK is rising. This study aims to identify contributory maternal and pregnancy factors that can explain the known association between area deprivation and infant mortality.
Methods: A cohort study was conducted using Clinical Practice Research Datalink (CPRD) primary care data between 2004 and 2019 linked to the Index of Multiple Deprivation (IMD), and infant mortality from the Office for National Statistics death data.
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