Previous studies demonstrate that people high in delusional ideation exhibit a data-gathering bias on inductive reasoning tasks. The current study set out to investigate the factors that may underpin such a bias by examining healthy individuals, classified as either high or low scorers on the Peters et al. Delusions Inventory (PDI). More specifically, whether high PDI scorers have a relatively poor appreciation of sample size and heterogeneity when making statistical judgments. In Expt 1, high PDI scorers made higher probability estimates when generalizing from a sample of 1 with regard to the heterogeneous human property of obesity. In Expt 2, this effect was replicated and was also observed in relation to the heterogeneous property of aggression. The findings suggest that delusion-prone individuals are less appreciative of the importance of sample size when making statistical judgments about heterogeneous properties; this may underpin the data gathering bias observed in previous studies. There was some support for the hypothesis that threatening material would exacerbate high PDI scorers' indifference to sample size.
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http://dx.doi.org/10.1348/000712609X479384 | DOI Listing |
JMIR Form Res
January 2025
Department of Psychology, Lakehead University, Thunder Bay, ON, Canada.
Background: The minimal clinically important difference (MCID) is an important threshold to consider when evaluating the meaningfulness of improvement following an intervention. The JoyPop app is an evidence-based smartphone app designed to improve resilience and emotion regulation. Information is needed regarding the JoyPop app's MCID among culturally diverse youth.
View Article and Find Full Text PDFActa Microbiol Immunol Hung
January 2025
1Department of Biomedical Sciences, Faculty of Health Sciences, International Hellenic University, 57400 Thessaloniki, Greece.
The spread of NDM-1-harboring Klebsiella pneumoniae is a worldwide concern. In this study the whole-genome sequence (WGS) of a carbapenem- and colistin-resistant K. pneumoniae 838Gr strain is presented.
View Article and Find Full Text PDFJ Microsc
January 2025
Ningbo Key Laboratory of Micro-Nano Motion and Intelligent Control, Ningbo University, Ningbo, PR China.
The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image optimisation and deep learning. Firstly, a dataset containing eight types of microorganisms is constructed, and an augmentation strategy based on single and multisamples processing is designed to address the issues of sample deficiency and uneven distribution.
View Article and Find Full Text PDFMater Horiz
January 2025
Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081, Ulm, Germany.
This work involves the preparation of dual surrogate-imprinted polymers (D-MIPs) for the capture of SARS-CoV-2. To achieve this goal, an innovative and novel dual imprinting approach using carboxylated-polystyrene (PS-COOH) nanoparticles with a diameter of 100 nm and a SARS-CoV-2 Spike-derived peptide was carried out at the surface of amine-functionalized silica (PS-NH) microspheres with a diameter of 500 nm. Firstly, PS-COOH nanoparticles with the same size and spherical shape as the SARS-CoV-2 virus were employed to form hemispherical indentations (HI) at the surface of the PS-NH microspheres (obtaining dummy particle-imprinted polymers, HI-MIPs).
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
A complex-valued neural process method, combined with modal depth functions (MDFs) of the ocean waveguide, is proposed to reconstruct the acoustic field. Neural networks are used to describe complex Gaussian processes, modeling the distribution of the acoustic field at different depths. The network parameters are optimized through a meta-learning strategy, preventing overfitting under small sample conditions (sample size equals the number of array elements) and mitigating the slow reconstruction speed of Gaussian processes (GPs), while denoising and interpolating sparsely distributed acoustic field data, generating dense field data for virtual receiver arrays.
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