In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the new model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/03640210701530755 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Comparison of optimization algorithms for automated method development of gradient profiles.
J Chromatogr A
December 2024
Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, Science Park 904, the Netherlands; Centre for Analytical Sciences Amsterdam (CASA), Amsterdam, the Netherlands; AI4Science Lab, Informatics Institute, University of Amsterdam, Science Park 904, the Netherlands. Electronic address:
Optimization algorithms play an important role in method development workflows for gradient elution liquid chromatography. Their effectiveness has not been evaluated for chromatographic method development using standardized comparisons across factors such as sample complexity, chromatographic response functions (CRFs), gradient complexity, and application type. This study compares six optimization algorithms - Bayesian optimization (BO), differential evolution (DE), a genetic algorithm (GA), covariance-matrix adaptation evolution strategy (CMA-ES), random search, and grid search - for the development of gradient elution LC methods.
View Article and Find Full Text PDFUsing supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.
Child Abuse Negl
January 2025
Johns Hopkins School of Medicine, United States of America. Electronic address:
Background: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.
Methods: We analyzed data from the Maryland Health Services Cost Review Commission (2015-2020) for patients aged 0-19 years.
Individualized morphological covariation network aberrance associated with seizure relapse after antiseizure medication withdrawal.
Neurol Sci
January 2025
Epilepsy Center, Department of Neurology, West China Hospital of Sichuan University, Chengdu, China.
This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups.
View Article and Find Full Text PDFTrajectories of breast density change over time and subsequent breast cancer risk: longitudinal study.
BMJ
December 2024
Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea.
Objective: To identify clusters of women with similar trajectories of breast density change over four longitudinal assessments and to examine the association between these trajectories and the subsequent risk of breast cancer.
Design: Retrospective cohort study.
Setting: Data from the national breast cancer screening programme, which is embedded in the National Health Insurance Service database in Korea.
Efficacy of Segmentation for Hyperspectral Target Detection.
Sensors (Basel)
January 2025
Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva blvd 1, Beer-Sheva 84105, Israel.
Algorithms for detecting point targets in hyperspectral imaging commonly employ the spectral inverse covariance matrix to whiten inherent image noise. Since data cubes often lack stationarity, segmentation appears to be an attractive preprocessing operation. Surprisingly, the literature reports both successful and unsuccessful segmentation cases, with no clear explanations for these divergent outcomes.
View Article and Find Full Text PDFWant 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!