Accurately estimating and assessing real-world quantities (e.g., how long it will take to get to the train station; the calorie content of a meal) is a central skill for adaptive cognition. To date, theoretical and empirical work on the mental resources recruited by real-world estimation has focused primarily on the role of domain knowledge (e.g., knowledge of the metric and distributional properties of objects in a domain). Here we examined the role of basic numeric abilities - specifically, symbolic-number mapping - in real-world estimation. In Experiment 1 ( ) and Experiment 2 ( ), participants first completed a country-population estimation task (a task domain commonly used to study real-world estimation) and then completed a number-line task (an approach commonly used to measure symbolic-number mapping). In both experiments, participants with better performance in the number-line task made more accurate estimates in the estimation task. Moreover, Experiment 2 showed that performance in the number-line task predicts estimation accuracy independently of domain knowledge. Further, in Experiment 2 the association between estimation accuracy and symbolic-number mapping did not depend on whether the number-line task involved small numbers (up to 1000) or large numbers that matched the range of the numbers in the estimation task (up to 100,000,000). Our results show for the first time that basic numeric abilities contribute to the estimation of real-world quantities. We discuss implications for theories of real-world estimation and for interventions aiming to improve people's ability to estimate real-world quantities.
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http://dx.doi.org/10.3758/s13423-024-02575-4 | DOI Listing |
Randomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk remains unclear. In this study, we developed TrialTranslator, a framework designed to systematically evaluate the generalizability of RCTs for oncology therapies.
View Article and Find Full Text PDFPsychon Bull Rev
January 2025
Department of Business and Information Science, Japan International University, Tsukuba, Japan.
Previous research has suggested that numerosity estimation and counting are closely related to distributed and focused attention, respectively (Chong & Evans, WIREs Cognitive Science, 2(6), 634-638, 2011). Given the critical role of color in guiding attention, this study investigated its effects on numerosity processing by manipulating both color variety (single color, medium variety, high variety) and spatial arrangement (clustered, random). Results from the estimation task revealed that high color variety led to a perceptual bias towards larger quantities, regardless of whether colors were clustered or randomly arranged.
View Article and Find Full Text PDFJCO Clin Cancer Inform
January 2025
Emory University School of Medicine, Atlanta, GA.
Purpose: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non-small cell lung cancer (NSCLC) with PD-L1 expression ≥50%. We sought to find out if a machine learning (ML) algorithm can perform better as a predictive biomarker than PD-L1 alone.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Mathematics, College of Science, University of Hafr Al Batin, Hafar Al-Batin, Saudi Arabia.
In this paper, we propose a new flexible statistical distribution, the Topp-Leone Exponentiated Chen distribution, to model real-world data effectively, with a particular focus on COVID-19 data. The motivation behind this study is the need for a more flexible distribution that can capture various hazard rate shapes (e.g.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
Background: Many independent studies have found rare variants associated with AD. Current gene-based tests for rare-variants generally consider the impact of low-frequency coding variants as an independent effect from the common regulatory variants that surround them. In this work, we propose to increase the statistical power of kernel-based rare-variant association tests by accounting for the surrounding cis-regulatory variants' effects on gene expression.
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