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Updated: Jan 13, 2026

Measuring Attentional Biases for Threat in Children and Adults
Published on: October 19, 2014
成人における数値バイアスを測定するための新しいタスク
Shachar Hochman1, Mattan S Ben-Shachar2, Roi Cohen Kadosh3
1Department of Psychology and the Zelman Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel; School of Psychology, University of Surrey, Guildford, United Kingdom.
Abstract:
Numerical bias is the spontaneous tendency to base decisions on numerical rather than equally available non-numerical information. We introduce the Congruent Learning-Incongruent Probe (CLIP) task, a computerised paradigm for indexing numerical bias in adults. The task presents digit pairs that vary in numerical value and physical size, organised into blocks. In feedback-based learning trials, digits are congruent (larger number in larger font) and participants learn which stimulus is "correct" for that block. In subsequent no-feedback probe trials (test trials), the same pairs are presented incongruently, revealing whether choices are spontaneously driven by numerical or physical dimensions. A sample of 129 adults completed a multi-day battery to validate the CLIP task. Drift-diffusion modelling indicated substantial individual differences in numerical bias. Higher numerical bias correlated positively with maths fluency and quantitative reasoning, paralleling child findings on spontaneous focus on numerosity (SFON) and maths competence. To establish convergent validity, we also administered a numerical Stroop task that requires suppressing numerical information; individuals with stronger numerical bias showed larger interference and facilitation effects. These findings validate the CLIP task as a reliable measure of numerical bias and, more broadly, highlight how variability in spontaneous numerical processing shapes cognitive-control demands, illuminating the interplay between domain-specific biases and executive function.
関連する概念動画
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Binet's Contribution to Measures of Intelligence
Confirmation Biases
Stereotypes, Prejudice, and Discrimination

