Localizing structure in individual differences: A visual illusion case study

doi:10.3758/s13414-025-03156-w

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Localizing structure in individual differences: A visual illusion case study

2025-11-26

PRE

Mahbod Mehrvarz *

Hrithik Popat

Jeffrey N. Rouder

DOI：10.3758/s13414-025-03156-w

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Abstract

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Are people who are susceptible to one illusion also susceptible to others? Previous research has shown small correlations, but might small values reflect attenuation from measurement error from trial-to-trial variation? To assess measurement error, we develop a set of novel data visualizations and hierarchical models. Data from 149 participants on two variants of the five illusions were collected using an adjustment paradigm. The results showed low trial-noise and strong between-subject variability (e.g., signal-to-noise ratio approximate to 1.14\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx 1.14$$\end{document}, reliability approximate to 0.93\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx 0.93$$\end{document}). Correlations across illusions are low, around 0.22 +/- 0.07\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.22 \pm 0.07$$\end{document}. A Bayesian hierarchical analysis reveals minimal attenuation from measurement error in these values. Though correlations are low, latent variable analysis reveals a common latent factor that loads on all tasks and explains about 23.3% of the variance in illusion susceptibility.

Keywords:

Individual differences

Visual illusions

Factor analysis

Hierarchical models

Reliability