A hierarchical signal detection model with unequal variance for binary responses
Bibliographic Data
| ID | 21640459 |
|---|---|
| Authors | Martin Lages (0000-0002-4962-3116, University of Glasgow, corresponding author) |
| Year | 2024 |
| Volume | 31 |
| Issue | 6 |
| Pages | 2534-2557 |
| Publication date | 2024-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Psychonomic Bulletin & Review (JOURNAL) |
| Journal identifiers | ISSN: 1069-9384 • E-ISSN: 1531-5320 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.3758/s13423-024-02504-5 |
| PMID | 38806791 |
| OpenAlex | W4399113918 |
| Language | EN |
| Citations received | 1 |
| References cited | 51 |
Gaussian signal detection models with equal variance are commonly used in simple yes–no detection and discrimination tasks whereas more flexible models with unequal variance require additional information. Here, a hierarchical Bayesian model with equal variance is extended to an unequal-variance model by exploiting variability of hit and false-alarm rates in a random sample of participants. This hierarchical model is investigated analytically, in simulations and in applications to existing data sets. The results suggest that signal variance and other parameters can be accurately estimated if plausible assumptions are met. It is concluded that the model provides a promising alternative to the ubiquitous equal-variance model for binary data
Analysis of variance · Bayesian probability · Binary data · Binary number · Data mining · False alarm · Gaussian · Hierarchical database model · Multilevel model · One-way analysis of variance · Statistics · Variance-based sensitivity analysis · Advanced Statistical Methods and Models · Advanced Statistical Process Monitoring · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Artificial Intelligence
Bayesian Rationality
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
No free lunch theorems for optimization
A decision-making theory of visual detection.
On the psychophysical law.
Inference from Iterative Simulation Using Multiple Sequences
BRMS
Fitting Linear Mixed-Effects Models Using lme4
Mixed-effects modeling with crossed random effects for subjects and items
Bayesian Measures of Model Complexity and Fit
Elementary Signal Detection Theory
Detection Theory
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |