ArticleJournal of vision2021
Hierarchical Bayesian modeling of contrast sensitivity functions in a within-subject design.
Article in Journal of vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Hierarchical Bayesian augmented Hebbian reweighting model of perceptual learning.Journal of vision · 2025Article
- Using Hierarchical Bayesian Modeling to Enhance Statistical Inference on Contrast Sensitivity.Translational vision science & technology · 2024Article
- How the window of visibility varies around polar angle.Journal of vision · 2024Article
- Predicting contrast sensitivity functions with digital twins.Scientific reports · 2024Article
- Presaccadic Attention Enhances and Reshapes the Contrast Sensitivity Function Differentially around the Visual Field.eNeuro · 2024Article
- Active mutual conjoint estimation of multiple contrast sensitivity functions.Journal of vision · 2024Article
- Bayesian adaptive method for estimating speed-accuracy tradeoff functions of multiple task conditions.Behavior research methods · 2024Article
- How the window of visibility varies around polar angle.bioRxiv : the preprint server for biology · 2024Article
- Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study.JMIR formative research · 2024Article
- Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling.Journal of vision · 2024Article
- Contrast response function estimation with nonparametric Bayesian active learning.Journal of vision · 2024Article
- Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling.Research square · 2023Article
- Quantification of expected information gain in visual acuity and contrast sensitivity tests.Scientific reports · 2023Article
- Quantification of Expected Information Gain in Visual Acuity and Contrast Sensitivity Tests.Research square · 2023Article
- Collective endpoint of visual acuity and contrast sensitivity function from hierarchical Bayesian joint modeling.Journal of vision · 2023Article
- Hierarchical Bayesian perceptual template modeling of mechanisms of spatial attention in central and peripheral cuing.Journal of vision · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Recent development of the quick contrast sensitivity function (qCSF) method has made it possible to obtain accurate, precise, and efficient contrast sensitivity function (CSF) assessment. To improve statistical inference on CSF changes in a within-subject design, we developed a hierarchical Bayesian model (HBM) to compute the joint distribution of CSF parameters and hyperparameters at test, subject, and population levels, utilizing information within- and between-subjects and experimental conditions. We evaluated the performance of the HBM relative to a non-hierarchical Bayesian inference procedure (BIP) on an existing CSF dataset of 112 subjects obtained with the qCSF method in three luminance conditions (Hou, Lesmes, Kim, Gu, Pitt, Myung, & Lu, 2016). We found that the average d's of the area under log CSF (AULCSF) and CSF parameters between pairs of luminance conditions at the test-level from the HBM were 33.5% and 103.3% greater than those from the BIP analysis of AULCSF. The increased d' resulted in greater statistical differences between experimental conditions across subjects. In addition, simulations showed that the HBM generated accurate and precise CSF parameter estimates. These results have strong implications for the application of HBM in clinical trials and patient care.
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