Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling

Zhao, Yukai; Liu, Jiajuan; Dosher, Barbara Anne; Lu, Zhong-Lin

doi:10.1007/s41465-024-00300-6

Cited by 2 publications

References 67 publications

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Predicting contrast sensitivity functions with digital twins

Zhao,

Lesmes,

Dorr

et al. 2024

Sci Rep

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We developed and validated digital twins (DTs) for contrast sensitivity function (CSF) across 12 prediction tasks using a data-driven, generative model approach based on a hierarchical Bayesian model (HBM). For each prediction task, we utilized the HBM to compute the joint distribution of CSF hyperparameters and parameters at the population, subject, and test levels. This computation was based on a combination of historical data (N = 56), any new data from additional subjects (N = 56), and “missing data” from unmeasured conditions. The posterior distributions of the parameters in the unmeasured conditions were used as input for the CSF generative model to generate predicted CSFs. In addition to their accuracy and precision, these predictions were evaluated for their potential as informative priors that enable generation of synthetic quantitative contrast sensitivity function (qCSF) data or rescore existing qCSF data. The DTs demonstrated high accuracy in group level predictions across all tasks and maintained accuracy at the individual subject level when new data were available, with accuracy comparable to and precision lower than the observed data. DT predictions could reduce the data collection burden by more than 50% in qCSF testing when using 25 trials. Although further research is necessary, this study demonstrates the potential of DTs in vision assessment. Predictions from DTs could improve the accuracy, precision, and efficiency of vision assessment and enable personalized medicine, offering more efficient and effective patient care solutions.

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Predicting contrast sensitivity functions with digital twins

Zhao,

Lesmes,

Dorr

et al. 2024

Sci Rep

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Hierarchical Bayesian Augmented Hebbian Reweighting Model of Perceptual Learning

Lu,

Yang,

Dosher

2024

Preprint

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The Augmented Hebbian Reweighting Model (AHRM) has been effectively utilized to model the collective performance of observers in various perceptual learning studies. In this work, we have introduced a novel hierarchical Bayesian Augmented Hebbian Reweighting Model (HB-AHRM) to simultaneously model the learning curves of individual participants and the entire population within a single framework. We have compared its performance to that of a Bayesian Inference Procedure (BIP), which independently estimates the posterior distributions of model parameters for each individual subject without employing a hierarchical structure. To cope with the substantial computational demands, we developed an approach to approximate the likelihood function in the AHRM with feature engineering and linear regression, increasing the speed of the estimation procedure by 20,000 times. The HB-AHRM has enabled us to compute the joint posterior distribution of hyperparameters and parameters at the population, observer, and test levels, facilitating statistical inferences across these levels. While we have developed this methodology within the context of a single experiment, the HB-AHRM and the associated modeling techniques can be readily applied to analyze data from various perceptual learning experiments and provide predictions of human performance at both the population and individual levels. The likelihood approximation concept introduced in this study may have broader utility in fitting other stochastic models lacking analytic forms.

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Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling

Cited by 2 publications

References 67 publications

Predicting contrast sensitivity functions with digital twins

Predicting contrast sensitivity functions with digital twins

Hierarchical Bayesian Augmented Hebbian Reweighting Model of Perceptual Learning

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