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  1. Artikel: Active Mutual Conjoint Estimation of Multiple Contrast Sensitivity Functions.

    Marticorena, Dom Cp / Wong, Quinn Wai / Browning, Jake / Wilbur, Ken / Davey, Pinakin / Seitz, Aaron R / Gardner, Jacob R / Barbour, Dennis L

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: Recent advances in nonparametric Contrast Sensitivity Function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to ... ...

    Abstract Recent advances in nonparametric Contrast Sensitivity Function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine Learning CSF (MLCSF) estimation with Gaussian processes allows for design optimization in the kernel, acquisition function and underlying task representation, to name a few. This paper describes a novel kernel for psychometric function estimation that is more flexible than a kernel based on signal detection theory. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.
    Sprache Englisch
    Erscheinungsdatum 2024-02-13
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2024.02.12.24302700
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Buch ; Online: Distributional Latent Variable Models with an Application in Active Cognitive Testing

    Kasumba, Robert / Marticorena, Dom CP / Pahor, Anja / Ramani, Geetha / Goffney, Imani / Jaeggi, Susanne M / Seitz, Aaron / Gardner, Jacob R / Barbour, Dennis L

    2023  

    Abstract: Cognitive modeling commonly relies on asking participants to complete a battery of varied tests in order to estimate attention, working memory, and other latent variables. In many cases, these tests result in highly variable observation models. A near- ... ...

    Abstract Cognitive modeling commonly relies on asking participants to complete a battery of varied tests in order to estimate attention, working memory, and other latent variables. In many cases, these tests result in highly variable observation models. A near-ubiquitous approach is to repeat many observations for each test, resulting in a distribution over the outcomes from each test given to each subject. In this paper, we explore the usage of latent variable modeling to enable learning across many correlated variables simultaneously. We extend latent variable models (LVMs) to the setting where observed data for each subject are a series of observations from many different distributions, rather than simple vectors to be reconstructed. By embedding test battery results for individuals in a latent space that is trained jointly across a population, we are able to leverage correlations both between tests for a single participant and between multiple participants. We then propose an active learning framework that leverages this model to conduct more efficient cognitive test batteries. We validate our approach by demonstrating with real-time data acquisition that it performs comparably to conventional methods in making item-level predictions with fewer test items.

    Comment: 9 pages, 6 figures
    Schlagwörter Computer Science - Artificial Intelligence ; Computer Science - Human-Computer Interaction
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-12-14
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  3. Artikel: Contrast Response Function Estimation with Nonparametric Bayesian Active Learning.

    Marticorena, Dom Cp / Wong, Quinn Wai / Browning, Jake / Wilbur, Ken / Jayakumar, Samyukta / Davey, Pinakin / Seitz, Aaron R / Gardner, Jacob R / Barbour, Dennis L

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning ... ...

    Abstract Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast Sensitivity Functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This paper describes the development of the Machine Learning Contrast Response Function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the MLCSF was evaluated in order to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential.
    Precis: Machine learning classifiers enable accurate and efficient contrast sensitivity function estimation with item-level prediction for individual eyes.
    Sprache Englisch
    Erscheinungsdatum 2023-10-05
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2023.05.11.23289869
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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