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Zhao Y, Liu J, Dosher BA, Lu ZL. Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling. J Vis 2024; 24:8. [PMID: 38780934 PMCID: PMC11131338 DOI: 10.1167/jov.24.5.8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/13/2024] [Indexed: 05/25/2024] Open
Abstract
Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.
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Affiliation(s)
- Yukai Zhao
- Center for Neural Science, New York University, New York, NY, USA
| | - Jiajuan Liu
- Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Barbara Anne Dosher
- Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Zhong-Lin Lu
- Division of Arts and Sciences, NYU Shanghai, Shanghai, China
- Center for Neural Science and Department of Psychology, New York University, New York, NY, USA
- NYU-ECNU Institute of Brain and Cognitive Neuroscience, Shanghai, China
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Zhao Y, Liu J, Dosher BA, Lu ZL. Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling. RESEARCH SQUARE 2023:rs.3.rs-3649060. [PMID: 38045291 PMCID: PMC10690334 DOI: 10.21203/rs.3.rs-3649060/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The learning curve serves as a crucial metric for assessing human performance in perceptual learning. It may encompass various component processes, including general learning, between-session forgetting or consolidation, and within-session rapid relearning and adaptation or deterioration. Typically, empirical learning curves are constructed by aggregating tens or hundreds of trials of data in blocks or sessions. Here, we devised three inference procedures for estimating the trial-by-trial learning curve based on the multi-component functional form identified in Zhao et al. (submitted): general learning, between-session forgetting, and within-session rapid relearning and adaptation. These procedures include a Bayesian inference procedure (BIP) estimating the posterior distribution of parameters for each learner independently, and two hierarchical Bayesian models (HBMv and HBMc) computing the joint posterior distribution of parameters and hyperparameters at the population, subject, and test levels. The HBMv and HBMc incorporate variance and covariance hyperparameters, respectively, between and within subjects. We applied these procedures to data from two studies investigating the interaction between feedback and training accuracy in Gabor orientation identification across about 2000 trials spanning six sessions (Liu et al., 2010, 2012) and estimated the trial-by-trial learning curves at both the subject and population levels. The HBMc generated best fits to the data and the smallest half width of 68.2% credible interval of the learning curves compared to the BIP and HBMv. The parametric HBMc with the multi-component functional form provides a general framework for trial-by-trial analysis of the component processes in perceptual learning and for predicting the learning curve in unmeasured time points.
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Affiliation(s)
- Yukai Zhao
- Center for Neural Science, New York University, New York, USA
| | - Jiajuan Liu
- Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Barbara Anne Dosher
- Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Zhong-Lin Lu
- Division of Arts and Sciences, NYU Shanghai, Shanghai, China
- Center for Neural Science and Department of Psychology, New York University, New York, USA
- NYU-ECNU Institute of Brain and Cognitive Neuroscience, Shanghai, China
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3
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Wang P, Reynaud A. The Random Step Method for Measuring the Point of Subjective Equality. Vision (Basel) 2023; 7:74. [PMID: 37987294 PMCID: PMC10661322 DOI: 10.3390/vision7040074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/03/2023] [Accepted: 11/11/2023] [Indexed: 11/22/2023] Open
Abstract
Points of Subjective Equality (PSE) are commonly measured using staircase or constant stimuli methods. However, the staircase method is highly dependent on the step size, and the constant stimuli method is time-consuming. Thus, we wanted to develop an efficient and quick method to estimate both the PSE and the slope of the psychometric function. We developed a random-step algorithm in which a one-up-one-down rule is followed but with a random step size in a pre-defined range of test levels. Each stimulus would be chosen depending on the previous response of the subject. If the subject responded "up", any random level in the lower range would be picked for the next trial. And if the subject responded "down", any random level in the upper range would be picked for the next trial. This procedure would result in a bell-shaped distribution of the test levels around the estimated PSE, while a substantial amount of trials would still be dispersed at both bounds of the range. We then compared this method with traditional constant stimuli procedure on a task based on the Pulfrich phenomenon while the PSEs of participants could be varied using different neutral density filters. Our random-step method provided robust estimates of both the PSE and the slope under various noise levels with small trial counts, and we observed a significant correlation between the PSEs obtained with the two methods. The random-step method is an efficient way to measure the full psychometric function when testing time is critical, such as in clinical settings.
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Affiliation(s)
| | - Alexandre Reynaud
- McGill Vision Research Unit, Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H3G 1A4, Canada;
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Kwon M, Lee SH, Ahn WY. Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:798-804. [PMID: 36805245 DOI: 10.1016/j.bpsc.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/21/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.
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Affiliation(s)
- Mina Kwon
- Department of Psychology, Seoul National University, Seoul, Korea
| | - Sang Ho Lee
- Department of Psychology, Seoul National University, Seoul, Korea; Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Korea
| | - Woo-Young Ahn
- Department of Psychology, Seoul National University, Seoul, Korea; Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Korea.
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Lu ZL, Dosher BA. Current directions in visual perceptual learning. NATURE REVIEWS PSYCHOLOGY 2022; 1:654-668. [PMID: 37274562 PMCID: PMC10237053 DOI: 10.1038/s44159-022-00107-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 06/06/2023]
Abstract
The visual expertise of adult humans is jointly determined by evolution, visual development, and visual perceptual learning. Perceptual learning refers to performance improvements in perceptual tasks after practice or training in the task. It occurs in almost all visual tasks, ranging from simple feature detection to complex scene analysis. In this Review, we focus on key behavioral aspects of visual perceptual learning. We begin by describing visual perceptual learning tasks and manipulations that influence the magnitude of learning, and then discuss specificity of learning. Next, we present theories and computational models of learning and specificity. We then review applications of visual perceptual learning in visual rehabilitation. Finally, we summarize the general principles of visual perceptual learning, discuss the tension between plasticity and stability, and conclude with new research directions.
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Affiliation(s)
- Zhong-Lin Lu
- Division of Arts and Sciences, New York University Shanghai, Shanghai, China
- Center for Neural Science, New York University, New York, NY, USA
- Department of Psychology, New York University, New York, NY, USA
- Institute of Brain and Cognitive Science, New York University - East China Normal University, Shanghai, China
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Zhao Y, Lesmes LA, Dorr M, Bex PJ, Lu ZL. Psychophysical Validation of a Novel Active Learning Approach for Measuring the Visual Acuity Behavioral Function. Transl Vis Sci Technol 2021; 10:1. [PMID: 33505768 PMCID: PMC7794273 DOI: 10.1167/tvst.10.1.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 12/01/2020] [Indexed: 11/24/2022] Open
Abstract
Purpose To evaluate the performance of the quantitative visual acuity (qVA) method in measuring the visual acuity (VA) behavioral function. Methods We evaluated qVA performance in terms of the accuracy, precision, and efficiency of the estimated VA threshold and range in Monte Carlo simulations and a psychophysical experiment. We also compared the estimated VA threshold from the qVA method with that from the Electronic Early Treatment Diabetic Retinopathy Study (E-ETDRS) and Freiburg Visual Acuity Text (FrACT) methods. Four repeated measures with all three methods were conducted in four Bangerter foil conditions in 14 eyes. Results In both simulations and psychophysical experiment, the qVA method quantified the full acuity behavioral function with two psychometric parameters (VA threshold and VA range) with virtually no bias and with high precision and efficiency. There was a significant correlation between qVA estimates of VA threshold and range in the psychophysical experiment. In addition, qVA threshold estimates were highly correlated with those from the E-ETDRS and FrACT methods. Conclusions The qVA method can provide an accurate, precise, and efficient assessment of the full acuity behavioral function with both VA threshold and range. Translational Relevance The qVA method can accurately, precisely, and efficiently assess the full VA behavioral function. Further research will evaluate the potential value of these rich measures for both clinical research and patient care.
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Affiliation(s)
- Yukai Zhao
- Center for Neural Science, New York University, New York, NY, USA
| | | | - Michael Dorr
- Adaptive Sensory Technology, San Diego, CA, USA.,Technical University of Munich, Munich, Germany
| | - Peter J Bex
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Zhong-Lin Lu
- Center for Neural Science, New York University, New York, NY, USA.,Division of Arts and Sciences, NYU Shanghai, Shanghai, China.,Department of Psychology, New York University, New York, NY, USA.,NYU-ECNU Institute of Brain and Cognitive Neuroscience, Shanghai, China
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Xu P, Lesmes LA, Yu D, Lu ZL. Mapping the Contrast Sensitivity of the Visual Field With Bayesian Adaptive qVFM. Front Neurosci 2020; 14:665. [PMID: 32733188 PMCID: PMC7358309 DOI: 10.3389/fnins.2020.00665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 05/29/2020] [Indexed: 11/13/2022] Open
Abstract
Current clinical evaluation, which focuses on central vision, could be improved through characterization of residual vision with peripheral testing of visual acuity, contrast sensitivity, color vision, crowding, and reading speed. Assessing visual functions in addition to light sensitivity, a comprehensive visual field map (VFM) would be valuable for detecting and managing eye diseases. In a previous study, we developed a Bayesian adaptive qVFM method that combines a global module for preliminary assessment of the VFM's shape and a local module for assessment at individual retinal locations. The method was validated in measuring the light sensitivity VFM. In this study, we extended the qVFM method to measure contrast sensitivity across the visual field. In both simulations and psychophysics, we sampled 64 visual field locations (48 x 48 deg) and compared the qVFM method with a procedure that tested each retinal location independently (qFC; Lesmes et al., 2015). In each trial, subjects were required to identify a single optotype (size: 2.5 x 2.5 deg), one of 10 filtered Sloan letters. To compare the accuracy and precision of the two methods, three simulated eyes were tested in 1,280 trials with each method. In addition, data were collected from 10 eyes (5 OS, 5 OD) of five normal observers. For simulations, the average RMSE of the estimated contrast sensitivity with the qVFM and qFC methods were 0.057 and 0.100 after 320 trials, and 0.037 and 0.041 after 1,280 trials [all in log10 units, represent as log(sensitivity)], respectively. The average SD of the qVFM and qFC estimates were 0.054 and 0.096 after 320 trials, and 0.032 and 0.041 after 1,280 trials, respectively. The within-run variability (68.2% HWCIs) were comparable to the cross-run variability (SD). In the psychophysics experiment, the average HWCI of the estimated contrast sensitivity from the qVFM and qFC methods across the visual field decreased from 0.33 on the first trial to 0.072 and 0.16 after 160, and to 0.060 and 0.10 after 320 trials. The RMSE between the qVFM and qFC estimates started at 0.26, decreased to 0.12 after 160 and to 0.11 after 320 qVFM trials. The qVFM provides an accurate, precise, and efficient mapping of contrast sensitivity across the entire visual field. The method might find potential clinical applications in monitoring vision loss, evaluating therapeutic interventions, and developing effective rehabilitation for visual diseases.
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Affiliation(s)
- Pengjing Xu
- College of Optometry, The Ohio State University, Columbus, OH, United States
| | - Luis A. Lesmes
- Adaptive Sensory Technology, Inc., San Diego, CA, United States
| | - Deyue Yu
- College of Optometry, The Ohio State University, Columbus, OH, United States
| | - Zhong-Lin Lu
- Division of Arts and Sciences, NYU Shanghai, Shanghai, China
- Center for Neural Science and Department of Psychology, New York University, New York, NY, United States
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai, China
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Wu D, Zhang P, Li C, Liu N, Jia W, Chen G, Ren W, Sun Y, Xiao W. Perceptual Learning at Higher Trained Cutoff Spatial Frequencies Induces Larger Visual Improvements. Front Psychol 2020; 11:265. [PMID: 32153473 PMCID: PMC7047335 DOI: 10.3389/fpsyg.2020.00265] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 02/04/2020] [Indexed: 12/29/2022] Open
Abstract
It is well known that extensive practice of a perceptual task can improve visual performance, termed perceptual learning. The goal of the present study was to evaluate the dependency of visual improvements on the features of training stimuli (i.e., spatial frequency). Twenty-eight observers were divided into training and control groups. Visual acuity (VA) and contrast sensitivity function (CSF) were measured and compared before and after training. All observers in the training group were trained in a monocular grating detection task near their individual cutoff spatial frequencies. The results showed that perceptual learning induced significant visual improvement, which was dependent on the cutoff spatial frequency, with a greater improvement magnitude and transfer of perceptual learning observed for those trained with higher spatial frequencies. However, VA significantly improved following training but was not related to the cutoff spatial frequency. The results may broaden the understanding of the nature of the learning rule and the neural plasticity of different cortical areas.
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Affiliation(s)
- Di Wu
- Department of Medical Psychology, Air Force Medical University, Xi'an, China
| | - Pan Zhang
- Department of Psychology, The Ohio State University, Columbus, OH, United States
| | - Chenxi Li
- School of Nursing, Yueyang Vocational Technical College, Yueyang, China
| | - Na Liu
- Department of Nursing, Air Force Medical University, Xi'an, China
| | - Wuli Jia
- Department of Psychology, School of Education Science, Huaiyin Normal University, Huai'an, China
| | - Ge Chen
- School of Arts and Design, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Weicong Ren
- Department of Psychology, Hebei Normal University, Shijiazhuang, China
| | - Yuqi Sun
- Department of Systems Neuroscience, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Wei Xiao
- Department of Medical Psychology, Air Force Medical University, Xi'an, China
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Zhang P, Zhao Y, Dosher BA, Lu ZL. Evaluating the performance of the staircase and quick Change Detection methods in measuring perceptual learning. J Vis 2020; 19:14. [PMID: 31323664 PMCID: PMC6645707 DOI: 10.1167/19.7.14] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The staircase method has been widely used in measuring perceptual learning. Recently, Zhao, Lesmes, and Lu (2017, 2019) developed the quick Change Detection (qCD) method and applied it to measure the trial-by-trial time course of dark adaptation. In the current study, we conducted two simulations to evaluate the performance of the 3-down/1-up staircase and qCD methods in measuring perceptual learning in a two-alternative forced-choice task. In Study 1, three observers with different time constants (40, 80, and 160 trials) of an exponential learning curve were simulated. Each simulated observer completed staircases with six step sizes (1%, 5%, 10%, 20%, 30%, and 60%) and a qCD procedure, each starting at five levels (+50%, +25%, 0, −25%, and −50% different from the true threshold in the first trial). We found the following results: Staircases with 1% and 5% step sizes failed to generate more than five reversals half of the time; and the bias and standard deviations of thresholds estimated from the post hoc segment-by-segment qCD analysis were much smaller than those from the staircase method with the other four step sizes. In Study 2, we simulated thresholds in the transfer phases with the same time constants and 50% transfer for each observer in Study 1. We found that the estimated transfer indexes from qCD showed smaller biases and standard deviations than those from the staircase method. In addition, rescoring the simulated data from the staircase method using the Bayesian estimation component of the qCD method resulted in much-improved estimates. We conclude that the qCD method characterizes the time course of perceptual learning and transfer more accurately, precisely, and efficiently than the staircase method, even with the optimal 10% step size.
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Affiliation(s)
- Pan Zhang
- Laboratory of Brain Processes (LOBES), Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Yukai Zhao
- Laboratory of Brain Processes (LOBES), Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Barbara Anne Dosher
- Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Zhong-Lin Lu
- Laboratory of Brain Processes (LOBES), Department of Psychology, The Ohio State University, Columbus, OH, USA
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Xu P, Lesmes LA, Yu D, Lu ZL. A novel Bayesian adaptive method for mapping the visual field. J Vis 2019; 19:16. [PMID: 31845976 PMCID: PMC6917184 DOI: 10.1167/19.14.16] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 10/04/2019] [Indexed: 11/24/2022] Open
Abstract
Measuring visual functions such as light and contrast sensitivity, visual acuity, reading speed, and crowding across retinal locations provides visual-field maps (VFMs) that are extremely valuable for detecting and managing eye diseases. Although mapping light sensitivity is a standard glaucoma test, the measurement is often noisy (Keltner et al., 2000). Mapping other visual functions is even more challenging. To improve the precision of light-sensitivity mapping and enable other VFM assessments, we developed a novel hybrid Bayesian adaptive testing framework, the qVFM method. The method combines a global module for preliminary assessment of the VFM's shape and a local module for assessing individual visual-field locations. This study validates the qVFM method in measuring light sensitivity across the visual field. In both simulation and psychophysics studies, we sampled 100 visual-field locations (60° × 60°) and compared the performance of qVFM with the qYN procedure (Lesmes et al., 2015) that measured light sensitivity at each location independently. In the simulations, a simulated observer was tested monocularly for 1,000 runs with 1,200 trials/run, to compare the accuracy and precision of the two methods. In the experiments, data were collected from 12 eyes (six left, six right) of six human subjects. Subjects were cued to report the presence or absence of a target stimulus, with the luminance and location of the target adaptively selected in each trial. Both simulations and a psychological experiment showed that the qVFM method can provide accurate, precise, and efficient mapping of light sensitivity. This method can be extended to map other visual functions, with potential clinical signals for monitoring vision loss, evaluating therapeutic interventions, and developing effective rehabilitation for low vision.
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Affiliation(s)
- Pengjing Xu
- College of Optometry, The Ohio State University, Columbus, OH, USA
| | | | - Deyue Yu
- College of Optometry, The Ohio State University, Columbus, OH, USA
| | - Zhong-Lin Lu
- Division of Arts and Sciences, NYU Shanghai, Shanghai, China
- Center for Neural Science and Department of Psychology, New York University, New York, NY, USA
- NYU-ECNU Institute of Cognitive Neuroscience at NYU Shanghai, Shanghai, China
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The multivariate adaptive design for efficient estimation of the time course of perceptual adaptation. Behav Res Methods 2019; 52:1073-1090. [PMID: 31676968 DOI: 10.3758/s13428-019-01301-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In experiments on behavioral adaptation, hundreds or even thousands of trials per subject are often required in order to accurately recover the many psychometric functions that characterize adaptation's time course. More efficient methods for measuring perceptual changes over time would be beneficial to such efforts. In this article, we propose two methods to adaptively select the optimal stimuli sequentially in an experiment on adaptation: These are the minimum entropy (ME) method and the match probability (MP) method. The ME method minimizes the uncertainty about the joint posterior distribution of the function parameters at each trial and is mathematically equivalent to Zhao, Lesmes, and Lu's (2019) method, which efficiently measures time courses of perceptual change by maximizing information gain. The MP method selects the next stimulus that makes the value of the psychometric function closest to .5-that is, where the probability of choosing either one of the two options for each stimulus is closest to .5. We extended Zhao et al.'s (2019) work by evaluating the ME method in a new domain (contrast adaptation) with two simulation studies that compared it to MP and two other methods (i.e., traditional staircase and random methods), and also explored the optimal block length. ME outperformed the other three methods in general, and using fewer longer blocks generally produced better parameter recovery than using more shorter blocks.
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Zhang P, Zhao Y, Dosher BA, Lu ZL. Assessing the detailed time course of perceptual sensitivity change in perceptual learning. J Vis 2019; 19:9. [PMID: 31074765 PMCID: PMC6510278 DOI: 10.1167/19.5.9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 03/08/2019] [Indexed: 11/24/2022] Open
Abstract
The learning curve in perceptual learning is typically sampled in blocks of trials, which could result in imprecise and possibly biased estimates, especially when learning is rapid. Recently, Zhao, Lesmes, and Lu (2017, 2019) developed a Bayesian adaptive quick Change Detection (qCD) method to accurately, precisely, and efficiently assess the time course of perceptual sensitivity change. In this study, we implemented and tested the qCD method in assessing the learning curve in a four-alternative forced-choice global motion direction identification task in both simulations and a psychophysical experiment. The stimulus intensity in each trial was determined by the qCD, staircase or random stimulus selection (RSS) methods. Simulations showed that the accuracy (bias) and precision (standard deviation or confidence bounds) of the estimated learning curves from the qCD were much better than those obtained by the staircase and RSS method; this is true for both trial-by-trial and post hoc segment-by-segment qCD analyses. In the psychophysical experiment, the average half widths of the 68.2% credible interval of the estimated thresholds from the trial-by-trial and post hoc segment-by-segment qCD analyses were both quite small. Additionally, the overall estimates from the qCD and staircase methods matched extremely well in this task where the behavioral rate of learning is relatively slow. Our results suggest that the qCD method can precisely and accurately assess the trial-by-trial time course of perceptual learning.
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Affiliation(s)
- Pan Zhang
- Laboratory of Brain Processes (LOBES), Departments of Psychology, The Ohio State University, Columbus, OH, USA
| | - Yukai Zhao
- Laboratory of Brain Processes (LOBES), Departments of Psychology, The Ohio State University, Columbus, OH, USA
| | - Barbara Anne Dosher
- Department of Cognitive Sciences and Institute of Mathematical Behavioral Sciences, University of California, Irvine, CA, USA
| | - Zhong-Lin Lu
- Laboratory of Brain Processes (LOBES), Departments of Psychology, The Ohio State University, Columbus, OH, USA
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