發(fā)布時(shí)間：2018-03-11 19:41

  本文選題：心理物理學(xué)函數(shù)　切入點(diǎn)：高斯過程　出處：《中國科學(xué)技術(shù)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：無論是在基礎(chǔ)研究還是臨床應(yīng)用上,對(duì)比敏感度函數(shù)在預(yù)測被試者視覺功能上都有重要且不可替代的作用。研究發(fā)現(xiàn)對(duì)比敏感度函數(shù)可以解釋為一個(gè)二維的心理物理學(xué)函數(shù),進(jìn)而可以采用比傳統(tǒng)方法更高效的二維自適應(yīng)貝葉斯方法進(jìn)行估計(jì)。本研究中,我們首先研究如何采用二維貝葉斯估計(jì)通過行為學(xué)數(shù)據(jù)估計(jì)對(duì)比敏感度函數(shù),這些數(shù)據(jù)由常見試驗(yàn)方法如階梯法、普賽(Ψ)法、以及二維貝葉斯自適應(yīng)方法。我們進(jìn)行了大量的仿真(simulation)實(shí)驗(yàn),并通過心理物理學(xué)實(shí)驗(yàn)驗(yàn)證實(shí)驗(yàn)結(jié)果。我們研究發(fā)現(xiàn)二維貝葉斯估計(jì)相比于心理物理學(xué)研究中常用的一維貝葉斯估計(jì)有更高的估計(jì)效率——可以在僅僅四分之一的采樣數(shù)就能達(dá)到相同的估計(jì)精度。進(jìn)一步地,我們比較了不同采樣方法下,二維貝葉斯估計(jì)的效率。我們發(fā)現(xiàn)估計(jì)的效率及精確度相似,這提示傳統(tǒng)的階梯法、普賽法和現(xiàn)代的二維自適應(yīng)方法的采樣效率是類似的,而前人研究中的二維自適應(yīng)方法的更高的估計(jì)效率主要來自于二維貝葉斯估計(jì)方法的使用,而不是更好的采樣方法。心理物理學(xué)函數(shù)(Psychometric function,PF)描述了被試的反應(yīng)如何隨知覺刺激強(qiáng)度而變化,是心理物理學(xué)研究中的基本測量數(shù)據(jù)。一般地,該函數(shù)通過特定的數(shù)學(xué)模型如韋伯(Weibull)或邏輯斯特(Logistic)對(duì)實(shí)驗(yàn)數(shù)據(jù)擬合得到,這被稱為有參(Model-based)方法。有參方法在模型正確定義時(shí)有很好的估計(jì)效率,但當(dāng)模型錯(cuò)誤時(shí)估計(jì)的效率和精度將明顯降低。我們進(jìn)一步提出了一種非參貝葉斯估計(jì)(model-free Bayesian inference)方法——高斯過程分類(Gaussian Processes Classification),來從行為學(xué)數(shù)據(jù)中估計(jì)心理物理學(xué)函數(shù)。這一非參方法僅僅假設(shè)了函數(shù)的連續(xù)性和平滑性,不做任何關(guān)于函數(shù)形狀的假設(shè)。我們采用蒙特-卡洛(Monte-Carlo)仿真模擬該非參方法、傳統(tǒng)的有參的最大似然法(maximum likelihood,ML)以及另一非參心理物理學(xué)方法——局部線性擬合法(local linear fitting,LLF),對(duì)一理想心理物理學(xué)函數(shù)進(jìn)行估計(jì)。我們通過統(tǒng)計(jì)分析研究了通過該非參方法對(duì)心理物理學(xué)函數(shù)兩個(gè)關(guān)鍵參數(shù)——閾值(threshold)和斜率(slope)——的估計(jì)精度,發(fā)現(xiàn)高斯過程分類方法在估計(jì)心理物理學(xué)函數(shù)時(shí)常常比其余兩種方法精度和效率更高。我們最后將高斯過程分類擴(kuò)展到對(duì)一種二維心理物理學(xué)函數(shù)——對(duì)比敏感度函數(shù)的估計(jì)與擬合中。通過蒙特卡洛仿真,我們大量的統(tǒng)計(jì)分析了高斯過程分類在估計(jì)對(duì)比敏感度的關(guān)鍵性質(zhì)峰值增益(peak gain)、對(duì)數(shù)對(duì)比敏感度函數(shù)下面積(area under log contrast sentivity function,AULCSF)以及局部損傷(local deficits)時(shí)的精度和效率,并與傳統(tǒng)二維最大似然法及一維估計(jì)方法進(jìn)行比較,發(fā)現(xiàn)在估計(jì)正常人的CSF時(shí),最大似然法精度略好于高斯過程分類,但估計(jì)有局部損傷的CSF時(shí),最大似然法精度明顯變差,而高斯過程分類依然保持著不錯(cuò)的估計(jì)精度�？紤]到實(shí)際實(shí)驗(yàn)條件的復(fù)雜性以及高斯過程分類的可靠性和適應(yīng)性,我們建議在測量對(duì)比敏感度時(shí)應(yīng)用高斯過程分類方法。
[Abstract]:Whether it is in the basic research and clinical application, contrast sensitivity function was tested in the prediction of visual function has an important and irreplaceable role. The study found that contrast sensitivity function can be interpreted as a two-dimensional psychophysical function, which can be used for two-dimensional adaptive Bias method is more efficient than the traditional method of estimation. In this study, we first study how to use the two-dimensional Bias estimated by behavioral data to estimate the contrast sensitivity function, these data by common test methods such as gradient method, pusai (PSI) method, and two adaptive Vee Bias method. We have done a lot of simulation experiments (simulation), and verified by the experimental results of psychophysical experiments. We found that the two-dimensional Bias estimation compared to one-dimensional Bias in psychophysical studies commonly used estimation has higher estimation efficiency- Can achieve the same estimation accuracy in only 1/4 of the number of sampling can. Further, we compare the different sampling methods, the efficiency of two dimensional Bayesian estimation. We found that the efficiency and accuracy of estimation is similar, suggesting that the step of the traditional methods, the sampling efficiency of adaptive methods and modern Pusaifa is similar, but the higher the estimation efficiency of adaptive methods in previous studies mainly from two-dimensional Bayes estimation method is used, and the sampling method is not better. The psychophysical function (Psychometric function, PF) describes the subject's response to sensory stimuli with intensity changes, is the basic data in psychophysical studies. In general, this function through mathematics such as Webb model specific (Weibull) or (Logistic) logic of fitting the experimental data, this is called ginseng (Model- Based) method. Parametric method in model estimation when the correct definition of efficiency is very good, but when the efficiency and accuracy of estimation error of the model will be significantly reduced. We further propose a nonparametric Bayesian estimation method (model-free Bayesian inference) - Gauss (Gaussian Processes Classification) classification process, from behavioral psychophysics function estimation data. This method assumes that the only non parametric function continuity and smoothness, don't do anything about the shape of the function hypothesis. We use Monte Carlo (Monte-Carlo) simulation of the non parametric method, the traditional maximum likelihood parameters (maximum likelihood, ML) and other non participation psychophysical methods -- local linear fitting (local linear, fitting, LLF), to an ideal psychophysical function was estimated by statistical analysis. We studied the non parametric Methods two key parameters of psychophysical function threshold (threshold) and slope (slope) - estimation accuracy classification method in the estimation process found Gauss psychophysical function than the other two methods have higher precision and efficiency. Finally, we will process the expansion of the Gauss classification of a two-dimensional function estimation and fitting of psychophysics the contrast sensitivity function. Through Monte Carlo simulation, we analyzed a large number of Gauss classification in the estimation of key properties of gain peak contrast sensitivity (peak gain), the logarithm of the contrast sensitivity function (area under log contrast area under sentivity function, AULCSF) and local damage (local deficits) the accuracy and efficiency of the then, the method is compared with the maximum likelihood method and the traditional one-dimensional two-dimensional estimation, found in the estimation of normal CSF, accuracy of maximum likelihood method Slightly better than the Gauss classification, but it is estimated that local damage CSF, accuracy of maximum likelihood method was worse, but Gauss still maintained the estimation accuracy of classification is good. Considering the actual experimental conditions and the complexity of Gauss classification reliability and adaptability, we suggest in the measurement of contrast sensitivity when using the Gauss process classification method.

【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：O212.8

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