本文選題：失匹配負(fù)波　切入點(diǎn)：擴(kuò)散模型　出處：《大連理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：失匹配負(fù)波(Mismatch Negativity, MMN)是一種聲音變化檢測機(jī)制,MMN可能與聽力的預(yù)注意認(rèn)知過程相關(guān)聯(lián)。因此,有人認(rèn)為它反映了在聽覺皮層的“原始智能”,但其是否是腦的自動加工的指標(biāo),一直是MMN研究的爭論問題,同時(shí)也是解釋MMN形成機(jī)理的一個(gè)重要論據(jù)。 盡管很多的實(shí)驗(yàn)范式都對MMN的自動與非自動加工性質(zhì)提出了自己的解釋,但目前鮮有實(shí)驗(yàn)范式能夠很好的控制受試者的注意力,致使在非注意通路中會引入受試者細(xì)微的注意力資源的變化。為了解決這一問題,很多學(xué)者利用注意力缺失的患者進(jìn)行實(shí)驗(yàn),但因受試本身的大腦功能已經(jīng)出現(xiàn)缺陷,而在臨床上這一缺陷產(chǎn)生的原因尚不明確,無法確定這一缺陷會對哪些因素產(chǎn)生影響,因此在這種情況下得到的結(jié)果就不能反推出正常受試的結(jié)果。 針對以上問題,本文擬對跨通路延遲反應(yīng)實(shí)驗(yàn)范式進(jìn)行改進(jìn),使實(shí)驗(yàn)范式能夠更好的控制受試者的注意力資源,并在實(shí)驗(yàn)過程中采集受試者的行為數(shù)據(jù),然后利用擴(kuò)散模型對行為數(shù)據(jù)進(jìn)行擬合,將擬合出的參數(shù)作為受試者注意力資源分配的指標(biāo)。由于在ERP數(shù)據(jù)采集過程中信號的信噪比較低,引入了較多的干擾,對MMN的成分的提取產(chǎn)生了影響,因此本文采用傳統(tǒng)疊加平均加、vavelet Indepandent Component Analysis算法來提取MMN成分。最后結(jié)合MMN的峰潛伏期與峰振幅,從而明確MMN與注意力間的關(guān)系,確定其加工的性質(zhì)。 經(jīng)過實(shí)驗(yàn)與數(shù)據(jù)分析后發(fā)現(xiàn),擴(kuò)散模型所擬合出的行為數(shù)據(jù)參數(shù)可以解釋實(shí)驗(yàn)設(shè)計(jì)中各條件對受試者注意力資源變化的潛在心理變化,定量地確定受試者注意力。此外MMN的峰潛伏期隨實(shí)驗(yàn)條件變化顯著,并且其與擴(kuò)散模型的參數(shù)有顯著的相關(guān)性：與邊界間隔α的相關(guān)系數(shù)為r=-0.63,與非決策時(shí)間Ter的相關(guān)系數(shù)為r=0.63,與漂移率v的相關(guān)系數(shù)為r=0.58。結(jié)果證明擴(kuò)散模型的參數(shù)可以作為MMN與受試者注意力資源分配的指標(biāo),且MMN的峰潛伏期隨受試者注意力呈正相關(guān)性,因此我們認(rèn)為MMN具有半自動加工的性質(zhì)。本文利用受試者生理數(shù)據(jù)與行為數(shù)據(jù)的對MMN的加工性質(zhì)進(jìn)行研究的一個(gè)創(chuàng)新,這也為今后的其它ERP成分加工性質(zhì)的研究提供了一條新的有益途徑。
[Abstract]:Mismatched negative wave mismatch Negativity (MMN) is a sound change detection mechanism that may be associated with the prenotice cognitive process of hearing. Therefore, it is thought to reflect the "primordial intelligence" in the auditory cortex, but whether it is an indicator of automatic processing of the brain. It has been a controversial issue in MMN research and an important argument to explain the formation mechanism of MMN. Although many experimental paradigms have provided their own explanations for the automatic and non-automatic processing properties of MMN, there are few experimental paradigms that can well control the subjects' attention. In order to solve this problem, many researchers used attention-deficit patients to conduct experiments, but because the brain function of the subjects themselves has been defective, However, the causes of this defect in clinic are not clear, and it is impossible to determine which factors will be affected by the defect, so the results obtained in this case can not reverse the results of normal subjects. In view of the above problems, this paper intends to improve the experimental paradigm of cross-pathway delay response, so that the experimental paradigm can better control the attention resources of the subjects, and collect the behavior data of the subjects during the experiment. Then the diffusion model is used to fit the behavioral data, and the fitted parameters are used as indicators for the allocation of attention resources. Because of the low signal-to-noise ratio (SNR) of the signal in the process of ERP data acquisition, more interference is introduced. It has an effect on the extraction of MMN components. In this paper, the traditional superposition average plus Indepandent Component Analysis algorithm is used to extract MMN components. Finally, combining the peak latency and amplitude of MMN, the relationship between MMN and attention is clarified. Determine the processing properties. After experiment and data analysis, it is found that the behavioral data parameters fitted by diffusion model can explain the potential psychological changes of each condition in the experimental design to the change of the subjects' attention resources. In addition, the peak latency of MMN varied significantly with the experimental conditions. And it has significant correlation with the parameters of diffusion model: the correlation coefficient with boundary interval 偽 is r-0.63, the correlation coefficient with non-decision time Ter is 0.63, and the correlation coefficient with drift rate v is rn0.58. the results show that the parameters of diffusion model can be obtained. As an indicator of the allocation of attention resources between MMN and subjects, The peak latency of MMN is positively correlated with the subjects' attention, so we think that MMN has the property of semi-automatic processing. This paper makes use of subjects' physiological data and behavioral data to study the processing properties of MMN. It also provides a new and beneficial way to study the processing properties of other ERP components in the future.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7;R318

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