Exploring behavioral and neural rhythms of visual working memory with an adaptive time-frequency analysis

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姓名

王行葦(Wang, Hangwei) 查詢紙本館藏

畢業系所

認知與神經科學研究所

論文名稱

(Exploring behavioral and neural rhythms of visual working memory with an adaptive time-frequency analysis)

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至系統瀏覽論文 (2026-7-30以後開放)

摘要(中)

先前的一些研究已表明，節律性或許是在認知過程中普遍存在的特征。例如在對注意力採樣（Fiebelkorn等人，2013）和聽覺工作記憶（Wöstmann等人，2020）等認知過程的研究中，均發現參與者的行為表現隨著刺激呈現的時間間隔改變存在節律性變化。這些結果也暗示，行為表現以及與之相關的神經活動中的節律成分可能是各種認知任務中的一般現象。以上結果引出了我們的問題：這種節律性特征是否也存在於視覺工作記憶過程中;如果其存在，當有著多個需要記憶的對象時其會如何表現;以及何為與其相關的神經生理學特徵。
在目前的研究中，我們設計了一個視覺工作記憶顏色回憶任務（Zhang & Luck，2008）的變體。參與者們需要按順序記憶兩個不同物體的顏色，並隨後在一個顏色環上盡可能精確地選出對於其中一個目標物體他們所記憶的顏色。兩個對象的呈現之間的時間間隔會依實驗設計而改變以探索其對參與者們記憶能力的影響。我們使用了具有自適應性的希爾伯特-黃轉換（Hilbert-Huang Transform, HHT）方法來探索參與者的行為和腦電圖（EEG）資料中的節律性成分。我們發現，在參與者們對於第一個呈現的物體的記憶精度隨兩個物體呈現的時間間隔改變而變化的波動中存在顯著的α波段（9~10 Hz）節奏分量。我們也發現事件相關模式（event-related mode, ERM）的某些特徵與行為表現的波動表現出顯著的相關性，包括在第二個物體呈現前的α波段成分和以及在第二個物體呈現之後產生的N1成分的振幅。這些結果表明，在視覺工作記憶的行為學以及腦電圖結果的波動中存在節律性成分，暗示或許當記憶的對象以特定的節律呈現時，參與者的記憶性能可以得到改善。這一研究也驗證了HHT方法用於分析行為和電生理資料的節律性特征的可行性。

摘要(英)

Previous studies have demonstrated that rhythmic components were pervasive in cognitive processes, such as attentional sampling (Fiebelkorn et al., 2013) and auditory working memory (Wöstmann et al., 2020). These results demonstrated that rhythmic components in behavioral performance and related neural activity might be a general phenomenon in a wide range of cognitive tasks. And it leads to our questions: does this rhythm also exist in the visual working memory process; if affirmative, how does it work with multiple objects which need to be encoded; and what the neurophysiological features are related to it.
In the current study, I designed a variant of the visual working memory color recall task (Zhang & Luck, 2008). Participants were required to memorize the colors of two objects and select the color of one of the objects on a color wheel after a period of retention time. The inter-stimulus interval (ISI) between the objects was variable to detect its influence on their memory performance. An adaptive Hilbert-Huang Transform (HHT) method was applied to explore the rhythmic components in both behavioral data and electrophysiological activity. In the behavioral data, a significant alpha band (9~10 Hz) rhythmic component was detected in the fluctuation of the memory precision of the first object which varied with the change of ISI. In addition, several features of event-related mode (ERM) showed significant correlations with behavioral fluctuation, including the alpha-band EEG component before the onset of the second object and the amplitude of N1 component after the onset of the second object. These results demonstrated the existence of rhythmic components in the behavioral and neural fluctuation of visual working memory, indicating that memory performance might be improved when the targets were presented with specific rhythms. The current study also verified the feasibility of the HHT method in analyzing the rhythmic features of behavioral and electrophysiological data.

關鍵字(中)

★ 節律性
★ 視覺工作記憶
★ 顏色回憶作業
★ 腦電圖
★ 希爾伯特-黃轉換
★ 事件相關模式

關鍵字(英)

★ rhythm
★ visual working memory
★ color recall task
★ EEG
★ Hilbert-Huang Transform
★ event-related mode

論文目次

摘要 i
Abstract iii
致謝 v
Table of contents vi
List of Figures ix
Chapter 1: Introduction 1
1.1 Rhythms in the cognitive process 1
1.1.1 Rhythms in cognition 1
1.1.2 The electrophysiology features of cognition rhythms 3
1.2 Visual working memory 4
1.3 Research aims 6
Chapter 2: Method 8
2.1 Participants 8
2.2 Apparatus and Stimulus materials 8
2.2.1 Apparatus 8
2.2.2 Stimuli and Materials 8
2.3 Procedure 9
2.4 EEG recording 13
2.5 Adaptive time-frequency decomposition 13
2.5.1 Empirical mode decomposition 14
2.5.2 Hilbert spectral analysis 15
2.6 Data Analysis 17
2.6.1 Behavioral data analysis 17
2.6.1.1 The mixture model analysis 17
2.6.1.2 Time-frequency decomposition of behavioral data 19
2.6.2 EEG analysis 21
2.6.2.1 EEG data preprocessing 21
2.6.2.2 Time-frequency decomposition of EEG data 23
2.6.3 Event-related mode analysis 24
2.6.4 Statistic and correlation analysis 25
2.6.4.1 Behavioral data analyses 25
2.6.4.2 EEG data analyses 25
2.6.4.3 Correlation analyses between ERM and behavioral data 28
Chapter 3: Results 35
3.1 Behavioral results 35
3.2 EEG results 40
3.3 ERM and correlation analyses results 42
Chapter 4: Discussions 47
Reference 52

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指導教授

阮啟弘(Juan, Chi-Hung)

審核日期

2022-8-16

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