透過注意力的調控,我們可以將訊息暫時地維持在工作記憶表徵。然而,我們對於注意力與工作記憶互動的大腦神經機制仍缺乏清楚的認識。本論文利用行為測量方法、腦電波測量,以及功能性磁振造影,探討注意力調控在工作記憶中運作的神經表徵。第一章介紹本論文的研究背景與研究假設;第二章我介紹所使用的神經影像技術與資料分析原理。第三章利用事件相關電位檢驗當個體從工作記憶表徵中選擇目標項目時,是否涉及具有空間與視覺特定性的腦電波活化,且此一活化與個體從知覺表徵中選擇目標項目時所引起的活化相同。第四章與第五章利用事件相關功能性磁振造影探討注意力調控的有效性是否影響工作記憶表徵的維持與提取,研究結果展現了有效的注意力調控會引發視覺皮質強烈的神經活化以維持記憶表徵,且注意力調控的有效性與記憶提取的困難度在大腦前額葉與後頂葉皮質的活化上也會產生顯著的交互作用,這些與注意力調控有關的大腦區域反映了由前而後的階層性關係。第六章透過功能性聯結的分析方法,展現注意力調控在工作記憶中運作的神經網路,以及大腦區域間的交互作用關係。最後,第七章針對我的研究結果進行綜合性的討論,對注意力在工作記憶中運作提出一個整合性的認知神經看法。
Attention and working memory are intrinsically bound, but the neural mechanisms of their interaction are still less understood. The goal of this thesis is to establish a framework that provides neural representations for understanding the operation of attentional control in working memory with the evidence integrated from behavioural measures, event-related potentials (ERPs), and functional magnetic resonance imaging (fMRI). Chapter 3 demonstrates the two ERPs experiments that examine whether selecting relevant target items from within working memory representations involves spatially specific, retinotopic biasing of neural activity in a manner analogous to that which occurs during visual search for target items in perceptual domain. Chapter 4 describes an event-related fMRI experiment that investigates the neural correlates of the effectiveness of orienting attention during retention and the mnemonic evaluation whilst comparing working memory representations with current perceptual stimuli. Chapter 5 shows an event-related fMRI study that demonstrates that the neural activity in the posterior areas is modulated by reflectively transient attention-based operation during working memory retention. Finally, I identify the neural network of the brain regions associated with the top-down attentional operation in working memory representations using coherence analysis in Chapter 6. In conclusion, I investigate the interaction within a distributed neural network for supporting attentional operation and how the cognitive framework of attentional operation in working memory can be implemented with a dynamic neural network in the brain. Based on the findings and implications, directions for future research are discussed.