人類的視覺經驗深受視覺注意力好壞的影響。視覺注意力在這裡著重於它的選擇性，其定義如下：犧牲對其他事物的處理，同時將有限的資源用於處理特定的事物。視覺注意力障礙通常伴隨著反應時間的改變和反應正確率的降低，所以反應時間和反應正確率是評量視覺注意力的重要參數。
本論文的研究動機主要源於以下的問題：（一）認知障礙病人的復健過程中，常需預測個案在某視覺刺激類型下的注意力反應，以作為治療策略之參考。在一般探討視覺注意力的實驗裏，受測者被要求對帶有特定特徵的目標作視覺搜尋，以得到在這樣的視覺刺激類型下視覺注意力的反應值，通常為一曲線值。此曲線值再與標準曲線比對關係以判別好壞。然而標準曲線值之獲得多來自曠日費時的統計過程。（二）文獻中之視覺搜尋實驗結果，均只能解釋在特定情況下的注意力反應，無法合理推廣到更豐富、更複雜的真實世界。所以我們需要一項能模擬真實環境與個案反應的工具，以提供我們重要的量化參數。（三）近幾年來，不斷的有各種演算模型被發展來模擬心智的運作機制，以解釋大量的實驗資料。有一些描述視覺搜尋的演算模型能合理解釋視覺注意力的機制。我們計畫應用這些模型當作模擬工具。然而在文獻中未見其說明如何去預測和評量視覺注意力。
因此本研究目的在嘗試找出演算模型與實際人類視覺反應時間的關係，並應用此關係去評量視覺注意力。在初步的實驗裡，我們建立了眼球動作的量測系統，並應用此量測系統量測正常受試者在一項視覺搜尋任務裡的反應時間，同時也以演算模型算出標準的反應時間。另外我們利用此量化方法去檢驗酒精對受試者的視覺注意力之影響。最後我們嘗試對一名臨床病人施測，並提出了個案報告。
根據本研究結果顯示，正常受試者部分，演算模型算出的反應時間與實際的量測資料呈現線性相關。酒精測試以及臨床病人部分，量化參數顯示出其注意力的差異。演算模型算出的反應時間一定程度地呈現了真實測試的結果。
綜合以上結果，本論文所提出之量化分析概念，可以進一步發展出能適當預測視覺注意力的方法，以協助認知心理學的學者及治療者分析和評量視覺注意力。

The influence of visual attention on human visual experiences is significant for cognitive psychology. In this thesis, visual attention is denoted by selective attention, defined as follows: the allocation of limited processing resources to some stimuli of tasks at the expense of others. Visual attention deficits are usually accompanied with increasing response time and decreasing correct rate.
The study is motivated by the following observations: (1) In terms of cognitive rehabilitation, practitioners often need to predict attention response in real world scenes for strategies of clinical interventions. In studies on visual attention, a program of research, in which subjects are asked to visual search for the specific target, has been conducted for capturing responses in specific visual stimulus. The result, a profile, is compared with a standard profile for estimating visual attention. However, this standard profile usually is based on the statistical analysis, which is a costly and time-consuming process. (2) Although these studies have provided much valuable information on various patterns between visual stimulus and visual attention, they only explain the results in specific stimulus. We could not predict the performance of clients conveniently for real world images. We need a tool, in which any simulation experiment can be replicated exactly, to estimate the performance in any task. (3) In recent years, a variety of computational models have been proposed to describe the data obtained from experiments or explain the cognitive mechanisms underlying performance in a task. An existing model could be adaptive to simulate and explain the process of visual attention. We have proposed the model for the second problem.
The purpose of this study is (1) to clarify a proposed model related with response time, and (2) to calculate a standard profile based on this proposed model for estimating visual attention. In the experiment for (1), the same visual stimulus was tested by human subjects and the model. A linear relationship was given by the comparison of the results. In the experiment for (2), the attention state of subjects was affected by alcohol. For clinical purpose, a case report was described. The effectiveness of the proposed estimation was examined. The results suggested that the proposed model could estimate visual attention.
It concluded that the proposed model could be developed to an adequate estimation for visual attention. This will be conducive to quantitative analysis of visual attention for psychologists and clinical practitioners.

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