過去對介面代理人擬真的研究多著重在外在的展現，如容貌、表情及肢體動作對於使用者感受所產生的影響，雖然另外有少數討論語言行為與非語言行為的搭配，然而擬真的呈現不僅於表面的言行與面貌，更透過隱晦的角色特質改變使用者感受，而除此之外，過往的擬真代理人也研究鮮少提及擬真對於使用者執行任務的輔助成效。
本研究欲探討角色特質的擬真對使用者是否有其正負面影響，並且討論不同任務情境之下角色特質的呈現對使用者是否會有不同的感受。實驗設計受測者在面對角色擬真有無的兩種代理人時，於兩種複雜度需求不同的任務中，對任務效率與受測者感受會產生何種影響。
研究結果顯示，當受測者在高複雜度任務場合下，擬真代理人能另受測者在執行任務時保有正面感受，同時降低各方面認知的突兀感。但在高複雜度壓力下，受測者易疏忽代理人的擬真表現甚至影響任務執行，此時無擬真代理人反而能營造專業感，進而提升受測者的信任度，只是在高複雜度下，受測者若面對無擬真代理人也會快速累積疲勞感等負面情緒。
受測者在低複雜度任務場合時，擬真代理人能夠帶給受測者更高度的任務專注度、提升任務資訊的理解力，以及正面影響受測者的情緒。雖然部分受測者因接受擬真而忽略任務資訊，但大多數的受測者在低任務複雜度且有擬真代理人的場合下，普遍展現了正面的情緒反應及任務效率。低複雜度下，無擬真的代理人同樣帶給受測者專業感以及較快速的疲勞累積，此時受測者對於再互動意願同樣多半偏低。

Previous studies have depth explorations of agents’ realism. Most of researches look into external characteristics like appearance, emotion expression or body gesture and some discussions about collocation of verbal and nonverbal behavior but lack of research on characters’ traits. Character traits are also important elements of agents which may help tasks and improve relation between human and computer. Our study tries to evaluate if agents with character traits can influence users’ cognition and perception.
This study was a 2 x 2 between-subjects design. The independent variables were task complexity (simple vs. complexity) and character behavior of agents (realism vs. unrealism). The dependent variables were participants’ cognitive load, performance and perception. Participants should try to read and find key information of tasks and all information was provided from agents.
The results found participants who faced complex tasks ignored the character behavior of agents, so they could concentrate on their tasks. It helps participants’ task performance and cognitive load. There are no differences of task performance and cognitive load between two complexity groups. But participants faced complex tasks showed that they like realistic agent more than unrealistic agent even if character behavior would increase their cognitive load.
Participants who faced simple tasks could find the character behavior of agents and they enjoyed alone with agent which showed character behavior. They got close to agent and hope to interact again. However, some participants didn’t like extreme behavior of agent.
But participants who faced simple task and unrealistic agent didn’t like agent significantly. They complained that agent without behavior was cold and it was too hard to get close. They believed that agent with realistic behavior can improve relationship between them and task performance.
Character traits of agent may improve human-computer interaction, but also increase users’ cognitive load. This study suggests that application of agent should think out task complexity and users’ preferences. Designers should try to balance quantity of task information and behavior from agents.

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