Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
PLoS One. 2021 Apr 23;16(4):e0250326. doi: 10.1371/journal.pone.0250326. eCollection 2021.
With the growing utility of today's conversational virtual assistants, the importance of user motivation in human-artificial intelligence interactions is becoming more obvious. However, previous studies in this and related fields, such as human-computer interaction, scarcely discussed intrinsic motivation (the motivation to interact with the assistants for fun). Previous studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation (the motivation to interact with the assistant for utilitarian purposes). The current study aims to cover intrinsic motivation by taking an affective engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty. Experiments in which these two factors are manipulated by making participants believe they are interacting with the smart speaker "Amazon Echo" are conducted. Intrinsic motivation is measured both by using questionnaires and by covertly monitoring a five-minute free-choice period in the experimenter's absence, during which the participants could decide for themselves whether to interact with the virtual assistants. Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation. However, the results did not support our hypothesis that expectation and uncertainty have an interaction effect on intrinsic motivation. We then revised our hypothetical model of action selection accordingly and conducted a verification experiment of the effects of uncertainty. Results of the verification experiment showed that reducing uncertainty encourages more interactions and causes the motivation behind these interactions to shift from non-intrinsic to intrinsic.
随着当今会话式虚拟助手的应用越来越广泛，用户动机在人机交互中的重要性变得越来越明显。然而，之前在人机交互等相关领域的研究几乎没有讨论内在动机（即因乐趣而与助手互动的动机）。之前的研究要么将动机视为一个不可分割的概念，要么专注于非内在动机（即出于功利目的与助手互动的动机）。本研究旨在通过情感工程方法涵盖内在动机。提出了一个新的动机模型，其中内在动机受到用户与虚拟助手交互的两个因素的影响：能力预期和不确定性。通过让参与者相信他们正在与智能扬声器“Amazon Echo”交互来进行操纵这两个因素的实验。通过使用问卷和在实验者不在场的情况下对五分钟的自由选择期进行秘密监测来测量内在动机，在此期间，参与者可以自行决定是否与虚拟助手互动。第一个实验的结果表明，高期望比低期望产生更多的内在动机交互。然而，结果并不支持我们的假设，即期望和不确定性对内在动机有交互作用。然后，我们相应地修改了我们的行动选择假设模型，并进行了不确定性影响的验证实验。验证实验的结果表明，降低不确定性会鼓励更多的互动，并使这些互动背后的动机从非内在动机转变为内在动机。
