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Extrapolating significance of text-based autonomous vehicle scenarios to multimedia scenarios and implications for user-centered design

机译:基于文本的自主车辆情景对多媒体情景的推断与用户中心设计的影响

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Extrapolation from low-fidelity design iterations is especially critical in HRI. An initial proposal for low-fidelity to higher fidelity extrapolation is developed using insights from cognitive multimedia learning theory to account for the effects of prototype medium and three types of cognitive demands. Inspired by Donald Norman and others, our proposal leverages tightly controlled and multi-authored scenarios through crowdsourcing to create additional potential evidence as a kind of experimental “stress test.” We motivate our proposal by investigating the intersection of emotion and human control, which is understudied outside of autonomous vehicles (AV) and HRI research. Evidence for positively moderated emotional effects in text-based AV scenarios as well as tentative evidence for our extrapolation proposal are identified.
机译:低保真设计迭代的外推在HRI尤其重要。 利用认知多媒体学习理论的见解来制定低保性对更高保真外推的初步提案,以解释原型培养基和三种认知需求的影响。 通过唐纳德诺曼和其他人的启发,我们的提案通过众包利用紧密控制和多撰写的情景,以创造额外的潜在证据作为一种实验性的“压力测试”。 我们通过调查情感和人类控制的交叉来激励我们的提议,这是在自治车辆(AV)和HRI研究之外的内容。 确定了基于文本的AV情景的积极性情绪影响的证据以及我们推断提案的暂定证据。

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