基于文本的自动驾驶汽车场景对多媒体场景的外推意义及其对以用户为中心的设计的启示

K. Robinson, L. Robert, R. Eglash
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引用次数: 0

摘要

在HRI中,低保真设计迭代的外推尤其重要。利用认知多媒体学习理论的见解,提出了从低保真到高保真外推的初步建议,以解释原型媒介和三种类型的认知需求的影响。受Donald Norman等人的启发,我们的建议通过众包的方式,利用严格控制和多作者的场景来创造额外的潜在证据,作为一种实验性的“压力测试”。我们通过研究情感和人类控制的交集来激发我们的提议,这在自动驾驶汽车(AV)和HRI研究之外还没有得到充分的研究。确定了基于文本的AV场景中积极调节情绪影响的证据以及我们外推建议的初步证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extrapolating significance of text-based autonomous vehicle scenarios to multimedia scenarios and implications for user-centered design
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.
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