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People Interpret Robotic Non-linguistic Utterances Categorically

机译:人们分类地解释机器人非语言话语

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摘要

We present results of an experiment probing whether adults exhibit categorical perception when affectively rating robot-like sounds (Non-linguistic Utterances). The experimental design followed the traditional methodology from the psychology domain for measuring categorical perception: stimulus continua for robot sounds were presented to subjects, who were asked to complete a discrimination and an identification task. In the former subjects were asked to rate whether stimulus pairs were affectively different, while in the latter they were asked to rate single stimuli affectively. The experiment confirms that Non-linguistic Utterances can convey affect and that they are drawn towards prototypical emotions, confirming that people show categorical perception at a level of inferred affective meaning when hearing robot-like sounds. We speculate on how these insights can be used to automatically design and generate affect-laden robot-like utterances.
机译:我们展示了实验探测成人是否表现出在情感额定机器人的声音(非语言话语)时表现出分类感知。 实验设计遵循了来自心理学领域的传统方法,用于测量分类感知:机器人声音的刺激持续存在于受试者,他们被要求完成歧视和识别任务。 在前面的主题中被要求评估刺激对是否有情感地不同,而在后者中,他们被要求情感地评估单一刺激。 该实验证实,非语言话语可以传达影响,并且它们被朝着原型情绪引起的,确认人们在听到机器人的声音时,人们表现出在推断的情感意义的程度上的分类感知。 我们推测如何使用这些见解如何自动设计和生成影响的机器人样的话语。

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