Number 5 is alive! Attribution of knowledge and intention in human-robot interactions

机器人将在许多工作领域变得越来越普遍，从医疗保健到制造再到物流。然而，由于机器人的外表和行为与人类不同，它们可能不会触发同样的精神状态归因过程，否则会促进流畅的社交互动。这可能会严重破坏他们与其他人的互动和被他人接受，特别是在无焦虑合作至关重要的领域(例如医疗保健)。这一跨学科研究项目将巴赫在人类社会互动和感知方面的专业知识与詹纳奇尼在工程学和机器人学方面的专业知识相结合，揭示了人们在多大程度上自发地将与其他人类相同的精神状态归因于机器人，这种归因依赖于形态特征(例如眼睛)和行为特征(例如生物运动、有效的目标寻求)，以及操纵这些特征是否可以鼓励或阻止人们将机器人视为与人类相似的交互伙伴。它依赖于巴赫开发的两个成熟的任务，这两个任务有力地衡量了独立研究流中社会意义形成的两个核心组成部分：(1)人们如何根据他们归因于他们的意图预测另一个演员的行为，以及(2)他们如何从另一个演员对其拥有的特定视觉视角中获得另一个演员的知识。通过改变机器人的形态特征和行为，我们将能够测量这两种核心类型的精神状态归因，并将它们与交互质量和心灵感知的明确评级联系起来。通过这样做，这个项目不仅将为机器人和其他人类的精神状态提供新的见解，还将提供新的方法，赋予机器人特征，让人们以心灵的方式看到它们，并更自信地与它们合作。

Robots will become increasingly common in many fields of work, from health care to fabrication to logistics. However, because robots look and act differently than humans, they may not trigger the same mental state attribution process that otherwise promote fluent social interactions. This can severely undermine their interactions with and acceptance by other people, especially in fields where anxiety-free cooperation is crucial (e.g., health care). This interdisciplinary research project combines Bach's expertise on human social interaction and perception with Giannaccini's expertise in engineering and robotics to reveal the extent to which people spontaneously attribute the same mental states to robots as to other humans, on which morphological (e.g., eyes) and behavioural features (e.g., biological motion, efficient goal seeking) such attributions rely, and whether manipulating these features can either encourage or discourage people from seeing robots as human-like interaction partners. It relies on two well-established tasks developed by Bach, which robustly measure two central components of social sense-making in independent research streams: (1) how people predict another actor's behaviour from the intentions they attribute to them, and (2) how they derive another actor's knowledge from the particular visual perspective this actor has upon it. By varying the robots' morphological features and behaviours, we will be able to measure these two central types of mental state attribution and link them to explicit ratings of interaction quality and mind perception. In doing so, this project will not only provide new insights into how mental states are attributed to robots - and other humans - but also provide novel methods to give robots characteristics that allow people to "see" them in a mentalistic way and cooperate with them more confidently.