Center for Humans and Machines, Max Planck Institute for Human Development.
School of Collective Intelligence, Mohammed VI Polytechnic University.
Cogn Sci. 2023 Apr;47(4):e13288. doi: 10.1111/cogs.13288.
Interactions between humans and bots are increasingly common online, prompting some legislators to pass laws that require bots to disclose their identity. The Turing test is a classic thought experiment testing humans' ability to distinguish a bot impostor from a real human from exchanging text messages. In the current study, we propose a minimal Turing test that avoids natural language, thus allowing us to study the foundations of human communication. In particular, we investigate the relative roles of conventions and reciprocal interaction in determining successful communication. Participants in our task could communicate only by moving an abstract shape in a 2D space. We asked participants to categorize their online social interaction as being with a human partner or a bot impostor. The main hypotheses were that access to the interaction history of a pair would make a bot impostor more deceptive and interrupt the formation of novel conventions between the human participants. Copying their previous interactions prevents humans from successfully communicating through repeating what already worked before. By comparing bots that imitate behavior from the same or a different dyad, we find that impostors are harder to detect when they copy the participants' own partners, leading to less conventional interactions. We also show that reciprocity is beneficial for communicative success when the bot impostor prevents conventionality. We conclude that machine impostors can avoid detection and interrupt the formation of stable conventions by imitating past interactions, and that both reciprocity and conventionality are adaptive strategies under the right circumstances. Our results provide new insights into the emergence of communication and suggest that online bots mining personal information, for example, on social media, might become indistinguishable from humans more easily.
人类与机器人之间的交互在网上越来越常见，促使一些立法者通过法律要求机器人披露其身份。图灵测试是一个经典的思维实验，用于测试人类通过文本信息交流来区分机器人冒充者和真实人类的能力。在当前的研究中，我们提出了一个最小化的图灵测试，避免使用自然语言，从而使我们能够研究人类交流的基础。具体来说，我们研究了规范和相互作用在确定成功交流中的相对作用。我们任务中的参与者只能通过在 2D 空间中移动一个抽象形状来进行交流。我们要求参与者将其在线社交互动归类为与人伙伴或机器人冒充者进行的互动。主要假设是，访问一对交互的历史记录将使机器人冒充者更具欺骗性，并阻止人类参与者之间形成新的规范。通过复制他们之前的交互，人类无法通过重复之前有效的方式成功进行交流。通过比较模仿相同或不同对偶行为的机器人，我们发现当冒充者模仿参与者自己的伙伴的行为时，更难检测到冒充者，从而导致交互不太规范。我们还表明，当机器人冒充者阻止常规性时，互惠性有利于交流成功。我们的结论是，机器冒充者可以通过模仿过去的交互来避免被检测到并中断稳定规范的形成，并且互惠性和常规性在适当的情况下都是适应性策略。我们的研究结果为交流的出现提供了新的见解，并表明例如在社交媒体上挖掘个人信息的在线机器人可能更容易与人类无法区分。
