Informal verbal interaction is the core matrix for human social life. A mechanism for coordinating this basic mode of interaction is a system of turn-taking that regulates who is to speak and when. Yet relatively little is known about how this system varies across cultures. The anthropological literature reports significant cultural differences in the timing of turn-taking in ordinary conversation. We test these claims and show that in fact there are striking universals in the underlying pattern of response latency in conversation. Using a worldwide sample of 10 languages drawn from traditional indigenous communities to major world languages, we show that all of the languages tested provide clear evidence for a general avoidance of overlapping talk and a minimization of silence between conversational turns. In addition, all of the languages show the same factors explaining within-language variation in speed of response. We do, however, find differences across the languages in the average gap between turns, within a range of 250 ms from the cross-language mean. We believe that a natural sensitivity to these tempo differences leads to a subjective perception of dramatic or even fundamental differences as offered in ethnographic reports of conversational style. Our empirical evidence suggests robust human universals in this domain, where local variations are quantitative only, pointing to a single shared infrastructure for language use with likely ethological foundations.

A fundamental puzzle in the organization of social interaction concerns how one individual elicits a response from another. This article asks what it is about some sequentially initial turns that reliably mobilizes a coparticipant to respond and under what circumstances individuals are accountable for producing a response. Whereas a linguistic approach suggests that this is what “questions” (more generally) and interrogativity (more narrowly) are for, a sociological approach to social interaction suggests that the social action a person is implementing mobilizes a recipient's response. We find that although both theories have merit, neither adequately solves the puzzle. We argue instead that different actions mobilize response to different degrees. Speakers then design their turns to perform actions, and with particular response-mobilizing features of turn-design speakers can hold recipients more accountable for responding or not. This model of response relevance allows sequential position, action, and turn design to each contribute to response relevance.

Conversation Analysis - September 2009

A prerequisite for social coordination is bidirectional communication between teammates, each playing two roles simultaneously: as receptive listeners and expressive speakers. For robots working with humans in complex situations with multiple goals that differ in importance, failure to fulfill the expectation of either role could undermine group performance due to misalignment of values between humans and robots. Specifically, a robot needs to serve as an effective listener to infer human users’ intents from instructions and feedback and as an expressive speaker to explain its decision processes to users. Here, we investigate how to foster effective bidirectional human-robot communications in the context of value alignment—collaborative robots and users form an aligned understanding of the importance of possible task goals. We propose an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users through explanations. To learn from human feedback, our XAI system integrates a cooperative communication model for inferring human values associated with multiple desirable goals. To be interpretable to humans, the system simulates human mental dynamics and predicts optimal explanations using graphical models. We conducted psychological experiments to examine the core components of the proposed computational framework. Our results show that real-time human-robot mutual understanding in complex cooperative tasks is achievable with a learning model based on bidirectional communication. We believe that this interaction framework can shed light on bidirectional value alignment in communicative XAI systems and, more broadly, in future human-machine teaming systems.