Interpersonal Stance in Conflict Conversation: Police Interviews

In this work we focus on the dynamics of the conflict that often arises in a police interview between suspects and police officers. Police interviews are a special type of social encounter, primarily because of the authority role of the police interviewer and the often uncooperative stance that the suspect takes: a conflict situation. The skill to resolve or reduce the conflict, to make an uncooperative suspect more cooperative, requires training of the police officer. Leary's interactional circumplex [2] is used in police interview training as a theoretical framework to understand how suspects take stance during an interview and how this is related to the stance that the interviewer takes. The circumplex consists of two axes, power (dominance-submission) and affiliation (opposed-together) and is divided in stances. Leary predicts the dynamics between the stances of interactants which he calls “interpersonal reflexes‿. Acts on the power dimension are complementary (dominant invokes submissive and vice versa) and acts on the affiliation dimension are symmetric (together invokes together, and opposed invokes opposed). Currently, officers practice applying this theory with expensive actors that are sparsely available. Artificial conversational characters that play the role of a suspect in a police interrogation game, a game where policemen can practice applying Leary's theory, would allow for cheaper training and fewer restriction in time and location of the training. Building artificial suspects requires explicit models of strategies and tactics that policemen apply and explicit models of the relevant internal psycho-social mechanisms that underlie the behaviors of a suspect in a police interview. Therefore, we annotated (practice) police interviews on the stance the suspect (professional actors) and police officer take towards each other. Depending on the part, up to nine independent annotators labeled the stance of the speech contributions in three police interviews (using audio and video). In the interviews, one or two officers interviewed one suspect. The result was a small corpus of 50 minutes and 1300 contributions annotated on stance. First, we investigated whether different observers (annotators) agree on the type of stance that suspects and policemen take by having all annotators annotate a small part of the corpus. Labeling stance on the level of speech segments is difficult. Even when the annotators were allowed to discuss, they were often unable to come to an unanimous agreement of the stance displayed. We found that although inter-annotator agreement on stance labeling is low (Krippendorf's a = 0:24), a majority voting “meta-annotator‿ was able to reveal the important dynamics and trends in stance taking in a police interview with relative high “inter-meta-annotator" accuracy (Cohen's = 0:55) [3]. The results of the meta-annotator showed that police officers generally take a dominant-together stance. This is part of their taught strategy. According to Leary's theory this stance would make the suspect move to a submissive-together stance, resulting in a cooperative dialogue. Indeed, our meta-annotator showed that a suspect goes from a typical opposed stance at the start of the interview to a more cooperative stance later. This shows the correctness of Leary's theory in the special type of conversations, police interviews, where conflict is abundant and interactants are engaged in uncooperative dialogue. It also shows the applicability of the theory in modeling an artificial suspect. Annotations showed that the trend towards cooperation in suspects is not always visible and sometimes destroyed. This occurs when suspects felt disrespected or threatened by the interviewer, showing that Leary's theory alone is insufficient to model a police interview or a convincing artificial suspect. Other psycho-social theories (e.g. face threats [1]) should be taken into account in future models for artificial suspects and perhaps be made explicit in the police training

Reducing social diabetes distress with a conversational agent support system: a three-week technology feasibility evaluation

BackgroundPeople with diabetes mellitus not only have to deal with physical health problems, but also with the psycho-social challenges their chronic disease brings. Currently, technological tools that support the psycho-social context of a patient have received little attention.ObjectiveThe objective of this work is to determine the feasibility and preliminary efficacy of an automated conversational agent to deliver, to people with diabetes, personalised psycho-education on dealing with (psycho-)social distress related to their chronic illness.MethodsIn a double-blinded between-subject study, 156 crowd-workers with diabetes received a social help program intervention in three sessions over three weeks. They were randomly assigned to receive support from either an interactive conversational support agent (n=79) or a self-help text from the book “Diabetes burnout” as a control condition (n=77). Participants completed the Diabetes Distress Scale (DDS) before and after the intervention, and after the intervention, the Client Satisfaction Questionnaire (CSQ-8), Feeling of Being Heard (FBH), and System Usability Scale (SUS).ResultsResults indicate that people using the conversational agent have a larger reduction in diabetes distress (M=−0.305, SD=0.865) than the control group (M=0.002, SD=0.743) and this difference is statistically significant (t(154)=2.377, p=0.019). A hypothesised mediation effect of “attitude to the social help program” was not observed.ConclusionsAn automated conversational agent can deliver personalised psycho-education on dealing with (psycho-)social distress to people with diabetes and reduce diabetes distress more than a self-help book.Ethics, Study Registration and Open ScienceThis study has been preregistered with the Open Science Foundation (osf.io/yb6vg) and has been accepted by the Human Research Ethics Committee - Delft University of Technology under application number 1130. The data and analysis script are available: https://surfdrive.surf.nl/files/index.php/s/4xSEHCrAu0HsJ4P

Telepresence Robots in Daily Life - Technical Report

Mobile remote presence systems (MRPs) are the logical next step in telepresence, but what are the ethical, social, legal, and technical implications of such systems going into the wide wild world? We explored these potential issues by immersing ourselves in a range of possible applications by re-purposing commercially available MRPs. This researcher-as-experimental-subject (RAES) approach allowed us to quickly identify many possible issues that could arise from use of the technology. Considering such issues can help further the use of telepresence robots in real-life settings. Furthermore, we suggest that the RAES approach could be helpful in finding interesting issues that might arise when new technologies are introduced to the consumer market

The recognition of acted interpersonal stance in police interrogations

This research aims at finding how suspects in police interrogations express their interpersonal stance -in terms of T.Leary’s interpersonal circumplex- through body postures and facial expressions and how this can be simulated by virtual humans. Therefore, four types of stances were acted by eight actors. To see if the resulted postures are valid, short recordings were shown online in a survey to subjects who were asked to describe them by a selection of a number of adjectives. Results of this annotation task show that some stance types are better recognized than others. Validity (recognizing the intended stance) and inter-rater agreement do not always go hand in hand. The body postures and facial expressions of the best recognized fragments are annotated so they can be implemented in the artificial agent. The results of this study are used in a serious game for police interrogation training where the role of the suspect is played by an artificial embodied conversational agent

Beyond simulations : serious games for training interpersonal skills in law enforcement

Serious games can be used to improve people's social awareness by letting them experience difficult social situations and learn from these experiences. However, we assert that, when moving beyond the strict realism that social simulations offer, techniques from role play may be used that offer more possibilities for feedback and reflection. We discuss the design of two such serious games for interpersonal skills training in the domain of law enforcement. These games feature intelligent virtual agents with which trainees have to interact across different scenarios to improve their social awareness. By interacting with the virtual agents, trainees experience how their behaviour influences the course of the intervention and its outcomes. We discuss how we intend to improve the learning experience in these serious games by including meta-techniques from role play. We close by describing the current and future implementations of our serious games

Computational models of social and emotional turn-taking for embodied conversational agents: a review

The emotional involvement of participants in a conversation not only shows in the words they speak and in the way they speak and gesture but also in their turn-taking behavior. This paper reviews research into computational models of embodied conversational agents. We focus on models for turn-taking management and (social) emotions. We are particularly interested in how in these models emotions of the agent itself and those of the others in uence the agent's turn-taking behavior and vice versa how turn-taking behavior of the partner is perceived by the agent itself. The system of turn-taking rules presented by Sacks, Schegloff and Jefferson (1974) is often a starting point for computational turn-taking models of conversational agents. But emotions have their own rules besides the "one-at-a-time" paradigm of the SSJ system. It turns out that almost without exception computational models of turn-taking behavior that allow "continuous interaction" and "natural turntaking" do not model the underlying psychological, affective, attentional and cognitive processes. They are restricted to rules in terms of a number of supercially observable cues. On the other hand computational models for virtual humans that are based on a functional theory of social emotion do not contain explicit rules on how social emotions affect turn-taking behavior or how the emotional state of the agent is affected by turn-taking behavior of its interlocutors. We conclude with some preliminary ideas on what an architecture for emotional turn-taking should look like and we discuss the challenges in building believable emotional turn-taking agents