Measuring warmth and competence at inter-group, interpersonal and individual levels / Medición de la cordialidad y la competencia en los niveles intergrupal, interindividual e individual

Adapting conversational strategies in information-giving human-agent interaction

In this work, we focus on human-agent interaction where the role of the socially interactive agent is to optimize the amount of information to give to a user. In particular, we developed a dialog manager able to adapt the agent's conversational strategies to the preferences of the user it is interacting with to maximize the user's engagement during the interaction. For this purpose, we train an agent in interaction with a user using the reinforcement learning approach. The engagement of the user is measured using their non-verbal behaviors and turn-taking status. This measured engagement is used in the reward function, which balances the task of the agent (giving information) and its social goal (maintaining the user highly engaged). Agent's dialog acts may have different impact on the user's engagement depending on several factors, such as their personality, interest in the discussion topic, and attitude toward the agent. A subjective study was conducted with 120 participants to measure how third-party observers can perceive the adaptation of our dialog model. The results show that adapting the agent's conversational strategies has an influence on the participants' perception.

Self-Stigma Among People With Mental Health Problems in Terms of Warmth and Competence

Introduction

Self-stigma arising from public stigma is a heavy burden for people suffering from mental health problems. Both public stigma and self-stigma encompass the same three elements: stereotype, prejudice, and discrimination. Public stigma has already been successfully explored by the Stereotype Content Model (SCM) and the Behaviors from Intergroup Affect and Stereotypes (BIAS) map. However, this is not the case for self-stigma. Therefore, this is the first study that applies SCM and the BIAS map to self-stigma by examining whether the effects of self-stereotypes on self-directed discrimination would be mediated by self-directed prejudices in people with mental health problems.

Method

Within a total sample of N = 823 participants, who took part in an online survey, n = 336 people reported mental health problems. Mental health and self-stereotypes (warmth, competence), self-directed prejudice (negative emotions), and self-directed discrimination (active/passive self-harm) were assessed.

Results

Structural equation modeling supported the hypothesis that the stereotype dimensions warmth and competence negatively related to prejudice, while stronger prejudice was associated with more discrimination (active/passive self-harm). Prejudice fully mediated the relationship between stereotypes and discrimination. The indirect effects of warmth and competence on active and passive self-harm were moderated by competence and warmth.

Discussion

Implications for further research on self-stigma and the usage of SCM and BIAS map are discussed.

Adaptation Mechanisms in Human–Agent Interaction: Effects on User’s Impressions and Engagement

Adaptation is a key mechanism in human–human interaction. In our work, we aim at endowing embodied conversational agents with the ability to adapt their behavior when interacting with a human interlocutor. With the goal to better understand what the main challenges concerning adaptive agents are, we investigated the effects on the user’s experience of three adaptation models for a virtual agent. The adaptation mechanisms performed by the agent take into account the user’s reaction and learn how to adapt on the fly during the interaction. The agent’s adaptation is realized at several levels (i.e., at the behavioral, conversational, and signal levels) and focuses on improving the user’s experience along different dimensions (i.e., the user’s impressions and engagement). In our first two studies, we aim to learn the agent’s multimodal behaviors and conversational strategies to dynamically optimize the user’s engagement and impressions of the agent, by taking them as input during the learning process. In our third study, our model takes both the user’s and the agent’s past behavior as input and predicts the agent’s next behavior. Our adaptation models have been evaluated through experimental studies sharing the same interacting scenario, with the agent playing the role of a virtual museum guide. These studies showed the impact of the adaptation mechanisms on the user’s experience of the interaction and their perception of the agent. Interacting with an adaptive agent vs. a nonadaptive agent tended to be more positively perceived. Finally, the effects of people’s a priori about virtual agents found in our studies highlight the importance of taking into account the user’s expectancies in human–agent interaction.

Indigenous ethnic identity, in-group warmth, and psychological wellbeing: A longitudinal study of Māori

Longitudinal studies into the relationship between affect (positive or negative feelings) towards one’s own ethnic group and wellbeing are rare, particularly for Indigenous peoples. In this paper, we test the longitudinal effects of in-group warmth (a measure of ethnic identity affect) and ethnic identity centrality on three wellbeing measures for New Zealand Māori: life satisfaction (LS), self-esteem (SE), and personal wellbeing (PW). Longitudinal panel data collected from Māori (N = 3803) aged 18 or over throughout seven annual assessments (2009–2015) in the New Zealand Attitudes and Values Study were analyzed using latent trajectory models with structured residuals to examine cross-lagged within-person effects. Higher in-group warmth towards Māori predicted increases in all three wellbeing measures, even more strongly than ethnic identity centrality. Bi-directionally, PW and SE predicted increased in-group warmth, and SE predicted ethnic identification. Further, in sample-level (between-person) trends, LS and PW rose, but ethnic identity centrality interestingly declined over time. This is the first large-scale longitudinal study showing a strong relationship between positive affect towards one’s Indigenous ethnic group and wellbeing. Efforts at cultural recovery and restoration have been a deliberate protective response to colonization, but among Māori, enculturation and access to traditional cultural knowledge varies widely. The data reported here underline the role of ethnic identity affect as an important dimension of wellbeing and call for continued research into the role of this dimension of ethnic identity for Indigenous peoples.

Stereotypes of Social Groups in Mainland China in Terms of Warmth and Competence: Evidence from a Large Undergraduate Sample

The Stereotype Content Model (SCM) has been validated in multiple countries and regions. However, previous validation studies in China have been limited by small sample size. The current research increased the sample size (n = 184 in the pilot study; n1 = 1315 and n2 = 268 in the formal study) to validate the SCM in mainland China in study 1. Supporting the SCM, 41 social groups were clustered into four quadrants based on warmth and competence dimensions. 35 of the 41 target groups (85.37%) receive ambivalent stereotype. Perceived warmth and competence were positively correlated (r = 0.585, p < 0.001). Status and competence were positively related (r = 0.81, p < 0.001), and competition and warmth were negatively related (r = -0.77, p < 0.001). In addition, 24 typical social groups were selected and a list of stereotype words for these groups was developed in study 2 (n1 = 48, n2 = 52). The implications of the emerging social groups and the applications of this stereotype word list are discussed.

Managing an Agent's Self-Presentational Strategies During an Interaction

In this paper we present a computational model for managing the impressions of warmth and competence (the two fundamental dimensions of social cognition) of an Embodied Conversational Agent (ECA) while interacting with a human. The ECA can choose among four different self-presentational strategies eliciting different impressions of warmth and/or competence in the user, through its verbal and non-verbal behavior. The choice of the non-verbal behaviors displayed by the ECA relies on our previous studies. In our first study, we annotated videos of human-human natural interactions of an expert on a given topic talking to a novice, in order to find associations between the warmth and competence elicited by the expert's non-verbal behaviors (such as type of gestures, arms rest poses, smiling). In a second study, we investigated whether the most relevant non-verbal cues found in the previous study were perceived in the same way when displayed by an ECA. The computational learning model presented in this paper aims to learn in real-time the best strategy (i.e., the degree of warmth and/or competence to display) for the ECA, that is, the one which maximizes user's engagement during the interaction. We also present an evaluation study, aiming to investigate our model in a real context. In the experimental scenario, the ECA plays the role of a museum guide introducing an exposition about video games. We collected data from 75 visitors of a science museum. The ECA was displayed in human dimension on a big screen in front of the participant, with a Kinect on the top. During the interaction, the ECA could adopt one of 4 self-presentational strategies during the whole interaction, or it could select one strategy randomly for each speaking turn, or it could use a reinforcement learning algorithm to choose the strategy having the highest reward (i.e., user's engagement) after each speaking turn.