Women earn less than men in technical fields. Competing theories have been offered to explain this disparity. Some argue that women underperform in negotiating their salary, in-part due to language in job descriptions, called gender triggers, which leave women feeling disadvantaged in salary negotiations. Others point to structural and institutional bias: i.e., recruiters make better offers to men even when women exhibit equal negotiation skills. As a final salary is co-constructed though an interaction between employees and recruiters, it is difficult to disentangle these views. Here, we discuss how intelligent virtual agents serve as powerful methodological tools that lend new insight into this psychological debate. We use virtual negotiators to examine the impact of gender triggers on computer science (CS) undergraduates that engaged in a simulated salary negotiation with an automated recruiter. We find that, regardless of gender, CS students are reluctant to negotiate, and this hesitancy likely lowers their starting salary. Even when they negotiate, students show little skill in discovering tradeoffs that could enhance their salary, highlighting the need for negotiation training in technical fields. Most importantly, we find little evidence that gender triggers impact women's negotiated outcomes, at least within the field of CS. We argue that findings that emphasize women's individual deficits may reflect a lack of experimental control, which intelligent agents can help correct, and that structural and institutional explanations of inequity deserve greater attention.

A crucial aspect of virtual gaming experiences is the avatar: the player's virtual self-representation. While research has demonstrated benefits to using self-similar avatars in some virtual experiences, such avatars sometimes produce a more negative experience for women. To help researchers and game designers assess the cost-benefit tradeoffs of self-similar avatars, we compared players' performance and subjective experience in a search and rescue computer game when using two different photorealistic avatars: their own self or a friend, and when playing either a social (rescuing people) or a nonsocial (rescuing gems) version of the game. There was no effect of avatar appearance on players' performance or subjective experience in either game version, but we also found that women's experience with self-similar avatars was no more negative than men's. Our results suggest that avatar appearance may not make a difference to players in certain game contexts.

This work explores the extent to which social dialogue can mitigate (or exacerbate) the loss of trust caused when robots make conversational errors. Our study uses a NAO robot programmed to persuade users to agree with its rankings on two tasks. We perform two manipulations: (1) The timing of conversational errors - the robot exhibited errors either in the first task, the second task, or neither; (2) The presence of social dialogue - between the two tasks, users either engaged in a social dialogue with the robot or completed a control task. We found that the timing of the errors matters: replicating previous research, conversational errors reduce the robot's influence in the second task, but not on the first task. Social dialogue interacts with the timing of errors, acting as an intensifier: social dialogue helps the robot recover from prior errors, and actually boosts subsequent influence; but social dialogue backfires if it is followed by errors, because it extends the period of good performance, creating a stronger contrast effect with the subsequent errors. The design of social robots should therefore be more careful to avoid errors after periods of good performance than early on in a dialogue.

This work explores whether culture impacts the extent to which social dialogue can mitigate (or exacerbate) the loss of trust caused when agents make conversational errors. Our study uses an agent designed to persuade users to agree with its rankings on two tasks. Participants from the U.S. and Japan completed our study. We perform two manipulations: (1) The presence of conversational errors -- the agent exhibited errors in the second task or not; (2) The presence of social dialogue -- between the two tasks, users either engaged in a social dialogue with the agent or completed a control task. Replicating previous research, conversational errors reduce the agent's influence. However, we found that culture matters: there was a marginally significant three-way interaction with culture, presence of social dialogue, and presence of errors. The pattern of results suggests that, for American participants, social dialogue backfired if it is followed by errors, presumably because it extends the period of good performance, creating a stronger contrast effect with the subsequent errors. However, for Japanese participants, social dialogue if anything mitigates the detrimental effect of errors; the negative effect of errors is only seen in the absence of a social dialogue. Agent design should therefore take the culture of the intended users into consideration when considering use of social dialogue to bolster agents against conversational errors.

We present the results of a study in which humans negotiate with computerized agents employing varied tactics over a repeated number of economic ultimatum games. We report that certain agents are highly effective against particular classes of humans: several individual difference measures for the human participant are shown to be critical in determining which agents will be successful. Asking for favors works when playing with pro-social people but backfires with more selfish individuals. Further, making poor offers invites punishment from Machiavellian individuals. These factors may be learned once and applied over repeated negotiations, which means user modeling techniques that can detect these differences accurately will be more successful than those that don't. Our work additionally shows that a significant benefit of cooperation is also present in repeated games---after sufficient interaction. These results have deep significance to agent designers who wish to design agents that are effective in negotiating with a broad swath of real human opponents. Furthermore, it demonstrates the effectiveness of techniques which can reason about negotiation over time.

Social robots establish rapport with human users. This work explores the extent to which rapport-building can benefit (or harm) conversations with robots, and under what circumstances this occurs. For example, previous work has shown that agents that make conversational errors are less capable of influencing people than agents that do not make errors [1]. Some work has shown this effect with robots, but prior research has not considered additional factors such as the level of rapport between the person and the robot. We predicted that building rapport through a social dialogue (such as an ice-breaker) could mitigate the detrimental effect of a robot's errors on influence. Our study used a Nao robot programmed to persuade users to agree with its rankings on two "survival tasks" (e.g., lunar survival task). We manipulated both errors and social dialogue:the robot either exhibited errors in the second survival task or not, and users either engaged in an ice-breaker with the robot between the two survival tasks or completed a control task. Replicating previous research, errors tended to reduce the robot's influence in the second survival task. Contrary to our prediction, results revealed that the ice-breaker did not mitigate the effect of errors, and if anything, errors were more harmful after the ice-breaker (intended to build rapport) than in the control condition. This backfiring of attempted rapport-building may be due to a contrast effect, suggesting that the design of social robots should avoid introducing dialogues of incongruent quality.

We demonstrate two agents, a robot and a virtual human, which can be used for studying factors that impact social influence. The agents engage in dialogue scenarios that build familiarity, share information, and attempt to influence a human participant. The scenarios are variants of the classical “survival task,” where members of a team rank the importance of a number of items (e.g., items that might help one survive a crash in the desert). These are ranked individually and then re-ranked following a team discussion, and the difference in ranking provides an objective measure of social influence. Survival tasks have been used in psychology, virtual human research, and human-robot interaction. Our agents are operated in a “Wizard-of-Oz” fashion, where a hidden human operator chooses the agents’ dialogue actions while interacting with an experiment participant.

Recent advances in scanning technology have enabled the widespread capture of 3D character models based on human subjects. Intuition suggests that, with these new capabilities to create avatars that look like their users, every player should have his or her own avatar to play video games or simulations. We explicitly test the impact of having one's own avatar (vs. a yoked control avatar) in a simulation (i.e., maze running task with mines). We test the impact of avatar identity on both subjective (e.g., feeling connected and engaged, liking avatar's appearance, feeling upset when avatar's injured, enjoying the game) and behavioral variables (e.g., time to complete task, speed, number of mines triggered, riskiness of maze path chosen). Results indicate that having an avatar that looks like the user improves their subjective experience, but there is no significant effect on how users perform in the simulation.

Even with the rise in use of TeleMedicine for health care and mental health, research suggests that clinicians may have difficulty reading nonverbal cues in computer-mediated situations. However, the recent progress in tracking affective markers (i.e., displays of emotional expressions on face and in voice) has opened the door to new clinical applications that might help health care providers better read nonverbal behaviors when employing TeleMedicine. For example, an interface that automatically quantified affective markers could assist clinicians in their assessment of and treatment for psychological distress (i.e., symptoms of depression and Post-traumatic Stress Disorder (PTSD)). To move towards this prospect, we will show that clinicians' judgments of these nonverbal affective markers (e.g., smile, frown, eye contact, tense voice) could be informed by such technology. The results of our evaluation suggest that clinicians' ratings of nonverbal affective markers are less predictive of psychological distress than automatically quantified affective markers. Because such quantifications are more strongly associated with psychological distress than clinician ratings of these same nonverbal behaviors, an affective interface providing quantifications of nonverbal affective markers could potentially improve assessment of psychological distress..