Lynn Carol Miller - US grants

DESCRIPTION (provided by applicant): With a second wave of sexual risk taking (i.e., unprotected anal sex) among MSM (CDC, 1999a, 1999b), especially those who are young (18-30) and men of color, it is clear that new approaches to HIV prevention--that build on past HIV prevention and persuasion research--are needed. Emerging technologies, such as interactive video (IAV) on DVD, delivered in a hip' manner may gain or recapture the attention of MSM who have tuned out more traditional PSAs, brochures, and interventions. New longitudinal experimental research by our team (Read et al., 2002) demonstrates that white men exposed to an IAV (with white actors) that incorporated behavior change elements (e.g., self-efficacy, skills, behavioral intention) showed dramatically higher rates of safer sex behavior than white men given counseling alone. In the current research, through a series of interviews, pilot studies, and one large experimental longitudinal study, we seek to achieve the following goals: (1) Replicate and extend that earlier work to two additional populations of MSM (African Americans and Latinos) and develop a template for the development of effective HIV prevention IAV for targeted audiences, tailored to the specific choices of the user to maximize HIV prevention efforts. Such a template could afford rapid technology transfer of effective behavioral interventions to individuals and clinics, locally, nationally, and globally, (2) assess the role of IAV in increasing condom use by comparing IAV alone to a no counseling control, to HIV prevention counseling alone, and to counseling plus IAV, (3) assess whether IAV is effective in reducing HIV risk because of its interactivity (feedback to and choice for the user). We will manipulate interactivity by comparing the conditions in 2 above to a "yoked" condition in which participants passively receive the video stream of a participant in the interactive condition. (4) Assess the psychological factors that mediate and moderate the impact of the IAV on changes in condom use over time.

[unreadable] DESCRIPTION (provided by applicant): HIV prevention efforts for men who have sex with men (MSM) seem stalled: 18,000 MSM are still diagnosed with Human Immunodeficiency Virus (HIV) annually. MSM under age 25 are especially likely to have unprotected anal intercourse (UAI) with casual partners. Traditional interventions have reduced UAI but these more conscious, deliberative, and cognitive approaches don't address a more automatic, affect-based route to decision-making. Experience with risk cues is needed to produce such automatic, affect-based risk reduction: But, real-life experience could be catastrophic. SOLVE (Socially Optimized Learning in Virtual Environments) is a new approach to HIV prevention that integrates traditional cognitive approaches (e.g., social-cognitive interventions modeling cognitive and behavioral skills), while addressing MSM's affect- based and reactive risky decision-making processes, by giving them experience with risk cues in a safe, virtual environment. This approach has been found to reduce UAI compared to wait-list and "standard of care" one-on-one counseling controls. Across the three ethnic populations of MSM (Black/African-American, Latino/Hispanic, White/Caucasian) in our ongoing work, preliminary findings are stronger for younger (18-24 year old) MSM who take greater risks (i.e., 2 or more UAI with non-primary sex partners in the last 90 days). Furthermore, virtual risk taking was uniquely predictive of future risk-taking, even accounting for traditional self-report measures (e.g., intent, self- efficacy). However, our work using interactive video (SOLVE-IAV) is limited by the number of potential learning situations that IAV technology affords and by IAV's inability to enable a more personalized virtual experience for the user that may enhance the user's sense of "presence" in the experience -- a factor also related to change in UAI. Our first specific aim in the current proposal, following formative research, is to create SOLVE-IT, a virtual environment covering a range of test-situations or "contexts of risk" for diverse MSM that, using Intelligent Agent and Gaming technologies, would be delivered and assessed "on-line" over the web nationally. Our second specific aim is to test the effectiveness of SOLVE-IT for young high risk MSM (Black/African-Americans, Latino/Hispanics, White/Caucasians) compared to a wait-list control group using a 3- month longitudinal randomized control trial (RCT). UAI change with casual partners is the primary dependent variable. Additional exploratory questions are examined. [unreadable] [unreadable] PUBLIC HEALTH RELEVANCE: If this work is successful it would provide further evidence for the effectiveness of an innovative, integrative approach for reducing MSM's sexual risk-taking, that would thereby reduce adverse health outcomes (e.g., HIV transmission). More broadly, the work would advance the science of optimizing personalized risk-reduction, a technology- enabled science that could provide health applications for reducing risky decisions that can adversely impact public health -- all readily available to the public over the web. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Risky decision-making leads to pervasive negative health outcomes (e.g., alcohol and drug abuse, risky sexual decisions, accidents). One central characteristic of such individuals (e.g., risky men who have sex with men (MSM)) is that they continue to engage in behaviors with very rewarding short-term consequences, but extremely negative long-term consequences, including medical, social and legal problems. Why do they have such difficulties making the right choices? A growing body of research suggests that motivated human decision-making is the result of a dynamic interplay among three systems: (1) a relatively automatic appetitive system, which has been called the Impulsive System, (2) an executive control system, which has been called the Reflective System[11], and (3) a neural system that translates interoceptive signals into what one experiences as a feeling of desire, or urge [5,12] that may help propel individuals towards reward, and inhibit cognitive resources needed for self-control. Unfortunately, we lack a systematic understanding of how these complex neural systems interact with each other and with various social and contextual factors to produce risk-taking, when, for whom, and why. This gap impedes more rapid advancements in prevention and intervention science. Adequate computational tools could help address this critical barrier, and better advance a cumulative science, but they are currently lacking. This project aims to address this gap by developing generalizable computational tools: A validated neurobiologically based, neural network model of the interaction of these systems could transform our ability to advance theory and effective interventions. To this end, a team of social scientists, neuroscientists, and computational neuroscientists will (a) develop biologically-based computational models that leverage and integrate existing neural network models that view behavior as emergent from approach and avoid motivational structures[13,14] and, at a different level of scale, neural network models that simulate the underlying biological basis of incentive processing and learning, executive function, and decision-making [15,16]; (b) test, validate, and refine the model by predicting the neural and behavioral responses of a subsample from 180 young MSM (sexually risky, sexually risky methamphetamine users, and non-risky) from a completed NIDA imaging study on risky decision-making; (c) assess its generalizability via focused tests, and cross validate with additional NIDA data subsamples; and (d) conduct exploratory computational analyses aimed at concurrently predicting MSM's sequential neural and behavioral dynamics in a virtual date simulation over time, and using the model to explore what interventions, when, and for whom might more effectively reduce risk-taking. A deeper understanding of these neural systems and their interactions, will transform our ability to advance theory, design effective risk-reduction interventions and enhance societal health and well-being, while reducing economic costs.

Support groups help people to learn from others who share similar experiences; they are known to be effective in reducing stress caused by negative life events. With broader access to the Internet, support groups have also expanded into video conferencing format. In-person and remote support groups are led by facilitators with wide ranging backgrounds and qualifications. Unfortunately, such facilitators often suffer from burnout, leading to support group closure. Hence, automated facilitators offer a way for maintaining support groups when human facilitators are unavailable. The main aims of this project are (i) to identify and evaluate the characteristics of an effective autonomous group facilitator; (ii) to study and develop computational methods for measuring individual engagement and group cohesion in video-mediated multiparty interaction; and (iii) to develop and evaluate an autonomous group facilitator that can maximize group cohesion through computational means. To achieve these aims, this project builds and studies an autonomous agent facilitator in the form of a socially assistive robot for remote support groups via Zoom or a similar platform. The interpersonal connectedness and alliances in a group make a support group more effective. Therefore, the project will enable the robot facilitator to choose the facilitation strategy that increases group members’ participation and connectedness. This research advances AI technologies for understanding human-robot interaction and contributes to the development of technologies that can broaden access to mental health support. The project activities will include annual outreach sessions for local inner-city K-12 students demonstrating the automated facilitator and discussing stress management, to educate about STEM and mental health. This project will also broaden participation in computing through the K-12 outreach activities and through training and mentoring five undergraduate researchers per year from systematically underserved groups.<br/><br/>This project advances the state-of-the-art in socially interactive agents and robots capable of interacting with multiple users, in video-mediated interaction. The research incorporates the study of expressive robot and agent embodiment, algorithm for autonomous conversation facilitation, and user engagement for novel facilitation strategies. To this end, the project will first use a human-driven agent, through a Wizard-of-Oz (WoZ) strategy, to design the agent’s action space (both verbal and nonverbal behaviors) necessary for moderating a support group. The WoZ study will also test the hypothesis that an embodied agent facilitator is as effective as a human facilitator in engaging users and projecting competence to group participants. After coding the data recorded during the WoZ study, multimodal machine learning models will be trained for automatic recognition of engagement and conversational stages and acts. Group cohesion will be assessed based on dyadic engagement and individual responses, through network analysis. The research team will finally build an autonomous facilitator leveraging a reinforcement learning model that optimizes for increasing group cohesion. The autonomous facilitator will be evaluated against a second agent that optimizes for equal access to the conversational floor, in terms of individual engagement and group cohesion assessed by post-session questionnaires. This work will build technologies for automated group facilitation that can assist to bridge the gaps in delivering support groups when human facilitators are absent or in short supply.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.