Month: March 2019

Evan M. Kleiman
Department of Psychology
Rutgers University

In other areas of science (biology, chemistry, etc.), we understand phenomena of interest by directly observing and studying them as they occur. Historically, however, we have not done this in the study of suicide because, until recently, the tools to do so have not been available. Indeed, this lack of information regarding the real-time occurrence of suicidal thoughts and behaviors may be a reason why despite all of the knowledge about suicidal thoughts and behaviors that has accumulated over the past 100 years, the suicide death rate in the United States is the same now as it was 100 years ago. The goal of my presentation will be to discuss how two new technologies—smartphone-based real-time monitoring (also called Ecological Momentary Assessment or Experience Sampling) and wearable physiological monitoring—offer to help us better understand suicidal thoughts and behaviors.

First, I will begin the presentation with an overview of findings from two smartphone-based real-time monitoring studies that describe how suicidal thoughts fluctuate throughout the day and how we can use these fluctuations to identify meaningful subtypes of individuals at risk for suicidal behaviors. Second, I will discuss findings from several other real-time monitoring studies on factors that predict suicidal thinking over just a few hours. Third, I will discuss new findings that use wearable physiological monitors to detect distress associated with suicidal thinking. Finally, I will conclude the presentation by discussing how integrating these new technologies offers great promise to go beyond improving our understanding of suicidal thoughts and behaviors to creating interventions to prevent suicidal thoughts and behaviors.

Leor Hackel
Department of Psychology
Rutgers University

People’s social and personal well-being hinges on the ability to form social bonds; in turn, this requires interacting with others and learning whether to spend time with them again in the future. How do we learn about others during such interactions? On one hand, people often learn through positive and negative feedback—a type of learning rooted in reward-based reinforcement. Yet, in social interactions, people often look beyond the immediate reinforcing value of an interaction to encode higher-level social impressions, and these may also impact future choices. Here, I will present a program of research investigating how we learn about people by making choices and experiencing feedback. This work demonstrates that people gravitate not only toward partners who provide rewarding outcomes (e.g., a valued gift), but also to those who display valued social traits (e.g., generosity). Both types of learning involve ventral striatum, while trait-based learning further recruits neural regions associated with social impression updating. Moreover, people use trait knowledge to select social partners in a flexible way across contexts. Finally, I will consider how this model can inform social deficits in disorders such as Borderline Personality Disorder.