9 October 2019: Spontaneous thought and creativity in mania

Judith Mildner and Elyssa Barrick
Department of Psychology
Princeton University

In our daily lives, thoughts often unfold spontaneously and freely, for instance during dreaming, mind wandering, daydreaming, or creative ideation. One theory of spontaneous thought suggests that these thoughts arise from a dynamic exploration of semantic and episodic memory that is influenced by the thinker’s current state. While creativity is typically considered a trait, like intelligence, this view of spontaneous thought suggests that a key component of creativity, creative ideation, is in fact more state-like. In this project, we aim to investigate the changing dynamics of spontaneous thought and creative ideation by examining thought in patients with bipolar disorder. How do patterns of spontaneous thought change between manic and euthymic states in these patients? And how do these changes relate to creative thought?

11 September 2019: Using Computational Modeling to Study the Impact of Individual Differences and Contextual Cues on Avoidance Behavior

Jony Sheynin
Department of Psychiatry and Behavioral Science
Texas A&M University Health Science Center

Exaggerated avoidance behavior is a predominant symptom in all anxiety disorders and posttraumatic stress disorder (PTSD), and its degree often parallels the development and persistence of these conditions. Both human and non-human animal studies suggest that individual differences as well as various contextual cues may impact avoidance behavior. Specifically, I have reported that female sex and inhibited temperament, two anxiety vulnerability factors, are associated with greater avoidance behavior in humans, as demonstrated on a computer-based task. I have also reported that avoidance is attenuated by the administration of explicit visual signals during “non-threat” periods (i.e., safety signals). Here, I demonstrate how a reinforcement-learning network model was used to investigate the underlying mechanisms of these empirical findings. Model simulations suggest that differences in relative sensitivity to reward and punishment might underlie the longer avoidance duration demonstrated by females, whereas greater sensitivity to punishment might underlie the greater avoidance rate demonstrated by inhibited individuals. Simulations also suggest that safety signals attenuate avoidance behavior by strengthening the competing approach response. Lastly, several predictions generated by the model suggest that extinction-based cognitive-behavioral therapies might benefit from the use of safety signals, especially if given to individuals with high reward sensitivity and during longer safe periods. Overall, this work highlights the utility of computational modeling for studying the impact of individual differences and contextual cues on avoidance behavior.

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15 May 2019: The depressed decision-maker

Joseph Kable
Department of Psychology
University of Pennsylvania

Depression is characterized by obvious changes in decision-making that cause distress and impairment. Here I will discuss some of our recent work aimed at understanding the specificity and dimensionality of decisional impairments in depression. We tested a large group of participants diagnosed with Major Depressive Disorder (MDD, n = 64) and healthy controls (n=64) on a comprehensive battery of nine value-based decision-making tasks which yield ten distinct measures. MDD participants were worse at learning from rewards and punishments, had more pessimistic predictions about the future, and were less persistent; but showed no differences in time or risk preferences or social bargaining behavior. Performance on learning, expectation, and persistence tasks each loaded on unique dimensions in a factor analysis and accounted for unique variance in predicting depressed status. Computational modeling of reward and punishment learning further revealed evidence for both hyposensitivity to outcomes and reduced learning rates in MDD relative to healthy controls. Our results highlight several learning and decision making processes that are the impacted in MDD whose more detailed study could shed light on distinct dimensions of this heterogeneous disorder.

New CCNP Publication: The Two Cultures of Computational Psychiatry

CCNP Researcher Daniel Bennett, along with CCNP Co-Directors Yael Niv and Steve Silverstein, have a new perspectives piece in the most recent issue of JAMA Psychiatry entitled “The Two Cultures of Computational Psychiatry”. Computational psychiatry is a rapidly growing research field that uses tools from cognitive science, computational neuroscience, and machine learning to study difficult psychiatric questions. In this paper, CCNP investigators discuss two distinct research approaches that are encompassed within this field: explanatory modelling and machine learning. Explanatory modelling research aims to explain the computational-biological mechanisms of psychiatric illness, whereas machine learning research uses advanced statistical tools to predict psychiatric outcomes from large-scale datasets. In the case of machine learning, this may also involve classifying patients into subgroups based on previously unknown combinations of variables, which may help in the characterization of heterogeneity across diagnoses, and the individualization of treatment You can find the full article here.

24 April 2019: Psychiatry & Violence: Biological & Psychosocial Assessment

Jay Singh
Department of Psychiatry
University of Pennsylvania

The relationship between mental health and violence remains a controversial one. However, much attention has been paid to this relationship in recent years, with the number of secure inpatient beds in psychiatric hospitals on the rise and surveys of the criminal justice system establishing prisons and jails as the largest providers of mental healthcare in the United States. Hence, establishing valid and reliable methods of identifying patients who will commit violent acts has become an important health and safety issue. In this seminar, we will discuss current evidence-based methods of assessing violence risk in psychiatric populations, including both biological (neurological correlates and genetic markers) and psychosocial (actuarial risk assessment tools and structured professional judgment instruments) methods.

*Please note that this meeting will take place at the Princeton Neuroscience Institute this week.*

3 April 2019: How can new technology help us better understand suicidal thoughts and behaviors?

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.

20 March 2019: Instrumental learning in social interaction

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.