23 June 2021: Using quantitative models to improve treatments for destructive behavior in children with autism

Wayne Fisher
Rutgers University Center for Autism Research, Education, and Services (RU-CARES)
Robert Wood Johnson Medical School

The most important advancement in the treatment of destructive behavior has been the development of functional analysis (FA), which is used to prescribe effective treatments, such as functional communication training (FCT). With FCT, the consequence that historically reinforced destructive behavior is delivered contingent on an appropriate communication response and problem behavior is correlated with extinction. Although this approach can be highly effective, many pitfalls and practical challenges arise when this treatment is implemented by caregivers in natural community settings. In this presentation, I will present data and describe a line of research routed in behavioral momentum theory and the generalized matching law aimed at increasing the effectiveness, generality, and durability of FCT for individuals with ASD who display destructive behavior in typical community settings. Specifically, I focus on: (a) applications based on the matching law that can be used to prevent extinction bursts when treatment is initiated; (b) stimulus-control procedures that can be used to promote the rapid transfer of treatment effects to novel therapists, contexts, and caregivers without reemergence of destructive behavior; and (c) stimulus- and consequence-control procedures that can be used as “behavioral inoculation” to prevent resurgence of problem when caregivers do not implement treatment procedures with pristine procedural integrity.

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26 May 2021: Computational phenotyping in Borderline Personality Disorder using a Social Hierarchy Probe

Iris Vilares
Department of Psychology
University of Minnesota

Dysfunction in social interactions is a hallmark feature of several psychiatric disorders. However, due to their complexity, social interactions are also extremely hard to study. Experimental economic games have been successfully used to quantitatively analyze behaviors and motives relevant to social interactions, and the emerging field of computational psychiatry has been applying these techniques to understand apparent aberrant social decision-making in people with psychiatric disorders.

One important aspect of social interactions that has scantly been studied with experimental economic games is how much people value being in a more dominant social hierarchy position. A mal-adaptive reaction to social dominance may present a significant source of vulnerability for neuropsychiatric disorders, and can be particularly relevant for personality disorders that have trouble sustaining social relations, such as borderline personality disorder (BorPD).

Here, we were interested in knowing how people with BorPD value and behave in social interactions when there are differences in social dominance, and how (or if) these differ from controls. Moreover, we were interested in finding computational phenotypes of these behaviors. For this, we had participants (169 controls and 312 BorPD patients) play a multi-round Social Hierarchy game where money could be used to increase (or maintain) social status and applied computational models to the obtained behavior.

We found no difference between BorPD patients and Controls in the amount of money spent to become (or remain) in the dominant position, the challenge rate, or the number of rounds in the dominant position. However, we found that BorPDs in the dominant position transferred more money to the other player when first alpha. In addition, they finished the game with a more equitable distribution of points between them and the game partner. The computational model revealed promising computational phenotypes and suggests that BorPD patients may have a higher disutility from losing their status, and this was associated with higher self-reported feelings of shame and aggression tendencies. Overall, our results indicate that BorPDs and Controls value social dominance similarly but that they may be particularly sensitive to losing status once they have it. In addition, our results suggest BorPDs, when in control, may be especially prosocial, and offer specific computational parameters that can be used to quantitatively characterize and phenotype each individual.

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12 May 2021: Risky business: the effects of reward cues on decision making in models of addiction

Mariya V. Cherkasova
Department of Psychology
West Virginia University

Reward cues can potently influence behaviour. In people with addictive disorders cue-reactivity predicts addictive behaviour such as drug-seeking as well as relapse. In this talk I will present studies looking at the effects of reward cues on decision making as a candidate mechanism whereby exposure to cues may bias behavior. Mirroring earlier findings in rodents, my work in humans suggests that reward cues can promote riskier choice and that these effects may depend on dopamine signaling. Individual differences in the propensity to attribute motivational salience to reward cues may modulate these risk-promoting effects.

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14 April 2021: Why Modeling Time and Context is Critical for (Some) Mental Health Problems

Peter Hitchcock
Department of Cognitive, Linguistic, & Psychological Sciences
Brown University

Computational psychiatry has made important advances and proof-of-principle demonstrations, but it still seems far away from influencing routine clinical practice. Why? I will argue that the field has had difficulty recognizing the variability among mental health problems—and, consequently, the need to model context and temporal dynamics for many problems. Modeling context and temporal dynamics is challenging conceptually and logistically; it would be much easier not to do so. I will suggest three heuristics for deciding whether such modeling is necessary for a given mental health problem. As a case study, I will apply a critical lens to my own developing research program on rumination and worry and their relations to depression and anxiety disorders. I will argue that modeling time and context is indeed critical for these disorders. I will draw out the implications for my research, with an eye toward general principles for modeling problems of sufficient complexity that they are best understood as interacting elements unfolding in context over time.

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17 March 2021: Computational Approaches to Understanding Suicide

Alex Millner
Department of Psychology
Harvard University

This presentation will be divided into two fairly distinct parts. First, I’ll discuss Pavlovian biases in the context of aversive states. Traditionally, aversive Pavlovian biases are associated with inhibition; however, this had only been tested in the context of avoiding punishment. Using a novel behavioral task and computational model, we show that the effect of Pavlovian control depends on the proximity of the aversive state: when escaping an ongoing aversive state, there is a Pavlovian bias for vigorous, active responses whereas when avoiding a potential aversive state, Pavlovian control favors inhibition. Escape-related Pavlovian biases have relevance for many psychiatric disorders. For example, decades of theories and clinical accounts have argued that suicidal thoughts and behaviors are mostly driven by a desire to escape aversive internal states and we show that people with a history of suicidal thoughts and behaviors show an increased Pavlovian bias for escape. In the second part, I will critique current approaches to computational psychiatry (such as my study discussed in the first part) and offer a complementary approach that includes developing formal theories of clinical states, such as suicidal thoughts and behaviors. I will present some very preliminary work in this area, with a focus on outlining the advantages and challenges of this novel approach.

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3 March 2021: Navigating social space

Daniela Schiller
Departments of Neuroscience and Psychiatry
Friedman Brain Institute
Icahn School of Medicine at Mt. Sinai

How do we place ourselves within a social structure? Social encounters provide opportunities to become intimate or estranged from others and to gain or lose power over them. The locations of others on the axes of power and intimacy can serve as reference points for our own position in the social space. The goal of our research is to uncover the neural encoding of these social coordinates. This talk will describe recent experiments tracking the online neural encoding of the perceived locations of others relative to us through dynamic interactions with multiple peers. The talk will also describe initial attempts to uncover a “grid-like” representation of social space, as well as preliminary findings from studies testing these predictions in psychiatric patients presenting with a broad dimensional range of psychopathology. Altogether, the results suggest that navigational computations are potentially crucial for representing and tracking dynamic social relationships, and imply that beyond framing physical locations, the hippocampus and related regions compute a more general, inclusive, abstract, and multidimensional cognitive map consistent with its role in episodic memory.

24 February 2021: Probing the neurobiology of addiction pathology

Stephanie Groman
Department of Neuroscience
University of Minnesota

Although most individuals will use a drug of abuse at least once in their lifetime, only a subset of these individuals will ever become addicted. This suggests that some individuals may be more vulnerable to developing an addiction compared to others and, importantly, if we can identify the neurobiological mechanisms mediating this susceptibility, that addiction may be a preventable disorder. Our work in rats has been using a reinforcement-learning framework to probe the neurobiology of addiction pathology and, notably, to delineate the mechanisms of addiction susceptibility from those that are disrupted by drug use. Here, I will present data indicating that the decision-making processes that predict drug use differ from those that are disrupted following drug use and describe how we have been using this framework to elucidate the neural circuits and biological mechanisms of addiction pathology. This multidisciplinary work – integrating complex behavioral assessments with computational, viral, and neuroimaging approaches in rats – highlights the power of preclincial work for uncovering the mechanisms of addiction.

View this recorded session here.