CCNP Meetings

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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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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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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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.

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.

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David Lydon-Staley
Annenberg School for Communication
University of Pennsylvania

Humans are complex dynamic systems, with feelings, thoughts, and actions that are interconnected and that change over time. I will provide an overview of efforts to realize this complex systems perspective of human behavior across three studies. Using intensive repeated measures data collected online and in the wild, the studies will demonstrate how zooming into processes unfolding over the course of minutes, hours, and days can provide insight into depression, nicotine withdrawal, and curiosity.

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Jennifer Siegel
Zuckerman Institute
Columbia University

Borderline Personality Disorder (BPD) is a serious mental disorder characterized by marked interpersonal disturbances, including difficulties trusting others and volatile impressions of others’ moral character, often resulting in premature relationship termination. The ability to build and maintain healthy relationships depends on our ability to use social information to build accurate representations of others and their mental states through social learning. One aspect of social learning that is especially relevant to forming and maintaining relationships is inferring others’ moral character, i.e., whether they are helpful and trustworthy, or harmful and untrustworthy. We introduce a novel computational assay of moral inference to investigate how patients with BPD form beliefs about the moral character of others and incorporate new information into existing beliefs. We find that the computational mechanisms of moral inference differed for untreated BPD patients relative to matched non-BPD control participants and BPD patients treated in a Democratic Therapeutic Community (DTC). In untreated BPD patients, beliefs about harmful agents were more certain and less amenable to updating relative to both non-BPD control participants and DTC-treated participants.The findings suggest that DTC may help the maintenance of social relationships in BPD by increasing patients’ openness to learning about adverse interaction partners. The results provide mechanistic insights into social deficits in BPD and demonstrate the potential for combining objective behavioral paradigms with computational modelling as a tool for assessing BPD pathology and treatment outcomes.

Frederike Petzschner
Institut für Biomedizinische Technik
Universität Zürich und ETH Zürich

While OCD and gambling are often conceptualized as disorders of compulsivity arising from dysfunctional beliefs, the nature of the beliefs leading to symptom manifestation remains unclear. Computational modeling can help disentangle the complex interplay between beliefs and symptoms, identify core components, and suggest targets for novel treatment approaches.

This talk covers three lines of work that center around a computational informed understanding of the origins of compulsive behavior: The first part relates individual structure learning differences to obsessive-compulsive traits and SSRIs – the first-line pharmacological treatment of OCD. The second part delves deeper into the origin of compulsive behavior. While there are a number of theoretical explanatory frameworks mostly centered around belief alterations, there is no clear consensus on what beliefs are necessary or sufficient to cause compulsive behavior.
We used a minimal model approach (based on a POMDP) to address this question and pinpoint to the belief about the success of preventive actions as being centrally important for eliciting and maintaining compulsive behavior. Finally, the third part of the talk highlights that similar beliefs about the efficacy of one’s actions may not only be associated with compulsive behavior in OCD but may also play an essential role in the compulsive aspect of pathological gambling.

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Michael J Frank
Carney Center for Computational Brain Science
Brown University

Computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. This talk has two parts: in the first I describe an example of how simplified models of brain function can be useful for linking neural circuits to decision making functions. The models provide a mechanistic interpretation of changes in decision making in patient populations. However, for quantitative fitting purposes, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models like the drift diffusion model are often fit to data for convenience even when other models might be superior. In the second part of the talk I will present a new method using artificial neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations linking brain mechanisms to behavior and for interrogating alterations in patient populations.

View this recorded session here.