10 May 2023: Interactions between neuromodulatory systems and cortex during initiation and switching of behaviour

Matthew Rushworth
Department of Experimental Psychology and Wellcome Trust Centre for Integrative Neuroimaging (WIN)
University of Oxford, UK

Voluntary actions are ones that appear to be initiated by people and other animals in the absence of any external instruction or cue.  However, the likelihood that voluntary actions are initiated depends on identifiable features of both the current and the recent environment, recent behaviour, and the consequences that will ensure if the action is made (for example, will it lead to reward or not).  These factors mediate their influence on the initiation of voluntary action via a distributed neural circuit spanning cortical regions such as the anterior cingulate and insula cortex and subcortical nuclei including habenula, basal forebrain, ventral tegmental area, substantia nigra, and raphe nucleus.  I review a series of recent studies conducted in non-human and human primates using a combination of neuroimaging, temporary inactivation via ultrasound stimulation, and pharmacological manipulation that begin to dissect these different influences on voluntary behaviour and to trace the anatomical pathways through which each operates on voluntary behaviour.  The timing of action initiation depends both on reward expectations and on recent behaviour and these influences are governed by changes in activity in the basal forebrain and anterior cingulate cortex, altered by disruption of activity in either of these areas, and by cholinergic manipulations.  However, whether or not an action is initiated also depends on the richness/sparseness of opportunities in the environment in general and this is tracked by activity in the raphe nucleus and altered by serotonergic manipulation.

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12 April 2023: Active Learning Impairments in Substance Use Disorders when Solving the Explore-Exploit Dilemma: Longitudinal Stability, Symptom Prediction, and Replication

Ryan Smith
Laureate Institute for Brain Research
University of Tulsa

Computational modeling is a promising approach for parsing behavioral processes and dysfunctions in individuals with substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. In this talk, I will describe a study in which we assessed baseline and 1-year follow-up data on a sample of healthy controls (HCs; N = 48) and treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) who completed a standard ‘three-armed bandit’ task designed to assess explore-exploit behavior. I will also describe a pre-registered replication study with a new sample of 168 individuals with SUDs and 99 HCs. Computational models – based on the notion of active learning – were fit to behavior on the task. Relative to HCs, participants with SUDs were found at baseline to show slower learning rates in response to negative outcomes and less precise action selection. We then repeated these analyses when the same individuals returned and re-performed the task 1 year later to assess the stability of these baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); (b) relationships between model parameters at baseline and follow-up were all significant and ranged from small to moderate (.25 < ICCs < .54); and (c) learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 < rs < .43, .002 < ps < .02). Differences in learning rates for losses replicated in the second sample, and model parameters could jointly differentiate specific substance disorders when combining samples. These findings suggest that processing dysfunctions involving learning to arbitrate between exploration and exploitation may show some stability throughout the recovery period. At the same time, individual computational differences at baseline had some predictive value for changes in substance use severity. Taken together, these results suggest active learning models may allow measurement of trait dysfunctions that could have predictive utility for substance use severity.

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29 March 2023: Intracranial recordings of value: unveiling the neurophysiological substrates of human decision-making with intracranial recordings

Ignacio Saez
Departments of Neuroscience, Neurology and Neurosurgery
Icahn School of Medicine at Mount Sinai

My research focuses on the study of the neural basis of human cognition, particularly decision-making, through the examination of invasive brain activity in neurosurgical patients. I combine human electrophysiological and electrochemical recordings during decision-making tasks with computational models of reward and choice. This experimental combination provides access to fast, highly detailed, high signal-to-noise, multi-areal neural activity from the human brain, which is highly suited to studying the relationship of distributed neural activity to aspects of cognition and the use of neurostimulation techniques to modulate behavior and treat cognitive aspects of disease. In this talk, I will cover the different methodologies we employ in the lab, as well as current research efforts in the study of decision-making and the combination of computational psychiatry and intracranial approaches. 

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15 March 2023: Navigating our uncertain social worlds

Oriel FeldmanHall
Department of Department of Cognitive, Linguistic and Psychological Sciences
Brown University

Interacting with others is one of the most inherently uncertain acts we embark on. There are a multitude of unknowns, including how to express ourselves, who to confide in, or whether to engage in risky behavior with our peers. All this uncertainty makes successfully navigating the social world a tremendous challenge. Combining behavioral and neuroscientific methods, we explore the social and emotional factors that shape and ultimately guide how humans learn to make adaptive decisions amongst this great uncertainty. In particular, we borrow models from the animal learning literature, and methods from computational neuroscience and machine learning, to examine how humans experience, process, and resolve this uncertainty to make more adaptive decisions.  

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1 March 2023: Motivation for emotional pleasure and pain in psychopathology

Yael Milgram
Department of Psychology
Harvard University

Emotion regulation deficits contribute to many mental health disorders. Most research sought to understand these deficits by investigating how people regulate emotions – specifically, which emotion regulation strategies people use and how effectively they implement them. However, emotion regulation strategies are used at the service of attaining desired emotional states. Therefore, people who struggle with psychopathology might differ not only in the strategies they use, but also in the emotional states they desire. In this talk, I will present evidence suggesting that some clinical populations differ from non-clinical populations in the degree to which they are motivated to experience painful and pleasant emotions, with an emphasis on Major Depressive Disorder. I will then present studies testing the implications of these differences for the use of emotion regulation strategies, emotional experiences, and mental health. Finally, I will offer a new perspective for understanding these findings, according to which motivation to experience painful emotions in psychopathology might reflect a form of emotional self-harm.

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15 February 2023: Computational Models of Compulsivity

Frederike Petzschner
Department of Psychiatry and Human Behavior
Brown University

OCD has been conceptualized as a disorder arising from dysfunctional beliefs, such as overestimating threads or pathological doubts. Yet, how these beliefs lead to compulsions and obsessions remains unclear. Here, we develop a computational model to examine the specific beliefs that trigger and sustain compulsive behavior in a simple handwashing scenario. Our results demonstrate that a single belief disturbance – a lack of trust in one’s avoidance action– can trigger and maintain compulsions and is directly linked to compulsion severity. This distrust can further explain a number of seemingly unrelated phenomena in OCD including the role of not-just-right feelings, intolerance to uncertainty, overestimation of threat or perfectionisms, and deficits in reversal and state learning. In conclusion, our findings shed new light on the underlying beliefs that drive compulsive behavior in OCD, providing a step forward in building a more comprehensive theory of this complex condition.

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1 February 2023: Workshop on Psychopathology Diagnosis

Gal Shoval
Princeton Neuroscience Institute
Princeton University

Gal Shoval, the clinical supervisor of the CCNP, will present the principles of diagnostic procedures of psychopathology and discuss different challenges in making diagnosis in therapy and research. The participants are welcome to prepare ahead and share some of their own concerns and prior experience for a vigorous discussion.

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14 December 2022: Too Much of a Good Thing? Modeling Excess Goal Pursuit in Anorexia Nervosa

Ann Haynos
Department of Psychology
Virginia Commonwealth University

Historically, physical and mental health concerns have been conceptualized as resulting from deficits in the ability to pursue long-term goals resulting from dysfunctions in decision-making abilities and underlying executive functioning brain circuitry. However, this perspective neglects a subset of health concerns that arise from “too much of a good thing” or excess pursuit of goals that society typically encourages (e.g., order/organization, work or academic performance). This talk will focus on one such presentation: the over-pursuit of weight loss that characterizes anorexia nervosa. In this talk, I will present data from a series of studies using a neuroeconomic approach and computational modeling (i.e., Hierarchical Drift Diffusion Modeling) of data derived from a translational foraging task to demonstrate that: 1) anorexia nervosa may be maintained by over-use of computational strategies (i.e., rule-based or value-congruent decision-making) designed to maximize positive outcomes; 2) these strategies appear to be supported by over-engagement of executive functioning circuits conventionally considered to be positive for supporting mental health; and 3) this phenotype expands beyond restrictive eating disorders to other mental health concerns characterized by excess goal pursuit (e.g., “work addiction”). These findings suggest that, paradoxically, that it may be paramount to reduce use of “good” executive functioning skills to treat this group of clinical concerns. Finally, I will provide a roadmap for future research aimed at understanding the computational pathways into severe and underserved problems of excess goal pursuit.

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30 November 2022: Temporal context effects in risky monetary decision-making

Hayley Brooks
Department of Psychology
University of Denver

Up until the late 20th century, influential decision theorists did not consider recent events (e.g. previous choices or outcomes) relevant to risk taking, in part because risky decision-making settings are not inherently contextual or temporal in nature. For example, an outcome on a previous trial does not causally influence or directly alter the outcome on a subsequent trial. However, over the last three decades, research spanning psychology, economics and neuroscience has begun to explore the possibility that risky decision-making may instead be fundamentally contextually sensitive. Using a combination of approaches, including computational modeling of behavior, physiological arousal, and fMRI, my research examines how risky monetary decision-making is temporally context-dependent. I will present results from a series of studies that demonstrate both value-dependent and value-independent effects of temporal context on risky choice behavior at multiple timescales. These data also suggest that risky choices reflect not only temporal context, but how people compare that context to their evolving expectations (i.e., a dynamic reference point). These results are perplexing because relying on recent events at any timescale appears to be at odds with the assumed goal of risky decision-making: to maximize payoff. I will discuss potential mechanisms, including physiological arousal and the hypothesized neural mechanisms, that may support context effects in risk. Finally, I will present a new project leveraging insights from emotion regulation and cognitive control research to understand how using cognitive strategies to change goals may mitigate such temporal context effects in an effort to improve risky decision-making.

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