15 January 2020: Credit assignment to state-independent task representations

Nitzan Shahar

Psychology Department

Tel-Aviv University

Natural environments are feature-rich and only a subset of these features are considered to predict action-outcome associations. To enable accurate action-outcome predictions a decision-maker is faced with a challenge, namely that only a portion of the information in the environment is predictive of a desired outcome. Here, we highlight the tendency of individuals to assign credit to outcome-irrelevant task representations. We demonstrate that value is assigned to these representations in a model-free and state-independent manner. We further show the association between these low-level value associations and a more sophisticated model-based system, and propose how model-free representations might be regulated according to a model of the environment. Finally, we suggest that a deficit in the regulation of outcome-irrelevant model-free associations might lead to behavioral abnormalities such as compulsive behavior.

4 December 2019: Integrating complex and personalized data to understand brain development and psychopathology

Theodore Satterthwaite
Department of Psychiatry
University of Pennsylvania School of Medicine

Increasingly, large-scale studies of brain development incorporate complex imaging data alongside diverse clinical and cognitive measures.  However, it remains a challenge to integrate these high-dimensional data, and generate measures tailored to the individual. Here I will focus on recent work illustrates how data integration and personalization can inform our understanding of normal and abnormal brain development.  Specifically, I will demonstrate how machine learning techniques can help identify links between dimensions of psychopathology in youth and abnormalities within functional brain networks.  Furthermore, I will present unpublished work that uses a combination of functional imaging data and machine learning techniques to identify personalized networks in the developing brain. Together, such approaches may evolve to help produce biomarkers of risk and resilience in youth.

20 November 2019: The power of closing the loop: controlling and understanding mood via the Mood-Machine-Interface

Hanna Keren
Mood Brain and Development Unit
National Institute of Mental Health

Implementing analytic tools from engineering can bring a great promise to the study of human dynamics. In this talk I will show how a closed-loop control strategy can be a powerful tool for controlling and characterizing mood. I will present the Mood-Machine-Interface, an efficient mood modification algorithm, that can shift mood by adjusting reward values according to individual mood sensitivity. This method ensures that the created positive and negative environments are potent across individuals and across time. The paradigm can be used to generate substantial mood changes in healthy, depressed, adult and adolescent individuals. And moreover, by creating a non-random environment, it enabled us to model how mood changes are affected by previous events. This model shows that early experiences carry a strong influence on mood, which is mediated by neural signals in the ACC, thus providing a neuro-computational account for mood regulation. Overall, this paradigm demonstrates how closing the loop can open the door to understanding mood dynamics and to developing individualized treatments of mood disorders.

6 November 2019: Brain network organization as the computational architecture of cognition: Implications for mental health

Michael W. Cole
Associate Professor
Center for Molecular and Behavioral Neuroscience (CMBN)
Rutgers University – Newark
http://www.colelab.org

Understanding neurocognitive computations – such as those disrupted in mental disorders – will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. Brain connectivity clearly has something to do with it, and decades of “connectionist” (and recent “deep learning”) theory suggests connectivity patterns specify distributed neural computations. I will share my laboratory’s efforts to map the human brain’s functional network organization and to determine how that organization shapes distributed cognitive processes and mental health. This has involved identification of “flexible hubs” – cortical regions that adaptively shift their connectivity to implement instructed task procedures. We have recently linked flexible hubs to the regulation of mental health. Also central to these efforts is the estimation of activity flow – the movement of evoked activity through brain network connections. Estimating activity flow quantifies the likely contribution of a network organization (such as one estimated using resting-state functional connectivity) to function-specific activity patterns (such as fMRI responses to cognitive events). I will cover application of activity flow mapping to predict whole-brain activation patterns across a variety of tasks, such as tasks involving working memory and rapid instructed task learning, as well as in the context of pre-clinical Alzheimer’s disease. These developments promise to better integrate theoretical/computational neuroscience and empirical neuroscience with mutual benefits across both fields.

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.