Monday, 16 April 2018
Nebraska Union Auditorium
Elaine Fox (University of Oxford)
The contribution of cognitive biases and genes to psychological wellbeing
A growing body of scientific evidence is uncovering the potential biological and psychological mechanisms that underlie differential sensitivity to the environment. Differential sensitivity refers to the notion that people differ in their emotional reactivity to both adverse as well as to positive and highly supportive environments. A relatively neglected factor in this research has been the role of systematic biases in cognitive processing that are known to be influential in determining differential sensitivity to environmental events. The CogBIAS project is investigating both cognitive and genetic markers of psychological wellbeing in adolescent and adult populations. Drawing on longitudinal as well as experimental studies, we are examining the role of multiple cognitive biases, and polygenic sensitivity scores (PSS), in establishing a spectrum of cumulative ‘risk’ or ‘enhancement’ that might tip the developmental trajectory towards vulnerability or resilience.
How emotion and motivation influence goal directed behavior differently from childhood to adulthood
The transformation from young child to mature adult brings with it changes in nearly every domain of psychological functioning. Central to this psychological growth is a collection of affective-motivational processes that evolve with development to shape what and how we feel, what we value, and how we marshal these signals to shape our moment-to-moment actions. In my talk, I will propose that children and adolescents undergo dynamic, complex changes in emotional and motivated processes, which is rooted in representational and brain development. One line of work on emotional representational development shows that key “building blocks” of emotion such as semantic understanding of emotion, abstraction of emotion concepts, and insight into one’s own feeling state evolve along partially distinct trajectories, and continue actively through adolescence. Charting emotional development in this way holds implications for clinical and emotion-regulatory challenges that children and adolescents face. A second, related line of work focuses on how children and adolescents detect valued cues in the environment and use them to guide learning and goal direction action. Our work demonstrates that throughout adolescence, individuals are fine-tuning the capacity to use value signals to guide momentary adaptive behaviors which ultimately serve one’s goals. This emergence of value-guided action is dependent on development of coordinated activity between key brain systems important for assigning value to actions and integrating that with moment-to-moment cognitive demands. More generally, this work reveals that affective-motivational development emerges along surprisingly late developmental trajectory, which holds implications for developmental theory and understanding of the core organization of these constructs.
Evolving perspectives on emotion, emotion regulation and their social context
Successfully navigating our complex social world involves at least three abilities: perceiving and interpreting other people’s actions and status in our groups, having emotional responses as a consequence of these perceptions and interpretations, and as needed, being able to exert top-down control over all of the above. This talk will describe the evolution of a general purpose, multi-level, model that helps organize our understanding of the psychological and neural mechanisms underlying these abilities. Towards that end, the talk will begin with a brief description of the starting point for the model - the study of the self-regulation of emotion - and then will transition into a discussion of how the model can be elaborated to account for perceiving and regulating emotion and status in social contexts. The talk concludes by considering broader implications of the model.
Tuesday, 17 April 2018
Nebraska Union Ballroom
Deanna Barch (Washington University in St. Louis)
Mechanisms of motivational impairments in psychosis and depression
The Research Domain Criteria (RDoC) initiative has recognized the importance of studying motivation and hedonic processing in psychopathology and includes a “positive valence” system domain that captures many relevant constructs. This talk will review behavioral and neuroimaging studies examining impairments in these constructs in individuals with psychosis versus depressive pathology, as there appear to be important differences in patterns and neural alterations associated with reward and hedonic function in psychosis versus depression. In depressive pathology, impairments in the experience of pleasure may propagate forward and lead to impairments in other aspects of the positive valence system that are reliant on hedonic responses, such as anticipation, learning, effort, and action selection. Such pleasure impairments in the context of depression could reflect disruption in dopamine and/or opioid signaling in the striatum related to depression more generally, or more specifically to anhedonia symptoms. In contrast, the existing data indicate relatively intact in-the-moment pleasure experience in psychosis, but disruptions in other components involved in the positive valence system. Specifically, individuals with schizophrenia exhibit altered reward prediction and associated striatal and prefrontal activation, impaired reward learning, impaired reward-modulated action selection, and impaired effort-cost decision making, which may combine together to disrupt goal-directed behavior and function in everyday life.
Motivation: A valuation systems perspective
Why do people do what they do? The field of motivation seeks to answer this question by examining the forces that energize and direct behavior. This field is currently witnessing a renaissance of research interest that has resulted in an exciting but fragmented picture. In this talk, we offer an integrative framework that draws upon control systems and predictive coding principles. We see the mind as having evolved to produce adaptive action using noisy sensory input. We identify discrepancy reduction as a universal computational strategy that is used to select mental models that can stand in for the world and to select actions that can change the world. Motivation arises from discrepancies between mental models of the world and our needs and goals. These discrepancies are computed across valuation systems that give rise to action tendencies by assessing the overlap between action outcomes and desired states. Discrepancy detection and action potentiation can occur on multiple hierarchical levels and across multiple valuation systems before being resolved on the level of motor output. This model integrates known bottom-up and top-down motivational forces and provides an integrative account of why people and other organisms do what they do.
Towards a deep science of affect and motivation
Although traditionally considered separately, some of the earliest behavioral science accounts implied links between affect and motivation. To examine these links, “deep science” frameworks that seek to explicitly connect levels of analysis may complement more extant “broad science” approaches that seek to more exhaustively characterize a single level of analysis (e.g., at the circuit, experiential, or behavioral levels). For example, emerging literature has begun to link dopamine efflux in the nucleus accumbens to the affective state of positive arousal, which can then potentiate approach behavior. Over the past decade, advances in the temporal, spatial, and chemical resolution of brain imaging and manipulation methods in humans and other animals have begun to mechanistically link these levels of analysis. These findings imply that further research might explore links between norepinephrine release, negative arousal, and avoidance behavior. In the near future, a deep science of affect and motivation may provide the most direct route for translating circuit-based knowledge to behavioral interventions.
Tor Wager (University of Colorado at Boulder)
Reproducible, generalizable brain models of affective processes
Recent years have seen dramatic advancement in the measurement of biology at a systems level. Researchers routinely obtain thousands or millions of simultaneous measures of dynamic systems. In humans, this includes neuroimaging, which can be used to probe the brain bases of affect and emotion in increasingly sophisticated ways. Neuroimaging can provide measures of activity in 300,000 brain locations and 60 billion functional associations every second. However, the complexity of these measures presents new challenges in maintaining scientific transparency and reproducibility. In this talk, I describe several new models of the brain bases of affective processes, including models that predict the intensity of negative affect, autonomic responses, prosocial emotions, and pain. These models reduce complex neuroimaging data to measures that can be readily replicated and generalized across laboratories. They can be tested prospectively on new participants, providing unbiased estimates of effect size that are often dramatically larger than single regions from standard brain maps. By asking which stimuli and psychological states these measures respond to across studies, we can induce the nature of their associated psychological constructs, providing a foundation for understanding how affect and emotion are generated in the brain.