Andrew Kayser - US grants

DESCRIPTION (provided by applicant): Defining the structure of a canonical decision making circuit, from its inputs to its outputs, is one of the principal goals of cognitive neuroscience. T this end, visual perceptual decision making is a fundamental cognitive function in which visual information provides the basis for choosing an appropriate response. In the simplest case, the link between sensory evidence and a behavioral choice is binary: an oncoming car suddenly veers into one's lane and a quick decision must be made - to swerve left or right. However, when vision is compromised, whether through disease or trauma, these decision mechanisms must adapt to degraded input, impaired associative visual processing, or both. As a consequence, visual misperceptions occur more frequently, and the process of translating sensation to action becomes increasingly susceptible to errors of identification and categorization that must be recognized. Understanding the neural mechanisms by which visual perception interacts with higher-order categorization and error detection is thus essential to understanding abnormalities in patients. In this proposal, we build upon previous work studying perceptual decision making in primates and humans to evaluate the process by which humans make higher-order visual decisions. One hypothesis is that both lower-order (i.e. perceptual) and higher-order (e.g. categorization) decisions may differ in the source of their inputs but are mediated by the same decision making network. An alternative hypothesis argues that categorical uncertainty, whether related to the separation of object classes or to the separation of correct from erroneous responses, represents a more abstract feature related to confidence and context that engages different decision processes. Under this hypothesis, perceptual and categorical uncertainty should activate different circuits. By using a combination of behavioral psychophysics, mathematical models, functional MRI, and EEG in a well-validated visual paradigm, here we will attempt to define the brain networks and mechanisms that allow humans to make visual categorization decisions and to detect errors.

Project Summary Despite the widespread nature of social deficits in patients with alcohol use disorders (AUDs) and the foundational importance of adequate social function for therapies (which often directly depend upon group and other interpersonal interactions), the behavioral and neural basis of social impairments in AUDs remains understudied. A significant challenge for understanding such impairments is to link high-level theories of social behavior and cognition with the computations performed by brain circuits. Specifically, how does the brain translate social perception into social valuation, and how does such valuation influence social actions? We propose to leverage recent developments in economic theory and cognitive neuroscience to bridge this divide using a computational, model-based approach. In this proposal, we hypothesize that social impairments in subjects with alcohol use disorders are manifest in the perception of potential social partners and the value consequently assigned to them, impacting the actions that result. By evaluating well-established economic games, we will quantify how subjects with AUDs divide rewards between themselves and anonymous partners in different social contexts. We will manipulate both incentives related to how subjects divide monetary resources between themselves and various social partners, as well as the characteristics of the partners themselves by employing validated quantitative measures of social perception. To unravel the neural mechanisms supporting the above choices, we will use functional MRI to assess brain regions whose activity we hypothesize will vary parametrically with monetary decisions. We thus seek to broaden our understanding of the computations and circuits underlying social behavior. Moreover, we believe that a model-based understanding of these behaviors and neural circuits may someday guide more robust and quantitative assessments of social function in patients with alcohol use disorders, with possible implications for both clinical evaluation and treatment.

PROJECT SUMMARY A significant challenge for understanding social dysfunctions observed in mental illness is to link high-level theories of social behavior and cognition with the computations performed by brain circuits. Specifically, how does the brain translate social perception into social valuation, and how does such valuation influence social actions? We propose to leverage recent developments in economic theory and cognitive neuroscience to bridge this divide using a computational, model-based approach. In this proposal, we hypothesize that social behavior is underpinned by brain mechanisms that are influenced by the neurotransmitter dopamine, and that these mechanisms can be captured by computational models that integrate internal representations of social experience, and parameters relevant to dopamine tone, to inform social actions. Social valuation thus critically, and quantitatively, depends upon both internal social representations and the neurochemistry of the actor within the social environment. To assess this hypothesis, we pursue two approaches to evaluate dopamine tone: one in which we use an FDA-approved medication, tolcapone, to influence dopamine metabolism, and one in which we perform PET imaging to measure dopamine release and baseline dopamine receptor D2/D3 occupancy. We then apply a model of social valuation to subjects' behavior, and search for neural correlates of this valuation using functional MRI (fMRI). To this end, we bring together a group of experts in (1) the neuroeconomics and modeling of social and non-social decision-making, (2) cognitive neuroscience, (3) the pharmacology of frontostriatal circuits, and (4) neuroimaging. We thus seek to broaden our understanding of the computations and circuits underlying social behavior. Moreover, we believe that a model-based understanding of these behaviors and neural circuits may guide more robust predictions of the effects of pharmacological manipulations on social valuation, and provide quantitative tools to assess the effects of such manipulations in patient populations, with possible therapeutic implications.