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High-probability grants
According to our matching algorithm, Catherine Hartley is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2016 — 2021 |
Hartley, Catherine |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Career: Neurodevelopment of Value-Based Learning and Decision-Making @ Joan and Sanford I. Weill Medical College of Cornell University
Learning to make choices that bring about good outcomes and avoid bad ones is a lifelong challenge. Such learning is particularly consequential during adolescence, when increased exploration and autonomy confer many opportunities to make new choices. Poor decision-making represents one of the greatest perils of adolescence, with mortality rates doubling during this developmental stage, in large part due to risky or impulsive actions. However, the neural and cognitive mechanisms underpinning this developmental period of increased risky decision-making are not well understood. The overarching goal of this proposal is to characterize how the dynamic developmental trajectory of brain circuitry involved in learning through positive and negative experience shapes real-world choices. Drawing upon behavioral, computational, and neuroimaging approaches, this work will examine how neurocognitive changes in value-based learning may give rise to a window of increased risky and impulsive decision-making during adolescence. A refined understanding of the mechanisms underlying developmental changes in real-world decision-making has broad relevance across a number of societal domains including public policy, adolescent health, and juvenile justice.
This proposal will test whether risky and impulsive choice may stem in part from developmental changes in two specific aspects of how individuals learn from experience: (i) the relative weighting individuals place upon positive versus negative outcomes of past actions and (ii) the degree to which individuals form and recruit mental models of the potential future consequences of their actions. A developmental sample of participants, spanning late childhood to young adulthood, will complete sequential decision-making tasks in which they make a series of choices that can yield good or bad outcomes. By applying computational reinforcement-learning models to participants' choices in these tasks, we can precisely quantify developmental changes in these component processes of learning. Functional magnetic resonance imaging (fMRI) will be used to characterize corresponding developmental changes in the brain circuitry engaged during learning. We will assess whether these neurocognitive changes in learning are predictive of participants' reports of their real-world risky and impulsive behavior, and whether they have distinct explanatory power from that of behavioral economic tasks that are commonly used to index risky and impulsive choice tendencies, but do not involve learning. A longitudinal follow-up session will enable assessment of the covariance between changes in learning and real-world risky and impulsive behavior as participants advance in their transition through adolescence. This work holds the potential to provide a more detailed mechanistic account of
how reinforcement learning mediates the dynamic relationship between brain and behavior over the course of development.
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1 |
2021 |
Hartley, Catherine Alexandra |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Interactive Development of Reinforcement Learning and Adaptive Memory
PROJECT SUMMARY Anxiety and depression are increasingly recognized as disorders that are developmental in origin. While vulnerability to anxiety and depression is heightened prior to adulthood, the developmental factors that give rise to this increased risk are not well understood. Two characteristic learning and memory biases are implicated in the etiology of anxiety and depression: preferential processing of negatively valenced information and a tendency to form overly general memories and value associations. Despite their apparent clinical relevance, studies to date linking these learning and memory biases to psychiatric risk have relied largely on recollective measures that do not enable the study of how they may arise over development through value-based learning and memory encoding processes. In this proposal, we will leverage computational modeling and neuroimaging approaches to elucidate how mechanistic relations between learning computations and memory formation underlie valence and overgeneralization biases across development from childhood to adulthood. Aim 1 will characterize how valence biases in learning change over development, how they influence incidental memory for episodic details of valenced outcomes, and how they arise through neural computations. Aim 2 will characterize, across development, how generality of learned representations adapts across contexts, how the specificity of memory representations changes with time, and how neural representations support the use of multiple levels of abstraction to guide learning and memory. Aim 3 will characterize how valence and generalization biases change longitudinally with age and assess their relation to real-world autobiographical memory and clinical symptomatology. The significance of the proposed research lies in its potential to: 1) provide a theoretical account relating valence and generalization biases in value-based learning and corresponding biases in episodic and autobiographical memory; 2) elucidate the neurocognitive mechanisms underlying these biases; 3) delineate normative longitudinal developmental changes in these processes from childhood to adulthood; and 4) establish whether computational phenotypes capturing these biases predict anxious and depressive symptomatology.
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0.958 |