We are testing a new system for linking grants to scientists.
The funding information displayed below comes from the
NIH Research Portfolio Online Reporting Tools and the
NSF Award Database.
The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
You can help! If you notice any innacuracies, please
sign in and mark grants as correct or incorrect matches.
Sign in to see low-probability grants and correct any errors in linkage between grants and researchers.
High-probability grants
According to our matching algorithm, Brandon M. Turner is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2012 — 2013 |
Turner, Brandon |
F32Activity Code Description: To provide postdoctoral research training to individuals to broaden their scientific background and extend their potential for research in specified health-related areas. |
Analyzing Complex Stochastic Models Using Approximate Bayesian Computation
DESCRIPTION (provided by applicant): There are a number of theoretical and practical reasons to adopt the Bayesian framework over classical techniques. Recently, many psychological models have benefited from Bayesian analyses, and in particular, they have benefited from hierarchical analyses that provide information on multiple levels. However, the class of models that are capable of enjoying the benefits of Bayesian analyses has been limited to models that possess tractable likelihood functions. These models are typically referred to as simulation-based models, and as a result of their complicated or intractable likelihood functions, Bayesian analyses are not possible. However, a new technique, called approximate Bayesian computation (ABC), allows researchers to circumvent the evaluation of the likelihood by simulating the model. Our proposed research will extend the application of ABC to complex, stochastic models of computational neuroscience. For these models, we will be interested in fitting hierarchical and finite mixture versions of the models to examine individual differences and explore the role of experimental design optimization in model selection. PUBLIC HEALTH RELEVANCE: The analysis of stochastic complex models of computational neuroscience will advance our understanding of the underlying mechanisms in the cognitive system guiding the behavior of interest. Introducing the general framework for Bayesian likelihood-free inference will have a broad impact on any field employing computational models. This will be of particular interest in computational neuroscience where models of human behavior are often very sophisticated and have complex likelihood functions.
|
0.599 |
2015 — 2018 |
Lu, Zhong-Lin [⬀] Turner, Brandon (co-PI) |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Ncs-Fo: Collaborative Research: Understanding Individual Differences in Cognitive Performance: Joint Hierarchical Bayesian Modeling of Behavioral and Neuroimaging Data
Understanding the complex determinants of individual health and wellbeing is critical for the promotion and maintenance of a healthy world population. Wellbeing may be understood not only as the absence of physical and mental illness but also as the quality of life and optimal functioning of individuals. It is well known that individuals vary tremendously in terms of cognitive abilities and dispositions, as seen from performance on high-order cognitive tasks, decision-making preferences, and emotional competencies. However, the neural underpinnings of much of this variability are poorly understood: It is unclear how individual differences in brain structure and function across tasks and processes are linked to abilities and competencies. This project explores a mathematical and computational framework for investigating a large-sample neuroimaging and behavioral dataset in order to improve our understanding of individual differences in cognitive performance. An ultimate goal of the project is to predict individual cognitive performance in novel, real-world situations based on observed (past) behavioral and neuroimaging data and contribute to the understanding of cognitive health and wellbeing of individuals. The project will also offer many training opportunities for the next generation of scientists.
The technical approach will build on and integrate recent advances in cognitive science, neuroscience, statistics, and machine learning. Statistical models will integrate data from both brain imaging and behavioral tests to generate predictions that otherwise may not be possible with a single source of data. The research will go beyond establishing and explaining individual differences to predicting individual cognitive performance in a variety of tasks.
|
0.915 |