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, Erie Dell Boorman is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2020 — 2021 |
Boorman, Erie D |
R56Activity Code Description: To provide limited interim research support based on the merit of a pending R01 application while applicant gathers additional data to revise a new or competing renewal application. This grant will underwrite highly meritorious applications that if given the opportunity to revise their application could meet IC recommended standards and would be missed opportunities if not funded. Interim funded ends when the applicant succeeds in obtaining an R01 or other competing award built on the R56 grant. These awards are not renewable. |
Model-Based Credit Assignment @ University of California At Davis
Abstract How the brain forms, tunes, and uses predictive models that specify the causal links between stimuli in the environment, our choices, and their outcomes is a fundamental question in Psychology and Neuroscience. A great deal of progress has been made identifying the neural computations theorized to form and update predictive models. This research has played a central role in the rise of computational psychiatry, but its relevance to clinical disorders has been limited in part by the use of relatively simple learning/choice paradigms that capture only a narrow subset of the structural complexity of real-world learning. In order to make sound predictions in a complex world, the brain needs to attribute good and bad outcomes to their most likely causes, a problem known as ?credit assignment?. There is little understanding of how outcomes are attributed to their most likely causes in structured real-world environments. Most real-world learning occurs in complex and structured environments, such as hierarchical systems (e.g. seasonal events, social hierarchies, contextual rules, etc.). Recent evidence suggests that humans can use an understanding of the environment?s causal structure to attribute outcomes to their most likely causes (which I call ?model-based credit assignment)?, rather than simply attributing them to the most recently experienced stimuli and choices that were made (which I call ?model-free? credit assignment), as standard models have proposed. The purpose of the present proposal is to develop the first neural model of model-based credit assignment. We hypothesize that the brain reinstates the cause when a reinforcement outcome is experienced to associate with the outcome. In other words, so that ?fire-together/wire-together? plasticity mechanisms can link a choice with an outcome, the choice representation and the outcome representation must both be active at the same time even though the causal choice or event may have actually occurred some time beforehand. To test this and other predictions, we will integrate mathematical descriptions of learning and decision making with ?representational? analysis methods that allow inferences to be made about the information represented in brain areas, applied to fMRI and scalp EEG data. fMRI will reveal how neural learning signals update neural representations of likely causes during learning, while EEG will reveal the timing of the hypothesized reinstatement. These experiments will set the stage to apply the insights gained to investigate how the brain attributes outcomes to more abstract ?latent? causes in hierarchically structured environments prevalent in the real world. The proposed project will thus move this general program of research strategy toward learning tasks that better reflect the complexity and structure in many real-world learning/choice situations important for both typical and atypical individuals.
|
0.905 |
2021 |
Boorman, Erie D |
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. |
Cognitive Maps For Goal-Directed Decision Making @ University of California At Davis
Abstract Cognitive maps refer to internal representations of spatial and non-spatial relationships between entities (people or things) or events in the external world. There has been widespread excitement generated by recent discoveries that even continuous non-spatial task dimensions may be organized and ?navigated? as a cognitive map. These studies suggest the neural representations and computations revealed in physical space may be only one instance of a general mechanism for organizing and ?navigating? any behaviorally-relevant continuous task dimensions (e.g. space, time, sound frequency, metric length). This insight raises the intriguing possibility that the well-established coding principles revealed during spatial navigation can also be used to understand flexible decision making in abstract and discrete tasks that are commonplace in the real world when they are based on a cognitive map, such as whom to collaborate with or where to eat. A cognitive map of an environment or task is incredibly powerful because it enables inferences to be made from limited experiences that can dramatically accelerate new learning and even guide novel decisions never faced before. Moreover, similar tasks that share an overall structure can be directly related to one another, thereby facilitating rapid generalization from one task or entity to another. Despite this wide-ranging importance for flexible cognition, we have only a basic understanding of how cognitive maps enable such novel inferences and generalization. Better understanding the mechanisms involved also carry significant clinical implications. Indeed, abnormal inferences, cognitive flexibility, and generalization are thought to core dysfunctions in several psychiatric conditions, ranging from schizophrenia to obsessive compulsive disorder to certain expressions of mood disorder. It follows that developing a mechanistic model of these component processes in humans has the potential to inform principled investigations into biomarkers and treatment targets for these disorders. The goal of this proposal is to develop a new neural model of how cognitive maps of abstract and discrete tasks are represented neurally and used to guide novel inferences during decision making in the human brain. We have developed a new experimental paradigm that induces people to form abstract and discrete cognitive maps (e.g. of social networks) and perform novel inferences during decision making. To develop our model, we will combine computational models of learning and inference in this paradigm with geometric models of neural coding derived from spatial navigation and ?representational? and computational functional magnetic resonance imaging analysis methods that allow inferences to be made about the information represented and computations performed in different brain structures, respectively. The insights gained from this research will lead to substantial theoretical advances in models of goal-directed decision making, cognitive flexibility, and memory, with implications for typical and atypical individuals.
|
0.905 |