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Nathaniel Douglass Daw

New York University

$7,015,253
Attributed
$10,542,391
Total exposure
8
Grants
5
Lead (contact PI)

Attributed= this PI's even-split share of every grant they're on (the fair, additive number). Exposure = full size of all those grants.

Funding over time

peak $1.9M · FY200925
$2M$1.5M$1M$500K$0
'09
'10
'11
'12
'13
'14
'15
'16
'17
'18
'19
'20
'21
'22
'23
'24
'25

Funding mix

By agency

NIH$10,542,391 · 8

By mechanism

R01$8,989,189 · 6
T32$867,485 · 1
P50$685,717 · 1

Top collaborators

Most similar at New York University

Same institution · by research overlap

Others in their field

Top investigators on “Brain

Research focus

BrainDecision MakingLearningBehaviorRewardsFunctional DisorderTrainingMental DisordersPsychological ReinforcementChoice BehaviorCognitiveBehavioralRelating To Nervous SystemFunctional Magnetic Resonance ImagingComputational NeuroscienceAreaMapsTheoriesInsightBaseDiagnosisCognitive NeuroscienceComplexPattern

Grant awards (26)

Value networks and hippocampal non-local representations$798,883
R01 · FY2025 · MH
NRSA Training Grant in Quantitative Neuroscience$376,355
T32 · FY2025 · MH
Computational Modeling Core (C)$282,470
P50 · FY2025 · MH · contact PI
CRCNS: Computational Foundations for Externalizing/Internalizing Psychopathology$205,000
R01 · FY2025 · MH · contact PI
Value networks and hippocampal non-local representations$813,843
R01 · FY2024 · MH
NRSA Training Grant in Quantitative Neuroscience$491,130
T32 · FY2024 · MH
Computational Modeling Core (C)$403,247
P50 · FY2024 · MH · contact PI
CRCNS: Computational Foundations for Externalizing/Internalizing Psychopathology$205,000
R01 · FY2024 · MH · contact PI
CRCNS: Computational Foundations for Externalizing/Internalizing Psychopathology$202,708
R01 · FY2023 · MH · contact PI
Differentiating reward seeking and loss avoidance with reference-dependent learning models$526,636
R01 · FY2022 · MH
Differentiating reward seeking and loss avoidance with reference-dependent learning models$526,636
R01 · FY2021 · MH
Differentiating reward seeking and loss avoidance with reference-dependent learning models$508,411
R01 · FY2020 · MH
Differentiating reward seeking and loss avoidance with reference-dependent learning models$526,341
R01 · FY2019 · MH
CRCNS: Computational and neural mechanisms of memory-guided decisions$326,829
R01 · FY2018 · DA · contact PI
CRCNS: Representational foundations of adaptive behavior in natural and artificial$373,529
R01 · FY2017 · MH · contact PI
CRCNS: Computational and neural mechanisms of memory-guided decisions$328,543
R01 · FY2017 · DA · contact PI
CRCNS: Representational foundations of adaptive behavior in natural and artificial$384,420
R01 · FY2016 · MH · contact PI
CRCNS: Computational and neural mechanisms of memory-guided decisions$326,999
R01 · FY2016 · DA · contact PI
CRCNS: Representational foundations of adaptive behavior in natural and artificial$425,468
R01 · FY2015 · MH · contact PI
CRCNS: Computational and neural mechanisms of memory-guided decisions$329,880
R01 · FY2015 · DA · contact PI
CRCNS: Computational and neural mechanisms of memory-guided decisions$347,054
R01 · FY2014 · DA · contact PI
CRCNS: Reinforcement learning in multi-dimensional action spaces$360,319
R01 · FY2013 · MH · contact PI
CRCNS: Reinforcement learning in multi-dimensional action spaces$375,031
R01 · FY2012 · MH · contact PI
CRCNS: Reinforcement learning in multi-dimensional action spaces$373,997
R01 · FY2011 · MH · contact PI
CRCNS: Reinforcement learning in multi-dimensional action spaces$364,442
R01 · FY2010 · MH · contact PI
CRCNS: Reinforcement learning in multi-dimensional action spaces$359,220
R01 · FY2009 · MH · contact PI