A striking range of mental disorders, from OCD to schizophrenia, is accompanied by aberrant decision-making and also by dysfunction in the dopamine system and its targets in the forebrain. Although celebrated computational work posits roles for this system together with the posterior parietal cortex in learning and decision-making for simple choice problems, it requires a tremendous leap of faith to imagine how these simple computational mechanisms can be "scaled up" from the laboratory to address real-world human behavior of the sort that is clinically problematic for patients with these disorders. One understudied aspect of this problem is the high dimensionality of the space of candidate actions, notably the involvement of multiple effectors such as hands and eyes. This project proposes a theoretical framework for more realistic learning and decision problems involving multiple effectors, and leverages it in experiments probing how the brain copes with learning and decision-making in these cases. The core idea is that the brain should divide-and-conquer: treating, e.g., hand and eye movements independently to simplify learning when their consequences are independent, but that it must evaluate actions jointly across effectors when this is not the case. Learning tasks manipulating this independence are used to: (1) test whether humans and animals learn to solve decision problems by separating or coordinating effector choices to efficiently harvest rewards;these tasks are combined with electrophysiological recordings and fMRI to (2) test whether separate or conjoint neural value maps are maintained for action values across effectors, as appropriate to the problem;and multiarea recordings are used to (3) test whether coordinated choices increase neural interactions between effector-specific motor maps. The work makes innovative use of computational theory for experimental design and analysis, in order to connect experimental observations across species, measurement types (spiking, local field potentials, fMRI), and scales (neuronal, systems). It also introduces a new laboratory microcosm for the computations needed to scale up existing decision theories toward clinically relevant real-world behaviors. In principle, quantitative theories of the brain's decision and learning systems hold important promise for the numerous serious mental illnesses that center around these systems, such as improved procedures for diagnosis or screening candidate treatments. This project aims to "scale up" such theories -- which are, in practice, too simple to deliver on this promise -- toward explaining the interacting neural circuits that control realistic behaviors more like those that are problematic for patients with mental illnesses.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-IFCN-B (50))
Program Officer
Glanzman, Dennis L
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
New York University
Schools of Arts and Sciences
New York
United States
Zip Code
Madlon-Kay, Seth; Pesaran, Bijan; Daw, Nathaniel D (2013) Action selection in multi-effector decision making. Neuroimage 70:66-79
Bornstein, Aaron M; Daw, Nathaniel D (2012) Dissociating hippocampal and striatal contributions to sequential prediction learning. Eur J Neurosci 35:1011-23
Dean, Heather L; Hagan, Maureen A; Pesaran, Bijan (2012) Only coherent spiking in posterior parietal cortex coordinates looking and reaching. Neuron 73:829-41
Bornstein, Aaron M; Daw, Nathaniel D (2011) Multiplicity of control in the basal ganglia: computational roles of striatal subregions. Curr Opin Neurobiol 21:374-80
Cools, Roshan; Nakamura, Kae; Daw, Nathaniel D (2011) Serotonin and dopamine: unifying affective, activational, and decision functions. Neuropsychopharmacology 36:98-113
Simon, Dylan Alexander; Daw, Nathaniel D (2011) Neural correlates of forward planning in a spatial decision task in humans. J Neurosci 31:5526-39
Li, Jian; Schiller, Daniela; Schoenbaum, Geoffrey et al. (2011) Differential roles of human striatum and amygdala in associative learning. Nat Neurosci 14:1250-2
Markowitz, David A; Wong, Yan T; Gray, Charles M et al. (2011) Optimizing the decoding of movement goals from local field potentials in macaque cortex. J Neurosci 31:18412-22
Daw, Nathaniel D; Gershman, Samuel J; Seymour, Ben et al. (2011) Model-based influences on humans' choices and striatal prediction errors. Neuron 69:1204-15
Markowitz, David A; Shewcraft, Ryan A; Wong, Yan T et al. (2011) Competition for visual selection in the oculomotor system. J Neurosci 31:9298-306

Showing the most recent 10 out of 14 publications