Affiliation: Princeton University
- Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brainYael Niv
Psychology Department and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540, USA
J Neurosci 32:551-62. 2012..This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms...
- Cost, benefit, tonic, phasic: what do response rates tell us about dopamine and motivation?Yael Niv
Gatsby Computational Neuroscience Unit, UCL, London, United Kingdom
Ann N Y Acad Sci 1104:357-76. 2007....
- Dialogues on prediction errorsYael Niv
Center for the Study of Brain, Mind and Behavior and Department of Psychology, Green Hall, Princeton University, Princeton, NJ 08544, USA
Trends Cogn Sci 12:265-72. 2008..Here, we provide answers to ten simple questions about prediction errors, with the aim of exposing both the strengths and the limitations of this active area of neuroscience research...
- Parkinson's disease: fighting the will?Yael Niv
Center for the Study of Brain, Mind and Behavior, Princeton University, Princeton, New Jersey 08544, USA
J Neurosci 27:11777-9. 2007
- The effects of neural gain on attention and learningEran Eldar
Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA
Nat Neurosci 16:1146-53. 2013....
- Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal gangliaCarlos Diuk
Psychology Department and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA
J Neurosci 33:5797-805. 2013..Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously...
- Exploring a latent cause theory of classical conditioningSamuel J Gershman
Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
Learn Behav 40:255-68. 2012..Through a wide range of simulations, we demonstrate where the theory succeeds and where it fails as a general account of classical conditioning...
- Learning latent structure: carving nature at its jointsSamuel J Gershman
Princeton Neuroscience Institute and Psychology Department, Princeton University, USA
Curr Opin Neurobiol 20:251-6. 2010..We survey an emerging literature on 'structure learning'--using experience to infer the structure of a task--and how this can be of service to RL, with an emphasis on structure in perception and action...
- Orbitofrontal cortex as a cognitive map of task spaceRobert C Wilson
Department of Psychology and Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA Electronic address
Neuron 81:267-79. 2014..In addition, we generate a number of testable experimental predictions that can distinguish our theory from other accounts of OFC function...
- Context, learning, and extinctionSamuel J Gershman
Psychology Department, Princeton University, Princeton, NJ 08540, USA
Psychol Rev 117:197-209. 2010..Moreover, in both paradigms, context dependence is absent in younger animals, or if hippocampal lesions are made prior to training. The authors suggest an explanation in terms of a restricted capacity to infer new causes...
- The misbehavior of value and the discipline of the willPeter Dayan
Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London, UK
Neural Netw 19:1153-60. 2006..The misbehavior created by Pavlovian values can be quite debilitating; we discuss how it may be disciplined...
- A neural signature of hierarchical reinforcement learningJosé J F Ribas-Fernandes
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
Neuron 71:370-9. 2011..The results reported support the relevance of HRL to the neural processes underlying hierarchical behavior...
- The effects of motivation on response rate: a hidden semi-Markov model analysis of behavioral dynamicsEran Eldar
Princeton Neuroscience Institute, Princeton University, Green Hall, Princeton, NJ 08540, USA
J Neurosci Methods 201:251-61. 2011..These results demonstrate the utility of our analysis method, and provide a precise quantification of the effects of motivation on response rates...
- Hierarchically organized behavior and its neural foundations: a reinforcement learning perspectiveMatthew M Botvinick
Princeton Neuroscience Institute, Department of Psychology, Princeton University, Green Hall, Princeton, NJ 08540, USA
Cognition 113:262-80. 2009..Here and at many other points, hierarchical reinforcement learning offers an appealing framework for investigating the computational and neural underpinnings of hierarchically structured behavior...
- Tonic dopamine: opportunity costs and the control of response vigorYael Niv
Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel
Psychopharmacology (Berl) 191:507-20. 2007....
- Choice valuesYael Niv
Nat Neurosci 9:987-8. 2006
- A normative perspective on motivationYael Niv
Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel
Trends Cogn Sci 10:375-81. 2006..However, in novel states, we propose that outcome-independent, global effects of the utilities can 'energize' habitual actions...
- Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral controlNathaniel D Daw
Gatsby Computational Neuroscience Unit, University College London, Alexandra House, 17 Queen Square, London WC1N 3AR, UK
Nat Neurosci 8:1704-11. 2005..This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system...
- Actor-critic models of the basal ganglia: new anatomical and computational perspectivesDaphna Joel
Department of Psychology, Tel Aviv University, Ramat Aviv, Israel
Neural Netw 15:535-47. 2002..We conclude with a short discussion of the dual role of the dopamine signal in RL and in behavioral switching...