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...
- 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...
- 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....
- 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
- 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...
- 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...
- 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, Computer Science Department, Princeton University, Princeton, New Jersey 08540, and Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California 94720
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...
- 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...