2025
Bein, O. & Niv, Y. (2025). Schemas, reinforcement learning, and the medial prefrontal cortex. Nature Reviews Neuroscience. [PDF]
Mirea, D.-M., Nook, E.C., Niv, Y. (2025). Cognitive Modeling of Real-World Behavior for Understanding Mental Health. [PDF]
2024
Amir, N., Niv Y., & Langdon, A. (2024). States as goal-directed concepts: an epistemic approach to state-representation learning. Reinforcement Learning Journal. [PDF]
Amir, N., Tiomkin S., & Langdon, A. (2024). Learning telic-controllable state representations. Finding the Frame: Reinforcement Learning Conference 2024 workshop. [PDF]
Bedder, R. L., Hitchcock, P., & Sharp, P. (2024). Unravelling Repetitive Negative Thinking With Reinforcement Learning. [PDF]
Berwian, I., Hitchcock, P., Pisupati, S., Schoen, G. & & Niv, Y. (2024). Using computational models of learning to advance cognitive behavioral therapy. [PDF]
Berwian, I., Pisupati, S., Chiu, J. C., Ren, Y. & Niv, Y. (2024). Selective maintenance of negative memories as a mechanism of spontaneous recovery of fear after extinction. [PDF]
Mirea, D.-M.*, Shin, Y. S.*, DuBrow, S., & Niv, Y. (2024). The Ubiquity of Time in Latent-cause Inference. Journal of Cognitive Neuroscience. [PDF]
Mirea, D.-M., Mildner, J. N., Kelley, S., Gillan, C., Nook, E. C., & Niv, Y. (2024). Depression is associated with higher sensitivity to social media rewards. [PDF]
Monfils, M-H., Lee, H. J., Raskin, M., Niv, Y., Shumake, J., Telch, M., Smits, J. & Otto, M. (2024). Fear attenuation collaborations to optimize translation. Behavioral Neuroscience. [PDF]
Pisupati, S., Langdon, A., Konova A. B., & Niv, Y. (2024). The utility of a latent-cause framework for understanding addiction phenomena. Addiction Neuroscience. [PDF]
Zorowitz, S., Karni, G., Paredes, N., Daw, N. D. & Niv, Y. (2024). Improving the reliability of the Pavlovian go/no-go task for computational psychiatry research. [PDF]
2023
Bedder, R., Pisupati, S., & Niv, Y. (2023). Modelling rumination as a state-inference process. Cognitive Science Conference Proceedings 2023. [PDF]
Bennett*, D., Radulescu*, A., Zorowitz, S., Felso, V., & Niv, Y. (2023). Affect-congruent attention modulates generalized reward expectations. [PDF]
Berwian, I., Pisupati, S., & Niv, Y. (2023). A reinforcement learning framework to illuminate change mechanisms underlying specific psychotherapy interventions. [PDF]
Pisupati*, S., Berwian*, I., Chiu, J., Ren, Y., & Niv, Y. (2023). Human inductive biases for aversive continual learning — a hierarchical Bayesian nonparametric model. Proceedings of Machine Learning Research. [PDF]
Rouhani, N., Niv, Y., Frank, M. J., & Schwabe, L. (2023). Multiple routes to enhanced memory for emotionally relevant events. [PDF]
Takahashi, Y., Stalnaker, T., Mueller, L. E., Harootonian, S., Langdon, A. J., & Schoenbaum, G. (2023). Dopaminergic prediction errors in the ventral tegmental area reflect a multithreaded predictive model. [PDF]
Zorowitz, S., & Niv, Y. (2023). Improving the reliability of cognitive task measures: A narrative review. [PDF]
Zorowitz, S., Solis, J., Niv, Y., & Bennett, D. (2023). Inattentive responding can induce spurious associations between task behavior and symptom measures. [PDF]
2022
Barbosa, J., Stein, H., Zorowitz, S., Niv, Y., Summerfield, C., Soto-Faraco, S., & Hyafil, A. (2022). A practical guide for studying human behavior in the lab. [PDF]
Bellamy, P., Haynes, C., Martin, L., Mirabile, S., & Niv, Y. (2022). A guide for writing anti-racist tenure and promotion letters. [PDF]
Langdon, A., Botvinick, M., Nakahara, H., Tanaka, K., Matsumoto, M., & Kanai, R. (2022). Meta-learning, social cognition and consciousness in brains and machines. Neural Networks. [PDF]
Pisupati, S., & Niv, Y. (2022). The challenges of lifelong learning in biological and artificial systems. Trends in Cognitive Sciences. [PDF]
Song, M., Baah, P., Cai, M. B., & Niv, Y. (2022). Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning. [PDF]
Song, M., Jones, C. E., Monfils, M.-H., & Niv, Y. (2022). Explaining the effectiveness of fear extinction through latent-cause inference. Neurons, Behavior, Data analysis, and Theory. [PDF]
Song, M., Takahashi, Y., Burton, A., Roesch, M., Schoenbaum, G., Niv, Y., & Langdon, A. (2022). Minimal cross-trial generalization in learning the representation of an odor-guided choice task. PLoS Computational Biology. [PDF]
Weber, I., Zorowitz, S., Niv, Y., & Bennet, D. (2022). The effects of induced positive and negative affect on Pavlovian-instrumental interactions. Cognition and Emotion. [PDF, Preregistration]
2021
Bennett, D., Davidson, G., & Niv, Y. (2021). A model of mood as integrated advantage. Psychological Review. [PDF]
Bennett, D., Niv, Y., & Langdon, A. (2021). Value-free reinforcement learning: Policy optimization as a minimal model of operant behavior. Current Opinion in Behavioral Sciences. [PDF]
Chan, S. C., Schuck, N. W., Lopatina, N., Schoenbaum, G., & Niv, Y. (2021). Orbitofrontal cortex and learning predictions of state transitions. [PDF]
Eldar*, E., Felso*, V., Cohen, J., & Niv, Y. (2021). A pupillary index of susceptibility to decision biases. [PDF]
Hayden, B. Y., & Niv, Y. (2021). The case against economic values in the orbitofrontal cortex (or anywhere else in the brain). Behavioral Neuroscience. [PDF]
Hitchcock, P., Forman, E., Rothstein, N., Zhang, F., Kounios, J., Niv, Y., & Sims, C. (2021). Rumination derails reinforcement learning with possible implications for ineffective behavior. Clinical Psychological Science. [PDF]
Langdon, A. J., & Chaudhuri, R. (2021). An evolving perspective on the dynamic brain: Notes from the Brain Conference on Dynamics of the brain: Temporal aspects of computation. European Journal of Neuroscience. [PDF]
Niv, Y., Hitchcock, P., Berwian, I. M., & Schoen, G. (2021). Toward precision cognitive behavioral therapy via reinforcement learning theory (Chapter 12). In: LM Williams and LM Hack (Eds). Precision Psychiatry. American Psychiatric Association. [PDF]
Niv, Y. (2021). The primacy of behavioral research for understanding the brain. Behavioral Neuroscience. [PDF]
Radulescu, A., Shin, Y. S., & Niv, Y. (2021). Human representation learning. Annual Reviews in Neuroscience. [PDF]
Rouhani, N., & Niv, Y. (2021). Signed and unsigned reward prediction errors dynamically enhance learning and memory. eLife. [PDF]
Shin, Y. S., & Niv, Y. (2021). Biased evaluations emerge from inferring hidden causes. Nature Human Behaviour. [PDF]
Zorowitz, S., Bennett, D., Choe, G., & Niv, Y. (2021). A recurring reproduction error in the administration of the generalized anxiety disorder scale. Lancet Psychiatry. [PDF]
2020
Bennett, D. & Niv, Y. (2020). Opening Burton’s Clock: Psychiatric Insights from Computational Cognitive Models. [PDF]
Cai, M. B., Shvartsman, M., Wu, A., Zhang, H., & Ju, X. (2020). Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. Neuropsychologia.[PDF]
Daniel, R., Radulescu, A., & Niv, Y. (2020). Intact reinforcement learning but impaired attentional control during multidimensional probabilistic learning in older adults. Journal of Neuroscience. [PDF]
Drummond, N., & Niv, Y. (2020). Model-based decision making and model-free learning. Current Biology. [PDF]
Langdon, A., & Daw, N. (2020). Beyond the average view of dopamine. Trends in Cognitive Sciences. [PDF]
Radulescu, A., Holmes, K. & Niv, Y. (2020). On the convergent validity of risk sensitivity measures. [PDF]
Rouhani, N., Norman, K. A., Niv, Y., & Bornstein, A. M. (2020). Reward prediction errors create event boundaries in memory. [PDF]
Sharpe, M. J., Batchelor, H. M., Mueller, L. E., Chang, C. Y., Maes, E. J., Niv, Y., & Schoenbaum, G. (2020). Dopamine transients do not act as model-free prediction errors during associative learning.Nature Communications. [PDF]
2019
Bennett, D., Silverstein, S., & Niv, Y. (2019). The two cultures of computational psychiatry. [PDF]
Bravo-Hermsdorff, G., Felso, V., Ray, E., Gunderson, L. M., Helander, M. E., Maria, J., & Niv, Y. (2019). Gender and collaboration patterns in a temporal scientific authorship network. Applied Network Science. [PDF]
Cai, M. B., Schuck, N., Pillow, J., & Niv, Y. (2019). Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. [PDF]
Langdon, A., Song, M., & Niv, Y. (2019). Uncovering the ‘state’: Tracing the hidden state representations that structure learning and decision-making. Behavioural Processes. [PDF]
Langdon, A. J., Hathaway, B. A., Zorowitz, S., Harris, C. B. W., & Winstanley, C. A. (2019). Relative insensitivity to time-out punishments induced by win-paired cues in a rat gambling task. Psychopharmacology. [PDF]
McDougle, S., Butcher, P., Parvin, D., Mushtaq, F., Niv, Y., Ivry, R., & Taylor, J. (2019). Neural signatures of prediction errors in a decision-making task are modulated by action execution failures. [PDF]
Niv, Y. (2019). Learning task-state representations. Nature Neuroscience. [PDF]
Radulescu, A., & Niv, Y. (2019). State representation in mental illness. [PDF]
Radulescu, A., Niv, Y., & Ballard, I. (2019). Holistic reinforcement learning: The role of structure and attention. [PDF]
Rouhani, N., & Niv, Y. (2019). Depressive symptoms bias the prediction-error enhancement of memory towards negative events in reinforcement learning. Psychopharmacology. [PDF]
Schuck, N., & Niv, Y. (2019). Sequential replay of nonspatial task states in the human hippocampus. [PDF]
Sharpe, M., Batchelor, H. M., Mueller, L., Chang, C. Y., Maes, E., Niv, Y., & Schoenbaum, G. (2019). Dopamine transients delivered in learning contexts do not act as model-free prediction errors. [PDF]
Zhou, J., Gardner, M. P. H., Stalnaker, T., Ramus, S., Wikenheiser, A., Niv, Y., & Schoenbaum, G. (2019). Rat orbitofrontal ensemble activity contains multiplexed but dissociable representations of value and task structure in an odor sequence task. [PDF]
2018
Hermsdorff, G. B., Pereira, T., & Niv, Y. (2018). Quantifying humans’ priors over graphical representations of tasks. Springer Proceedings in Complexity. [PDF]
Langdon, A., Sharpe, M., Schoenbaum, G., & Niv, Y. (2018). Model-based predictions for dopamine. Current Opinion in Neurobiology. [PDF]
Niv, Y. (2018). Deep down, you are a scientist. [PDF]
Rouhani, N., Norman, K., & Niv, Y. (2018). Dissociable effects of surprising rewards on learning and memory. Journal of Experimental Psychology: Learning Memory and Cognition. [PDF]
Schuck, N., Wilson, R., & Niv, Y. (2018). A state representation for reinforcement learning and decision-making in the orbitofrontal cortex. [PDF]
Sharpe, M., Chang, C. Y., Liu, M., Batchelor, H. M., Mueller, L., Jones, J., Niv, Y., & Schoenbaum, G. (2018). Dopamine transients are sufficient and necessary for acquisition of model-based associations. Nature Neuroscience. [PDF]
Sharpe, M., Stalnaker, T., Schuck, N., Killcross, S., Schoenbaum, G., & Niv, Y. (2018). An integrated model of action selection: Distinct modes of cortical control of striatal decision making. Annual Review of Psychology. [PDF]
2017
Auchter, A., Cormack, L., Niv, Y., Gonzalez-Lima, F., & Monfils, M.-H. (2017). Reconsolidation-extinction interactions in fear memory attenuation: The role of inter-trial interval variability. Frontiers in Behavioral Neuroscience. [PDF]
Cohen, J., Daw, N., Engelhardt, B., Hasson, U., Li, K., Niv, Y., Norman, K., Pillow, J., Ramadge, P., Turk-Browne, N., & Willke, T. (2017). Computational approaches to fmri analysis. Nature Neuroscience. [PDF]
DuBrow, S., Rouhani, N., Niv, Y., & Norman, K. (2017). Does mental context drift or shift? Current Opinion in Behavioral Sciences. [PDF]
Gershman, S., Monfils, M.-H., Norman, K., & Niv, Y. (2017). The computational nature of memory modification. eLife. [PDF]
Leong*, Y. C., Radulescu*, A., Daniel, R., DeWoskin, V., & Niv, Y. (2017). Dynamic interaction between reinforcement learning and attention in multidimensional environments. Neuron. [PDF]
Sharpe, M., Marchant, N., Whitaker, L., Richie, C., Zhang, Y., Campbell, E., Koivula, P., Necarsulmer, J., Mejias-Aponte, C., Morales, M., Pickel, J., Smith, J., Niv, Y., Shaham, Y., Harvey, B., & Schoenbaum, G. (2017). Lateral hypothalamic GABAergic neurons encode reward predictions that are relayed to the ventral tegmental area to regulate learning. Current Biology. [PDF]
2016
Arkadir, D., Radulescu, A., Raymond, D., Lubarr, N., Bressman, S., Mazzoni, P., & Niv, Y. (2016). DYT1 dystonia increases risk taking in humans. eLife. [PDF]
Cai, M. B., Schuck, N., Lee, Luxburg, Guyon, & Garnett. (2016). A Bayesian method for reducing bias in neural representational similarity analysis. Proceedings of Neural Information Processing Systems. [PDF]
Chan, S. C. Y., Niv*, Y., & Norman*, K. A. (2016). A probability distribution over latent causes, in the orbitofrontal cortex. Journal of Neuroscience. [PDF]
Eldar, E., Cohen, J., & Niv, Y. (2016). Amplified selectivity in cognitive processing implements the neural gain model of norepinephrine function. Behavioral and Brain Sciences. [PDF]
Eldar, E., Niv, Y., & Cohen, J. (2016). Do you see the forest or the tree? neural gain and breadth versus focus in perceptual processing. Psychological Science. [PDF]
Eldar*, E., Rutledge*, Dolan, & Niv, Y. (2016). Mood as representation of momentum. Trends in Cognitive Sciences. [PDF]
Kurth-Nelson, Z., O’Doherty, J. P., Barch, D. M., Denève, S., Durstewitz, D., Frank, M. J., Gordon, J. A., Mathew, S. J., Niv, Y., Ressler, K., & Tost, H. (2016). Computational approaches for studying mechanisms of psychiatric disorders. [PDF]
Niv, Y., & Langdon, A. (2016). Reinforcement learning with Marr. Current Opinion in Behavioral Sciences. [PDF]
Radulescu, A., Daniel, R., & Niv, Y. (2016). The effects of aging on the interaction between reinforcement learning and attention. Psychology and Aging. [PDF]
Schuck, N., Cai, M. B., Wilson, R., & Niv, Y. (2016). Human orbitofrontal cortex represents a cognitive map of state space. Neuron. [PDF]
Takahashi*, Y., Langdon*, A., Niv, Y., & Schoenbaum, G. (2016). Temporal specificity of reward prediction errors signaled by putative dopamine neurons in rat VTA depends on ventral striatum. Neuron. [PDF]
2015
Daniel, R., Schuck, N., & Niv, Y. (2015). How to divide and conquer the world, one step at a time. Proceedings of the National Academy of Sciences. [Link]
Dunsmoor, J., Niv, Y., Daw, N., & Phelps, E. (2015). Rethinking extinction. Neuron. [Link]
Eldar, E., & Niv, Y. (2015). Interaction between emotional state and learning underlies mood instability. Nature Communications. [Link]
Gershman, S., & Niv, Y. (2015). Novelty and inductive generalization in human reinforcement learning. Topics in Cognitive Science. [Link]
Gershman, S., Norman, K., & Niv, Y. (2015). Discovering latent causes in reinforcement learning. Current Opinion in Behavioral Sciences. [Link]
Niv, Y., Daniel, R., Geana, A., Gershman, S. J., Leong, Y. C., Radulescu, A., & Wilson, R. C. (2015). Reinforcement learning in multidimensional environments relies on attention mechanisms. Journal of Neuroscience. [Link]
Niv, Y., Langdon, A., & Radulescu, A. (2015). A free-choice premium in the basal ganglia. Trends in Cognitive Sciences. [Link]
Sharpe, M., Wikenheiser, A., Niv, Y., & Schoenbaum, G. (2015). The state of the orbitofrontal cortex. Neuron. [Link]
Wilson, R. C., & Niv, Y. (2015). Is model fitting necessary for model-based fMRI? PLoS Computational Biology. [Link]
2014
Geana, A., & Niv, Y. (2014). Causal model comparison shows that human representation learning is not Bayesian. Cold Spring Harbor Symposia on Quantitative Biology. [Link]
Gershman, S., Radulescu, A., Norman, K., & Niv, Y. (2014). Statistical computations underlying the dynamics of memory updating. PLoS Computational Biology. [Link]
Solway*, A., Diuk*, C., C ́ordova, N., Yee, D., Barto, A., Niv, Y., & Botvinick, M. (2014). Optimal behavioral hierarchy. PLoS Computational Biology. [Link]
Soto, F., Gershman, S., & Niv, Y. (2014). Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization. Psychological Review. [Link]
Wilson, R., Takahashi, Y., Schoenbaum, G., Niv, Y., Lee, Luxburg, Guyon, & Garnett. (2014). Orbitofrontal cortex as a cognitive map of task space. Neuron. [Link]
Diuk, C., Schapiro, A., Córdova, N., Ribas-Fernandes, J., Niv, Y., & Botvinick, M. (2013). Divide and conquer: Hierarchical reinforcement learning and task decomposition in humans. Computational and robotic models of the hierarchical organization of behavior. [PDF]
Diuk, C., Tsai, K., Wallis, Botvinick, M., & Niv, Y. (2013). Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia. Journal of Neuroscience. [Link]
Eldar, E., Cohen, J., & Niv, Y. (2013). The effects of neural gain on attention and learning. Nature Neuroscience. [Link]
Gershman, S., Jones, C., Norman, K., Monfils, M.-H., & Niv, Y. (2013). Gradual extinction prevents the return of fear: Implications for the discovery of state. Frontiers in Behavioral Neuroscience. [Link]
Gershman, S., & Niv, Y. (2013). Perceptual estimation obeys Occam’s razor. Frontiers in Psychology. [Link]
2013
Niv, Y. (2013). Neuroscience: Dopamine ramps up. Nature. [Link]
Schoenbaum, G., Stalnaker, T., & Niv, Y. (2013). How did the chicken cross the road? with her striatal cholinergic interneurons, of course. Neuron. [Link]
2012
Gershman, S., & Niv, Y. (2012). Exploring a latent cause theory of classical conditioning. Learning & Behavior. [Link]
Lucantonio, F., Stalnaker, T., Shaham, Y., Niv, Y., & Schoenbaum, G. (2012). The impact of orbitofrontal dysfunction on cocaine addiction. Nature Neuroscience. [Link]
Niv, Y., Edlund, J., Dayan, P., & O’Doherty, J. (2012). Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain. Journal of Neuroscience. [Link]
Wilson, R., & Niv, Y. (2012). Inferring relevance in a changing world. Frontiers in Human Neuroscience. [Link]
2011
Eldar, E., Morris, G., & Niv, Y. (2011). The effects of motivation on response rate: A hidden semi-markov model analysis of behavioral dynamics. Journal of Neuroscience Methods. [Link]
McDannald, M., Lucantonio, F., Burke, K., Niv, Y., & Schoenbaum, G. (2011). Ventral striatum and orbitofrontal cortex are both required for model-based, but not model-free, reinforcement learning. Journal of Neuroscience. [Link]
Niv, Y., & Chan, S. (2011). On the value of information and other rewards. Nature Neuroscience. [Link]
Ribas-Fernandes, J., Solway, A., Diuk, C., McGuire, J., Barto, A., Niv, Y., & Botvinick, M. (2011). A neural signature of hierarchical reinforcement learning. Neuron. [Link]
Takahashi, Y., Roesch, M., Wilson, R., Toreson, K., O’Donnell, P., Niv, Y., & Schoenbaum, G. (2011). Expectancy-related changes in firing of dopamine neurons depend on orbitofrontal cortex. Nature Neuroscience. [Link]
2010
Dayan, P., Daw, N. D., & Niv, Y. (2010). Learning, action, inference and neuromodulation. In: Encyclopedia of Neuroscience. [Link]
Gershman, S., Blei, D., & Niv, Y. (2010). Context, learning, and extinction. Psychological Review. [Link]
Gershman, S., & Niv, Y. (2010). Learning latent structure: Carving nature at its joints. Current Opinion in Neurobiology. [Link]
2009
Botvinick, M., Niv, Y., & Barto, A. (2009). Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective. Cognition. [Link]
Niv, Y. (2009). Reinforcement learning in the brain. Journal of Mathematical Psychology. [PDF]
Niv, Y., & Montague, P. R. (2009). Theoretical and empirical studies of learning. In: Neuroeconomics. Academic Press. [Link]
Todd, M., Niv, Y., & Cohen, J. C. (2009). Learning to use working memory in partially observable environments through dopaminergic reinforcement. Advances in Neural Information Processing Systems. [PDF]
2008
Dayan, P., & Niv, Y. (2008). Reinforcement learning: The good, the bad and the ugly. Current Opinion in Neurobiology. [Link]
Niv, Y., & Schoenbaum, G. (2008). Dialogues on prediction errors. Trends in Cognitive Sciences. [Link]
Schiller, D., Levy, I., Niv, Y., LeDoux, J., & Phelps, E. (2008). From fear to safety and back: Reversal of fear in the human brain. Journal of Neuroscience. [Link]
Takahashi, Y. (2008). Silencing the critics: Understanding the effects of cocaine sensitization on dorsolateral and ventral striatum in the context of an actor/critic model. Frontiers in Neuroscience. [Link]
2007
Niv, Y. (2007a). Cost, benefit, tonic, phasic: What do response rates tell us about dopamine and motivation? Annals of the New York Academy of Sciences. [Link]
Niv, Y. (2007b). The effects of motivation on habitual instrumental behavior [Doctoral dissertation]. The Hebrew University of Jerusalem. [PDF]
Niv, Y., Daw, N., Joel, D., & Dayan, P. (2007). Tonic dopamine: Opportunity costs and the control of response vigor. Psychopharmacology. [Link]
Niv, Y., & Rivlin-Etzion, M. (2007). Parkinson’s disease: Fighting the will? Journal of Neuroscience. [Link]
2006
Daw, N. D., Niv, Y., & Dayan, P. (2006). Actions, policies, values, and the basal ganglia. In: Recent breakthroughs in basal ganglia research. [PDF]
Dayan, P., Niv, Y., Seymour, B., & Daw, N. (2006). The misbehavior of value and the discipline of the will. Neural Networks. [Link]
Niv, Y., Daw, N., & Dayan, P. (2006). Choice values. Nature Neuroscience. [Link]
Niv, Y., Joel, D., & Dayan, P. (2006). A normative perspective on motivation. Trends in Cognitive Science. [Link]
2005
Daw, N., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience. [Link]
Niv, Y., Daw, N. & Dayan, P. (2005). How fast to work: Response vigor, motivation and tonic dopamine. Proceedings of Neural Information Processing Systems. [PDF]
Niv, Y., Duff, M., & Dayan, P. (2005). Dopamine, uncertainty and TD learning. Behavioral and Brain Functions. [Link]
2002
Joel, D., Niv, Y., & Ruppin, E. (2002). Actor-critic models of the basal ganglia: New anatomical and computational perspectives. Neural Networks. [Link]
Niv, Y., Joel, D., Meilijson, I., & Ruppin, E. (2002a). Evolution of reinforcement learning in foraging bees: A simple explanation for risk averse behavior. Neurocomputing. [Link]
Niv, Y., Joel, D., Meilijson, I., & Ruppin, E. (2002b). Evolution of reinforcement learning in uncertain environments: A simple explanation for complex foraging behaviors. Adaptive Behavior. [PDF]
2001
Niv, Y. (2001). Evolution of reinforcement learning in uncertain environments: Emergence of risk-aversion and matching [Masters dissertation]. Tel-Aviv University. [PDF]