Bennett, D., Radulescu, A., Zorowitz, S., Felso, V., & Niv, Y. (2023). Affect-congruent
attention modulates generalized reward expectations. PDF

Zorowitz, S., Niv, Y., & Bennett, D. (2023). Inattentive responding can induce spurious associations between task behavior and symptom measures. PDF

Zorowitz, S., & Niv, Y. (2023). Improving the reliability of cognitive task measures: A narrative review. PDF

Bedder, R., Pisupati, S., & Niv, Y. (2023). Modelling rumination as a state-inference process. Cognitive Science Conference Proceedings 2023. 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.

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 behaviour and symptom measures. PDF


Weber, I., Zorowitz, S., Niv, Y., & Bennett, D. (2022). The effects of induced positive and negative affect on pavlovian-instrumental interactions. Cognition and Emotion. PDF

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, 145, 80–89. 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, 18. 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


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, 41, 114–121. PDF

Chan, S. C., Schuck, N. W., Lopatina, N., Schoenbaum, G., & Niv, Y. (2021). Orbitofrontal cortex and learning predictions of state transitions. Behavioral Neuroscience. PDF

Eldar*, E., Felso*, V., Cohen, J., & Niv, Y. (2021). A pupillary index of susceptibility to decision biases. Nature Human Behavior. PDF

Hayden, B. Y., & Niv, Y. (2021). The case against economic values in the orbitofrontal cortex (or anywhere else in the brain). Behavioral Neuroscience, 135, 192–201. 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, Hitchcock, Berwian, I., & Schoen. (2021). Toward precision cognitive behavioral therapy via reinforcement learning theory (ch. 12). 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, 10. 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


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, 40(5), 1084–1096. PDF

Drummond, N., & Niv, Y. (2020). Model-based decision making and model-free learning. Current Biology, 30, 860–865. PDF

Langdon, A., & Daw, N. (2020). Beyond the average view of dopamine. Trends in Cognitive Sciences. PDF

Rouhani, N., Norman, K. A., Niv, Y., & Bornstein, A. M. (2020). Reward prediction errors create event boundaries in memory. Cognition. 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, 11, 106. PDF


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, 4, 112. 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. PLoS computational biology. PDF

Langdon, A., Song, M., & Niv, Y. (2019). Uncovering the ‘state’: Tracing the hidden state representations that structure learning and decision-making. Behavioural Processes, 167, 103891. 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, 236, 2543–2556. 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. Current Biology. PDF

Niv, Y. (2019). Learning task-state representations. Nature Neuroscience, 22, 1544–1553. 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, 236, 2425–2435. PDF

Schuck, N., & Niv, Y. (2019). Sequential replay of nonspatial task states in the human hippocampus. Science. 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. bioRxiv. 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. Current Biology, 29, 897–907.e3. PDF


Hermsdorff, G. B., Pereira, T., & Niv, Y. (2018). Quantifying humans’ priors over graphical representations of tasks. Springer Proceedings in Complexity, 281–290. PDF

Langdon, A., Sharpe, M., Schoenbaum, G., & Niv, Y. (2018). Model-based predictions for dopamine. Current Opinion in Neurobiology, 49, 1–7. 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, 44, 1430–1443. 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, 21, 1493. 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


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, 11. 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, 20, 304–313. PDF

DuBrow, S., Rouhani, N., Niv, Y., & Norman, K. (2017). Does mental context drift or shift? Current Opinion in Behavioral Sciences, 17, 141–146. PDF

Gershman, S., Monfils, M.-H., Norman, K., & Niv, Y. (2017). The computational nature of memory modification. eLife, 6. PDF

Leong*, Y. C., Radulescu*, A., Daniel, R., DeWoskin, V., & Niv, Y. (2017). Dynamic interaction between reinforcement learning and attention in multidimensional environments. Neuron, 93, 451–463. 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, 27, 2089––2100.e5. PDF


Arkadir, D., Radulescu, A., Raymond, D., Lubarr, N., Bressman, S., Mazzoni, P., & Niv, Y. (2016). Dyt1 dystonia increases risk taking in humans. eLife, 5. PDF

Cai, M. B., Schuck, N., Lee, Luxburg, Guyon, & Garnett. (2016). A bayesian method for reducing bias in neural representational similarity analysis. Curran Associates, Inc. PDF

Chan, Niv*, Y., & Norman*, K. (2016). A probability distribution over latent causes, in the orbitofrontal cortex. Journal of Neuroscience, 36, 7817–7828. PDF

Eldar, E., Cohen, J., & Niv, Y. (2016). Amplified selectivity in cognitive processing implements the neural gain model of norepinephrine function. The Behavioral and brain sciences, 39, e206. 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, 27, 1632–1643. PDF

Eldar*, E., Rutledge*, Dolan, & Niv, Y. (2016). Mood as representation of momentum. Trends in Cognitive Sciences, 20, 15–24. PDF

Kurth-Nelson, Z., O’Doherty, J. P., Barch, D. M., Den`eve, S., Durstewitz, D., Frank, M. J., Gordon, J. A., Mathew, S. J., Niv, Y., Ressler, K., & Tost, H. (2016, November). Computational approaches for studying mechanisms of psychiatric disorders. The MIT Press. PDF

Niv, Y., & Langdon, A. (2016). Reinforcement learning with marr. Current Opinion in Behavioral Sciences, 11, 67–73. PDF

Radulescu, A., Daniel, R., & Niv, Y. (2016). The effects of aging on the interaction between reinforcement learning and attention. Psychology and Aging, 31, 747–757. PDF

Schuck, N., Cai, M. B., Wilson, R., & Niv, Y. (2016). Human orbitofrontal cortex represents a cognitive map of state space. Neuron, 91, 1402–1412. 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, 91, 182–193. PDF


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, 112, 2929–2930. PDF

Dunsmoor, J., Niv, Y., Daw, N., & Phelps, E. (2015). Rethinking extinction. Neuron, 88, 47–63. PDF

Eldar, E., & Niv, Y. (2015). Interaction between emotional state and learning underlies mood instability. Nature Communications, 6, 6149. PDF

Gershman, S., & Niv, Y. (2015). Novelty and inductive generalization in human reinforcement learning. Topics in Cognitive Science, 7, 391–415. PDF

Gershman, S., Norman, K., & Niv, Y. (2015). Discovering latent causes in reinforcement learning. Current Opinion in Behavioral Sciences, 5, 43–50. PDF

Niv, Y., Daniel, R., Geana, A., Gershman, S., Leong, Y. C., Radulescu, A., & Wilson, R. (2015). Reinforcement learning in multidimensional environments relies on attention mechanisms. Journal of Neuroscience, 35, 8145–8157. PDF

Niv, Y., Langdon, A., & Radulescu, A. (2015). A free-choice premium in the basal ganglia. Trends in Cognitive Sciences, 19, 4–5. PDF

Sharpe, M., Wikenheiser, A., Niv, Y., & Schoenbaum, G. (2015). The state of the orbitofrontal cortex. Neuron, 88, 1075–1077. PDF

Wilson, R., & Niv, Y. (2015). Is model fitting necessary for model-based fmri? PLoS Comput Biol, 11, e1004237. PDF


Geana, A., & Niv, Y. (2014). Causal model comparison shows that human representation learning is not bayesian. Cold Spring Harbor Symposia on Quantitative Biology, 79, 161–168. PDF

Gershman, S., Radulescu, A., Norman, K., & Niv, Y. (2014). Statistical computations underlying the dynamics of memory updating. PLoS Computational Biology, 10, e1003939. PDF

Solway*, A., Diuk*, C., C ́ordova, N., Yee, D., Barto, A., Niv, Y., & Botvinick, M. (2014). Optimal behavioral hierarchy. PLoS Computational Biology, 10, e1003779. PDF

Soto, F., Gershman, S., & Niv, Y. (2014). Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization. Psychological Review, 121, 526–558. PDF

Wilson, R., Takahashi, Y., Schoenbaum, G., Niv, Y., Lee, Luxburg, Guyon, & Garnett. (2014). Orbitofrontal cortex as a cognitive map of task space. Neuron, 81, 267–279. PDF

Diuk, C., Schapiro, A., C ́ordova, N., Ribas-Fernandes, J., Niv, Y., & Botvinick, M. (2013). Divide and conquer: Hierarchical reinforcement learning and task decomposition in humans (Vol. 9783642398). 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, 33, 5797–5805. PDF

Eldar, E., Cohen, J., & Niv, Y. (2013). The effects of neural gain on attention and learning. Nature Neuroscience, 16, 1146–1153. PDF

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. Front Behav Neurosci, 7, 164. PDF

Gershman, S., & Niv, Y. (2013). Perceptual estimation obeys occam’s razor. Frontiers in Psychology, 4, 623. PDF


Niv, Y. (2013). Neuroscience: Dopamine ramps up. Nature, 500, 533–535. PDF

Schoenbaum, G., Stalnaker, T., & Niv, Y. (2013). How did the chicken cross the road? with her striatal cholinergic interneurons, of course. Neuron, 79, 3–6. PDF


Gershman, S., & Niv, Y. (2012). Exploring a latent cause theory of classical conditioning. Learn Behav, 40, 255–268. PDF

Lucantonio, F., Stalnaker, T., Shaham, Y., Niv, Y., & Schoenbaum, G. (2012). The impact of orbitofrontal dysfunction on cocaine addiction. Nature Neuroscience, 15, 358–366. PDF

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, 32, 551–562. PDF

Wilson, R., & Niv, Y. (2012). Inferring relevance in a changing world. Frontiers in Human Neuroscience, 5, 189. PDF


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, 201, 251–261. PDF

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, 31, 2700–2705. PDF

Niv, Y., & Chan, S. (2011). On the value of information and other rewards. Nature Neuroscience, 14, 1095–1097. PDF

Ribas-Fernandes, J., Solway, A., Diuk, C., McGuire, J., Barto, A., Niv, Y., & Botvinick, M. (2011). A neural signature of hierarchical reinforcement learning. Neuron, 71, 370–379. PDF

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, 14, 1590–1597. PDF


Dayan, P., Daw, N., & Niv, Y. (2010). Learning, action, inference and neuromodulation. PDF

Diuk, C., Botvinick, M., Barto, A., & Niv, Y. (2010). Hierarchical reinforcement learning: An fmri study of learning in a two-level gambling task. Neuroscience Meeting Planner.
Gershman, S., Blei, D., & Niv, Y. (2010). Context, learning, and extinction. Psychological Review, 117, 197–209. PDF

Gershman, S., Cohen, J., & Niv, Y. (2010). Learning to selectively attend. 32nd Annual Conference of the Cognitive Science Society. PDF

Gershman, S., & Niv, Y. (2010). Learning latent structure: Carving nature at its joints. Curr Opin Neurobiol, 20, 251–256. PDF

Niv, Y., & Gershman, S. (2010). Representation learning and reinforcement learning : An fmri study of learning to selectively attend. Society for Neuroscience Abstracts.
Todd, M., Cohen, J., & Niv, Y. (2010). Identifying internal representations of context in fmri. Society for Neuroscience Abstracts.
Wilson, R., Takahashi, Y., Roesch, M., Stalnaker, T., Schoenbaum, G., & Niv, Y. (2010). A computational model of the role of orbitofrontal cortex and ventral striatum in signalling reward expectancy in reinforcement learning. Society for Neuroscience Abstracts. PDF


Botvinick, M., Niv, Y., & Barto, A. (2009). Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective. Cognition, 113, 262–280. PDF

Niv, Y. (2009). Reinforcement learning in the brain. Journal of Mathematical Psychology, 53, 139–154. PDF

Niv, Y., & Montague, R. (2009). Theoretical and empirical studies of learning. Neuroeconomics, 331–351. PDF

Todd, M., Niv, Y., Cohen, J., Koller, Schuurmans, Bengio, & Bottou. (2009). Learning to use working memory in partially observable environments through dopaminergic reinforcement. Advances in Neural Information Processing Systems 21, 1689–1696. PDF


Dayan, P., & Niv, Y. (2008). Reinforcement learning: The good, the bad and the ugly. Current Opinion in Neurobiology, 18, 185–196. PDF

Niv, Y., & Schoenbaum, G. (2008). Dialogues on prediction errors. Trends in Cognitive Sciences, 12, 265–272. PDF

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, 28, 11517–11525. PDF

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, 2, 86–99. PDF


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, 1104, 357–376. PDF

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, 191, 507–520. PDF

Niv, Y., & Rivlin-Etzion, M. (2007). Parkinson’s disease: Fighting the will? Journal of Neuroscience, 27, 11777–11779. PDF


Daw, N., Niv, Y., Dayan, P., & Bezard. (2006). Actions, policies, values, and the basal ganglia. Nova Science Publishers Inc. PDF

Dayan, P., Niv, Y., Seymour, B., & Daw, N. (2006). The misbehavior of value and the discipline of the will. Neural Networks, 19, 1153–1160. PDF

Niv, Y., Daw, N., & Dayan, P. (2006). Choice values. Nature Neuroscience, 9, 987–988. PDF

Niv, Y., Edlund, J., Dayan, P., & O’Doherty, J. (2006). Neural correlates of risk-sensitivity: An fmri study of instrumental choice behavior. Society for Neuroscience Abstracts.
Niv, Y., Joel, D., & Dayan, P. (2006). A normative perspective on motivation. Trends in Cognitive Science, 10, 375–381. PDF


Daw, N., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience, 8, 1704–1711. PDF

Niv, Y., Daw, N., Dayan, P., Weiss, Sch ̈olkopf, & Platt. (2005). How fast to work: Response vigor, motivation and tonic dopamine. Neural Information Processing Systems, 18, 1019–1026. PDF

Niv, Y., Daw, N., Joel, D., & Dayan, P. (2005). Motivational effects on behavior: Towards a reinforcement learning model of rates of responding. CoSyNe. PDF

Niv, Y., Duff, M., & Dayan, P. (2005). Dopamine, uncertainty and td learning. Behavioral and Brain Functions, 1, 6. PDF


Joel, D., Niv, Y., & Ruppin, E. (2002). Actor-critic models of the basal ganglia: New anatomical and computational perspectives. Neural Networks, 15, 535–547. PDF

Niv, Y., Joel, D., Meilijson, I., & Ruppin, E. (2002a). Evolution of reinforcement learning in foraging bees: A simple explanation for risk averse behavior. Neurocomputing, 44-46, 951–956. PDF

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, 10, 5–24. PDF


Niv, Y., Joel, D., Meilijson, I., & Ruppin, E. (2001). Evolution of reinforcement learning in uncertain environments : Emergence of risk-aversion and matching [Doctoral dissertation]. Tel-Aviv University. PDF