2023
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
2022
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
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, 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
2020
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
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, 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
2018
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
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, 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
2017
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
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, 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
2014
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
2013
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
2012
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
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, 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
2010
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
2009
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
2008
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
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, 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
2006
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
2005
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
2002
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
2001
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