Preprints

• A Radulescu*, B van Opheusden*, F Callaway, TL Griffiths, JM Hillis. (2022). Modeling human eye movements during immersive visual search. bioArXiv. [pdf]
• D Bennett*, A Radulescu*, S Zorowitz, V Felso, Y Niv. (2021). Affect-congruent attention drives changes in reward expectations. PsyArXiv. [pdf]
• A Radulescu, K Holmes, Y Niv. On the convergent validity of risk sensitivity measures. (2020). PsyArXiv. [pdf] [data]

Journal articles

• A Radulescu, Y Shin, Y Niv. Human representation learning. (2021). Annual Review of Neuroscience, 44, in press.
• R Daniel, A Radulescu, Y Niv. Multidimensional probabilistic learning reveals impaired attentional control during reinforcement learning in older adults. (2020). Journal of Neuroscience, 40(5), 1084-1096. [pdf]
• A Radulescu, Y Niv. State representation in mental illness. (2019). Current Opinion in Neurobiology, 55, 160-166. [pdf]
• A Radulescu, Y Niv, IC Ballard. Holistic reinforcement learning: the role of structure and attention. (2019). Trends in Cognitive Sciences. [pdf]
• Y Leong*, A Radulescu*, R Daniel, V deWoskin, Y Niv. Dynamic interaction between reinforcement learning and attention in multidimensional environments. (2017). Neuron, 93(2), 451-463. [pdf]
• A Radulescu, R Daniel, Y Niv. The effects of aging on the interaction between reinforcement learning and attention. (2016) Psychology and Aging, 31(7), 747. [pdf]
• D Arkadir, A Radulescu, D Raymond, N Lubarr, SB Bressman, P Mazzoni, Y Niv. (2016). DYT1 dystonia increases risk taking in humans. eLife, 5, e14155. [pdf]
• Y Niv, R Daniel, A Geana, SJ Gershman, Y Leong, A Radulescu, RC Wilson. (2015). Reinforcement learning in multidimensional environments relies on attention mechanisms. Journal of Neuroscience, 35, 8145-8157. [pdf]
• SJ Gershman, A Radulescu, KA Norman, Y Niv. (2014). Statistical computations underlying the dynamics of memory updating. PLoS Computational Biology, 10, e1003939. [pdf]

Conference proceedings (peer-reviewed)

• A Radulescu, WK Vong, TM Gureckis. (2022). Name that state: How language affects human reinforcement learning. Proceedings of the Annual Meeting of the Cognitive Science Society 44. [pdf]
• A Radulescu*, B van Opheusden*, F Callaway, TL Griffiths, JM Hillis. (2020). From heuristic to optimal models in naturalistic visual search. Bridging AI and Cognitive Science workshop @ ICLR. [paper selected for a talk, 4/63 acceptance rate] [pdf]
• A Radulescu, Y Niv, ND Daw. (2019). A particle filtering account of selective attention during learning. Computational Cognitive Neuroscience (CCN). [pdf]
• G Davidson*, A Radulescu*, Y Niv. (2019). Contrasting the effects of prospective attention and retrospective decay in representation learning. Reinforcement Learning and Decision Making (RLDM). [pdf]
• A Radulescu, YC Leong, Y Niv. (2017). Reward sensitive attention dynamics during human reinforcement learning. Reinforcement Learning and Decision Making (RLDM). [pdf]
• P Hitchcock, A Radulescu, Y Niv, C Sims. (2017). Building on solid ground: establishing the stability of computational modeling parameters. In Hitchcock, P. (Chair), Introducing Computational Clinical Science: New Techniques to Improve Methods, Theory, Diagnosis, and Prediction. Symposium to be presented at 51st Annual Convention for the Association for Behavioral and Cognitive Therapies , San Diego, California.
• A Radulescu, R Daniel, Y Niv. (2013). Age related differenced in learning to selectively attend. Reinforcement Learning and Decision Making (RLDM). [pdf]

Commentaries

• Y Niv, A Langdon, A Radulescu. (2014). A free-choice premium in the basal ganglia. Trends in Cognitive Sciences, 19(1), 4-5. [pdf].

PhD thesis

• A Radulescu. (2020). Computational Mechanisms of Selective Attention during Reinforcement Learning. [pdf].

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