Publications of SL Master

Journal Article (2)

1.
Journal Article
Sargent, K.; Chavez-Baldini, U.; Master, S.; Verweij, K.; Lok, A.; Sutterland, A.; Vulink, N.; Denys, D.; Smit, D.; Nieman, D.: Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach. NeuroImage: Clinical 30, 102617, pp. 1 - 9 (2021)
2.
Journal Article
Master, S.; Eckstein, M.; Gotlieb, N.; Dahl, R.; Wilbrecht, L.; Collins, A.: Disentangling the systems contributing to changes in learning during adolescence. Developmental Cognitive Neuroscience 41, 100732, pp. 1 - 13 (2020)

Conference Paper (2)

3.
Conference Paper
Xia, L.; Master, S.; Eckstein, M.; Wilbrecht, L.; Collins, A.: Learning under uncertainty changes during adolescence. In: 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020): 5Developing a Mind: Learning in Humans, Animals, and Machines, pp. 716 - 722 (Eds. Denison, S.; Mack, M.; Xu, Y.; Armstrong, B.). 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020), Toronto, Canada, July 29, 2020 - August 01, 2020. Curran, Red Hook, NY, USA (2020)
4.
Conference Paper
Eckstein, M.; Master, S.; Dahl, R.; Wilbrecht, L.; Collins, A.: Modeling the development of decision making in volatile environments using strategies, reinforcement learning, and Bayesian inference. In: Conference on Cognitive Computational Neuroscience (CCN 2019), PS-1A.68, pp. 48 - 51. Conference on Cognitive Computational Neuroscience (CCN 2019), Berlin, Germany, September 13, 2019 - September 16, 2019. (2019)

Working Paper (2)

5.
Working Paper
Eckstein, M.; Master, S.; Dahl, R.; Wilbrecht, L.; Collins, A.: The Unique Advantage of Adolescents in Probabilistic Reversal: Reinforcement Learning and Bayesian Inference Provide Adequate and Complementary Models. (submitted)
6.
Working Paper
Eckstein, M.; Master, S.; Dahl, R.; Wilbrecht, L.; Collins, A.: Understanding the Unique Advantage of Adolescents in Stochastic, Volatile Environments: Combining Reinforcement Learning and Bayesian Inference. (submitted)
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