van Baar JM, Nassar M, Deng W, FeldmanHall O. Latent motives guide structure learning during adaptive social choice (2020) bioRxiv.
Nassar MR, Troiani V. The stability flexibility tradeoff and the dark side of detail (2020) bioRxiv.
Kao CH, Khambhati AN, Bassett DS, Nassar MR, McGuire JT, Gold JI, Kable JW. Functional brain network reconfiguration during learning in a dynamic environment (2020) Nature Communications.
He M, Heindel WC, Nassar MR, Seifert E, Festa EK. Age-related changes in the functional integrity of the phasic alerting system: A pupillometric investigation (2020) Neurobiology of Aging.
Nassar M, Bruckner R, Frank MJ. Statistical context dictates the relationship between feedback-related EEG signals and learning (2019) eLife.
Li Y, Nassar M, Kable J, Gold J. Individual neurons in the cingulate cortex encode action monitoring, not selection, during adaptive decision-making (2019) Journal of Neuroscience.
*Jang A, *Nassar M, Dillon D, Frank M. Positive reward prediction errors during decision making strengthen memory encoding. (2019) Nature Human Behaviour.
Nassar M, McGuire J, Ritz H, Kable J. Dissociable Forms of Uncertainty-Driven Representational Change Across the Human Brain (2019) Journal of Neuroscience.
Ritz H, Nassar M, Frank M, Shenhav A. A Control Theoretic Model of Adaptive Learning in Dynamic Environments (2018) Journal of Cognitive Neuroscience.
Nassar M, Helmers J, Frank M. Chunking as a rational strategy for lossy data compression in visual working memory. (2018) Psychological Review.
van den Bos W, Bruckner R, Lorenz R, Nassar M, Mata R, & Eppinger B. (2017) Computational models for Lifespan Cognitive Neuroscience: the missing link. Developmental Cognitive Neuroscience.
*Krishnamurthy K, *Nassar M, Sarode S, Gold J. (2017) Adaptive, arousal-related adjustments of perceptual biases optimize perception in a dynamic environment. Nature Human Behaviour
Jepma M, Murphy P, Nassar M, Brown S, van den Maagdenberg A, Koelewijn S, de Vries B, Rangel-Gomez M, Meter M, Nieuwenhuis S. (2016) Noradrenergic regulation of learning rate in a dynamic environment. PLOS Computational Biology.
Nassar M., Bruckner R., Eppinger B. (2016). What do we GANE with age: Implications of the GANE model for effective learning and decision-making across healthy aging. Behavioral Brain Sciences. [commentary on by Mather et al.]
Nassar M, Bruckner R, Gold J, Li SC, Hauke H, Eppinger B. (2016) Age differences in learning emerge from an insufficient representation of uncertainty in older adults. Nature Communications 10(7):11609
Nassar M, Frank M. (2016) Taming the beast: Extracting generalizable knowledge from computational models of cognition. Current Opinion in Behavioral Sciences 11, 49-54.
Liu D, Zhang Y, Gharavi R, Park H, Lee J, Siddiqui S, Tellejohan R, Nassar M, Cutler R, Jiang H, Becker K, Mattson M. (2015). The Mitochondrial Uncoupler DNP Triggers Brain Cell mTOR Signaling Network Reprogramming and CREB Pathway Upregulation. Journal of Neurochemistry. 134(4),677-92.
*McGuire J, *Nassar M, Gold J, Kable J. (2014) Functionally dissociable influences on learning rate in a dynamic environment. Neuron 84, 870-881
Wilson R, Nassar M, Gold J. (2013) A mixture of Delta-rules approximation to Bayesian inference in change-point problems. PLOS Computational Biology 9(7): e1003150.
Nassar M, Gold J. (2013) A healthy fear of the unknown: perspectives on the interpretation of parameter fits from computational models in neuroscience. PLOS Computational Biology 9(4): e1003015.
Nassar M, Rumsey K, Wilson R, Parikh K, Heasly B, Gold J. (2012) Rational regulation of learning dynamics by pupil-linked arousal systems. Nature Neuroscience 12:1040–1046.
Nassar M, Wilson R, Heasly B, Gold J. (2010) An approximately Bayesian delta-rule model explains the dynamics of belief updating in a changing environment. Journal of Neuroscience. 30(37): 12366-12378.
(* = equal contribution and listed alphabetically).