Elisabeth A. Karuza

Assistant Professor

Department of Psychology

Penn State University



Kahn, A.E., Karuza, E.A., Vettel, J.M., & Bassett, D.S. (2018). Network constraints on 

learnability of probabilistic motor sequences. Nature Human Behaviour, 2, 936–947. 


Sizemore A. E., Karuza, E. A., Giusti C., & Bassett, D. S. (2018). Knowledge gaps in the early 

growth of semantic networks. Nature Human Behaviour, 2, 682–692. 


Khambhati, A. N., Medaglia, J. D., Karuza, E. A., Thompson-Schill, S. L., & Bassett, D. S. 

(2018). Subgraphs of functional brain networks identify dynamical constraints of cognitive control. PLoS Computational Biology, 14, e1006234. https://doi.org/10.1371/journal.pcbi.1006234


Medaglia, J.D., Huang, W., Karuza, E.A., Kelkar, A., Thompson-Schill, S.L., Ribeiro, A., & 

Bassett, D.S. (2018). Functional alignment with anatomical networks is associated with cognitive flexibility. Nature Human Behaviour, 2, 156–164. 


Karuza, E.A., Emberson, L.L., Roser, M.E., Aslin, R.N., Cole, D., & Fiser, J. (2017). Neural 

signatures of spatial statistical learning: Characterizing the extraction of structure from complex visual scenes. Journal of Cognitive Neuroscience, 29, 1963–1976. 


Karuza, E.A., Kahn, A.E., Thompson-Schill, S.L., & Bassett, D.S. (2017). Process reveals 

structure: How a network is traversed mediates expectations about its architecture. Scientific Reports, 7, 12733. https://doi.org/10.1038/s41598-017-12876-5


Karuza, E.A., Balewski, Z.Z., Hamilton, R.H., Medaglia, J.D., Tardiff, N., & Thompson-Schill, 

S.L. (2016). Mapping the parameter space of tDCS and cognitive control via manipulation of current polarity and intensity. Frontiers in Human Neuroscience, 10, 665. https://doi.org/10.3389/fnhum.2016.00665.


Karuza, E. A., Li, P., Weiss, D. J., Bulgarelli, F., Zinszer, B. D., & Aslin, R. N. (2016). Sampling 

over nonuniform distributions: A neural efficiency account of the primacy effect in statistical learning. Journal of Cognitive Neuroscience, 28, 1484–1500. 


Karuza, E.A., Thompson-Schill, S.L., & Bassett, D.S. (2016). Local patterns to global 

architectures: Influences of network topology on human learning. Trends in Cognitive Sciences, 20, 629–640.


Karuza, E. A., Farmer, T. A., Fine, A. B., Smith, F. X., & Jaeger, T. F. (2014). On-line measures 

of prediction in a self-paced statistical learning task. Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 725–730.


Karuza, E.A.*, Emberson, L.L.*, & Aslin, R.N. (2014). Combining fMRI and behavioral 

measures to examine the process of human learning.  Neurobiology of Learning and Memory, 109, 193–206. 

*Co-first authors


Karuza, E.A., Newport, E.L., Aslin, R.N., Starling, S.J., Tivarus, M.E., & Bavelier, D. (2013). 

Neural correlates of statistical learning in a word segmentation ask: An fMRI study. Brain and Language, 127, 46–54. 

Check out my Google Scholar page for links to past and ongoing work (preprints)!



Humans are the ultimate pattern learners. We absorb a constant stream of complicated, noisy data and somehow emerge with a deep understanding of structures like language, categories, even what kinds of events are likely to follow one another in time. That “somehow” is the focus of my lab. Given known constraints on the human brain, how do learners extract the information they need from the environment, often without realizing they are doing it? To answer this question my lab takes a multi-pronged approach. We use a variety of behavioral methods to examine learners’ sensitivity to both the simple associations and network-level structures around them, with a particular focus on which patterns best facilitate learning.  We also study the neural mechanisms underlying pattern learning through brain imaging techniques such as fMRI. Finally, we investigate the conditions under which learning can be boosted or impeded, including asking whether brain stimulation might be a useful tool in this endeavor.