Preprints​

 

Ellis, C.T., Baldassano, C., Schapiro, A.C., Cai, M., & Cohen, J.D. (submitted). Facilitating open-science with realistic fMRI simulation: validation and application. [bioRxiv]

Denis, D., Schapiro, A.C., Poskanzer, C., Bursal, V., Charron, L., Morgan, A., & Stickgold, R. (submitted). Memories are selected for consolidation during sleep based on initial encoding strength: The roles of item exposure and visualization. [PsyArXiv]

 

2019

 

Richards, B.A., et al. (2019). A deep learning framework for neuroscience. Nature Neuroscience.

Antony, J.W., & Schapiro, A.C. (2019). Active and effective replay: Systems consolidation reconsidered again. Nature Reviews Neuroscience. [PDF]

Schapiro, A.C., Reid, A.G., Morgan, A., Manoach, D.S., Verfaellie, M., & Stickgold, R. (2019). The hippocampus is necessary for the consolidation of a task that does not require the hippocampus for initial learning. Hippocampus. [PDF]

2018

 

Cox, R., Van Bronkhorst, M.L.V., Bayda, M., Gomillion, H., Cho, E., Parr, E., Manickas-Hill, O.P., Schapiro, A.C., & Stickgold, R. (2018). Sleep selectively stabilizes contextual aspects of negative memories. Scientific Reports. [PDF]

Schapiro, A.C., McDevitt, E.A., Rogers, T.T., Mednick, S.C., & Norman, K.A. (2018). Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance. Nature Communications. [PDF] [Data on OpenNeuro]

Cox, R., Schapiro, A.C., & Stickgold, R. (2018). Variability and stability of large-scale cortical oscillation patterns. Network Neuroscience. [PDF]

2017

 

Honey, C.J., Newman, E.L., & Schapiro, A.C. (2017). Switching between internal and external modes: a multi-scale learning principle. Network Neuroscience. [PDF]

Schapiro, A.C.*, McDevitt, E.A.*, Chen, L., Norman, K.A., Mednick, S.C., & Rogers, T.T. (2017). Sleep benefits memory for semantic category structure while preserving exemplar-specific information. Scientific Reports. [PDF]

Cox, R., Schapiro, A.C., Manoach, D.S., & Stickgold, R. (2017). Individual differences in frequency and topography of slow and fast sleep spindles. Frontiers in Human Neuroscience. [PDF]

 

Schapiro, A.C., Turk-Browne, N.B., Botvinick, M.M., & Norman, K.A. (2017). Complementary learning systems within the hippocampus: A neural network modelling approach to reconciling episodic memory with statistical learning. Philosophical Transactions of the Royal Society B. [PDF] [Supplementary Material] [Model]

 

2016

 

Schapiro, A.C., Turk-Browne, N.B., Norman, K.A., & Botvinick, M.M. (2016). Statistical learning of temporal community structure in the hippocampus. Hippocampus. [PDF] [Supplementary Material]

 

Schlichting, M.L., Guarino, K.F., Schapiro, A.C., Turk-Browne, N.B., & Preston, A.R. (2016). Hippocampal structure predicts statistical learning and associative inference abilities during development. Journal of Cognitive Neuroscience.

 

2015

 

Schapiro, A.C., & Turk-Browne, N.B. (2015). Statistical Learning. In: Arthur W. Toga, editor. Brain Mapping: An Encyclopedic Reference. Academic Press: Elsevier; pp. 501-506. [PDF]

 

2014

 

Schapiro, A.C., Gregory, E., Landau, B., McCloskey, M., Turk-Browne, N.B. (2014). The necessity of the medial temporal lobe for statistical learning. Journal of Cognitive Neuroscience. [PDF]

 

2013

 

Schapiro, A.C., Rogers, T.T., Cordova, N.I., Turk-Browne, N.B., & Botvinick, M.M. (2013). Neural representations of events arise from temporal community structure. Nature Neuroscience. [PDF] [Supplementary Material] [Data on OpenNeuro]

 

Schapiro, A.C., McClelland, J.L., Welbourne, S.R., Rogers, T.T., & Lambon Ralph, M.A. (2013). Why bilateral damage is worse than unilateral damage to the brain. Journal of Cognitive Neuroscience. [PDF]

 

Gershman, S.J., Schapiro, A.C., Hupbach, A., Norman, K.A. (2013). Neural context reinstatement predicts memory misattribution. Journal of Neuroscience. [PDF]

Diuk, C., Schapiro, A.C., Cordova, N.I., Ribas-Fernandes, J., Niv, Y., & Botvinick, M.M. (2013). Divide and conquer: Task decompositions and hierarchical reinforcement learning in humans. In Computational and Robotic Models of the Hierarchical Organization of Behavior (pp. 271-291). Springer Berlin Heidelberg. [PDF]

2012 & earlier

 

Schapiro, A.C., Kustner, L.V., & Turk-Browne, N.B. (2012). Shaping of object representations in the human medial temporal lobe based on temporal regularities. Current Biology. [PDF] [Supplementary Material]

 

Schapiro, A.C. & McClelland, J.L. (2009). A connectionist model of a continuous developmental transition in the balance scale task. Cognition. [PDF]

 

Thomas, M.S.C., McClelland, J.L., Richardson, F M., Schapiro, A.C., & Baughman, F. (2009). Dynamical and Connectionist Approaches to Development: Toward a Future of Mutually Beneficial Coevolution. In J.P. Spencer, M. S. C. Thomas, & J. L. McClelland, (Eds). Toward a unified theory of development: Connectionism and dynamic systems theory re-considered. New York: Oxford. [DOI]