I am a post doc in Bob Stickgold's lab at Beth Israel Deaconess Medical Center / Harvard Medical School. I am interested in how we learn and represent information, especially structured information with temporal or semantic relationships that require integration over time. My research has explored the initial stages of acquiring such information as well as the offline consolidation processes that shape initial memory representations for longer term storage and use. I have found in fMRI experiments and in a patient case study that the hippocampus is involved in the rapid acquistion of new temporal information that requires integration across events. These findings are, on the surface, difficult to reconcile with the idea that the hippocampus specializes in memorizing the specifics of individual experiences, keeping related episodes separate to avoid interference. I show in recent work using a neural network model of the hippocampus, however, that these functions may coexist in separate anatomical pathways within the hippocampus. In my work on memory consolidation, I have found that sleep and offline replay benefit new semantic memories, with a particular focus on weaker memories -- those most in need of further processing. I am now developing a neural network model of learning and hippocampal-cortical interactions during sleep.
I was a grad student at Princeton with Ken Norman, Matt Botvinick, and Nick Turk-Browne, and worked with Jay McClelland as an undergrad at Stanford.
Schapiro, A.C., McDevitt, E.A., Rogers, T.T., Mednick, S.C., & Norman, K.A. (submitted). Human hippocampal replay during rest prioritizes weakly-learned information and predicts memory performance. [bioRxiv]
Cox, R., Schapiro, A.C., & Stickgold, R. (submitted). Variability and stability of large-scale cortical oscillation patterns. [bioRxiv]
Schapiro, A.C.*, McDevitt, E.A.*, Chen, L., Norman, K.A., Mednick, S.C., & Rogers, T.T. (in press). Sleep benefits memory for semantic category structure while preserving exemplar-specific information. Scientific Reports. [bioRxiv]
Honey, C.J., Newman, E.L., & Schapiro, A.C. (in press). Switching between internal and external modes: a multi-scale learning principle. Network Neuroscience. [DOI]
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]
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.
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]
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]
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]
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]
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] [DOI]
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]
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]