​Preprints​

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Solomon, S.H., Kay, K., & Schapiro, A.C. Recent statistics shift object representations in parahippocampal cortex. [bioRxiv]

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Smith, C.M., Thompson-Schill, S.L., & Schapiro, A.C. Rapid learning of temporal dependencies at multiple timescales. [bioRxiv]

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Tandoc, M.C., Dong, C.V., & Schapiro, A.C. Object feature memory is distorted by category structure. [PsyArXiv]

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Zhou, Z., Kahana, M., J., Schapiro, A.C. Replay as context-driven memory reactivation. [bioRxiv]

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2024

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Siefert, E.M., Uppuluri, S., Mu. J., Tandoc, M.C., Antony, J.W., & Schapiro, A.C. (2024). Memory reactivation during sleep does not act holistically on object memory. Journal of Neuroscience. [DOI]

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Solomon, S.H. & Schapiro, A.C. (2024). Structure shapes the representation of a novel category. Journal of Experimental Psychology: Learning, Memory, and Cognition. [DOI] 

APA Editor’s Choice article

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Mylonas, D., Schapiro, A.C., Verfaellie, M., Baxter, B., Vangel, M., Stickgold, R., & Manoach, D.S. (2024). Maintenance of procedural motor memory across brief rest periods requires the hippocampus. Journal of Neuroscience. [PDF]

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2023

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Sucevic, J. & Schapiro, A.C. (2023). A neural network model of hippocampal contributions to category learning. eLife. [DOI]

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Zhou, Z., Yeung, G., & Schapiro, A.C. (2023). Self-recovery of memory via generative replay. NeurIPS Workshop MemARI. [arXiv]

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Zhou, Z., Singh, D., Tandoc, M.C., Schapiro, A.C. (2023). Building integrated representations through interleaved learning.  Journal of Experimental Psychology: General. [PDF]

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Larson, O., Schapiro, A.C., & Gehrman, P.R. (2023). Effect of sleep manipulations on intrusive memories after exposure to an experimental analogue trauma: A meta-analytic review. Sleep Medicine Reviews. [PDF]

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2022

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Singh, D., Norman, K.A., Schapiro, A.C. (2022). A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation. Proceedings of the National Academy of Sciences. [PDF]

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Plate, R. C., Schapiro, A., & Waller, R. (2022). Emotional faces facilitate statistical learning. Affective Science. [PDF]

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Pudhiyidath, A., Morton, N.W., Viveros Duran, R., Schapiro, A.C., Momennejad, I.,  Hinojosa-Rowland, D.M., Molitor, R.J., & Preston, A.R. (2022). Representations of temporal community structure in hippocampus and precuneus predict inductive reasoning decisions. Journal of Cognitive Neuroscience. [PDF]

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Vogelstein, et al. (2022). Prospective learning: Back to the future. [arXiv]

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2021

 

Tandoc, M.C., Bayda, M., Poskanzer, C., Cho, E., Cox, R., Stickgold, R., & Schapiro, A.C. (2021). Examining the effects of time of day and sleep on generalization. PLOS One. [DOI]

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Cowan E.T., Schapiro, A.C., Dunsmoor, J.E., & Murty, V.P. (2021). Consolidation as an adaptive process. Psychonomic Bulletin & Review.

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Zeng, T., Tompary, A., Schapiro, A. C., & Thompson-Schill, S. (2021). Tracking the relation between gist and item memory over the course of long term memory consolidation. eLife. [DOI]

 

Kumar, M., Michael Anderson, Antony, J., Baldassano, C., Brooks, P. P., Cai, M. B., … Norman, K. A. (2021). BrainIAK: The Brain Imaging Analysis Kit. Aperture. [PDF]

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2020

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Solomon, S.H., & Schapiro, A.C. (2020). Semantic search as pattern completion across a concept. Trends in Cognitive Sciences. [PDF]

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Zhou, Z., Singh, D., Tandoc, M. & Schapiro, A. C. (2020). Interleaving facilitates the rapid formation of distributed representations. Proceedings of the 42nd Annual Conference of the Cognitive Science Society.

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Denis, D., Schapiro, A.C., Poskanzer, C., Bursal, V., Charron, L., Morgan, A., & Stickgold, R. (2020). Memories are selected for consolidation during sleep based on initial encoding strength: The roles of item exposure and visualization. Learning and memory. [PDF]

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Karnauskas, K.B., Miller, S.L., & Schapiro, A.C. (2020). Fossil fuel combustion is driving indoor CO2 toward levels harmful to human cognition. GeoHealth. [DOI]

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Ellis, C.T. , Baldassano, C., Schapiro, A.C., Cai, M.B., & Cohen, J.D. (2020). Facilitating open-science with realistic fMRI simulation: validation and application. PeerJ. [PDF]

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2019

 

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

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

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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]

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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]

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

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2017

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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]

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

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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]

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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. [DOI]

 

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. [PDF]

 

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]

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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]

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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]

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