Neural representations of events arise from temporal community structure

The Structure and Function of Complex Networks

Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719

Community structure in social and biological networks

Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502

Modularity and community structure in networks

Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256

Community detection in graphs

Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212

A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.

Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282

Family resemblances: Studies in the internal structure of categories

Rosch, E., & Mervis, C. B. (1975) · Cognitive Psychology · doi:10.1016/0010-0285(75)90024-9 · Cited by 5,933

Statistical learning by 8-month-old infants

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996) · Science · doi:10.1126/science.274.5294.1926 · Cited by 5,659

Maps of random walks on complex networks reveal community structure

Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583

Context theory of classification learning

Medin, D. L., & Schaffer, M. M. (1978) · Psychological Review · doi:10.1037/0033-295x.85.3.207 · Cited by 3,061

Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

(2018) · doi:10.5194/hess-22-6005-2018 · Cited by 2,114

What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior

Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.002 · Cited by 1,055

Discovering Event Structure in Continuous Narrative Perception and Memory

Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.041 · Cited by 952

The hippocampus as a predictive map

Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017) · Nature Neuroscience · doi:10.1038/nn.4650 · Cited by 1,000

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning

Schapiro, A. C., Turk‐Browne, N. B., Botvinick, M., & Norman, K. A. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0049 · Cited by 530

The Neural Representation of Sequences: From Transition Probabilities to Algebraic Patterns and Linguistic Trees

Dehaene, S., Meyniel, F., Wacongne, C., Wang, L., & Pallier, C. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.019 · Cited by 478

Learning, reward, and decision making

O'Doherty, J. P., Cockburn, J., & Pauli, W. M. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-010416-044216 · Cited by 475

Cognitive Network Neuroscience

Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00810 · Cited by 419

A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network

Wang, S., & Chen, H. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.160 · Cited by 434

If deep learning is the answer, what is the question?

Saxe, A., Nelli, S., & Summerfield, C. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-00395-8 · Cited by 397

The successor representation in human reinforcement learning

Momennejad, I., Russek, E. M., Cheong, J. H., Botvinick, M., Daw, N. D., & Gershman, S. J. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0180-8 · Cited by 399

A map of abstract relational knowledge in the human hippocampal-entorhinal cortex

Garvert, M. M., Dolan, R. J., & Behrens, T. E. (2017) · eLife · doi:10.7554/elife.17086 · Cited by 379

Predictive representations can link model-based reinforcement learning to model-free mechanisms

Russek, E. M., Momennejad, I., Botvinick, M., Gershman, S. J., & Daw, N. D. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005768 · Cited by 334

The Necessity of the Medial Temporal Lobe for Statistical Learning

Schapiro, A. C., Gregory, E., Landau, B., McCloskey, M. E., & Turk‐Browne, N. B. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00578 · Cited by 328

The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers

Botvinick, M., & Cohen, J. D. (2014) · Cognitive Science · doi:10.1111/cogs.12126 · Cited by 354

Semantic memory: A review of methods, models, and current challenges

Kumar, A. A. (2020) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01792-x · Cited by 347

Statistical learning of temporal community structure in the hippocampus

Schapiro, A. C., Turk‐Browne, N. B., Norman, K. A., & Botvinick, M. (2015) · Hippocampus · doi:10.1002/hipo.22523 · Cited by 306

Evidence for holistic episodic recollection via hippocampal pattern completion

Horner, A. J., Bisby, J. A., Bush, D., Lin, W., & Burgess, N. (2015) · Nature Communications · doi:10.1038/ncomms8462 · Cited by 323

Structuring knowledge with cognitive maps and cognitive graphs

Peer, M., Brunec, I. K., Newcombe, N. S., & Epstein, R. A. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.10.004 · Cited by 283

Model-based choices involve prospective neural activity

Doll, B. B., Duncan, K. D., Simon, D. A., Shohamy, D., & Daw, N. D. (2015) · Nature Neuroscience · doi:10.1038/nn.3981 · Cited by 290

Deep reinforcement learning and its neuroscientific implications

Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269

Memory hierarchies map onto the hippocampal long axis in humans

Collin, S. H. P., Milivojevic, B., & Doeller, C. F. (2015) · Nature Neuroscience · doi:10.1038/nn.4138 · Cited by 256

Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease

Madani, A., Ong, J. R., Tibrewal, A., & Mofrad, M. R. K. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0065-x · Cited by 250

Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

Marouf, M., Machart, P., Bansal, V., Kilian, C., Magruder, D. S., Krebs, C. F., & Bonn, S. (2020) · Nature Communications · doi:10.1038/s41467-019-14018-z · Cited by 261

Deep learning for drug response prediction in cancer

Baptista, D., Ferreira, P. G., & Rocha, M. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz171 · Cited by 243

Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition

Rogers, T. T., & McClelland, J. L. (2014) · Cognitive Science · doi:10.1111/cogs.12148 · Cited by 243

Transcending time in the brain: How event memories are constructed from experience

Clewett, D., DuBrow, S., & Davachi, L. (2019) · Hippocampus · doi:10.1002/hipo.23074 · Cited by 254

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107307 · Cited by 215

Interplay of approximate planning strategies

Huys, Q. J. M., Lally, N., Faulkner, P., Eshel, N., Seifritz, E., Gershman, S. J., Dayan, P., & Roiser, J. P. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1414219112 · Cited by 207

Optimizing real time fMRI neurofeedback for therapeutic discovery and development

Stoeckel, L. E., Garrison, K. A., Ghosh, S., Wighton, P., Hanlon, C. A., Gilman, J. M., Greer, S. M., Turk‐Browne, N. B., deBettencourt, M. T., Scheinost, D., et al. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.07.002 · Cited by 209

Constructing Experience: Event Models from Perception to Action

Richmond, L. L., & Zacks, J. M. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.08.005 · Cited by 208

Structured Event Memory: A neuro-symbolic model of event cognition.

Franklin, N. T., Norman, K. A., Ranganath, C., Zacks, J. M., & Gershman, S. J. (2020) · Psychological Review · doi:10.1037/rev0000177 · Cited by 177

Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network

Balaguer, J., Spiers, H., Hassabis, D., & Summerfield, C. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.037 · Cited by 163

Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks

Huang, X., Sun, W., Tseng, T.-L., Li, C., & Qian, W. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.02.003 · Cited by 163

Learning hierarchical sequence representations across human cortex and hippocampus

Henin, S. E., Turk‐Browne, N. B., Friedman, D. J., Liu, A. A., Dugan, P. C., Flinker, A., Doyle, W., Devinsky, O., & Melloni, L. (2021) · Science Advances · doi:10.1126/sciadv.abc4530 · Cited by 167

Learning structures: Predictive representations, replay, and generalization

Momennejad, I. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.02.017 · Cited by 159

Time and space in the hippocampus

Howard, M. W., & Eichenbaum, H. (2014) · Brain Research · doi:10.1016/j.brainres.2014.10.069 · Cited by 165

Model-based hierarchical reinforcement learning and human action control

Botvinick, M., & Weinstein, A. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0480 · Cited by 166

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

Karuza, E. A., Thompson‐Schill, S. L., & Bassett, D. S. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.06.003 · Cited by 157

Predicting drug-target interaction network using deep learning model

You, J., McLeod, R. D., & Hu, P. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.03.016 · Cited by 150

Nonmonotonic Plasticity: How Memory Retrieval Drives Learning

Ritvo, V. J. H., Turk‐Browne, N. B., & Norman, K. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.06.007 · Cited by 154

Optimal Behavioral Hierarchy

Solway, A., Diuk, C., Córdova, N. I., Yee, D. M., Barto, A. G., Niv, Y., & Botvinick, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003779 · Cited by 154

OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks

Kassis, T., Hernandez‐Gordillo, V., Langer, R., & Griffith, L. G. (2019) · Scientific Reports · doi:10.1038/s41598-019-48874-y · Cited by 141

Integrating memories to guide decisions

Shohamy, D., & Daw, N. D. (2015) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2015.08.010 · Cited by 152

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

Cho, Y., Kwon, J., Kim, K., Medina‐Inojosa, J. R., Jeon, K., Cho, S., Lee, S. Y., Park, J., & Oh, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-77599-6 · Cited by 148

Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices

Lui, H. W., & Chow, K. L. (2018) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2018.08.002 · Cited by 144

Boundaries Shape Cognitive Representations of Spaces and Events

Brunec, I. K., Moscovitch, M., & Barense, M. D. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.03.013 · Cited by 155

Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification

Yang, Q., Ji, H., Lü, H., & Zhang, Z. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.0c04071 · Cited by 135

The Perception of Relations

Hafri, A., & Firestone, C. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.01.006 · Cited by 146

Hippocampal neurons represent events as transferable units of experience

Sun, C., Yang, W., Martin, J., & Tonegawa, S. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0614-x · Cited by 135

Taking stock of value in the orbitofrontal cortex

Knudsen, E. B., & Wallis, J. D. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00589-2 · Cited by 143

Dissociating memory traces and scenario construction in mental time travel

Cheng, S., Werning, M., & Suddendorf, T. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.11.011 · Cited by 141

Single-Neuron Mechanisms Underlying Cost-Benefit Analysis in Frontal Cortex

Hosokawa, T., Kennerley, S. W., Sloan, J., & Wallis, J. D. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2221-13.2013 · Cited by 128

Hippocampal Neurons Construct a Map of an Abstract Value Space

Knudsen, E. B., & Wallis, J. D. (2021) · Cell · doi:10.1016/j.cell.2021.07.010 · Cited by 129

Reinforcement-learning in fronto-striatal circuits

Averbeck, B. B., & O’Doherty, J. P. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01108-0 · Cited by 130

Insight Reconfigures Hippocampal-Prefrontal Memories

Milivojevic, B., Vicente-Grabovetsky, A., & Doeller, C. F. (2015) · Current Biology · doi:10.1016/j.cub.2015.01.033 · Cited by 137

Structuring Memory Through Inference‐Based Event Segmentation

Shin, Y. S., & DuBrow, S. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12505 · Cited by 136

Complementary task representations in hippocampus and prefrontal cortex for generalizing the structure of problems

Samborska, V., Butler, J. L., Walton, M. E., Behrens, T. E. J., & Akam, T. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01149-8 · Cited by 141

Predictive Representations in Hippocampal and Prefrontal Hierarchies

Brunec, I. K., & Momennejad, I. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1327-21.2021 · Cited by 124

Statistical Learning

Schapiro, A. C., & Turk‐Browne, N. B. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00276-1 · Cited by 124

Optimization of selective laser melting process parameters for Ti-6Al-4V alloy manufacturing using deep learning

Nguyen, D. S., Park, H., & Lee, C. M. (2020) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2020.04.014 · Cited by 114

Pattern Completion in Multielement Event Engrams

Horner, A. J., & Burgess, N. (2014) · Current Biology · doi:10.1016/j.cub.2014.03.012 · Cited by 118

The ebb and flow of experience determines the temporal structure of memory

Clewett, D., & Davachi, L. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.08.013 · Cited by 121

Integration and differentiation of hippocampal memory traces

Brunec, I. K., Robin, J., Olsen, R. K., Moscovitch, M., & Barense, M. D. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.07.024 · Cited by 122

The neural bases for timing of durations

Tsao, A., Yousefzadeh, S. A., Meck, W. H., Moser, M., & Moser, E. I. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00623-3 · Cited by 114

The Necessity of Rostrolateral Prefrontal Cortex for Higher-Level Sequential Behavior

Desrochers, T. M., Chatham, C. H., & Badre, D. (2015) · Neuron · doi:10.1016/j.neuron.2015.08.026 · Cited by 110

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

Bassett, D. S., & Mattar, M. G. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.01.010 · Cited by 111

A Roadmap for Understanding Memory: Decomposing Cognitive Processes into Operations and Representations

Cowell, R. A., Barense, M. D., & Sadil, P. S. (2019) · eNeuro · doi:10.1523/eneuro.0122-19.2019 · Cited by 121

From a deep learning model back to the brain—Identifying regional predictors and their relation to aging

Levakov, G., Rosenthal, G., Shelef, I., Raviv, T. R., & Avidan, G. (2020) · Human Brain Mapping · doi:10.1002/hbm.25011 · Cited by 101

Personal Networks: Classic Readings and New Directions in Egocentric Analysis

Pescosolido, B. A., & Smith, E. B. (2021) · Cited by 102

Predicting memory from the network structure of naturalistic events

Lee, H., & Chen, J. (2022) · Nature Communications · doi:10.1038/s41467-022-31965-2 · Cited by 123

The structure of reinforcement-learning mechanisms in the human brain

O’Doherty, J. P., Lee, S. W., & McNamee, D. (2014) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2014.10.004 · Cited by 106

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU

Caicedo-Torres, W., & Gutiérrez, J. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103269 · Cited by 99

Discovery of hierarchical representations for efficient planning

Tomov, M. S., Yagati, S., Kumar, A., Yang, W., & Gershman, S. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007594 · Cited by 99

Collective minds: social network topology shapes collective cognition

Momennejad, I. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0315 · Cited by 105

Automated crater detection on Mars using deep learning

Lee, C. (2019) · Planetary and Space Science · doi:10.1016/j.pss.2019.03.008 · Cited by 93

Calibration after bootstrap for accurate uncertainty quantification in regression models

Palmer, G., Du, S., Politowicz, A. L., Emory, J. P., Yang, X., Gautam, A., Gupta, G., Li, Z., Jacobs, R. M., & Morgan, D. D. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00794-8 · Cited by 106

Neural networks for dimensionality reduction of fluorescence spectra and prediction of drinking water disinfection by-products

Peleato, N. M., Legge, R. L., & Andrews, R. C. (2018) · Water Research · doi:10.1016/j.watres.2018.02.052 · Cited by 92

Transferring structural knowledge across cognitive maps in humans and models

Mark, S., Moran, R., Parr, T., Kennerley, S. W., & Behrens, T. E. J. (2020) · Nature Communications · doi:10.1038/s41467-020-18254-6 · Cited by 95

Learning visual groups from co-occurrences in space and time

Isola, P., Zoran, D., Krishnan, D., & Adelson, E. H. (2015) · arXiv · doi:10.48550/arxiv.1511.06811 · Cited by 90

A non-spatial account of place and grid cells based on clustering models of concept learning

Mok, R. M., & Love, B. C. (2019) · Nature Communications · doi:10.1038/s41467-019-13760-8 · Cited by 98

Constructing and Forgetting Temporal Context in the Human Cerebral Cortex

Chien, H.-Y. S., & Honey, C. J. (2020) · Neuron · doi:10.1016/j.neuron.2020.02.013 · Cited by 90

Memory integration constructs maps of space, time, and concepts

Morton, N. W., Sherrill, K. R., & Preston, A. R. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.08.007 · Cited by 98

MarkBot – A Language Model-Driven Chatbot for Interactive Marketing in Post-Modern World

Kushwaha, A. K., & Kar, A. K. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10184-y · Cited by 105

Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction

Kwon, J.-M., Jeon, K.-H., Kim, H. M., Kim, M. J., Lim, S., Kim, K.-H., Song, P. S., Park, J., Choi, R. K., & Oh, B.-H. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224502 · Cited by 86

Individual Differences in Learning Social and Non-Social Network Structures

Tompson, S., Kahn, A. E., Falk, E., Vettel, J., & Bassett, D. (2017) · Journal of Experimental Psychology. Learning, Memory and Cognition · doi:10.1037/xlm0000580 · Cited by 67

How humans learn and represent networks

Lynn, C. W., & Bassett, D. S. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912328117 · Cited by 81

PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets

Sánchez‐Peralta, L. F., Pagador, J. B., Picón, A., Calderón, Á. J., Polo, F. J., Andraka, N., Bilbao, R., Glover, B. M., Saratxaga, C. L., & Sanchez-Margallo, F. M. (2020) · Applied Sciences · doi:10.3390/app10238501 · Cited by 95

Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification

Ji, H., Xu, Y., Lü, H., & Zhang, Z. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.8b05405 · Cited by 87

Attention-guided analysis of infrastructure damage with semi-supervised deep learning

Karaaslan, E., Bağcı, U., & Çatbaş, F. N. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103634 · Cited by 84

Hippocampal spatio-predictive cognitive maps adaptively guide reward generalization

Garvert, M. M., Saanum, T., Schulz, E., Schuck, N. W., & Doeller, C. F. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01283-x · Cited by 88

Dynamic representations in networked neural systems

Ju, H., & Bassett, D. S. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0653-3 · Cited by 79

Abstract task representations for inference and control

Vaidya, A. R., & Badre, D. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.03.009 · Cited by 79

A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation

Singh, D., Norman, K. A., & Schapiro, A. C. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2123432119 · Cited by 78

Tea With Milk? A Hierarchical Generative Framework of Sequential Event Comprehension

Kuperberg, G. R. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12518 · Cited by 90

More Than the Sum of Its Parts: A Role for the Hippocampus in Configural Reinforcement Learning

Duncan, K. D., Doll, B. B., Daw, N. D., & Shohamy, D. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.042 · Cited by 83

A neural network model of when to retrieve and encode episodic memories

Lu, Q., Hasson, U., & Norman, K. A. (2022) · eLife · doi:10.7554/elife.74445 · Cited by 90

Human hippocampal and entorhinal neurons encode the temporal structure of experience

Tacikowski, P., Kalender, G., Ciliberti, D., & Fried, I. (2024) · Nature · doi:10.1038/s41586-024-07973-1 · Cited by 85

Human information processing in complex networks

Lynn, C. W., Papadopoulos, L., Kahn, A. E., & Bassett, D. S. (2020) · Nature Physics · doi:10.1038/s41567-020-0924-7 · Cited by 75

Design Principles of the Hippocampal Cognitive Map

Stachenfeld, K., Botvinick, M., & Gershman, S. J. (2014) · Cited by 74

The structure of event representations: behavioral, imaging, and computational investigations

Schapiro, A. C., Rogers, T. T., & Botvinick, M. (2010) · eScholarship (California Digital Library)

Prediction‐Based Learning and Processing of Event Knowledge

McRae, K., Brown, K., & Elman, J. L. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12482 · Cited by 23

Investigating the Role of Predictive Representations in Implicit Event Boundaries, Statistical Learning, and Categorization

Savalia, T. (2025) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/55170

Evidence of dynamic “on the fly” representations in perceptionPreprint

Bai, D., Scholl, B. J., & Strickland, B. (2022) · OSF Preprints (OSF Preprints) · doi:10.17605/osf.io/kyxhj

Neural activity differentiates novel and learned event boundaries

Ezzyat, Y., & Clements, A. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.2246-23.2024 · Cited by 7