Kenneth A. Norman

Cognitive Neuroscience · 2003–2022

A researcher in the Science corpus: 14 papers; trusts 51 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 186 citations into it; #490 of 10,852 by trust here, #483 by citations
• Artificial Intelligence — 56 citations into it
• Cellular and Molecular Neuroscience — 43 citations into it; #267 of 2,998 by trust here, #802 by citations
• Developmental and Educational Psychology — 33 citations into it
• Neuroscience — 31 citations into it
• Social Psychology — 18 citations into it
• Statistics and Probability — 18 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Kenneth A. Norman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Stephen Grossberg
2. Randall C. O’Reilly
3. Geoffrey E. Hinton
4. James L. McClelland
5. Michael E. Hasselmo
6. D. Marr
7. Erkki Oja
8. Bruce L. McNaughton
9. Roger Ratcliff
10. Andrew P. Yonelinas

Who trusts Kenneth A. Norman overall

1. Sean M. Polyn
2. James V. Haxby
3. Michael E. Hasselmo
4. Gaël Varoquaux
5. Michael J. Kahana
6. Stephen D. Goldinger
7. Dharshan Kumaran
8. Russell A. Poldrack
9. Nikolaus Kriegeskorte
10. James L. McClelland

Papers from Kenneth A. Norman's perspective

Ordered by how strongly the researchers Kenneth A. Norman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources (2014) — 14 citations
• Shared computational principles for language processing in humans and deep language models (2022) — 506 citations
• Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data (2014) — 43 citations
• Offline replay supports planning in human reinforcement learning (2018) — 127 citations
• Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance (2017) — 210 citations
• Complementary learning systems within the hippocampus: A neural network modeling approach to reconciling episodic memory with statistical learning (2016) — 423 citations
• Discovering event structure in continuous narrative perception and memory (2016) — 817 citations
• Discovering latent causes in reinforcement learning (2015) — 152 citations
• Moderate Levels of Activation Lead to Forgetting In the Think/No-Think Paradigm (2013) — 113 citations
• The Successor Representation and Temporal Context (2012) — 120 citations
• A context maintenance and retrieval model of organizational processes in free recall (2009) — 705 citations
• Beyond mind-reading: multi-voxel pattern analysis of fMRI data. (2006) — 2,417 citations
• Category-Specific Cortical Activity Precedes Retrieval During Memory Search (2005) — 712 citations
• Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. (2003) — 1,364 citations