Mark S. Seidenberg

Developmental and Educational Psychology · 1989–2017

A researcher in the Science corpus: 12 papers; trusts 39 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Developmental and Educational Psychology — 35 citations into it; #238 of 8,223 by trust here, #510 by citations
• Cognitive Neuroscience — 29 citations into it; #859 of 10,852 by trust here, #2,039 by citations
• Cognitive Science & Psychology — 18 citations into it; #196 of 582 by trust here, #241 by citations
• Model Architectures — 13 citations into it
• Natural Language Processing — 13 citations into it
• Experimental and Cognitive Psychology — 10 citations into it; #1,010 of 9,731 by trust here, #2,846 by citations
• Linguistics — 9 citations into it
• Computational Linguistics & Semantics — 8 citations into it

Whom Mark S. Seidenberg trusts overall

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

1. James L. McClelland
2. David E. Rumelhart
3. Geoffrey E. Hinton
4. Barak A. Pearlmutter
5. Jenny R. Saffran
6. Michael W. Harm
7. David C. Plaut
8. Ronald J. Williams
9. Richard S. Sutton
10. Robert S. Siegler

Who trusts Mark S. Seidenberg overall

1. Jason D. Zevin
2. Jonathan Grainger
3. Marc Brysbaert
4. Michael W. Harm
5. Roger Ratcliff
6. Gene P. Ouellette
7. H. Baayen
8. M. L. Lambon Ralph
9. Randall C. O’Reilly
10. Kathleen Rastle

Papers from Mark S. Seidenberg's perspective

Ordered by how strongly the researchers Mark S. Seidenberg 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. Long Short-Term Memory
3. ImageNet classification with deep convolutional neural networks
4. Learning internal representations by error propagation
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Modulation of the semantic system by word imageability (2005) — 339 citations
• Semantic feature production norms for a large set of living and nonliving things (2005) — 1,118 citations
• Modeling the Successes and Failures of Interventions for Disabled Readers (2003) — 139 citations
• Language at the Speed of Sight: How We Read, Why So Many Can't, and What Can Be Done About It (2017) — 187 citations
• Simulating consistency effects and individual differences in nonword naming: A comparison of current models (2006) — 87 citations
• Connectionist Models in Developmental Cognitive Neuroscience : Critical Periods and the Paradox of Success (2005)
• ON THE BASES OF TWO SUBTYPES OF DEVELOPMENTAL DYSLEXIA (2005) — 393 citations
• Computing the meanings of words in reading: cooperative division of labor between visual and phonological processes. (2004) — 1,095 citations
• Deficits in phonology and past-tense morphology: What's the connection? (2003) — 149 citations
• Age of Acquisition Effects in Word Reading and Other Tasks (2002) — 405 citations
• Understanding normal and impaired word reading: computational principles in quasi-regular domains. (1996)
• A distributed, developmental model of word recognition and naming. (1989)