Laura Schulz

Developmental and Educational Psychology · 2001–2017

A researcher in the Science corpus: 16 papers; trusts 56 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Developmental and Educational Psychology — 94 citations into it; #749 of 8,223 by trust here, #232 by citations
• Cognitive Science & Psychology — 61 citations into it; #245 of 582 by trust here, #390 by citations
• Artificial Intelligence — 56 citations into it; #7,347 of 35,345 by trust here, #5,002 by citations
• Cognitive Neuroscience — 39 citations into it
• Social Psychology — 29 citations into it
• Cultural Studies — 24 citations into it; #592 of 609 by trust here, #290 by citations
• Education — 16 citations into it; #2,838 of 11,309 by trust here, #2,726 by citations
• Experimental and Cognitive Psychology — 12 citations into it

Whom Laura Schulz trusts overall

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

1. Alison Gopnik
2. Josh Tenenbaum
3. Susan Carey
4. Noah D. Goodman
5. Henry Wellman
6. Tomer D. Ullman
7. Clark Glymour
8. Rod D. Roscoe
9. B. Rehder
10. Tania Lombrozo

Who trusts Laura Schulz overall

1. Josh Tenenbaum
2. Tomer D. Ullman
3. Pedro Tsividis
4. Alison Gopnik
5. T. Griffiths
6. Brenden M. Lake
7. Noah D. Goodman
8. Tobias Gerstenberg
9. C. Cook
10. Julian Jara-Ettinger

Papers from Laura Schulz's perspective

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

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

Their papers

• A theory of causal learning in children: causal maps and Bayes nets. (2004) — 1,155 citations
• Where science starts: spontaneous experiments in preschoolers' exploratory play. (2011) — 300 citations
• Infants consider both the sample and the sampling process in inductive generalization (2010) — 224 citations
• The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery. (2011) — 662 citations
• Imagination and the generation of new ideas (2015) — 43 citations
• Lookit (Part 1): A New Online Platform for Developmental Research (2017) — 144 citations
• The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology. (2016) — 382 citations
• Constraints on Hypothesis Selection in Causal Learning (2015)
• I'd do anything for a cookie (but I won't do that): Children's understanding of the costs and rewards underlying rational action (2014) — 129 citations
• Information Selection in Noisy Environments with Large Action Spaces (2014) — 12 citations
• Finding new facts; thinking new thoughts. (2012) — 43 citations
• The origins of inquiry: inductive inference and exploration in early childhood. (2012) — 211 citations
• Preschool children learn about causal structure from conditional interventions. (2007) — 224 citations
• Causal learning : psychology, philosophy, and computation (2007) — 437 citations
• Intuitive Theories of Mind: A Rational Approach to False Belief (2005) — 123 citations
• Causal learning mechanisms in very young children: two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation. (2001) — 487 citations