Laura Schulz

111 papers receiving 5.8k citations

Laura Schulz's Hit Papers

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology 2016 · 241 citations
2410+7+14Years since publication250500750

Peers

Laura Schulz
Comparison fields: 5 of 84
  • Developmental and Educational Psychology 2.4k
  • General Decision Sciences 72
  • Experimental and Cognitive Psychology 295
  • Neuropsychology and Physiological Psychology 28
  • Cognitive Neuroscience 320
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Citations per year

Countries citing papers authored by Laura Schulz

Since Specialization
Citations

This map shows the geographic impact of Laura Schulz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Laura Schulz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laura Schulz more than expected).

Fields of papers citing papers by Laura Schulz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Laura Schulz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Laura Schulz. The network helps show where Laura Schulz may publish in the future.

Co-authors

The 25 scholars most cited alongside Laura Schulz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Laura Schulz Line = papers co-authored together Laura Schulz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 113 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
Hit paper breakdown →
2004828
2
The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery
Hit paper breakdown →
2011515
3 2001365
4 2007335
5
The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology
Hit paper breakdown →
2016241
6 2011232
7 2004189
8 2004188
9 2012188
10 2008166
11 2007165
12 2007159
13 2010159
14 2006149
15 2012144
16 2014125
17 2006124
18 2017118
19 2004114
20 2011111

About Laura Schulz

Laura Schulz is a scholar working on Developmental and Educational Psychology, General Decision Sciences, Artificial Intelligence, Cognitive Neuroscience and Computer Science Applications, having authored 113 papers that have together received 6.4k indexed citations. Recurring topics across this work include Child and Animal Learning Development (77 papers), Bayesian Modeling and Causal Inference (6 papers), Psychology of Moral and Emotional Judgment (3 papers), Decision-Making and Behavioral Economics (3 papers), Language Development and Disorders (2 papers), Evolution and Science Education (1 paper), Memory Processes and Influences (1 paper) and Advanced Causal Inference Techniques (1 paper). The work is most often cited by research in Developmental and Educational Psychology (2.4k citations), General Decision Sciences (72 citations), Experimental and Cognitive Psychology (295 citations), Neuropsychology and Physiological Psychology (28 citations) and Cognitive Neuroscience (320 citations). Laura Schulz has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Alison Gopnik C, Elizabeth Baraff Bonawitz, Hyowon Gweon C, Clark Glymour, Josh Tenenbaum, David Marc Sobel, Noah D. Goodman, Tamar Kushnir, Julian Jara‐Ettinger and David J. Danks. Their work appears in journals such as Cognitive Science, Child Development, Cognition, Developmental Psychology and Trends in Cognitive Sciences.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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