Laura Schulz

9.3k total citations · 2 hit papers
114 papers, 5.6k citations indexed

About

Laura Schulz is a scholar working on Developmental and Educational Psychology, Social Psychology and Education. According to data from OpenAlex, Laura Schulz has authored 114 papers receiving a total of 5.6k indexed citations (citations by other indexed papers that have themselves been cited), including 96 papers in Developmental and Educational Psychology, 28 papers in Social Psychology and 25 papers in Education. Recurrent topics in Laura Schulz's work include Child and Animal Learning Development (94 papers), Early Childhood Education and Development (14 papers) and Bayesian Modeling and Causal Inference (14 papers). Laura Schulz is often cited by papers focused on Child and Animal Learning Development (94 papers), Early Childhood Education and Development (14 papers) and Bayesian Modeling and Causal Inference (14 papers). Laura Schulz collaborates with scholars based in United States, United Kingdom and Germany. Laura Schulz's co-authors include Alison Gopnik, Elizabeth Bonawitz, Hyowon Gweon, Clark Glymour, Joshua B. Tenenbaum, David M. Sobel, Noah D. Goodman, Julian Jara‐Ettinger, Tamar Kushnir and David Danks and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Laura Schulz

113 papers receiving 5.1k citations

Hit Papers

A Theory of Causal Learni... 2004 2026 2011 2018 2004 2011 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Laura Schulz 3.8k 1.3k 1.3k 1.2k 921 114 5.6k
David M. Sobel 2.9k 0.7× 1.5k 1.1× 1.3k 1.0× 1.2k 1.0× 636 0.7× 148 5.4k
Fei Xu 4.4k 1.1× 1.2k 0.9× 1.8k 1.4× 895 0.7× 872 0.9× 112 6.7k
Michael C. Frank 3.7k 1.0× 624 0.5× 2.2k 1.7× 792 0.6× 1.6k 1.8× 243 7.8k
Ellen M. Markman 7.3k 1.9× 1.4k 1.1× 1.7k 1.3× 1.3k 1.1× 753 0.8× 114 9.1k
Arthur S. Reber 3.3k 0.9× 377 0.3× 3.1k 2.4× 1.4k 1.1× 1.0k 1.1× 93 6.8k
Michael McCloskey 3.5k 0.9× 1.7k 1.3× 3.7k 2.9× 1.2k 1.0× 736 0.8× 128 7.5k
Tom Trabasso 5.3k 1.4× 1.6k 1.2× 2.2k 1.7× 942 0.8× 1.6k 1.8× 86 8.7k
Vladimir M. Sloutsky 2.6k 0.7× 631 0.5× 1.3k 1.0× 486 0.4× 427 0.5× 157 4.1k
Thomas R. Shultz 1.5k 0.4× 386 0.3× 980 0.8× 1.1k 0.9× 706 0.8× 161 3.8k
Jean M. Mandler 5.0k 1.3× 1.1k 0.8× 2.5k 2.0× 1.6k 1.3× 959 1.0× 116 9.1k

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-authorship network of co-authors of Laura Schulz

This figure shows the co-authorship network connecting the top 25 collaborators of Laura Schulz. A scholar is included among the top collaborators of Laura Schulz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Laura Schulz. Laura Schulz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Schulz, Laura, et al.. (2023). Not Playing by the Rules: Exploratory Play, Rational Action, and Efficient Search. Open Mind. 7. 294–317. 6 indexed citations
2.
Wu, Yang, Laura Schulz, Michael C. Frank, & Hyowon Gweon. (2021). Emotion as Information in Early Social Learning. Current Directions in Psychological Science. 30(6). 468–475. 34 indexed citations
3.
Kominsky, Jonathan F., Tobias Gerstenberg, Mark Sheskin, et al.. (2021). The trajectory of counterfactual simulation in development.. Developmental Psychology. 57(2). 253–268. 26 indexed citations
4.
Leonard, Julia, et al.. (2020). Preschoolers are Sensitive to their Performance Over Time.. Cognitive Science. 4 indexed citations
5.
Schulz, Laura, et al.. (2018). Cognitive pragmatism: Children flexibly choose between facts and conjectures.. Cognitive Science. 2 indexed citations
6.
Jara‐Ettinger, Julian, et al.. (2018). Sensitivity to the Sampling Process Emerges From the Principle of Efficiency. OSF Preprints (OSF Preprints). 1 indexed citations
7.
Wu, Yang & Laura Schulz. (2017). What do you really think? Children's ability to infer others' desires when emotional expressions change between social and nonsocial contexts.. Cognitive Science. 1 indexed citations
8.
Jara‐Ettinger, Julian, et al.. (2015). Beliefs about desires: Children's understanding of how knowledge and preference influence choice.. Cognitive Science. 1 indexed citations
9.
Jara‐Ettinger, Julian, Laura Schulz, & Joshua B. Tenenbaum. (2015). The naïve utility calculus: Joint inferences about the costs and rewards of actions.. Cognitive Science. 3 indexed citations
10.
Jara‐Ettinger, Julian, Hyowon Gweon, Joshua B. Tenenbaum, & Laura Schulz. (2014). I’d do anything for a cookie (but I won’t do that): Children’s understanding of the costs and rewards underlying rational action. Cognitive Science. 36(36). 1 indexed citations
11.
Siegel, Max, et al.. (2014). Black boxes: Hypothesis testing via indirect perceptual evidence. Cognitive Science. 36(36). 8 indexed citations
12.
Gweon, Hyowon, et al.. (2014). To give a fish or to teach how to fish? Children weigh costs and benefits in considering what information to transmit.. Cognitive Science. 36(36). 5 indexed citations
13.
Gershman, Samuel J., et al.. (2014). Information Selection in Noisy Environments with Large Action Spaces. Cognitive Science. 36(36). 9 indexed citations
14.
Wu, Yang, Paul Muentener, & Laura Schulz. (2013). The invisible hand: Toddlers infer hidden agents when events occur probabilistically. Cognitive Science. 35(35). 2 indexed citations
15.
Kline, Melissa, Paul Muentener, & Laura Schulz. (2013). Transitive and periphrastic sentences affect memory for simple causal scenes. Cognitive Science. 35(35). 1 indexed citations
16.
Gweon, Hyowon, Joshua B. Tenenbaum, & Laura Schulz. (2009). What are you trying to tell me? A Bayesian model of how toddlers can simultaneously infer property extension and sampling processes. DSpace@MIT (Massachusetts Institute of Technology). 31(31). 1 indexed citations
17.
Bonawitz, Elizabeth, Adina S. Fischer, & Laura Schulz. (2008). Training a Bayesian: Three-and-a-half-year-olds' Reasoning about Ambiguous Evidence. eScholarship (California Digital Library). 30(30). 3 indexed citations
18.
Bonawitz, Elizabeth, et al.. (2007). Weighing the Evidence: Children's Naïve Theories of Balance Affect Their Exploratory Play. eScholarship (California Digital Library). 29(29). 5 indexed citations
19.
Gopnik, Alison, Laura Schulz, & Jessica A. Sommerville. (2005). Causal Determinism and Preschoolers' Causal Inferences. eScholarship (California Digital Library). 27(27). 1 indexed citations
20.
Goodman, Noah D., Chris L. Baker, Vikash K. Mansinghka, et al.. (2005). Intuitive Theories of Mind: A Rational Approach to False Belief. Proceedings of the Annual Meeting of the Cognitive Science Society. 28(28). 58 indexed citations

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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