Lael J. Schooler

4.7k total citations · 1 hit paper
53 papers, 2.8k citations indexed

About

Lael J. Schooler is a scholar working on Artificial Intelligence, General Decision Sciences and Cognitive Neuroscience. According to data from OpenAlex, Lael J. Schooler has authored 53 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 20 papers in General Decision Sciences and 19 papers in Cognitive Neuroscience. Recurrent topics in Lael J. Schooler's work include Decision-Making and Behavioral Economics (20 papers), Memory Processes and Influences (9 papers) and Neural and Behavioral Psychology Studies (9 papers). Lael J. Schooler is often cited by papers focused on Decision-Making and Behavioral Economics (20 papers), Memory Processes and Influences (9 papers) and Neural and Behavioral Psychology Studies (9 papers). Lael J. Schooler collaborates with scholars based in Germany, United States and Switzerland. Lael J. Schooler's co-authors include John R. Anderson, Ralph Hertwig, Jörg Rieskamp, Rui Mata, Wolfgang Gaissmaier, Julian N. Marewski, Gerd Gigerenzer, Thorsten Pachur, Shenghua Luan and Torsten Reimer and has published in prestigious journals such as PLoS ONE, Psychological Review and Psychological Science.

In The Last Decade

Lael J. Schooler

52 papers receiving 2.7k citations

Hit Papers

Reflections of the Environment in Memory 1991 2026 2002 2014 1991 200 400 600

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lael J. Schooler Germany 23 1.1k 1.0k 744 475 365 53 2.8k
Peter Juslin Sweden 28 1.1k 1.0× 1.5k 1.5× 572 0.8× 623 1.3× 581 1.6× 120 3.2k
David A. Lagnado United Kingdom 33 1.3k 1.2× 691 0.7× 942 1.3× 932 2.0× 178 0.5× 141 3.4k
Simon J. Handley United Kingdom 34 1.0k 0.9× 1.4k 1.4× 949 1.3× 948 2.0× 219 0.6× 103 3.0k
Daniel N. Osherson United States 38 1.1k 1.0× 756 0.8× 2.0k 2.7× 1.2k 2.4× 371 1.0× 140 4.7k
Danielle Navarro Australia 27 780 0.7× 362 0.4× 1.1k 1.4× 698 1.5× 143 0.4× 127 2.7k
Ilan Yaniv Israel 28 630 0.6× 1.0k 1.0× 280 0.4× 243 0.5× 649 1.8× 48 3.3k
Todd M. Gureckis United States 26 1.4k 1.3× 339 0.3× 729 1.0× 1.1k 2.3× 140 0.4× 93 3.6k
Jörg Rieskamp Switzerland 40 1.9k 1.8× 2.5k 2.5× 547 0.7× 549 1.2× 661 1.8× 127 5.2k
Ruth M. J. Byrne Ireland 32 1.3k 1.2× 1.5k 1.5× 1.9k 2.5× 1.2k 2.5× 161 0.4× 122 4.6k
Mike Oaksford United Kingdom 40 1.4k 1.3× 2.2k 2.2× 2.5k 3.4× 1.5k 3.2× 340 0.9× 118 5.6k

Countries citing papers authored by Lael J. Schooler

Since Specialization
Citations

This map shows the geographic impact of Lael J. Schooler'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 Lael J. Schooler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lael J. Schooler more than expected).

Fields of papers citing papers by Lael J. Schooler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lael J. Schooler. 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 Lael J. Schooler. The network helps show where Lael J. Schooler may publish in the future.

Co-authorship network of co-authors of Lael J. Schooler

This figure shows the co-authorship network connecting the top 25 collaborators of Lael J. Schooler. A scholar is included among the top collaborators of Lael J. Schooler 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 Lael J. Schooler. Lael J. Schooler 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.
Luan, Shenghua, et al.. (2020). Improving judgment accuracy by sequential adjustment. Psychonomic Bulletin & Review. 27(1). 170–177. 2 indexed citations
2.
Schooler, Lael J. & John Anderson. (2018). Recency and Context: An Environmental Analysis of Memory. Dermatology Online Journal. 22(7). 1 indexed citations
3.
Schooler, Lael J. & John R. Anderson. (2018). The Disruptive Potential of Immediate Feedback. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 11 indexed citations
4.
Marewski, Julian N., et al.. (2017). Architectural process models of decision making: Towards a model database.. Cognitive Science. 2 indexed citations
5.
Marewski, Julian N., et al.. (2016). An Ecological Model of Memory and Inferences. IRIS. 1883–1888. 3 indexed citations
6.
Pachur, Thorsten, et al.. (2016). Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture. Cognition. 157. 77–99. 18 indexed citations
7.
Pachur, Thorsten, Lael J. Schooler, & Jeffrey R. Stevens. (2014). We'll Meet Again: Revealing Distributional and Temporal Patterns of Social Contact. PLoS ONE. 9(1). e86081–e86081. 12 indexed citations
8.
Marewski, Julian N., et al.. (2013). Constraining ACT-R models of decision strategies: An experimental paradigm. SERVAL (Université de Lausanne). 35(35). 2201–2206. 6 indexed citations
9.
Olsson, Henrik, et al.. (2012). Mapping the Structure of Semantic Memory. Cognitive Science. 37(1). 125–145. 63 indexed citations
10.
Neth, Hansjörg, et al.. (2011). Ranking query results from Linked Open Data using a simple cognitive heuristic. KOPS (University of Konstanz). 55–60. 1 indexed citations
11.
Pachur, Thorsten, Peter M. Todd, Gerd Gigerenzer, Lael J. Schooler, & Daniel G. Goldstein. (2011). The Recognition Heuristic: A Review of Theory and Tests. Frontiers in Psychology. 2. 147–147. 80 indexed citations
12.
Volz, Kirsten G., Lael J. Schooler, & D. Yves von Cramon. (2010). It just felt right: The neural correlates of the fluency heuristic. Consciousness and Cognition. 19(3). 829–837. 19 indexed citations
13.
Katsikopoulos, Konstantinos V., Lael J. Schooler, & Ralph Hertwig. (2010). The robust beauty of ordinary information.. Psychological Review. 117(4). 1259–1266. 70 indexed citations
14.
Marewski, Julian N., Wolfgang Gaissmaier, Lael J. Schooler, Daniel G. Goldstein, & Gerd Gigerenzer. (2009). Do Voters Use Episodic Knowledge to Rely on Recognition. KOPS (University of Konstanz). 15 indexed citations
15.
Cokely, Edward T., et al.. (2009). On the link between cognitive control and heuristic processes. Max Planck Institute for Plasma Physics. 31(31). 2926–2931. 20 indexed citations
16.
Pachur, Thorsten, Rui Mata, & Lael J. Schooler. (2009). Cognitive aging and the adaptive use of recognition in decision making.. Psychology and Aging. 24(4). 901–915. 59 indexed citations
17.
Gaissmaier, Wolfgang, Lael J. Schooler, & Rui Mata. (2008). An ecological perspective to cognitive limits: Modeling environment-mind interactions with ACT-R. Judgment and Decision Making. 3(3). 278–291. 20 indexed citations
18.
Gaissmaier, Wolfgang & Lael J. Schooler. (2008). The smart potential behind probability matching. Cognition. 109(3). 416–422. 132 indexed citations
19.
Hertwig, Ralph, Stefan M. Herzog, Lael J. Schooler, & Torsten Reimer. (2008). Fluency heuristic: A model of how the mind exploits a by-product of information retrieval.. Journal of Experimental Psychology Learning Memory and Cognition. 34(5). 1191–1206. 161 indexed citations
20.
Schooler, Lael J. & Ralph Hertwig. (2005). How forgetting aids heuristic inference.. Psychological Review. 112(3). 610–628. 229 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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