Christopher G. Lucas

53 papers receiving 3.2k citations

Hit Papers

Advances in Neural Information Processing Systems 21200920262014202020092017202550010001.5k

Peers

Christopher G. Lucas
Comparison fields: 5 of 192
  • Artificial Intelligence 1000
  • Computer Vision and Pattern Recognition 477
  • Developmental and Educational Psychology 460
  • Cognitive Neuroscience 429
  • Atmospheric Science 265
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Countries citing papers authored by Christopher G. Lucas

Since Specialization
Citations

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

Fields of papers citing papers by Christopher G. Lucas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christopher G. Lucas. 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 Christopher G. Lucas. The network helps show where Christopher G. Lucas may publish in the future.

Co-authorship network of co-authors of Christopher G. Lucas

This figure shows the co-authorship network connecting the top 25 collaborators of Christopher G. Lucas. A scholar is included among the top collaborators of Christopher G. Lucas 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 Christopher G. Lucas. Christopher G. Lucas 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
#WorkIndexed citations
1 7
2
Embodied large language models enable robots to complete complex tasks in unpredictable environmentsbreakdown →
17
3 1
4 2
5 4
6 5
7 2
8
Learning hidden causal structure from temporal data
1
9 3
10
Investigating the Explore/Exploit Trade-off in Adult Causal Inferences
2
11
The human kernel
6
12
Learning to reason about desires: An infant training study
4
13
Inferring causal structure and hidden causes from event sequences.
1
14 64
15 139
16 25
17
A Bayesian model of navigation in squirrels
2
18
Developmental differences in learning the forms of causal relationships
17
19
Advances in Neural Information Processing Systems 21breakdown →
1571
20
A rational model of preference learning and choice prediction by children
25

About Christopher G. Lucas

Christopher G. Lucas is a scholar working on General Decision Sciences, Developmental and Educational Psychology and Cultural Studies, having authored 56 papers that have together received 3.4k indexed citations. Recurring topics across this work include Child and Animal Learning Development (23 papers), Decision-Making and Behavioral Economics (14 papers) and Bayesian Modeling and Causal Inference (13 papers). The work is most often cited by research in General Decision Sciences (129 citations), Developmental and Educational Psychology (460 citations) and Artificial Intelligence (1000 citations). Christopher G. Lucas has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include Thomas L. Griffiths, Joseph Jay Williams, Michael L. Kalish, Alison Gopnik, Margaret A. LeMone, Edward J. Zipser, David A. Dorward, Adriano G. Rossi, Sophie Bridgers and Neil Brice. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Geophysical Research Atmospheres and PLoS ONE.

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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2026