Adam N. Sanborn

3.0k citations
56 papers · 1.6k indexed · h-index 18

Adam N. Sanborn

53 papers receiving 1.5k citations

Peers

Adam N. Sanborn
Comparison fields: 5 of 132
  • General Decision Sciences 324
  • Cognitive Neuroscience 638
  • Experimental and Cognitive Psychology 399
  • Developmental and Educational Psychology 298
  • History and Philosophy of Science 89
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Chris Donkin Australia
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Citations per year

Countries citing papers authored by Adam N. Sanborn

Since Specialization
Citations

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

Fields of papers citing papers by Adam N. Sanborn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adam N. Sanborn. 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 Adam N. Sanborn. The network helps show where Adam N. Sanborn may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Adam N. Sanborn, 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 Adam N. Sanborn Line = papers co-authored together Adam N. Sanborn links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20254
2 20241
3 20241
4 20245
5 202310
6 20226
7
Local sampling with momentum accounts for human random sequence generation
20211
8 202043
9
Bayesian Inference Causes Incoherence in Human Probability Judgments.
20191
10
Exploring the Representation of Linear Functions
20191
11
Why Decisions Bias Perception: An Amortised Sequential Sampling Account.
20192
12
Mental Sampling in Multimodal Representations
20181
13 20174
14
Why Does Higher Working Memory Capacity Help You Learn
20171
15 201611
16
Look-Ahead Monte Carlo with People
20121
17
Identifying representations of categories of discrete items using Markov chain Monte Carlo with People.
20123
18
Computational Models of Intuitive Physics
20120
19
Hierarchical Learning of Dimensional Biases in Human Categorization
200915
20
Markov Chain Monte Carlo with People
200728

About Adam N. Sanborn

Adam N. Sanborn is a scholar working on General Decision Sciences, Experimental and Cognitive Psychology and Developmental and Educational Psychology, having authored 56 papers that have together received 1.6k indexed citations. Recurring topics across this work include Child and Animal Learning Development (16 papers), Decision-Making and Behavioral Economics (15 papers), Bayesian Modeling and Causal Inference (9 papers), Language and cultural evolution (8 papers), Categorization, perception, and language (7 papers), Neural dynamics and brain function (6 papers), Sleep and related disorders (6 papers) and Forecasting Techniques and Applications (6 papers). The work is most often cited by research in General Decision Sciences (324 citations), Cognitive Neuroscience (638 citations) and Experimental and Cognitive Psychology (399 citations). Adam N. Sanborn has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Thomas L. Griffiths, Nick Chater, Danielle Navarro, Nicole K. Y. Tang, Richard M. Shiffrin, Edward Vul, Vikash K. Mansinghka, Claire E. Goodchild, Andrew L. Cohen and Jian-Qiao Zhu. Their work appears in journals such as PLoS ONE, Psychological Review 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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