Joshua T. Abbott

15 papers receiving 314 citations

Peers

Joshua T. Abbott
Comparison fields: 5 of 64
  • Experimental and Cognitive Psychology 126
  • Cognitive Neuroscience 110
  • Artificial Intelligence 77
  • Social Psychology 70
  • Computer Vision and Pattern Recognition 63
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Countries citing papers authored by Joshua T. Abbott

Since Specialization
Citations

This map shows the geographic impact of Joshua T. Abbott'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 Joshua T. Abbott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua T. Abbott more than expected).

Fields of papers citing papers by Joshua T. Abbott

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joshua T. Abbott. 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 Joshua T. Abbott. The network helps show where Joshua T. Abbott may publish in the future.

Co-authorship network of co-authors of Joshua T. Abbott

This figure shows the co-authorship network connecting the top 25 collaborators of Joshua T. Abbott. A scholar is included among the top collaborators of Joshua T. Abbott 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 Joshua T. Abbott. Joshua T. Abbott is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
#WorkIndexed citations
1 0
2 74
3 16
4 70
5 3
6 37
7
Empirical Evidence for Markov Chain Monte Carlo in Memory Search.
18
8 13
9
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies
24
10
Constructing a hypothesis space from the Web for large-scale Bayesian word learning.
9
11
Predicting focal colors with a rational model of representativeness
4
12
Human memory search as a random walk in a semantic network
31
13
Exploring the influence of particle filter parameters on order effects in causal learning
16
14
Testing a Bayesian Measure of Representativeness Using a Large Image Database
4
15 2
16 12

About Joshua T. Abbott

Joshua T. Abbott is a scholar working on Experimental and Cognitive Psychology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 16 papers that have together received 333 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Face Recognition and Perception (3 papers). The work is most often cited by research in Experimental and Cognitive Psychology (126 citations), Cognitive Neuroscience (110 citations) and General Decision Sciences (8 citations). Joshua T. Abbott has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Thomas L. Griffiths, Joshua C. Peterson, Joseph L. Austerweil, Jenny M. Bosten, Alice E. Skelton, Anna Franklin, Terry Regier, J. P. Das, Yangqing Jia and Thomas Griffiths. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Experimental Psychology General and Cognitive Science.

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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