Allen Chang

592 total citations
10 papers, 298 citations indexed

About

Allen Chang is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Allen Chang has authored 10 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cognitive Neuroscience, 3 papers in Artificial Intelligence and 2 papers in Cellular and Molecular Neuroscience. Recurrent topics in Allen Chang's work include Memory and Neural Mechanisms (3 papers), Neural dynamics and brain function (2 papers) and Face Recognition and Perception (2 papers). Allen Chang is often cited by papers focused on Memory and Neural Mechanisms (3 papers), Neural dynamics and brain function (2 papers) and Face Recognition and Perception (2 papers). Allen Chang collaborates with scholars based in United States, Netherlands and Sweden. Allen Chang's co-authors include Michael A. Yassa, Elizabeth Murray, Maria Ly, Gizem Keceli, John P. Toscano, Philippe N. Tobler, Martin Bellander, Björn Lindström, David Schultner and David M. Amodio and has published in prestigious journals such as Nature Communications, Nature Neuroscience and Cerebral Cortex.

In The Last Decade

Allen Chang

10 papers receiving 290 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Allen Chang United States	6	99	61	49	44	29	10	298
Alain Lieury France	12	133 1.3×	42 0.7×	54 1.1×	48 1.1×	70 2.4×	59	423
Grant McQueen United Kingdom	12	149 1.5×	32 0.5×	38 0.8×	82 1.9×	47 1.6×	24	473
Jonathan J. Simone Canada	15	96 1.0×	28 0.5×	153 3.1×	103 2.3×	16 0.6×	28	490
Elena Davis United States	8	92 0.9×	33 0.5×	53 1.1×	43 1.0×	42 1.4×	12	385
Thao Tran United States	10	44 0.4×	17 0.3×	131 2.7×	161 3.7×	10 0.3×	22	452
Yelena Stukalin Israel	10	25 0.3×	26 0.4×	23 0.5×	62 1.4×	20 0.7×	22	287
Elizabeth Cameron Stade United States	7	69 0.7×	14 0.2×	76 1.6×	40 0.9×	91 3.1×	17	299
Li Gu China	14	177 1.8×	20 0.3×	112 2.3×	20 0.5×	86 3.0×	39	530
Roy de Kleijn Netherlands	12	195 2.0×	30 0.5×	10 0.2×	24 0.5×	69 2.4×	30	533
G. Brian Thompson New Zealand	16	178 1.8×	6 0.1×	18 0.4×	54 1.2×	52 1.8×	46	703

Countries citing papers authored by Allen Chang

Since Specialization

Citations

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

Fields of papers citing papers by Allen Chang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Allen Chang. 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 Allen Chang. The network helps show where Allen Chang may publish in the future.

Co-authorship network of co-authors of Allen Chang

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

All Works

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10 of 10 papers shown

1.

Lindström, Björn, Martin Bellander, David Schultner, et al.. (2021). Publisher Correction: A computational reward learning account of social media engagement. Nature Communications. 12(1). 1802–1802. 2 indexed citations

2.

Lindström, Björn, Martin Bellander, David Schultner, et al.. (2021). A computational reward learning account of social media engagement. Nature Communications. 12(1). 1311–1311. 84 indexed citations

3.

Chang, Allen, et al.. (2021). Dynamic Network Analysis Demonstrates the Formation of Stable Functional Networks During Rule Learning. Cerebral Cortex. 31(12). 5511–5525. 4 indexed citations

4.

Zhu, Henghui, Ioannis Ch. Paschalidis, Allen Chang, Chantal E. Stern, & Michael E. Hasselmo. (2020). A neural circuit model for a contextual association task inspired by recommender systems. Hippocampus. 30(4). 384–395. 8 indexed citations

5.

Chrastil, Elizabeth R., et al.. (2018). Converging meta-analytic and connectomic evidence for functional subregions within the human retrosplenial region.. Behavioral Neuroscience. 132(5). 339–355. 14 indexed citations

6.

Chang, Allen, Elizabeth Murray, & Michael A. Yassa. (2015). Mnemonic discrimination of similar face stimuli and a potential mechanism for the “other race” effect.. Behavioral Neuroscience. 129(5). 666–672. 13 indexed citations

7.

Murray, Elizabeth, Gizem Keceli, Allen Chang, et al.. (2014). Post-study caffeine administration enhances memory consolidation in humans. Nature Neuroscience. 17(2). 201–203. 160 indexed citations

8.

Chang, Allen & Eyal Amir. (2007). Reachability under uncertainty. Uncertainty in Artificial Intelligence. 41–48. 1 indexed citations

9.

Shahaf, Dafna, Allen Chang, & Eyal Amir. (2006). Learning partially observable action models: efficient algorithms. National Conference on Artificial Intelligence. 920–926. 9 indexed citations

10.

Chang, Allen & Eyal Amir. (2006). Goal achievement in partially known, partially observable domains. International Conference on Automated Planning and Scheduling. 59(5). 203–211. 3 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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