Daniel N. Albohn

861 total citations
26 papers, 213 citations indexed

About

Daniel N. Albohn is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Social Psychology. According to data from OpenAlex, Daniel N. Albohn has authored 26 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Cognitive Neuroscience, 14 papers in Experimental and Cognitive Psychology and 9 papers in Social Psychology. Recurrent topics in Daniel N. Albohn's work include Face Recognition and Perception (19 papers), Evolutionary Psychology and Human Behavior (14 papers) and Psychology of Moral and Emotional Judgment (5 papers). Daniel N. Albohn is often cited by papers focused on Face Recognition and Perception (19 papers), Evolutionary Psychology and Human Behavior (14 papers) and Psychology of Moral and Emotional Judgment (5 papers). Daniel N. Albohn collaborates with scholars based in United States, South Korea and Canada. Daniel N. Albohn's co-authors include Reginald B. Adams, Kestutis Kveraga, Hee Yeon Im, Troy G. Steiner, Alexander Todorov, Ursula Heß, Robert E. Kleck, Stefan Uddenberg, Bruce P. Mortenson and Karena S. Rush and has published in prestigious journals such as Scientific Reports, Annals of the New York Academy of Sciences and Experimental Brain Research.

In The Last Decade

Daniel N. Albohn

23 papers receiving 206 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel N. Albohn United States 7 145 98 51 35 23 26 213
Zhongqing Jiang China 10 126 0.9× 116 1.2× 47 0.9× 13 0.4× 28 1.2× 26 201
Weizhi Nan China 10 154 1.1× 54 0.6× 33 0.6× 18 0.5× 17 0.7× 28 220
Sofia Frade Portugal 7 167 1.2× 176 1.8× 122 2.4× 11 0.3× 13 0.6× 13 336
Linsey Raymaekers Netherlands 10 205 1.4× 45 0.5× 90 1.8× 45 1.3× 72 3.1× 19 312
Quentin Raffaelli Canada 5 210 1.4× 127 1.3× 23 0.5× 17 0.5× 34 1.5× 9 242
Laurie Bayet United States 10 153 1.1× 54 0.6× 41 0.8× 11 0.3× 35 1.5× 21 220
Kohinoor Monish Darda United Kingdom 10 122 0.8× 51 0.5× 88 1.7× 22 0.6× 10 0.4× 20 207
Brenda Nicodemus United States 10 115 0.8× 78 0.8× 32 0.6× 21 0.6× 17 0.7× 29 336
Paddy Ross United Kingdom 10 190 1.3× 93 0.9× 97 1.9× 6 0.2× 24 1.0× 14 258
Mariko Kikutani Japan 8 110 0.8× 126 1.3× 93 1.8× 23 0.7× 50 2.2× 21 272

Countries citing papers authored by Daniel N. Albohn

Since Specialization
Citations

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

Fields of papers citing papers by Daniel N. Albohn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel N. Albohn

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel N. Albohn. A scholar is included among the top collaborators of Daniel N. Albohn 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 Daniel N. Albohn. Daniel N. Albohn 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.
Albohn, Daniel N., Stefan Uddenberg, & Alexander Todorov. (2025). Individualized models of social judgments and context-dependent representations. Scientific Reports. 15(1). 4208–4208. 2 indexed citations
2.
Todorov, Alexander, DongWon Oh, Stefan Uddenberg, & Daniel N. Albohn. (2025). Face evaluation: Findings, methods, and challenges. Annals of the New York Academy of Sciences. 1545(1). 28–37. 3 indexed citations
3.
Uddenberg, Stefan, et al.. (2025). Capturing variability in children’s faces: an artificial, yet realistic, face stimulus set. Frontiers in Psychology. 16. 1454312–1454312.
4.
Albohn, Daniel N., Joel E. Martínez, & Alexander Todorov. (2024). Determinants of shared and idiosyncratic contributions to judgments of faces.. Journal of Experimental Psychology Human Perception & Performance. 50(11). 1117–1130. 4 indexed citations
5.
Im, Hee Yeon, et al.. (2023). Americans weigh an attended emotion more than Koreans in overall mood judgments. Scientific Reports. 13(1). 19323–19323. 2 indexed citations
6.
Albohn, Daniel N., et al.. (2022). The shared signal hypothesis: Facial and bodily expressions of emotion mutually inform one another. Attention Perception & Psychophysics. 84(7). 2271–2280. 4 indexed citations
7.
Albohn, Daniel N. & Reginald B. Adams. (2022). The Social Face Hypothesis. Affective Science. 3(3). 539–545. 1 indexed citations
8.
Adams, Reginald B., et al.. (2022). Angry White Faces: A Contradiction of Racial Stereotypes and Emotion-Resembling Appearance. Affective Science. 3(1). 46–61. 4 indexed citations
9.
Albohn, Daniel N., Stefan Uddenberg, & Alexander Todorov. (2022). A data-driven, hyper-realistic method for visualizing individual mental representations of faces. Frontiers in Psychology. 13. 997498–997498. 6 indexed citations
10.
Albohn, Daniel N., et al.. (2022). Facing social exclusion: a facial EMG examination of the reaffiliative function of smiling. Cognition & Emotion. 36(4). 741–749. 1 indexed citations
11.
Albohn, Daniel N. & Reginald B. Adams. (2021). The Expressive Triad: Structure, Color, and Texture Similarity of Emotion Expressions Predict Impressions of Neutral Faces. Frontiers in Psychology. 12. 612923–612923. 4 indexed citations
12.
Albohn, Daniel N. & Reginald B. Adams. (2020). Everyday Beliefs About Emotion Perceptually Derived From Neutral Facial Appearance. Frontiers in Psychology. 11. 264–264. 6 indexed citations
13.
Kveraga, Kestutis, et al.. (2019). Spatial and feature-based attention to expressive faces. Experimental Brain Research. 237(4). 967–975. 5 indexed citations
14.
Albohn, Daniel N., et al.. (2019). Emotional stereotypes on trial: Implicit emotion associations for young and old adults.. Emotion. 20(7). 1244–1254.
15.
Doren, Natalia Van, et al.. (2018). Culture Moderates the Relationship Between Emotional Fit and Collective Aspects of Well-Being. Frontiers in Psychology. 9. 1509–1509. 6 indexed citations
16.
Im, Hee Yeon, Reginald B. Adams, Noreen Ward, et al.. (2018). Neurodynamics and connectivity during facial fear perception: The role of threat exposure and signal congruity. Scientific Reports. 8(1). 2776–2776. 13 indexed citations
17.
Im, Hee Yeon, Sang Chul Chong, Troy G. Steiner, et al.. (2017). Cross-cultural and hemispheric laterality effects on the ensemble coding of emotion in facial crowds. PubMed. 5(2). 125–152. 16 indexed citations
18.
Im, Hee Yeon, et al.. (2017). Differential hemispheric and visual stream contributions to ensemble coding of crowd emotion. Nature Human Behaviour. 1(11). 828–842. 37 indexed citations
19.
Adams, Reginald B., et al.. (2016). What Facial Appearance Reveals Over Time: When Perceived Expressions in Neutral Faces Reveal Stable Emotion Dispositions. Frontiers in Psychology. 7. 986–986. 24 indexed citations
20.
Adams, Reginald B., Hee Yeon Im, Noreen Ward, et al.. (2016). Compound facial threat cue perception: Contributions of visual pathways, aging, and anxiety. Journal of Vision. 16(12). 1375–1375. 2 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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