Eva G. Krumhuber

4.7k total citations
94 papers, 2.9k citations indexed

About

Eva G. Krumhuber is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Social Psychology. According to data from OpenAlex, Eva G. Krumhuber has authored 94 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Cognitive Neuroscience, 53 papers in Experimental and Cognitive Psychology and 38 papers in Social Psychology. Recurrent topics in Eva G. Krumhuber's work include Face Recognition and Perception (47 papers), Evolutionary Psychology and Human Behavior (36 papers) and Psychology of Moral and Emotional Judgment (18 papers). Eva G. Krumhuber is often cited by papers focused on Face Recognition and Perception (47 papers), Evolutionary Psychology and Human Behavior (36 papers) and Psychology of Moral and Emotional Judgment (18 papers). Eva G. Krumhuber collaborates with scholars based in United Kingdom, Germany and Switzerland. Eva G. Krumhuber's co-authors include Antony S. R. Manstead, Arvid Kappas, Darren Cosker, Dennis Küster, Xijing Wang, Paul L. Rosin, D. Marshall, Klaus R. Scherer, Lina Skora and Gale Lucas and has published in prestigious journals such as PLoS ONE, Psychological Science and Sensors.

In The Last Decade

Eva G. Krumhuber

89 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eva G. Krumhuber United Kingdom 26 1.4k 1.4k 1.1k 400 343 94 2.9k
Rachael E. Jack United Kingdom 21 1.9k 1.3× 1.6k 1.1× 1.1k 1.0× 220 0.6× 497 1.4× 56 3.1k
Maria Gendron United States 24 1.7k 1.1× 1.4k 1.0× 1.4k 1.3× 280 0.7× 144 0.4× 46 3.2k
Gijsbert Bijlstra Netherlands 13 1.2k 0.8× 958 0.7× 557 0.5× 273 0.7× 484 1.4× 28 2.2k
Andrew P. Bayliss United Kingdom 27 2.6k 1.8× 955 0.7× 1.2k 1.1× 273 0.7× 194 0.6× 59 3.4k
Ron Dotsch Netherlands 31 2.8k 1.9× 2.5k 1.8× 1.5k 1.3× 1.4k 3.4× 574 1.7× 65 5.3k
Daniel C. Richardson United Kingdom 33 2.1k 1.5× 1.6k 1.1× 1.6k 1.5× 319 0.8× 252 0.7× 82 4.2k
Alan Cowen United States 21 907 0.6× 978 0.7× 806 0.7× 190 0.5× 205 0.6× 40 2.1k
Jamin Halberstadt New Zealand 34 1.7k 1.2× 1.4k 1.0× 1.5k 1.4× 942 2.4× 89 0.3× 122 3.8k
José Miguel Fernández Dols Spain 23 960 0.7× 1.0k 0.7× 1.4k 1.3× 482 1.2× 119 0.3× 74 2.5k
Dare A. Baldwin United States 36 2.3k 1.6× 1.0k 0.7× 1.9k 1.7× 572 1.4× 139 0.4× 76 7.1k

Countries citing papers authored by Eva G. Krumhuber

Since Specialization
Citations

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

Fields of papers citing papers by Eva G. Krumhuber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eva G. Krumhuber

This figure shows the co-authorship network connecting the top 25 collaborators of Eva G. Krumhuber. A scholar is included among the top collaborators of Eva G. Krumhuber 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 Eva G. Krumhuber. Eva G. Krumhuber 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.
Krumhuber, Eva G., et al.. (2025). How choice and motor mimicry affect affiliation: An fNIRS study. Imaging Neuroscience. 3.
2.
Küster, Dennis, et al.. (2023). Human and machine recognition of dynamic and static facial expressions: prototypicality, ambiguity, and complexity. Frontiers in Psychology. 14. 1221081–1221081. 4 indexed citations
3.
Krumhuber, Eva G., et al.. (2023). The reciprocal relationship between smiles and situational contexts. Cognition & Emotion. 37(7). 1230–1247. 1 indexed citations
4.
Krumhuber, Eva G., Lina Skora, Harold Hill, & Karen Lander. (2023). The role of facial movements in emotion recognition. Nature Reviews Psychology. 2(5). 283–296. 53 indexed citations
5.
Krumhuber, Eva G., et al.. (2022). Looking guilty: Handcuffing suspects influences judgements of deception. Journal of Investigative Psychology and Offender Profiling. 19(3). 231–247. 1 indexed citations
6.
Krumhuber, Eva G., et al.. (2021). Sitting in Judgment: How Body Posture Influences Deception Detection and Gazing Behavior. Behavioral Sciences. 11(6). 85–85. 2 indexed citations
7.
Bull, Peter, et al.. (2020). Veracity judgement, not accuracy: Reconsidering the role of facial expressions, empathy, and emotion recognition training on deception detection. Quarterly Journal of Experimental Psychology. 74(5). 910–927. 20 indexed citations
8.
Kunz, Miriam, et al.. (2020). Decoding of facial expressions of pain in avatars: does sex matter?. Scandinavian Journal of Pain. 21(1). 174–182. 8 indexed citations
9.
Krumhuber, Eva G., et al.. (2020). Acting Surprised: Comparing Perceptions of Different Dynamic Deliberate Expressions. Journal of Nonverbal Behavior. 45(2). 169–185. 6 indexed citations
10.
Krumhuber, Eva G., et al.. (2019). Emotion recognition from posed and spontaneous dynamic expressions: Human observers versus machine analysis.. Emotion. 21(2). 447–451. 47 indexed citations
11.
Krumhuber, Eva G., Yu‐Kun Lai, Paul L. Rosin, & Kurt Hugenberg. (2018). When facial expressions do and do not signal minds: The role of face inversion, expression dynamism, and emotion type.. Emotion. 19(4). 746–750. 16 indexed citations
12.
Skora, Lina, et al.. (2017). Measures and metrics for automatic emotion classification via FACET. UCL Discovery (University College London). 17 indexed citations
13.
Wang, Xijing & Eva G. Krumhuber. (2017). Described robot functionality impacts emotion experience attributions. UCL Discovery (University College London). 282–283. 1 indexed citations
14.
Tsankova, Elena, Eva G. Krumhuber, Arvid Kappas, et al.. (2015). The multi-modal nature of trustworthiness perception.. UCL Discovery (University College London). 147–152. 5 indexed citations
15.
Hofstede, Gert Jan, Samuel Mascarenhas, A. Silva, et al.. (2013). Traveller–Intercultural training with intelligent agents for young adults. Socio-Environmental Systems Modeling. 5 indexed citations
16.
Krumhuber, Eva G., Katja U. Likowski, & Peter Weyers. (2013). Facial Mimicry of Spontaneous and Deliberate Duchenne and Non-Duchenne Smiles. Journal of Nonverbal Behavior. 38(1). 1–11. 47 indexed citations
17.
Hall, Lynne, et al.. (2012). Incorporating Multi-Modal Evaluation into a Technology Enhanced Learning Experience. Socio-Environmental Systems Modeling. 1 indexed citations
18.
Krumhuber, Eva G., Antony S. R. Manstead, Darren Cosker, et al.. (2007). Facial dynamics as indicators of trustworthiness and cooperative behavior.. Emotion. 7(4). 730–735. 299 indexed citations
19.
Krumhuber, Eva G. & Arvid Kappas. (2005). Moving Smiles: The Role of Dynamic Components for the Perception of the Genuineness of Smiles. Journal of Nonverbal Behavior. 29(1). 3–24. 125 indexed citations
20.
Krumhuber, Eva G., Antony S. R. Manstead, Darren Cosker, Andrew Marshall, & Paul L. Rosin. (2005). Temporal dynamics of smiling: Human versus synthetic faces. ORCA Online Research @Cardiff (Cardiff University). 1 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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