Michał Kuniecki

510 total citations
25 papers, 354 citations indexed

About

Michał Kuniecki is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Social Psychology. According to data from OpenAlex, Michał Kuniecki has authored 25 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Cognitive Neuroscience, 8 papers in Experimental and Cognitive Psychology and 7 papers in Social Psychology. Recurrent topics in Michał Kuniecki's work include Neural and Behavioral Psychology Studies (6 papers), Visual perception and processing mechanisms (5 papers) and Memory and Neural Mechanisms (5 papers). Michał Kuniecki is often cited by papers focused on Neural and Behavioral Psychology Studies (6 papers), Visual perception and processing mechanisms (5 papers) and Memory and Neural Mechanisms (5 papers). Michał Kuniecki collaborates with scholars based in Poland, Czechia and Netherlands. Michał Kuniecki's co-authors include Szymon Wichary, Rolf Verleger, Hartwig R. Siebner, Piotr Jaśkowskí, Marek Binder, Andrzej Urbanik, Jan Christian Kaiser, A.M.L. Coenen, Aleksandra Domagalik and Kamila Śmigasiewicz and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Experimental Brain Research.

In The Last Decade

Michał Kuniecki

23 papers receiving 351 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michał Kuniecki Poland 10 239 97 91 27 23 25 354
Inês Almeida Portugal 12 231 1.0× 48 0.5× 93 1.0× 25 0.9× 18 0.8× 21 408
Elle van Heusden Netherlands 6 258 1.1× 80 0.8× 86 0.9× 28 1.0× 42 1.8× 11 363
Seppo J. Laukka Finland 13 290 1.2× 53 0.5× 97 1.1× 12 0.4× 18 0.8× 30 402
Artur Czeszumski Germany 7 322 1.3× 221 2.3× 73 0.8× 14 0.5× 20 0.9× 11 445
Lars Strother United States 14 364 1.5× 77 0.8× 87 1.0× 15 0.6× 13 0.6× 32 430
Katrin Herrmann United States 4 387 1.6× 52 0.5× 105 1.2× 32 1.2× 6 0.3× 6 457
Unni Sulutvedt Norway 8 285 1.2× 89 0.9× 108 1.2× 49 1.8× 30 1.3× 10 396
Joseph Schmidt United States 15 486 2.0× 48 0.5× 176 1.9× 36 1.3× 97 4.2× 34 657
Miranda Scolari United States 10 730 3.1× 74 0.8× 115 1.3× 16 0.6× 19 0.8× 22 799
Stefanie Rukavina Germany 6 178 0.7× 115 1.2× 161 1.8× 18 0.7× 12 0.5× 11 327

Countries citing papers authored by Michał Kuniecki

Since Specialization
Citations

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

Fields of papers citing papers by Michał Kuniecki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michał Kuniecki

This figure shows the co-authorship network connecting the top 25 collaborators of Michał Kuniecki. A scholar is included among the top collaborators of Michał Kuniecki 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 Michał Kuniecki. Michał Kuniecki 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.
Kuniecki, Michał, et al.. (2025). Pupil-Based Prediction of Affect in VR: A Machine Learning Approach. Homo Politicus (Academy of Humanities and Economics in Lodz). 1376–1377.
2.
Kuniecki, Michał, et al.. (2024). Like a human: The social facilitation/inhibition effect in presence of a virtual observer depends on arousal. Virtual Reality. 28(1). 3 indexed citations
3.
4.
Hohol, Mateusz, et al.. (2022). Restricting movements of lower face leaves recognition of emotional vocalizations intact but introduces a valence positivity bias. Scientific Reports. 12(1). 16101–16101. 2 indexed citations
5.
Schwertner, Emilia, et al.. (2022). Physiological reactions at encoding selectively predict recognition of emotional images. Biological Psychology. 175. 108429–108429. 4 indexed citations
6.
Kuniecki, Michał, et al.. (2021). Psychophysiology in Studying VR-Mediated Interactions: Panacea or a Trick? Valuable Applications, Limitations, and Future Directions. SHILAP Revista de lepidopterología. 2. 3 indexed citations
7.
Kuniecki, Michał, et al.. (2021). Psychophysiology, eye-tracking and VR: exemplary study design. 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). 9. 639–640. 4 indexed citations
8.
Kuniecki, Michał, et al.. (2020). Blue blood, red blood. How does the color of an emotional scene affect visual attention and pupil size?. Vision Research. 171. 36–45. 5 indexed citations
9.
Schwertner, Emilia, et al.. (2019). Phase of the menstrual cycle affects engagement of attention with emotional images. Psychoneuroendocrinology. 104. 25–32. 11 indexed citations
10.
Kuniecki, Michał, et al.. (2017). Effects of Scene Properties and Emotional Valence on Brain Activations: A Fixation-Related fMRI Study. Frontiers in Human Neuroscience. 11. 429–429. 9 indexed citations
11.
Kuniecki, Michał, et al.. (2015). The color red attracts attention in an emotional context. An ERP study. Frontiers in Human Neuroscience. 9. 212–212. 53 indexed citations
12.
Kuniecki, Michał, et al.. (2014). Emotional content of an image attracts attention more than visually salient features in various signal-to-noise ratio conditions. Journal of Vision. 14(12). 4–4. 33 indexed citations
13.
Barry, Robert J., et al.. (2011). Fast, transient cardiac accelerations and decelerations during fear conditioning in rats. Physiology & Behavior. 105(3). 607–612. 9 indexed citations
14.
Verleger, Rolf, et al.. (2010). The left visual-field advantage in rapid visual presentation is amplified rather than reduced by posterior-parietal rTMS. Experimental Brain Research. 203(2). 355–365. 25 indexed citations
15.
Verleger, Rolf, et al.. (2009). On how the motor cortices resolve an inter‐hemispheric response conflict: an event‐related EEG potential‐guided TMS study of the flankers task. European Journal of Neuroscience. 30(2). 318–326. 57 indexed citations
16.
Kuniecki, Michał, et al.. (2008). Neuronal mechanisms of face perception [Review]. Acta Neurobiologiae Experimentalis. 68(2). 229–252. 36 indexed citations
17.
Maes, Joseph H. R., et al.. (2007). N150 in amygdalar ERPs in the rat: Is there modulation by anticipatory fear?. Physiology & Behavior. 93(1-2). 222–228. 3 indexed citations
18.
Wronka, Eligiusz, et al.. (2007). The P3 produced by auditory stimuli presented in a passive and active condition: Modulation by visual stimuli. Acta Neurobiologiae Experimentalis. 67(2). 155–164. 9 indexed citations
19.
Kuniecki, Michał, et al.. (2003). Central control of heart rate changes during visual affective processing as revealed by fMRI. Acta Neurobiologiae Experimentalis. 63(1). 39–48. 46 indexed citations
20.
Kuniecki, Michał, A.M.L. Coenen, & Jan Christian Kaiser. (2002). Correlation between long latency evoked potentials from amygdala and evoked cardiac response to fear conditioned stimulus in rats. Acta Neurobiologiae Experimentalis. 62(2). 85–92. 15 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Inês Almeida Breakdown of academic impact, for papers by Elle van Heusden Breakdown of academic impact, for papers by Seppo J. Laukka Breakdown of academic impact, for papers by Artur Czeszumski Breakdown of academic impact, for papers by Lars Strother Breakdown of academic impact, for papers by Katrin Herrmann Breakdown of academic impact, for papers by Unni Sulutvedt Breakdown of academic impact, for papers by Joseph Schmidt Breakdown of academic impact, for papers by Miranda Scolari Breakdown of academic impact, for papers by Stefanie Rukavina
Rankless by CCL
2026