Jiří Lukavský

2.3k total citations · 1 hit paper
75 papers, 1.2k citations indexed

About

Jiří Lukavský is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Psychiatry and Mental health. According to data from OpenAlex, Jiří Lukavský has authored 75 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Cognitive Neuroscience, 20 papers in Experimental and Cognitive Psychology and 17 papers in Psychiatry and Mental health. Recurrent topics in Jiří Lukavský's work include Dementia and Cognitive Impairment Research (9 papers), Visual perception and processing mechanisms (8 papers) and Gaze Tracking and Assistive Technology (8 papers). Jiří Lukavský is often cited by papers focused on Dementia and Cognitive Impairment Research (9 papers), Visual perception and processing mechanisms (8 papers) and Gaze Tracking and Assistive Technology (8 papers). Jiří Lukavský collaborates with scholars based in Czechia, United States and Slovakia. Jiří Lukavský's co-authors include Hana Štěpánková, Cyril Brom, Miloslav Kopeček, Filip Děchtěrenko, Ondřej Bezdíček, Daniela Řı́pová, Kateřina Lukavská, Marek Preiss, Marek Urban and Kamila Urban and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Developmental Psychology.

In The Last Decade

Jiří Lukavský

68 papers receiving 1.1k citations

Hit Papers

ChatGPT improves creative problem-solving performance in ... 2024 2026 2025 2024 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. ChatGPT improves creative problem-solving performance in university students: An experimental study
    2024 150 citations Marek Urban, Filip Děchtěrenko et al. Computers & Education profile →

Peers

Jiří Lukavský
Jorge Oliveira Portugal
Farshid Faraji United States
Jacqueline R. Janowich United States
Camarin E. Rolle United States
Tara T. Lineweaver United States
Christopher R. Brydges United States
Benjamin Katz United States
Daniele Magistro United Kingdom
Michael A. Motes United States
Brian Harel United States
Jorge Oliveira Portugal
Jiří Lukavský
Citations per year, relative to Jiří Lukavský Jiří Lukavský (= 1×) peers Jorge Oliveira

Countries citing papers authored by Jiří Lukavský

Since Specialization
Citations

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

Fields of papers citing papers by Jiří Lukavský

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiří Lukavský

This figure shows the co-authorship network connecting the top 25 collaborators of Jiří Lukavský. A scholar is included among the top collaborators of Jiří Lukavský 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 Jiří Lukavský. Jiří Lukavský 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.
Urban, Marek, et al.. (2025). “ ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing. British Journal of Educational Technology. 56(5). 1897–1918. 4 indexed citations
2.
Tomeček, David, Iveta Fajnerová, Filip Děchtěrenko, et al.. (2025). Deviation From Typical Brain Activity During Naturalistic Stimulation Is Related to Personality Traits. Psychophysiology. 62(12). e70203–e70203.
3.
Urban, Marek, et al.. (2025). Prompting for creative problem-solving: A process-mining study. Learning and Instruction. 99. 102156–102156. 1 indexed citations
4.
Urban, Marek, et al.. (2024). ChatGPT improves creative problem-solving performance in university students: An experimental study. Computers & Education. 215. 105031–105031. 150 indexed citations breakdown →
5.
Daďová, Klára, et al.. (2024). Cognitive SuperAging in Physically Active Women (COSACTIW): Study Protocol and data from the NANOK. 18(1). 30–52. 1 indexed citations
6.
Lukavský, Jiří, et al.. (2023). Contextual animation in multimedia learning materials for pre-adolescents: The saga of null results continues. Learning and Instruction. 87. 101803–101803. 3 indexed citations
7.
Lukavský, Jiří & Hauke S. Meyerhoff. (2023). Gaze coherence reveals distinct tracking strategies in multiple object and multiple identity tracking. Psychonomic Bulletin & Review. 31(3). 1280–1289. 1 indexed citations
8.
Lukavský, Jiří, Lauri Oksama, & Filip Děchtěrenko. (2022). Multiple object tracking with extended occlusions. Quarterly Journal of Experimental Psychology. 76(9). 2094–2106.
9.
Děchtěrenko, Filip, et al.. (2022). Tracking multiple fish. PeerJ. 10. e13031–e13031. 1 indexed citations
10.
Kremláček, Jan, et al.. (2022). The intraocular implant and visual rehabilitation improve the quality of life of elderly patients with geographic atrophy secondary to age-related macular degeneration. Graefe s Archive for Clinical and Experimental Ophthalmology. 261(1). 263–272. 3 indexed citations
11.
Děchtěrenko, Filip & Jiří Lukavský. (2022). False memories when viewing overlapping scenes. PeerJ. 10. e13187–e13187. 2 indexed citations
12.
Lukavský, Jiří. (2019). Scene categorization in the presence of a distractor. Journal of Vision. 19(2). 6–6. 4 indexed citations
13.
Lukavský, Jiří, et al.. (2019). Anthropomorphisms in multimedia learning: Attract attention but do not enhance learning?. Journal of Computer Assisted Learning. 35(4). 555–568. 30 indexed citations
14.
Lukavský, Jiří & Filip Děchtěrenko. (2017). Visual properties and memorising scenes: Effects of image-space sparseness and uniformity. Attention Perception & Psychophysics. 79(7). 2044–2054. 20 indexed citations
15.
Lukavský, Jiří. (2010). THE EFFECTS OF COGNITIVE AND EMOTIONAL WORKLOAD ON PHYSIOLOGICAL REACTIONS IN WORD ASSOCIATION TEST IN HEALTHY ADULTS. Studia Psychologica. 52(1). 15–22. 2 indexed citations
16.
Lukavský, Jiří, et al.. (2010). SPIRITUALITY OF AMERICAN AND CZECH STUDENTS - A CROSS-CULTURAL COMPARISON. Studia Psychologica. 52(3). 243–251. 5 indexed citations
17.
Lukavský, Jiří & Filip Šmolík. (2009). Word Order and Case Inflection in Czech: On-line Sentence Comprehension in Children and Adults. eScholarship (California Digital Library). 31(31). 1 indexed citations
18.
Lukavský, Jiří, et al.. (2008). RELATIONSHIP OF CHILDREN'S DEPRESSION INVENTORY FACTOR STRUCTURE TO SCHOOL ACHIEVEMENT. Studia Psychologica. 50(4). 383–394. 1 indexed citations
19.
Preiss, Marek, et al.. (2007). Cognitive deficits in unipolar depression during remission - Auditory Verbal Learning Test findings. 11(11). 3 indexed citations
20.
Brom, Cyril, et al.. (2006). Affordances and level-of-detail AI for virtual humans. 7 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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