G. L.

  • I'm a postdoc working on various topics within applied cognitive psychology and research methods.
Background in very short
  • 03/2023 – present  University of Vienna (researcher)
  • 10/2022 – 02/2023 University of Konstanz (researcher; DFG project)
  • 04/2022 – 09/2022 Tilburg University (researcher; NWO project)
  • 10/2021 – 03/2022 Aoyama Gakuin University (JSPS fellow)
  • 03/2021 – 09/2021 University of Vienna (PostDocTrack fellow)
  • 03/2018 – 02/2021 University of Vienna (ÖAW-DOC PhD fellow)
  • 02/2020 University College London (visiting)
  • 08/2019 University of Gothenburg (visiting)
  • 04/2019 – 07/2019 Humboldt University of Berlin (visiting)
  • 03/2017 – 02/2018 European Investment Fund (IT consultant)
  • 10/2016 – 02/2017 Eurostat (statistician intern)
  • 2011 – 2016 University of Szeged (cognitive and neuropsychology studies)
  • 07/2016 – 09/2016 Maastricht University (research intern)
  • 03/2016 – 05/2016 University of Amsterdam (research intern)
  • 10/2015 – 02/2016 University of Klagenfurt (Erasmus+ & research intern)
  • 09/2014 – 01/2015 University of Angers (Campus Hungary)
  • 09/2012 – 02/2013 Bielefeld University (LLP Erasmus)
  • Email: lkcsgaspar@gmail.com
    In the unlikely case that I don't reply within a few days, feel free to message me again.
  • Python material   Thesis/internship
Publications (peer-reviewed international journal papers)
  1. Lukács, G., & Ansorge, U. (in press). Response time concealed information test using fillers in cybercrime and concealed identity scenarios. Memory. https://doi.org/10.1080/09658211.2023.2195179
  2. Lukács, G., & Gartus, A. (2023). Precise display time measurement in JavaScript for web-based experiments. Behavior Research Methods. https://doi.org/10.3758/s13428-022-01835-2 [pdf]
  3. Lukács, G., Kawai, C., Ansorge, U., & Fekete, A. (2023). Detecting concealed language knowledge via response times. Applied Linguistics Review. https://doi.org/10.1515/applirev-2020-0130
  4. Kawai, C., Zhang, Y., Lukács, G., Chu, W., Zheng, C., Gao, C., Gozli, D., Wang, Y., & Ansorge, U. (2023). The good, the bad, and the red: implicit color-valence associations across cultures. Psychological Research, 87(3), 704–724. https://doi.org/10.1007/s00426-022-01697-5 [pdf]
  5. Lukács, G. (2022). Prolonged response time concealed information test decreases probe-control differences but increases classification accuracy. Journal of Applied Research in Memory and Cognition, 11(2), 188–199. https://doi.org/10.1016/j.jarmac.2021.08.008 [pdf]
  6. Lukács, G., & Steyrl, D. (2022). Machine learning mega-analysis applied to the Response Time Concealed Information Test: No evidence for advantage of model-based predictors over baseline. Collabra: Psychology, 8(1), 32661. https://doi.org/10.1525/collabra.32661 [pdf]
  7. Lubczyk, T., Lukács, G., & Ansorge, U. (2022). Speed versus accuracy instructions in the response time concealed information test. Cognitive Research: Principles and Implications, 7(1), 1–11. https://doi.org/10.1186/s41235-021-00352-8 [pdf]
  8. Wojciechowski, J., & Lukács, G. (2022). Importance-related fillers improve the classification accuracy of the response time concealed information test in a crime scenario. Legal and Criminological Psychology, 27(1), 82–100. https://doi.org/10.1111/lcrp.12198 [pdf]
  9. Lukács, G. (2022). POSSA: Power simulation for sequential analyses and multiple hypotheses. Journal of Open Source Software, 7(76), 4643. https://doi.org/10.21105/joss.04643 [pdf]
  10. Lukács, G., & Ansorge, U. (2021). The mechanism of filler items in the Response Time Concealed Information Test. Psychological Research, 85(7), 2808–2828. https://doi.org/10.1007/s00426-020-01432-y [pdf]
  11. Kawai, C., Lukács, G., & Ansorge, U. (2021). A new type of pictorial database: The Bicolor Affective Silhouettes and Shapes (BASS). Behavior Research Methods, 53(6), 2558–2575. https://doi.org/10.3758/s13428-021-01569-7 [pdf]
  12. Lukács, G. (2021). SmaRT-CIT: Smartphone app for the Response Time Concealed Information Test with ready-to-use Android deployment. Journal of Open Research Software, 9(1), 1-7. https://doi.org/10.5334/jors.341 [pdf]
  13. Lukács, G. (2021). neatStats: An R package for a neat pipeline from raw data to reportable statistics in psychological science. The Quantitative Methods for Psychology, 17(1), 7-23. https://doi.org/10.20982/tqmp.17.1.p007 [pdf]
  14. Lukács, G. (2021). Addressing selective attrition in the enhanced response time‐based Concealed Information Test: A within‐subject replication. Applied Cognitive Psychology, 35(1), 243–250. https://doi.org/10.1002/acp.3759 [pdf]
  15. Lukács, G., & Specker, E. (2020). Dispersion matters: Diagnostics and control data computer simulation in Concealed Information Test studies. PLOS ONE, 15(10), e0240259. https://doi.org/10.1371/journal.pone.0240259 [pdf]
  16. Kawai, C., Lukács, G., & Ansorge, U. (2020). Polarities influence implicit associations between colour and emotion. Acta Psychologica, 209, 103143. https://doi.org/10.1016/j.actpsy.2020.103143 [pdf]
  17. Lukács, G., Kleinberg, B., Kunzi, M., & Ansorge, U. (2020). Response time Concealed Information Test on smartphones. Collabra: Psychology, 6(1), 4. https://doi.org/10.1525/collabra.255 [pdf]
  18. Lukács, G. & Ansorge, U. (2019). Information leakage in the response time-based Concealed Information Test. Applied Cognitive Psychology, 33(6), 1178-1196. https://doi.org/10.1002/acp.3565 [pdf]
  19. Lukács, G., Grządziel, A., Kempkes, M., & Ansorge, U. (2019). Item roles explored in a modified P300-Based CTP Concealed Information Test. Applied Psychophysiology and Biofeedback, 44(3), 195-209. https://doi.org/10.1007/s10484-019-09430-6 [pdf]
  20. Lukács, G. & Ansorge, U. (2019). Methodological improvements of the association-based Concealed Information Test. Acta Psychologica, 194, 7-16. https://doi.org/10.1016/j.actpsy.2019.01.010 [pdf]
  21. Lukács, G. (2019). CITapp – a response time-based Concealed Information Test lie detector web application. Journal of Open Source Software, 4(34), 1179. https://doi.org/10.21105/joss.01179 [pdf]
  22. Lukács, G., Kleinberg, B., & Verschuere, B. J. (2017). Familiarity-related fillers improve the validity of reaction time-based memory detection. Journal of Applied Research in Memory and Cognition, 6(3), 295-305. https://doi.org/10.1016/j.jarmac.2017.01.013 [pdf]
  23. Lukács, G., Gula, B., Hallgató, E., & Csifcsák, G. (2017). Association-based Concealed Information Test: A novel reaction time-based deception detection method. Journal of Applied Research in Memory and Cognition, 6(3), 283-294. https://doi.org/10.1016/j.jarmac.2017.06.001 [pdf]
  24. Lukács, G., Weiss, B., Dalos, V. D., Kilencz, T., Tudja, S., & Csifcsák, G. (2016). The first independent study on the complex trial protocol version of the P300-based Concealed Information Test: Corroboration of previous findings and highlights on vulnerabilities. International Journal of Psychophysiology, 110, 56-65. https://doi.org/10.1016/j.ijpsycho.2016.10.010 [pdf]