Publikationen

Preprints

García Alanis, J. C., Strelow, A. E., Dort, M., Christiansen, H., Pinquart, M., & Panitz, C. (2022). Expectation violations, expectation change, and expectation persistence: The scientific landscape as revealed by bibliometric network analyses. PsyArXiv. https://doi.org/10.31234/osf.io/73f52

Sadus, K., Göttmann, J., & Schubert, A.-L. (2021). Predictors of stockpiling behavior during the COVID-19 pandemic in Germany. PsyArXiv. https://doi.org/10.31234/osf.io/2m9nu

Schubert, A.-L., Löffler, C., Hein, J., Schröer, P., Teuber, A., Sadus, K., & Hagemann, D. (2020). Tic Toc: How working memory load affects intelligence test performance under different time constraints. PsyArXiv. https://doi.org/10.31234/osf.io/8nm4s

in press

Frischkorn, G. T., Hilger, K., Kretzschmar, A., & Schubert, A.-L. (in press). Intelligenzdiagnostik der Zukunft: Ein Plädoyer für eine prozessorientierte und biologisch inspirierte Intelligenzmessung. Psychologische Rundschau.

Schubert, A.-L., Löffler, C., & Hagemann, D. (in press). A Neurocognitive Psychometrics Account of Individual Differences in Attentional Control. Journal of Experimental Psychology: General. https://doi.org/10.31234/osf.io/vj7eb

2022

Löffler, C., Frischkorn, G. T., Rummel, J., Hagemann, D., & Schubert, A.-L. (2022). Do attentional lapses account for the worst performance rule? Journal of Intelligence,10(1), 2. https://dx.doi.org/10.3390/jintelligence10010002
    Mirman, D., Scheel, A. M., Schubert, A.-L., & McIntosh, R. D. (2022). Strengthening derivation chains in cognitive neuroscience: A special issue of Cortex. Cortex, 146, A1–A4.

    2021

    Euler, M. J., & Schubert, A.-L. (2021). Recent developments, current challenges, and future directions in electrophysiological approaches to studying intelligence. Intelligence, 88, 101569. https://doi.org/10.1016/j.intell.2021.101569
      Jungeblut, H. M., Hagemann, D., Löffler, C., & Schubert, A.-L. (2021). An Investigation of the Slope Parameters of Reaction Times and P3 Latencies in the Sternberg Memory Scanning Task – A Fixed-Links Model Approach. Journal of Cognition, 4(1), 26. https://doi.org/10.5334/joc.158

        Rummel, J., Hagemann, D., Steindorf, L., & Schubert, A.-L. (2021). How consistent is mind wandering across situations and tasks?—A latent state–trait analysis. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication. https://doi.org/10.1037/xlm0001041

        Schubert, A.-L., Ferreira, M. B., Mata, A., & Riemenschneider, B. (2021). A diffusion model analysis of belief bias: Different cognitive mechanisms explain how cognitive abilities and thinking styles contribute to conflict resolution in reasoning. Cognition, 211, 104629. https://doi.org/10.1016/j.cognition.2021.104629

        Schubert, A.-L., Hagemann, D., & Göttmann, J. (2021). Do individual effects reflect quantitative or qualitative differences in cognition? Journal of Cognition, 4(1), 50. http://doi.org/10.5334/joc.171

        Schubert, A.-L., Hagemann, D., Löffler, C., Rummel, J., & Arnau, S. (2021). A chronometric model of the relationship between frontal midline theta functional connectivity and human intelligence. Journal of Experimental Psychology. General, 150(1), 1–22. https://doi.org/10.1037/xge0000865

        2020

        Arnau, S., Löffler, C., Rummel, J., Hagemann, D., Wascher, E., & Schubert, A.-L. (2020). Inter-trial alpha power indicates mind wandering. Psychophysiology, 57(6), e13581. https://doi.org/10.1111/psyp.13581
          Klatt, L.-I., Schneider, D., Schubert, A.-L., Hanenberg, C., Lewald, J., Wascher, E., & Getzmann, S. (2020).
          Unraveling the relation between eeg correlates of attentional orienting and sound localization performance: A diffusion model approach. Journal of Cognitive Neuroscience, 32(5), 945–962. https://doi.org/10.1162/jocn_a_01525
            Lerche, V., von Krause, M., Voss, A., Frischkorn, G. T., Schubert, A.-L., & Hagemann, D. (2020). Diffusion modeling and intelligence: Drift rates show both domain-general and domain-specific relations with intelligence. Journal of Experimental Psychology. General, 149(12), 2207–2249. https://doi.org/10.1037/xge0000774
              Schubert, A.-L., & Frischkorn, G. T. (2020). Neurocognitive psychometrics of intelligence: How measurement advancements unveiled the role of mental speed in intelligence differences: Current Directions in Psychological Science, 29(2), 140–146. https://doi.org/10.1177/0963721419896365
                Schubert, A.-L., Frischkorn, G. T., & Rummel, J. (2020). The validity of the online thought-probing procedure of mind wandering is not threatened by variations of probe rate and probe framing. Psychological Research, 84(7), 1846–1856. https://doi.org/10.1007/s00426-019-01194-2
                  Schubert, A.-L., Hagemann, D., Löffler, C., & Frischkorn, G. T. (2020). Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence. Journal of Intelligence, 8(1), 1. https://doi.org/10.3390/jintelligence8010001
                    von Krause, M., Lerche, V., Schubert, A.-L., & Voss, A. (2020). Do Non-Decision Times Mediate the Association between Age and Intelligence across Different Content and Process Domains? Journal of Intelligence, 8(3), 33. https://doi.org/10.3390/jintelligence8030033

                    2019

                    Frischkorn, G. T., Schubert, A.-L., & Hagemann, D. (2019). Processing speed, working memory, and executive functions: Independent or inter-related predictors of general intelligence. Intelligence, 75, 95–110. https://doi.org/10.1016/j.intell.2019.05.003
                      Schubert, A.-L. (2019). A meta-analysis of the worst performance rule. Intelligence, 73, 88–100. https://doi.org/10.1016/j.intell.2019.02.003
                        Schubert, A.-L., Nunez, M. D., Hagemann, D., & Vandekerckhove, J. (2019). Individual differences in cortical processing speed predict cognitive abilities: A model-based cognitive neuroscience account. Computational Brain & Behavior, 2(2), 64–84. https://doi.org/10.1007/s42113-018-0021-5
                          Schubert, A.-L., & Rey-Mermet, A. (2019). Does process overlap theory replace the issues of general intelligence with the issues of attentional control? Journal of Applied Research in Memory and Cognition, 8(3), 277–283. https://doi.org/10.1016/j.jarmac.2019.06.004

                          2018

                          Frischkorn, G. T., & Schubert, A.-L. (2018). Cognitive models in intelligence research: Advantages and recommendations for their application. Journal of Intelligence, 6(3), 34. https://doi.org/10.3390/jintelligence6030034
                            Klotz, A.-L., Tauber, B., Schubert, A.-L., Hassel, A. J., Schröder, J., Wahl, H.-W., Rammelsberg, P., & Zenthöfer, A. (2018). Oral health-related quality of life as a predictor of subjective well-being among older adults—A decade-long longitudinal cohort study. Community Dentistry and Oral Epidemiology, 46(6), 631–638. https://doi.org/10.1111/cdoe.12416
                              Schubert, A.-L., Hagemann, D., Frischkorn, G. T., & Herpertz, S. C. (2018). Faster, but not smarter: An experimental analysis of the relationship between mental speed and mental abilities. Intelligence, 71, 66–75. https://doi.org/10.1016/j.intell.2018.10.005
                                Zähringer, J., Falquez, R., Schubert, A.-L., Nees, F., & Barnow, S. (2018). Neural correlates of reappraisal considering working memory capacity and cognitive flexibility. Brain Imaging and Behavior, 12(6), 1529–1543. https://doi.org/10.1007/s11682-017-9788-6

                                2017

                                Schubert, A.-L., Hagemann, D., & Frischkorn, G. T. (2017). Is general intelligence little more than the speed of higher-order processing? Journal of Experimental Psychology. General, 146(10), 1498–1512. https://doi.org/10.1037/xge0000325

                                  Schubert, A.-L., Hagemann, D., Voss, A., & Bergmann, K. (2017). Evaluating the model fit of diffusion models with the root mean square error of approximation. Journal of Mathematical Psychology, 77, 29–45. https://doi.org/10.1016/j.jmp.2016.08.004

                                  2016

                                  Bergmann, K., Schubert, A.-L., Hagemann, D., & Schankin, A. (2016). Age-related differences in the P3 amplitude in change blindness. Psychological Research, 80(4), 660–676. https://doi.org/10.1007/s00426-015-0669-6

                                    Schankin, A., Bergmann, K., Schubert, A.-L., & Hagemann, D. (2016). The allocation of attention in change detection and change blindness. Journal of Psychophysiology, 31(3), 94–106. https://doi.org/10.1027/0269-8803/a000172

                                      Schubert, A.-L., Frischkorn, G. T., Hagemann, D., & Voss, A. (2016). Trait characteristics of diffusion model parameters. Journal of Intelligence, 4(3), 7. https://doi.org/10.3390/jintelligence4030007

                                        Schubert, A.-L., Hagemann, D., Voss, A., Schankin, A., & Bergmann, K. (2015). Decomposing the relationship between mental speed and mental abilities. Intelligence, 51, 28–46. https://doi.org/10.1016/j.intell.2015.05.002

                                        2015

                                        Mata, A., Schubert, A.-L., & B. Ferreira, M. (2014). The role of language comprehension in reasoning: How “good-enough” representations induce biases. Cognition, 133(2), 457–463. https://doi.org/10.1016/j.cognition.2014.07.011