Publikationen

Preprints

García Alanis, J. C., Nunez, M. D., Wehrheim, M. H., Fiebach, C., Löffler, C., & Schubert, A.-L. (2023). The Devil's in the Variability: A Multidimensional Analysis of EEG Signal Dynamics and Their Relation to Behaviour. PsyArXiv. https://doi.org/10.31234/osf.io/4ug3y

Löffler, C., Sadus, K., Frischkorn, G. T., Hagemann, D., & Schubert, A.-L. (2024). The factor structure of executive functions measured with electrophysiological correlates: An event-related potential analysis. PsyArXiv. https://doi.org/10.31234/osf.io/kfqt4

Nunez, M. D., Schubert, A.-L., Frischkorn, G. T., & Oberauer, K. (2023). Cognitive models of decision-making with identifiable parameters: Diffusion Decision Models with within-trial noise. PsyArXiv. https://doi.org/10.31234/osf.io/h4fde

Schubert, A.-L., Frischkorn, G. T., Sadus, K., Welhaf, M., Kane, M. J., & Rummel, J. (2023). The brief mind wandering three-factor scale (BMW-3). PsyArXiv. https://doi.org/10.31234/osf.io/cy9rx

Strzelczyk, D., Clayson, P. E., Sigurdardottir, H. M., Mushtaq, F., Pavlov, Y. G., Devillez, H., … Langer, N. (2023). Contralateral delay activity as a marker of visual working memory capacity: a multi-site registered replication. PsyArXiv. https://doi.org/10.31234/osf.io/shdea

Wehrheim, M., Faskowitz, J., García Alanis, J. C., Schubert, A.-L., & Fiebach, C. (2023). Reliability of Variability and Complexity Measures for Task and Task-Free BOLD fMRI. PsyArXiv. https://doi.org/10.31234/osf.io/ves2t

In Press

Steinhilber, M., Schnuerch, M., & Schubert, A.-L. (in press). Sequential Analysis of Variance: Increasing Efficiency of Hypothesis Testing. Psychological Methods. https://doi.org/10.31234/osf.io/m64ne

2024

Löffler, C., Frischkorn, G. T., Hagemann, D., Sadus, K., & Schubert, A.-L. (2024). The common factor of executive functions measures nothing but speed of information uptake. Psychological Research. https://doi.org/10.1007/s00426-023-01924-7

Mirman, D., Scheel, A., Schubert, A.-L., & McIntosh, R. D. (2024). Strengthening derivation chains in cognitive neuroscience: Closing editorial. Cortex. https://doi.org/10.1016/j.cortex.2024.04.004

Schubert, A.-L., Löffler, C., Wiebel, C., Kaulhausen, F., & Baudson, T. G. (2024). Don’t waste your time measuring intelligence: Further evidence for the validity of a three-minute speeded reasoning test. Intelligence, 102, 101804. https://doi.org/10.1016/j.intell.2023.101804

Vermeent, S., Young, E. S., DeJoseph, M. L., Schubert, A.-L., & Frankenhuis, W. E. (2024). Cognitive deficits and enhancements in youth from adverse conditions: An integrative assessment using Drift Diffusion Modeling in the ABCD study. Developmental Science, e13478. https://doi.org/10.1111/desc.13478

2023

García Alanis, J. C., Strelow, A. E., Dort, M., Christiansen, H., Pinquart, M., & Panitz, C. (2023). Expectation violations, expectation change, and expectation persistence: The scientific landscape as revealed by bibliometric network analyses. Collabra: Psychology, 9(1), 73830. https://doi.org/10.1525/collabra.73830

Hagemann, D., Ihmels, M., Bast, N., Neubauer, A. B., Schankin, A., & Schubert, A.-L. (2023). Fluid intelligence is (much) more than working memory capacity: An experimental analysis. Journal of Intelligence,11(4), 4. https://doi.org/10.3390/jintelligence11040070

Jensen, M., García Alanis, J. C., Hüttenrauch, E., Winther-Jensen, M., Chavanon, M.-L., Andersson, G., & Weise, C. (2023). Does it matter what is trained? A randomized controlled trial evaluating the specificity of alpha/delta ratio neurofeedback in reducing tinnitus symptoms. Brain Communications, fcad185. https://doi.org/10.1093/braincomms/fcad185

Marquetand, C., Aboud, A., Hasfurther, M., Göttmann, J., Bahlmann, E., Busch-Tilge, C., Tilge, P., Ivannikova, M., Ensminger, S., Stierle, U., Reil, G.-H., & Reil, J.-C. (2023). New insights into the hemodynamics of pulmonary homograft patients under stress echocardiography: The contribution of pressure recovery. Echocardiography, 1-10. https://doi.org/10.1111/echo.15675

Nebe, S., Reutter, M., Baker, D., Bölte, J., Domes, G., Gamer, M., Gärtner, A., Gießing, C., Mann, C. G. née, Hilger, K., Jawinski, P., Kulke, L., Lischke, A., Markett, S., Meier, M., Merz, C., Popov, T., Puhlmann, L., Quintana, D., Schäfer, T., Schubert, A.-L., Sperl, M. F. J., Vehlen, A., Lonsdorf, T., & Feld, G. (2023). Enhancing precision in human neuroscience. eLife, 12, e85980. https://doi.org/10.7554/eLife.85980

Sadus, K., Schubert, A.-L., Löffler, C., & Hagemann, D. (2023). An explorative multiverse study for extracting differences in P3 latencies between young and old adults. Psychophysiology, e14459, https://doi.org/10.1111/psyp.14459

Schubert, A.-L., Löffler, C., Sadus, K., Göttmann, J., Hein, J., Schröer, P., Teuber, A., & Hagemann, D. (2023). Working memory load affects intelligence test performance by reducing the strength of relational item bindings and impairing the filtering of irrelevant information. Cognition, 236, 105438. https://doi.org/10.1016/j.cognition.2023.105438

2022

Dordevic, M., Hoelzer, S., Russo, A., García Alanis, J. C., & Müller, N. G. (2022). The role of the precuneus in human spatial updating in a real environment setting—A cTBS study. Life, 12(8), 1239. https://doi.org/10.3390/life12081239
    Frischkorn, G. T., Hilger, K., Kretzschmar, A., & Schubert, A.-L. (2022). Intelligenzdiagnostik der Zukunft: Ein Plädoyer für eine prozessorientierte und biologisch inspirierte Intelligenzmessung. Psychologische Rundschau, 73(3), 173-189. https://doi.org/10.1026/0033-3042/a000598
      Hilger, K., Spinath, F. M., Troche, S., & Schubert, A.-L. (2022). The biological basis of intelligence: Benchmark findings. Intelligence, 93, 101665. https://doi.org/10.1016/j.intell.2022.101665
        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. https://doi.org/10.1016/j.cortex.2021.12.002
            Radeck, L., Paech, B., Kramer-Gmeiner, F., Wettstein, M., Wahl, H.-W., Schubert, A.-L., & Sperling, U. (2022). Understanding IT-related Well-being, Aging and Health Needs of Older Adults with Crowd-Requirements Engineering. 2022 IEEE 30th International Requirements Engineering Conference Workshops (REW), 57–64. https://doi.org/10.1109/REW56159.2022.00018
              Sadus, K., Göttmann, J., & Schubert, A.-L. (2022). Predictors of stockpiling behavior during the COVID-19 pandemic in Germany. Journal of Public Health. https://doi.org/10.1007/s10389-022-01727-x

                Schönbrodt, F., Gärtner, A., Frank, M., Gollwitzer, M., Ihle, M., Mischkowski, D., Phan, L. V., Schmitt, M., Scheel, A. M., Schubert, A.-L., Steinberg, U., & Leising, D. (2022). Responsible Research Assessment I: Implementing DORA for hiring and promotion in psychology. Meta-PsychArchives. https://doi.org/10.23668/psycharchives.8162

                  Schubert, A.-L., Löffler, C., & Hagemann, D. (2022). A Neurocognitive Psychometrics Account of Individual Differences in Attentional Control. Journal of Experimental Psychology: General, 151(9), 2060-2082. https://doi.org/10.1037/xge0001184
                    Schubert, A.-L., Löffler, C., Hagemann, D., & Sadus, K. (2022). How Robust is the Relationship between Neural Processing Speed and Cognitive Abilities? Psychophysiology, e14165. https://doi.org/10.1111/psyp.14165
                      Thome, I., García Alanis, J. C., Volk, J., Vogelbacher, C., Steinsträter, O., & Jansen, A. (2022). Let’s face it: The lateralization of the face perception network as measured with fMRI is not clearly right dominant. NeuroImage, 263, 119587. https://doi.org/10.1016/j.neuroimage.2022.119587

                      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
                                                Kretzschmar, A., Spengler, M., Schubert, A.-L., Steinmayr, R., & Ziegler, M. (2018). The Relation of Personality and Intelligence—What Can the Brunswik Symmetry Principle Tell Us? Journal of Intelligence, 6(3), Article 3. https://doi.org/10.3390/jintelligence6030030
                                                  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

                                                        Frischkorn, G. T., Schubert, A.-L., Neubauer, A. B., & Hagemann, D. (2016). The Worst Performance Rule as Moderation: New Methods for Worst Performance Analysis. Journal of Intelligence, 4(3), 3. https://doi.org/10.3390/jintelligence4030009

                                                          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