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

Hunt, C., Löffler, C., Hülsemann, M., Schubert, A., & Meinhardt, G. (2025). Electrophysiological correlates of saliency and feature synergy. PsyArXiv. https://doi.org/10.31234/osf.io/37mut_v1

Jungeblut, H., Genç, E., Burke, M., Gajewski, P., Getzmann, S., Wascher, E., & Schubert, A.-L. (2025). Modeling white matter microstructure to understand individual differences in intelligence. PsyArXiv. https://doi.org/10.31234/osf.io/akvdn_v1

Kubik, V., Thielmann, I., Giesen, C. G., Koslowski, K., Feld, G., & Schubert, A. (2025). Attitudes Toward Open Science Practices Among German Psychologists. PsyArXiv. https://doi.org/10.31234/osf.io/mbcu6_v2

Lesche, S., Sadus, K., Schubert, A.-L., Löffler, C., & Hagemann, D. (2024). Automatically Extracting P3 Latencies Using a Dynamic Template Matching Algorithm. Authorea. 10.22541/au.173383976.68997762/v1

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

Oberauer, K., Schubert, A.-L., Fiebach, C., Frischkorn, G. T., & Nunez, M. D. (2025). The Signal-To-Noise Ratio Hypothesis of Intelligence. PsyArXiv. https://doi.org/10.31219/osf.io/nkms3_v1

Sadus, K., Schubert, A.-L., Lesche, S., Hemming, W., Löffler, C., & Hagemann, D. (2025). The Relationship Between Intelligence, Working Memory Capacity and Information Processing Speed During Encoding. PsyArXiv. https://doi.org/10.31219/osf.io/nbzru_v1

Schubert, A.-L., Steinhilber, M., Kang, H., & Quintana, D. S. (2025). Improving Statistical Reporting in Psychology. PsyArXiv. https://doi.org/10.31234/osf.io/5gr9n_v1

Steinhilber, M., Schnuerch, M., & Schubert, A.-L. (2025). The dark side of sequential testing: A simulation study on questionable research practices. PsyArXiv. https://doi.org/10.31234/osf.io/vkbu3_v1

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

in press

Hunt, C., Schubert, A.-L., & Meinhardt, G. (in press). Feature Synergy Enhances Saliency of Figure-Ground Segregation. Vision Research. https://doi.org/10.2139/ssrn.5163663

Vermeent, S., Schubert, A.-L., & Frankenhuis, W. (in press). Adversity is associated with lower general processing speed rather than specific executive functioning abilities. https://doi.org/10.31234/osf.io/kqhf7 Journal of Experimental Psychology: General.

2025

Nunez, M. D., Schubert, A.-L., Frischkorn, G. T., & Oberauer, K. (2025). Cognitive models of decision-making with identifiable parameters: Diffusion Decision Models with within-trial noise. Journal of Mathematical Psychology, 125, 102917 . https://doi.org/10.1016/j.jmp.2025.102917

Schubert, A.-L., Löffler, C., Jungeblut, H. M., & Hülsemann, M. (2025). Trait characteristics of midfrontal theta connectivity as a neurocognitive measure of cognitive control and its relation to general cognitive abilities. Journal of Experimental Psychology: General. Advanced online publication. https://doi.org/10.31234/osf.io/g27u5

Vermeent, S., Schubert, A.-L., DeJoseph, M. L., Denissen, J. J. A., van Gelder, J.-L., & Frankenhuis, W. E. (2025). Inconclusive evidence for associations between adverse experiences in adulthood and working memory performance. Royal Society Open Science, 12(1), 241837. https://doi.org/10.1098/rsos.241837

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., Frischkorn, G. T., Sadus, K., Welhaf, M. S., Kane, M. J., & Rummel, J. (2024). The brief mind wandering three-factor scale (BMW-3). Behavior Research Methods. https://doi.org/10.3758/s13428-024-02500-6

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

Steinhilber, M., Schnuerch, M., & Schubert, A.-L. (2024). Sequential analysis of variance: Increasing efficiency of hypothesis testing. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000677

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

Wehrheim, M. H., Faskowitz, J., Schubert, A.-L., & Fiebach, C. J. (2024). Reliability of variability and complexity measures for task and task-free BOLD fMRI. Human Brain Mapping, 45(10), e26778. https://doi.org/10.1002/hbm.26778

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