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Results: 85
Number of items: 85
  • Open Access
    Hadian Rasanan, A. H., Evans, N. J., Fontanesi, L., Manning, C., Huang-Pollock, C., Matzke, D., Heathcote, A., Rieskamp, J., Speekenbrink, M., Frank, M. J., Palminteri, S., Lucas, C. G., Busemeyer, J. R., Ratcliff, R., & Rad, J. A. (2024). Beyond discrete-choice options. Trends in Cognitive Sciences, 28(9), 857-870. https://doi.org/10.1016/j.tics.2024.07.004
  • Open Access
    Gronau, Q. F., Hinder, M. R., Salomoni, S. E., Matzke, D., & Heathcote, A. (2024). A unified account of simple and response-selective inhibition. Cognitive Psychology, 149, Article 101628. https://doi.org/10.31234/osf.io/aqk8d, https://doi.org/10.1016/j.cogpsych.2023.101628
  • Open Access
    Isherwood, S. J. S., Bazin, P., Miletić, S., Stevenson, N. R., Trutti, A. C., Tse, D. H. Y., Heathcote, A., Matzke, D., Innes, R. J., Habli, S., Sokołowski, D. R., Alkemade, A., Håberg, A. K., & Forstmann, B. U. (2023). Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI. NeuroImage, 271, Article 119988. https://doi.org/10.1016/j.neuroimage.2023.119988
  • Open Access
    Salomoni, S. E., Gronau, Q. F., Heathcote, A., Matzke, D., & Hinder, M. R. (2023). Proactive cues facilitate faster action reprogramming, but not stopping, in a response-selective stop signal task. Scientific Reports, 13, Article 19564. https://doi.org/10.1038/s41598-023-46592-0
  • Open Access
    Weigard, A., Matzke, D., Tanis, C., & Heathcote, A. (2023). A cognitive process modeling framework for the ABCD study stop-signal task. Developmental Cognitive Neuroscience, 59, Article 101191. https://doi.org/10.1016/j.dcn.2022.101191
  • Open Access
    Friehs, M., Siodmiak, J., Donzallaz, M. C., Matzke, D., Nummsen, O., Frings, C., & Hartwigsen, G. (2023). No effects of 1 Hz offline TMS on performance in the stop-signal game. Scientific Reports, 13, Article 11565. https://doi.org/10.1038/s41598-023-38841-z
  • Open Access
    Heathcote, A., & Matzke, D. (2023). The Limits of Marginality. Computational Brain & Behavior, 6(1), 28-34. https://doi.org/10.1007/s42113-021-00120-3
  • Isherwood, S., Bazin, P., Miletić, S., Stevenson, N., Trutti, A., H. Y. Tse, D., Heathcote, A., Matzke, D., Habli, S., Sokołowski, D. R., Alkemade, A., Håberg, A., Forstmann, B., & Innes, R. (2023, June 8). Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI data [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.22240393.v1
  • Open Access
    van Doorn, J., Haaf, J. M., Stefan, A. M., Wagenmakers, E.-J., Cox, G. E., Davis-Stober, C. P., Heathcote, A., Heck, D. W., Kalish, M., Kellen, D., Matzke, D., Morey, R. D., Nicenboim, B., van Ravenzwaaij, D., Rouder, J. N., Schad, D. J., Shiffrin, R. M., Singmann, H., Vasishth, S., ... Aust, F. (2023). Bayes Factors for Mixed Models: a Discussion. Computational Brain and Behavior, 6(1), 140–158. https://doi.org/10.1007/s42113-022-00160-3
  • Open Access
    Kucina, T., Wells, L., Lewis, I., de Salas, K., Kohl, A., Palmer, M. A., Sauer, J. D., Matzke, D., Aidman, E., & Heathcote, A. (2023). Calibration of cognitive tests to address the reliability paradox for decision-conflict tasks. Nature Communications, 14, Article 2234. https://doi.org/10.1038/s41467-023-37777-2
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