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pdficon smallChambers, C. D., Forstmann, B. U., & Pruszynski, J. (2016). Registered Reports at the European Journal of Neuroscience: Consolidating and extending peer-reviewed study pre-registration. European Journal of Neuroscience, doi: 10.1111/ejn.13519.

pdficon smallLy, A., Boehm, U., Heathcote, A., Turner, B. M., Forstmann, B. U., Marsman, M., & Matzke, D. (2016). A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. Computational Models of Brain and Behavior. John Wiley and Sons.

pdficon smallWinkel, J., Hawkins, G. E., Ivry, R. B., Brown, S. D., Cools, R., & Forstmann, B. U. (2016). Focal striatum lesions impair cautiousness in humans. Cortex, 85, 37-45.

pdficon smallForstmann, B. U., Keuken, M. C., & Alkemade, A. (2016). The Next Step for Imaging the Subthalamic Nucleus. Brain.

pdficon smallAron, A., Herz, D., Brown, P., Forstmann, B. U., & Zaghloul, K. (2016). Fronto-Subthalamic Circuits for Control of Action and Cognition. The Journal of Neuroscience, 36, 11489-11495.

pdficon smallKeuken, M. C., Schaefer, A., & Forstmann, B. U. (2016). Can We Rely on Susceptibility-Weighted Imaging (SWI) for Subthalamic Nucleus Identification in Deep Brain Stimulation Surgery? Neurosurgery, 79, e945-e946.

pdficon smallvan Maanen, L., Fontanesi, L., Hawkins, G. E., & Forstmann, B. U. (2016). Striatal activation reflects urgency in perceptual decision-making. NeuroImage, 139, 294-303.

pdficon smallVisser, E., Keuken, M. C., Forstmann, B. U., & Jenkinson, M. (2016). Automated segmentation of the substantia nigra, subthalamic nucleus and red nucleus in 7T data at young and old age. NeuroImage, 139, 324-336.

pdficon smallMittner, M., Hawkins, G. E., Boekel, W., & Forstmann, B. U. (2016). A neural model of mind wandering. Trends in Cognitive Sciences, 20, 570-578.

pdficon smallde Hollander, G., Labruna, L., Sellaro, R., Trutti, A., Colzato, L., Ratcliff, R., Ivry, R., & Forstmann, B. U. (2016). Transcranial direct current stimulation does not influence the speed-accuracy tradeoff in perceptual decision-making: Evidence from three independent replication studies. Journal of Cognitive Neuroscience, 7, 1-12.

pdficon smallde Hollander, G., Forstmann, B. U., & Brown, S. D. (2016). Different ways of linking behavioral and neural data via computational cognitive models. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 101-109.

pdficon smallde Hollander, G. (2016). Combining computational models of cognition and neural data to learn about mixed task strategies. The Journal of Neuroscience, 36(1), 1-3.

pdficon smallForstmann, B. U., Ratcliff, R., & Wagenmakers, E.-J. (2016). Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions. Annual Review of Psychology, 67, 641-666.

pdficon smallBoekel, W., Forstmann, B. U., & Wagenmakers, E.-J. (2016). Challenges in replicating brain-behavior correlations: Rejoinder to Kanai (2015) and Muhlert and Ridgway (2015). Cortex, 74, 348-352.

pdficon smallKarayanidis, F., Keuken, M. C., Wong, A. S., Rennie, J., L., de Hollander, G., Cooper, P. S., Fulham, W. R., Lenroot, R. Parsons, M. W., Philips, N., Mitchie, P. T., Forstmann, B. U. (2016). The Age-ility Project (Phase 1): Structural and functional imaging and electrophysiological data repository. NeuroImage, 124(B), 1137-1142.

pdficon smallten Kulve, J., van Bloemendaal, L., Balesar, R. A., IJzerman, R., Swaab, D. F., Diamant, M., ... Alkemade, J. M. (2016). Decreased Hypothalamic Glucagon-Like Peptide-1 Receptor Expression in Type 2 Diabetes Patients. Journal of clinical endocrinology and metabolism101(5), 2122-2129. DOI: 10.1210/jc.2015-3291 [details]

pdficon smallGayet, S., van Maanen, L., Heilbron, M., Paffen, C. L. E., & van der Stigchel, S. (2016). Visual input that matches the content of visual working memory requires less (not faster) evidence sampling to reach conscious accessJournal of Vision16, 1-20. [details]

pdficon smallvan Maanen, L. (2016). Is there evidence for a mixture of processes in speed-accuracy trade-off behavior? Topics in Cognitive Science8(1), 279-290. DOI: 10.1111/tops.12182 [details]

pdficon smallvan Maanen, L., Couto, J., & Lebreton, M. P. (2016). Three boundary conditions for computing the fixed-point property in binary mixture data. PLoS One[details]

pdficon smallAnders, R., Alario, F-X., & van Maanen, L. (2016). The shifted Wald distribution for response time data analysis. Psychological Methods21, 309-327. DOI: 10.1037/met0000066 [details]

pdficon smallvan Rijn, H., Borst, J., Taatgen, N. A., & van Maanen, L. (2016). On the Necessity of Integrating Multiple Levels of Abstraction in a Single Computational FrameworkCurrent Opinion in Behavioral Sciences11, 116-120. [details]