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People & Research at the CCNB

Working at the CCNB

Working at the CCNB
Image Credit: Elias Domsch

Our research spans all aspects of cognitive neuroscience, including investigations of the neural basis of sensory perception, working memory, decision making, action planning, emotion, language, and social interaction, among other topics. In addition to exploring the neural mechanisms underlying human behavior, the CCNB is active in developing new techniques for non-invasive imaging data acquisition and analysis that aim to expand our ability to understand the neural basis of cognition.

Research Groups

The CCNB comprises several research groups based at the Department of Education and Psychology as well as affiliated research groups situated at other institutions in the wider Berlin area.

CCNB research groups within the Education and Psychology Department

Affiliated CCNB research groups


Collaborations

The CCNB collaborates with several institutions both in Berlin and in greater Germany:


Publications

Here is a short selection of publications from the various working groups. For a complete list, please visit each research group's website (see above).

Affective Neuroscience and Emotional Modulation Group

  • Herrera-Melendez, A. L., Bajbouj, M., & Aust, S. (2019). Application of Transcranial Direct Current Stimulation in Psychiatry. Neuropsychobiology, 1-12.
  • Bajbouj, M., Alabdullah, J., Ahmad, S., Schidem, S., Zellmann, H., Schneider, F., & Heuser, I. (2018). Psychosocial care of refugees in Germany: insights from the emergency relief and development aid. Der Nervenarzt, 89(1), 1-7.
  • Accolla, E. A., Aust, S., Merkl, A., Schneider, G. H., Kühn, A. A., Bajbouj, M., & Draganski, B. (2016). Deep brain stimulation of the posterior gyrus rectus region for treatment resistant depression. Journal of affective disorders, 194, 33-37.
  • Lang, U. E., Hellweg, R., Kalus, P., Bajbouj, M., Lenzen, K. P., Sander, T., ... & Gallinat, J. (2005). Association of a functional BDNF polymorphism and anxiety-related personality traits. Psychopharmacology, 180(1), 95-99.

Biological Psychology and Cognitive Neuroscience Group

  • Nassar, M. R., Bruckner, R., & Frank, M. J. (2019). Statistical context dictates the relationship between feedback-related EEG signals and learning. eLife, 8:e46975
  • Nassar, M. R., Bruckner, R., Li, S.-C., Gold, J., Heekeren, H. R., & Eppinger, B. (2016). Age differences in learning emerge from an insufficient representation of uncertainty in older adults. Nature Communications, 7: 11609.
  • Green N., Bogacz R., Huebl J., Beyer A.K., Kühn A.A., Heekeren H.R. (2013). Reduction of Influence of Task Difficulty on Perceptual Decision Making by STN Deep Brain Stimulation, Curr Biol., pii: S0960-9822(13)00824-5. doi: 10.1016/j.cub.2013.07.001.
  • Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature reviews neuroscience9(6), 467.
  • Heekeren HR, Marrett S, Bandettini PA, Ungerleider LG (2004). A general mechanism for perceptual decision-making  in the human brain. Nature, 431 (7010):859-861.

Computational Cognitive Neuroscience Group

  • Ostwald D, Schneider S, Bruckner R, Horvath L (2019) Power, positive predictive value, and sample size calculations for random field theory-based fMRI inference BioRxiv Data & Code
  • Toelch U, Ostwald D (2018)  Digital Open Science – Teaching digital tools for reproducible and transparent research PLoS Biol 16(7):e2006022. PDF
  • Ostwald D, Schneider S, Bruckner R, Horvath L (2018) Random field theory-based p-values: a review of the SPM implementation ArXiv Data & Code
  • Starke L, Ostwald D (2017) Variational Bayesian parameter estimation techniques for the general linear model Frontiers in Neuroscience | Brain Imaging Methods 11:504 PDF BioRxiv Data & Code
  • Ostwald D, Starke L (2016) Probabilistic delay differential equation modeling of event-related potentials NeuroImage 136:227–257 PDF Supplementary Material Data & Code

Emmy Noether Group of Visual Cognition

  • Cichy RM & Kaiser D (2019) Deep neural networks as scientific models. Trends Cogn Sci 23(4): 305-317; doi: 10.1016/j.tics.2019.01.009.
  • Hebart MN, Bankson BB, Harel A, Baker CI*, Cichy RM* (2018) Representational dynamics of task context and its influence on visual object processing. eLife 2018; 7:e32816, doi: 10.7554/eLife.32816.
  • Cichy RM, Khosla A, Pantazis D, Torralba A, Oliva A (2016) Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci Reports 10(6): 27755, doi: 10.1038/srep27755.
  • Cichy RM, Pantazis D, Oliva A (2014) Resolving human object recognition in space and time. Nat Neurosci 17(3): 455-462; doi: 10.1038/NN.3635.
  • Cichy RM, Heinzle J, Haynes JD (2012) Imagery and perception share cortical representations of content and location. Cereb Cortex 22(2): 372-380; doi: 10.1093/cercor/bhr106

General and Neurocognitive Psychology Group

  • Ziegler, J, Montant, M, Briesemeister B, Brink, T, Wicker, B, Ponz, A, Bonnard, M, Jacobs, AM, Braun, M (2018). Do words stink? Neural re-use as a principle for understanding emotions in reading. Journal of Cognitive Neuroscience 30:7, pp. 1023–1032 doi:10.1162/jocn_a_01268
  • Hofmann, M. J., Biemann, C., Westbury, C., Murusidze, M., Conrad, M., & Jacobs, A. M. (2018). Simple Co-Occurrence Statistics Reproducibly Predict Association Ratings. Cognitive Science, 1–26. DOI: 10.1111/cogs.12662.
  • Willems, R. & Jacobs, A.M. (2016). Caring about Dostoyevsky: The untapped potential of studying literature. Trends in Cognitive Sciences, 20, 243-245. https://doi.org/10.1016/j.tics.2015.12.009
  • Jacobs AM (2015) Neurocognitive poetics: methods and models for investigating the neuronal and cognitive-affective bases of literature reception. Front. Hum. Neurosci. 9:186. doi: 10.3389/fnhum.2015.00186
  • Pehrs, C., Zaki, J., Schlochtermeier, L. H., Jacobs, A. M., Kuchinke, L., & Koelsch, S. (2015). The Temporal Pole Top-Down Modulates the Ventral Visual Stream During Social Cognition. Cerebral Cortex, bhv226.

Neurobiological Mechanisms of Therapeutic Intervention Group

  • Kallies, G., Rapp, M.A., Fydrich, T., Fehm, L., Tschorn, M., Terán, … Ströhle, A.*, Heinzel, S.*, & Heissel, A.* (2019). Serum brain-derived neurotrophic factor (BDNF) at rest and after acute aerobic exercise in major depressive disorder. Psychoneuroendocrinology, 102, 212-215. *equal contribution doi: https://doi.org/10.1016/j.psyneuen.2018.12.015
  • Heinzel, S., Kaufmann, C., Grützmann, R., Hummel, R., Klawohn, J., Riesel, A., … Kathmann, N. (2018). Neural correlates of working memory deficits and associations to response inhibition in obsessive compulsive disorder. NeuroImage: Clinical, 17, 426-434. doi: https://doi.org/10.1016/j.nicl.2017.10.039
  • Heinzel, S., Lorenz, R. C., Duong, Q.-L., Rapp, M. A., & Deserno, L. (2017). Prefrontal-parietal effective connectivity during working memory in older adults. Neurobiology of Aging, 57, 18–27. doi: https://doi.org/10.1016/j.neurobiolaging.2017.05.005
  • Heinzel, S., Lorenz, R. C., Pelz, P., Heinz, A., Walter, H., Kathmann, N., …, & Stelzel, C. (2016). Neural correlates of training and transfer effects in working memory in older adults. NeuroImage, 134, 236-249. doi: https://doi.org/10.101/j.neuroimage.2016.03.068
  • Heinzel, S., Lorenz, R. C., Brockhaus, W.-R., Wüstenberg, T., Kathmann, N., Heinz, A., & Rapp, M. A. (2014). Working memory load-dependent brain response predicts behavioral training gains in older adults. The Journal of Neuroscience, 34(4), 1224–1233. doi: https://doi.org/10.1523/JNEUROSCI.2463-13.2014

Neurocomputation and Neuroimaging Group

  • Schröder, P., Schmidt, T. T., & Blankenburg, F. (2019). Neural basis of somatosensory target detection independent of uncertainty, relevance, and reports. eLife, 8, e43410. doi: https://doi.org/10.7554/eLife.43410.001
  • Schmidt TT, Wu Y.-H., Blankenburg F (2017): Content-specific codes of parametric vibrotactile working memory in humans. Journal of Neuroscience 37(40):9771-9777. DOI: https://doi.org/10.1523/JNEUROSCI.1167-17.2017
  • Limanowski J, Blankenburg F (2013) Minimal self-models and the free energy principle. Front Hum Neurosci 7:547. DOI: 10.3389/fnhum.2013.00547
  • Spitzer B, Blankenburg F (2011) Stimulus-dependent EEG activity reflects internal updating of tactile working memory in humans. Proc Natl Acad Sci U S A 108:8444–8449. DOI: 10.1073/pnas.1104189108
  • Spitzer B, Wacker E, Blankenburg F (2010) Oscillatory correlates of vibrotactile frequency processing in human working memory. J Neurosci 30:4496–4502. DOI: 10.1523/JNEUROSCI.6041-09.2010

Neuroeconomics Group

  • Fochmann, M., Hechtner, F., Kirchler, E., & Mohr, P. N. (2019). When happy people make society unhappy: How incidental emotions affect compliance behavior.
  • Thomas, A. W., Molter, F., Krajbich, I., Heekeren, H. R., & Mohr, P. N. (2019). Gaze bias differences capture individual choice behaviour. Nature human behaviour, 3(6), 625.
  • Mohr, P. N., Heekeren, H. R., & Rieskamp, J. (2017). Attraction effect in risky choice can be explained by subjective distance between choice alternatives. Scientific reports, 7(1), 8942.
  • Mohr, P. N., Heekeren, H. R., & Rieskamp, J. (2017). Attraction effect in risky choice can be explained by subjective distance between choice alternatives. Scientific reports, 7(1), 8942.
  • Mohr, P. N., Li, S. C., & Heekeren, H. R. (2010). Neuroeconomics and aging: neuromodulation of economic decision making in old age. Neuroscience & Biobehavioral Reviews, 34(5), 678-688.