@article{HofmannDambacherJacobsetal.2014,
  author    = {Hofmann, Markus J. and Dambacher, Michael and Jacobs, Arthur M. and Kliegl, Reinhold and Radach, Ralph and Kuchinke, Lars and Plichta, Michael M. and Fallgatter, Andreas J. and Herrmann, Martin J.},
  title     = {Occipital and orbitofrontal hemodynamics during naturally paced reading: An fNIRS study},
  series = {NeuroImage : a journal of brain function},
  volume    = {94},
  journal   = {NeuroImage : a journal of brain function},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {1053-8119},
  doi       = {10.1016/j.neuroimage.2014.03.014},
  pages     = {193 -- 202},
  year      = {2014},
  abstract  = {Humans typically read at incredibly fast rates, because they predict likely occurring words from a given context. Here, we used functional near-infrared spectroscopy (fNIRS) to track the ultra-rapid hemodynamic responses of words presented every 280 ms in a naturally paced sentence context. We found a lower occipital deoxygenation to unpredictable than to predictable words. The greater hemodynamic responses to unexpected words suggest that the visual features of expected words have been pre-activated previous to stimulus presentation. Second, we tested opposing theoretical proposals about the role of the medial orbitofrontal cortex (OFC): Either OFC may respond to the breach of expectation; or OFC is activated when the present stimulus matches the prediction. A significant interaction between word frequency and predictability indicated OFC responses to breaches of expectation for low- but not for high-frequency words: OFC is sensitive to both, bottom-up processing as mediated by word frequency, as well as top-down predictions. Particularly, when a rare word is unpredictable, OFC becomes active. Finally, we discuss how a high temporal resolution can help future studies to disentangle the hemodynamic responses of single trials in such an ultra-rapid event succession as naturally paced reading. (C) 2014 Elsevier Inc. All rights reserved.},
  language  = {en}
}