@article{KayhanMatthesMarriottHaresignetal.2022,
  author    = {Kayhan, Ezgi and Matthes, Daniel and Marriott Haresign, Ira and B{\´a}nki, Anna and Michel, Christine and Langeloh, Miriam and Wass, Sam and Hoehl, Stefanie},
  title     = {DEEP: A dual EEG pipeline for developmental hyperscanning studies},
  series = {Developmental Cognitive Neuroscience},
  volume    = {54},
  journal   = {Developmental Cognitive Neuroscience},
  publisher = {Elsevier},
  address   = {Amsterdam, Niederlande},
  issn      = {1878-9307},
  doi       = {10.1016/j.dcn.2022.101104},
  pages     = {1 -- 11},
  year      = {2022},
  abstract  = {Cutting-edge hyperscanning methods led to a paradigm shift in social neuroscience. It allowed researchers to measure dynamic mutual alignment of neural processes between two or more individuals in naturalistic contexts. The ever-growing interest in hyperscanning research calls for the development of transparent and validated data analysis methods to further advance the field. We have developed and tested a dual electroencephalography (EEG) analysis pipeline, namely DEEP. Following the preprocessing of the data, DEEP allows users to calculate Phase Locking Values (PLVs) and cross-frequency PLVs as indices of inter-brain phase alignment of dyads as well as time-frequency responses and EEG power for each participant. The pipeline also includes scripts to control for spurious correlations. Our goal is to contribute to open and reproducible science practices by making DEEP publicly available together with an example mother-infant EEG hyperscanning dataset.},
  language  = {en}
}
@article{KoesterKayhanLangelohetal.2020,
  author    = {K{\"o}ster, Moritz and Kayhan, Ezgi and Langeloh, Miriam and Hoehl, Stefanie},
  title     = {Making sense of the world},
  series = {Perspectives on Psychological Science},
  volume    = {15},
  journal   = {Perspectives on Psychological Science},
  number    = {3},
  publisher = {Sage},
  address   = {London},
  issn      = {1745-6916},
  doi       = {10.1177/1745691619895071},
  pages     = {562 -- 571},
  year      = {2020},
  abstract  = {For human infants, the first years after birth are a period of intense exploration-getting to understand their own competencies in interaction with a complex physical and social environment. In contemporary neuroscience, the predictive-processing framework has been proposed as a general working principle of the human brain, the optimization of predictions about the consequences of one's own actions, and sensory inputs from the environment. However, the predictive-processing framework has rarely been applied to infancy research. We argue that a predictive-processing framework may provide a unifying perspective on several phenomena of infant development and learning that may seem unrelated at first sight. These phenomena include statistical learning principles, infants' motor and proprioceptive learning, and infants' basic understanding of their physical and social environment. We discuss how a predictive-processing perspective can advance the understanding of infants' early learning processes in theory, research, and application.},
  language  = {en}
}