@article{VanonciniHoehlElsneretal.2023,
  author    = {Vanoncini, Monica and H{\"o}hl, Stefanie and Elsner, Birgit and Wallot, Sebastian and Boll-Avetisyan, Natalie and Kayhan, Ezgi},
  title     = {Mother-infant social gaze dynamics relate to infant brain activity and word segmentation},
  series = {Developmental cognitive neuroscience : a journal for cognitive, affective and social developmental neuroscience},
  volume    = {65},
  journal   = {Developmental cognitive neuroscience : a journal for cognitive, affective and social developmental neuroscience},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1878-9293},
  doi       = {10.1016/j.dcn.2023.101331},
  pages     = {8},
  year      = {2023},
  abstract  = {The 'social brain', consisting of areas sensitive to social information, supposedly gates the mechanisms involved in human language learning. Early preverbal interactions are guided by ostensive signals, such as gaze patterns, which are coordinated across body, brain, and environment. However, little is known about how the infant brain processes social gaze in naturalistic interactions and how this relates to infant language development. During free-play of 9-month-olds with their mothers, we recorded hemodynamic cortical activity of ´social brain` areas (prefrontal cortex, temporo-parietal junctions) via fNIRS, and micro-coded mother's and infant's social gaze. Infants' speech processing was assessed with a word segmentation task. Using joint recurrence quantification analysis, we examined the connection between infants' ´social brain` activity and the temporal dynamics of social gaze at intrapersonal (i.e., infant's coordination, maternal coordination) and interpersonal (i.e., dyadic coupling) levels. Regression modeling revealed that intrapersonal dynamics in maternal social gaze (but not infant's coordination or dyadic coupling) coordinated significantly with infant's cortical activity. Moreover, recurrence quantification analysis revealed that intrapersonal maternal social gaze dynamics (in terms of entropy) were the best predictor of infants' word segmentation. The findings support the importance of social interaction in language development, particularly highlighting maternal social gaze dynamics.},
  language  = {en}
}
@article{VanonciniBollAvetisyanElsneretal.2022,
  author    = {Vanoncini, Monica and Boll-Avetisyan, Natalie and Elsner, Birgit and H{\"o}hl, Stefanie and Kayhan, Ezgi},
  title     = {The role of mother-infant emotional synchrony in speech processing in 9-month-old infants},
  series = {Infant behavior and development : an international \& interdisciplinary journal},
  volume    = {69},
  journal   = {Infant behavior and development : an international \& interdisciplinary journal},
  publisher = {Elsevier Science},
  address   = {Amsterdam [u.a.]},
  issn      = {0163-6383},
  doi       = {10.1016/j.infbeh.2022.101772},
  pages     = {13},
  year      = {2022},
  abstract  = {Rhythmicity characterizes both interpersonal synchrony and spoken language. Emotions and language are forms of interpersonal communication, which interact with each other throughout development. We investigated whether and how emotional synchrony between mothers and their 9-month-old infants relates to infants' word segmentation as an early marker of language development. Twenty-six 9-month-old infants and their German-speaking mothers took part in the study. To measure emotional synchrony, we coded positive, neutral and negative emotional expressions of the mothers and their infants during a free play session. We then calculated the degree to which the mothers' and their infants' matching emotional expressions followed a predictable pattern. To measure word segmentation, we familiarized infants with auditory text passages and tested how long they looked at the screen while listening to familiar versus novel words. We found that higher levels of predictability (i.e. low entropy) during mother-infant interaction is associated with infants' word segmentation performance. These findings suggest that individual differences in word segmentation relate to the complexity and predictability of emotional expressions during mother-infant interactions.},
  language  = {en}
}
@article{NguyenGeorgieKayhanetal.2021,
  author    = {Nguyen, Dong Hai Phuong and Georgie, Yasmin Kim and Kayhan, Ezgi and Eppe, Manfred and Hafner, Verena Vanessa and Wermter, Stefan},
  title     = {Sensorimotor representation learning for an "active self" in robots},
  series = {K{\"u}nstliche Intelligenz : KI ; Forschung, Entwicklung, Erfahrungen ; Organ des Fachbereichs 1 K{\"u}nstliche Intelligenz der Gesellschaft f{\"u}r Informatik e.V., GI / Fachbereich 1 der Gesellschaft f{\"u}r Informatik e.V},
  volume    = {35},
  journal   = {K{\"u}nstliche Intelligenz : KI ; Forschung, Entwicklung, Erfahrungen ; Organ des Fachbereichs 1 K{\"u}nstliche Intelligenz der Gesellschaft f{\"u}r Informatik e.V., GI / Fachbereich 1 der Gesellschaft f{\"u}r Informatik e.V},
  number    = {1},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {0933-1875},
  doi       = {10.1007/s13218-021-00703-z},
  pages     = {9 -- 35},
  year      = {2021},
  abstract  = {Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.},
  language  = {en}
}
@article{HafnerHommelKayhanetal.2022,
  author    = {Hafner, Verena and Hommel, Bernhard and Kayhan, Ezgi and Lee, Dongheui and Paulus, Markus and Verschoor, Stephan},
  title     = {Editorial: The mechanisms underlying the human minimal self},
  series = {Frontiers in psychology},
  volume    = {13},
  journal   = {Frontiers in psychology},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2022.961480},
  pages     = {4},
  year      = {2022},
  language  = {en}
}
@article{KoesterKayhanLangelohetal.2020,
  author    = {K{\"o}ster, Moritz and Kayhan, Ezgi and Langeloh, Miriam and H{\"o}hl, 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}
}
@article{BoyadzhievaKayhan2021,
  author    = {Boyadzhieva, Asena and Kayhan, Ezgi},
  title     = {Keeping the breath in mind},
  series = {Frontiers in neuroscience / Frontiers Research Foundation},
  volume    = {15},
  journal   = {Frontiers in neuroscience / Frontiers Research Foundation},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1662-453X},
  doi       = {10.3389/fnins.2021.647579},
  pages     = {13},
  year      = {2021},
  abstract  = {Scientific interest in the brain and body interactions has been surging in recent years. One fundamental yet underexplored aspect of brain and body interactions is the link between the respiratory and the nervous systems. In this article, we give an overview of the emerging literature on how respiration modulates neural, cognitive and emotional processes. Moreover, we present a perspective linking respiration to the free-energy principle. We frame volitional modulation of the breath as an active inference mechanism in which sensory evidence is recontextualized to alter interoceptive models. We further propose that respiration-entrained gamma oscillations may reflect the propagation of prediction errors from the sensory level up to cortical regions in order to alter higher level predictions. Accordingly, controlled breathing emerges as an easily accessible tool for emotional, cognitive, and physiological regulation.},
  language  = {en}
}
@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 H{\"o}hl, 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{MusculusTuenteRaabetal.2021,
  author    = {Musculus, Lisa and T{\"u}nte, Markus R. and Raab, Markus and Kayhan, Ezgi},
  title     = {An embodied cognition perspective on the role of interoception in the development of the minimal self},
  series = {Frontiers in Psychology},
  volume    = {12},
  journal   = {Frontiers in Psychology},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2021.716950},
  pages     = {7},
  year      = {2021},
  abstract  = {Interoception is an often neglected but crucial aspect of the human minimal self. In this perspective, we extend the embodiment account of interoceptive inference to explain the development of the minimal self in humans. To do so, we first provide a comparative overview of the central accounts addressing the link between interoception and the minimal self. Grounding our arguments on the embodiment framework, we propose a bidirectional relationship between motor and interoceptive states, which jointly contribute to the development of the minimal self. We present empirical findings on interoception in development and discuss the role of interoception in the development of the minimal self. Moreover, we make theoretical predictions that can be tested in future experiments. Our goal is to provide a comprehensive view on the mechanisms underlying the minimal self by explaining the role of interoception in the development of the minimal self.},
  language  = {en}
}
@article{KayhanHeilKwisthoutetal.2019,
  author    = {Kayhan, Ezgi and Heil, Lieke and Kwisthout, Johan and van Rooij, Iris and Hunnius, Sabine and Bekkering, Harold},
  title     = {Young children integrate current observations, priors and agent information to predict others' actions},
  series = {PLOS ONE / Public Library of Science},
  volume    = {14},
  journal   = {PLOS ONE / Public Library of Science},
  number    = {5},
  publisher = {PLOS},
  address   = {San Fransisco},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0200976},
  pages     = {16},
  year      = {2019},
  abstract  = {From early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agent's preferences from systematic violations of random sampling. We investigated whether and how young children infer an agent's sampling biases. Moreover, we examined whether pupil data of toddlers follow the predictions of a computational model based on the causal Bayesian network formalization of predictive processing. We formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to predict others' actions. We measured pupillary responses as a behavioral marker of 'prediction errors' (i.e., the perceived mismatch between what one's model of an agent predicts and what the agent actually does). Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to make predictions about agents and their actions. These findings shed light on the mechanisms behind toddlers' inferences about agent-caused events. To our knowledge, this is the first study in which young children's pupillary responses are used as markers of prediction errors, which were qualitatively compared to the predictions by a computational model based on the causal Bayesian network formalization of predictive processing.},
  language  = {en}
}