TY  - JOUR
A1  - Kayhan Wagner, Ezgi
A1  - Meyer, Marlene
A1  - O’Reilly, J.X.
A1  - Hunnius, Sabine
A1  - Bekkering, Harold
T1  - Nine-month-old infants update their predictive models of a changing environment
JF  - Developmental Cognitive Neuroscience : a journal for cognitive, affective and social developmental neuroscience
N2  - Humans generate internal models of their environment to predict events in the world. As the environments change, our brains adjust to these changes by updating their internal models. Here, we investigated whether and how 9-month-old infants differentially update their models to represent a dynamic environment. Infants observed a predictable sequence of stimuli, which were interrupted by two types of cues. Following the update cue, the pattern was altered, thus, infants were expected to update their predictions for the upcoming stimuli. Because the pattern remained the same after the no-update cue, no subsequent updating was required. Infants showed an amplified negative central (Nc) response when the predictable sequence was interrupted. Late components such as the PSW were also evoked in response to unexpected stimuli; however, we found no evidence for a differential response to the informational value of surprising cues at later stages of processing. Infants rather learned that surprising cues always signal a change in the environment that requires updating. Interestingly, infants responded with an amplified neural response to the absence of an expected change, suggesting a top-down modulation of early sensory processing in infants. Our findings corroborate emerging evidence showing that infants build predictive models early in life.
KW  - Internal models
KW  - Predictive models
KW  - Predictive processing
KW  - Development
KW  - Event-Related potentials
Y1  - 2019
U6  - https://doi.org/10.1016/j.dcn.2019.100680
SN  - 1878-9293
SN  - 1878-9307
VL  - 38
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Kayhan, Ezgi
A1  - Heil, Lieke
A1  - Kwisthout, Johan
A1  - van Rooij, Iris
A1  - Hunnius, Sabine
A1  - Bekkering, Harold
T1  - Young children integrate current observations, priors and agent information to predict others’ actions
JF  - PLOS ONE / Public Library of Science
N2  - 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.
Y1  - 2019
U6  - https://doi.org/10.1371/journal.pone.0200976
SN  - 1932-6203
VL  - 14
IS  - 5
PB  - PLOS
CY  - San Fransisco
ER  -