This study aims to investigate whether parental sensitivity is related to the level of correspondence within parent-child dyads in terms of the strategies that they use while monitoring animations...Show moreThis study aims to investigate whether parental sensitivity is related to the level of correspondence within parent-child dyads in terms of the strategies that they use while monitoring animations of social interactions. Higher levels of synchrony, or the level of correspondence in behavior between parents and their children, is related to better social and emotional developmental outcomes for the children. In total, 69 parents and their 12-months-old baby’s participated in an eye-tracking study, in which an animation was shown that depicted a situation, wherein a “baby figure” shows distress after it is separated from a “parent figure”. This so-called separation segment of the animation was followed by the so-called response segment, wherein either a reunion or further separation of the two characters was shown. Both the parental sensitivity during free-play and the relative fixation duration to the “parent figure” in regards to the “baby figure” were measured. Within this study, no relationship between parental sensitivity and the level of correspondence in monitoring strategies has been found. Moreover, there was no proof for the statement that overall the monitoring strategies of parents and their children correspond with each other while watching animations that depict a social interaction. The baby’s tend to look more at the “parent figure” than their parents do. The focus on the “parent figure” increased from the separation segment to the response segment, for both the parents and their baby’s, although this increase in fixation is bigger for the parents than for their baby’s. These results imply that further research into the possible precursors or influences on the correspondence of behaviors within parent-child dyads is necessary.Show less
Synchronization between brain signals can be quantified by mathematical approaches. Recent studies have proposed a large variety of synchrony methods to capture the synchrony in brain activity...Show moreSynchronization between brain signals can be quantified by mathematical approaches. Recent studies have proposed a large variety of synchrony methods to capture the synchrony in brain activity between interacting subjects. However, there is no detailed overview of how each synchrony method performs under different data characteristics. Here we investigate four synchrony methods, corr-entropy, S-estimator, Global field synchrony (GFS) and Omega complexity and this under varying data characteristics. These four synchrony methods are applied to time series simulated by two data generation mechanisms: Roessler system and linear multivariate autoregressive (MVAR) process. The simulated time series represent the brain activity of subjects and several data characteristics have been manipulated. The performance of each synchrony method is evaluated by root mean square error (RMSE) and the correlation coefficient between true and estimated synchrony values. Besides, the ANOVA analysis and effect sizes are introduced to test the influence of data characteristics on the performance of synchrony methods. The results show that the S-estimator is always the first or the second best performing method and corr-entropy outperforms other methods when it is applied to data generated by Roessler system. The coupling strength and the length of time series can interact with synchrony methods and significantly influence the performance of each method. Time series with high synchrony results in good performance of the S-estimator and poor performance of Corr-entropy. It turns out that the longer data length can lead to better performance of each synchrony method.Show less
Synchronization between brain signals can be quantifiedby mathematical approaches. Recent studies have proposed a large variety ofsynchrony methods tocapture the synchrony in brain activitybetween...Show moreSynchronization between brain signals can be quantifiedby mathematical approaches. Recent studies have proposed a large variety ofsynchrony methods tocapture the synchrony in brain activitybetween interacting subjects. However, there isno detailed overview ofhow each synchrony method performs under different data characteristics.Here we investigate four synchrony methods, corr-entropy, S-estimator, Globalfield synchrony (GFS) and Omega complexityand this under varying data characteristics. These four synchrony methods are applied to time series simulated by two data generation mechanisms: Roessler system and linear multivariate autoregressive (MVAR)process.The simulated time series represent the brain activity ofsubjects and several data characteristics have been manipulated.Theperformance of each synchrony method is evaluated by root mean square error (RMSE) and the correlation coefficient between true and estimated synchronyvalues. Besides, the ANOVA analysis and effect sizes are introduced to test the influence of data characteristicson the performance of synchrony methods.The results show that the S-estimator is always the first or the second best performing method and corr-entropy outperforms other methodswhen it is applied to data generated by Roessler system. Thecoupling strength and thelength of time series can interact with synchrony methodsand significantly influence the performanceof each method.Time series with high synchrony results in good performance of the S-estimator and poor performance of Corr-entropy. It turns out that the longer data length can lead to better performance of each synchrony method.Show less
